Two NPIs on the page
Synthetic document written for Parity. No real member, provider, or NPI is described; NPIs are invented and are not valid registry entries. Only the referring NPI is asked for. The rendering NPI is right next to it.
Field mean
99.8
across 28 models
Models scoring 100
27
of 28
Models scoring 0
0
of 28
What the model was given
--- PRIOR AUTHORISATION REQUEST ---
REFERRING: Priya Raghunathan, DO NPI 1730558812
SERVICING: Mesquite Surgical Partners NPI 1114477220
MEMBER DOB 1985-08-30
Procedure requested: diagnostic knee arthroscopy, right. Scheduled 2026-10-02.
DIAGNOSES
M23.221 Derangement of posterior horn of medial meniscus, right knee
M17.11 Unilateral primary osteoarthritis, right knee
MEDICATIONS: meloxicam 15 mg daily.
ALLERGIES: none known.json{
"kind": "set",
"gold": {
"diagnoses": [
"M23.221",
"M17.11"
],
"medications": [
"meloxicam"
],
"referring_npi": [
"1730558812"
],
"dates_of_service": [
"2026-10-02"
],
"allergies": []
}
}Every model's answer
Sorted by score. Open a row for the response and, where the vendor exposes it, the reasoning behind it.
100Claude Fable 5.1 · Anthropic8.8s · 93 out | 3.5s · 93 out | 4.6s · 93 out
json{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}json{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}json{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}100Gemini 3.7 Flash · Google5.4s · 596 out | 5.0s · 554 out | 3.3s · 509 out
Reasoning · 479 tokens
**Extracting Diagnoses**
I'm currently focused on identifying and extracting specific ICD-10-CM diagnosis codes as they appear verbatim within the prior authorization request. My immediate task involves parsing out the code "M23.221" from the example provided.
json{
"diagnoses": [
"M23.221",
"M17.11"
],
"medications": [
"meloxicam"
],
"referring_npi": [
"1730558812"
],
"dates_of_service": [
"2026-10-02"
],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 437 tokens
**Extracting Diagnostic Codes**
I'm focusing on identifying and extracting specific ICD-10-CM codes directly mentioned in the document, particularly those pertaining to the member's own health conditions. Currently, I've identified `M23.22` as a relevant diagnosis code.
json{
"diagnoses": [
"M23.221",
"M17.11"
],
"medications": [
"meloxicam"
],
"referring_npi": [
"1730558812"
],
"dates_of_service": [
"2026-10-02"
],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 392 tokens
**Extracting Diagnostic Codes**
I'm currently focusing on identifying and extracting all literal ICD-10-CM diagnosis codes from the document for item ABS-003. So far, I've pinpointed "M23.221" and "M17.11" for the "diagnoses" field.
json{
"diagnoses": [
"M23.221",
"M17.11"
],
"medications": [
"meloxicam"
],
"referring_npi": [
"1730558812"
],
"dates_of_service": [
"2026-10-02"
],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}100GPT-5.5 · OpenAI2.4s · 184 out | 4.0s · 168 out | 2.2s · 176 out
json{
"diagnoses": [
"M23.221",
"M17.11"
],
"medications": [
"meloxicam"
],
"referring_npi": [
"1730558812"
],
"dates_of_service": [
"2026-10-02"
],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 78 tokens
**Structuring output data**
I need to create JSON arrays that capture specific information. First, the date of service is scheduled for 2026-10-02, which I should include as a definite date rather than an anticipated one. There are also diagnoses to include, noted as M23.221 and M17.11. For medications, the current one is meloxicam, which I’ll note as the lower generic. Lastly, there are no known allergies, so that field will be empty, and I’ll add the referring NPI of 1730558812.json{
"diagnoses": [
"M23.221",
"M17.11"
],
"medications": [
"meloxicam"
],
"referring_npi": [
"1730558812"
],
"dates_of_service": [
"2026-10-02"
],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}json{
"diagnoses": [
"M23.221",
"M17.11"
],
"medications": [
"meloxicam"
],
"referring_npi": [
"1730558812"
],
"dates_of_service": [
"2026-10-02"
],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}100GPT-5.6 Sol · OpenAI1.9s · 88 out | 1.8s · 88 out | 3.1s · 132 out
json{
"diagnoses": [
"M23.221",
"M17.11"
],
"medications": [
"meloxicam"
],
"referring_npi": [
"1730558812"
],
"dates_of_service": [
"2026-10-02"
],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}json{
"diagnoses": [
"M23.221",
"M17.11"
],
"medications": [
"meloxicam"
],
"referring_npi": [
"1730558812"
],
"dates_of_service": [
"2026-10-02"
],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 42 tokens
**Clarifying JSON structure**
I need to focus on creating the exact JSON format requested. I understand we need to include scheduled date counts, but not anticipated or booked dates. Also, there’s medication—meloxicam—to mention. Since there are no known allergies, I'll leave that field empty. I'll ensure that any diagnoses are preserved literally and perhaps include relevant codes if necessary. It's important to get this right for clarity and accuracy!json{
"diagnoses": [
"M23.221",
"M17.11"
],
"medications": [
"meloxicam"
],
"referring_npi": [
"1730558812"
],
"dates_of_service": [
"2026-10-02"
],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}100Grok 4.6 · xAI7.7s · 483 out | 7.7s · 430 out | 8.7s · 604 out
Reasoning · 409 tokens
The user wants me to extract fields from a prior authorization document into a specific JSON format.
{
"diagnoses": [
"M23.221",
"M17.json{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 356 tokens
The user wants me to extract specific fields from the document into a JSON object.
json{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 541 tokens
The task is to extract specific fields from the document into a JSON object.
{
"diagnoses": ["M23.221", "M17.json{"diagnoses": ["M23.221", "M17.11"], "medications": ["meloxicam"], "referring_npi": ["1730558812"], "dates_of_service": ["2026-10-02"], "allergies": []}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}100Grok 4.5 · xAI5.5s · 384 out | 5.6s · 398 out | 5.1s · 361 out
Reasoning · 310 tokens
The task is to extract specific fields from the document into a JSON object.
json{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 324 tokens
The task is to extract specific fields from the prior authorization request document.
json{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 287 tokens
The task is to extract specific fields from the document into a JSON object.
json{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}100Gemini 3.1 Pro · Google9.5s · 906 out | 13.3s · 924 out | 11.9s · 1,137 out
Reasoning · 789 tokens
**My Thought Process for Extracting Data into JSON**
Alright, the core of this task is to meticulously parse a document and extract specific pieces of information, then structure them into a JSON array of strings. My primary objective here is accuracy and adherence to the provided field definitions.
First, let's tackle **"diagnoses"**. I'm looking for literal ICD-10-CM codes that explicitly describe the member's own conditions. In the text, I immediately spot "M23.221" and "M17.11". These clearly fit the criteria. So, I'll add these directly to my "diagnoses" array: `["M23.221", "M17.11"]`.
Next is **"medications"**. The requirement is for current medications, expressed as lower-case generic names. I see "meloxicam" in the text. A quick mental check confirms that "meloxicam" is already in its generic form, so no conversion is needed. My "medications" array will be `["meloxicam"]`.
Now for **"referring_npi"**. This is crucial: I need *only* the NPI of the referring or ordering provider, and it must be a 10-digit string. The text presents "Priya Raghunathan, DO NPI 1730558812. (referring)." It also mentions a "Servicing NPI is 1114477220". The instructions are clear to *exclude* the servicing NPI. Therefore, I will isolate and include only the referring NPI: `["1730558812"]`.
Moving on to **"dates_of_service"**. The rule here is to capture dates that are either requested or performed, in YYYY-MM-DD format, and specifically to *exclude* dates described as anticipated, not yet booked, or outside the current request. The document states "Scheduled 2026-10-02." The word "Scheduled" implies it's booked, not merely anticipated or outside the scope. So, I'll include this date: `["2026-10-02"]`.
Finally, **"allergies"**. The instruction is for allergies and intolerances, in lower-case, substance only, with no reaction descriptions. The text says "none known." This is where a slight deliberation occurs. Does "none known" constitute a value to be included, or does it imply the absence of any values? The instructions state "empty where the document does not supply the value". Since no specific allergy *substances* are supplied, and "none known" signifies the lack of any, the most appropriate representation is an empty array. Including `["none known"]` would imply an allergy was identified as such, which isn't the case here. Thus, my "allergies" array will be `[]`.
After reviewing all these extracted pieces against the requirements and cross-referencing them with the final desired JSON structure, I'm confident in the resulting output. The format is correct, and each field accurately reflects the parsed information according to the established rules. The final JSON output I've constructed is:
```json
{
"diagnoses": [
"M23.221",
"M17.11"
],
"medications": [
"meloxicam"
],
"referring_npi": [
"1730558812"
],
"dates_of_service": [
"2026-10-02"
],
"allergies": []
}
```
json{
"diagnoses": [
"M23.221",
"M17.11"
],
"medications": [
"meloxicam"
],
"referring_npi": [
"1730558812"
],
"dates_of_service": [
"2026-10-02"
],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 808 tokens
**My Thought Process for Extracting Prior Authorization Data**
Alright, my primary objective here is to meticulously extract specific data points from a Prior Authorisation Request document and structure them into a JSON format, adhering to a predefined set of keys. This requires careful parsing of the text to identify and isolate each piece of information.
First, I need to address the `"diagnoses"` key. The instructions clearly state to pull out the ICD-10-CM diagnosis codes that are literally present and describe the member's condition. I've identified `"M23.221"` and `"M17.11"` as fitting this criterion. I've also double-checked that these are indeed the member's conditions and not, for instance, family history.
Next up is `"medications"`. The requirement is for the lower-case generic name of current medications, without any dosage, route, or frequency details. I see `"meloxicam 15 mg daily"`. Extracting the generic name and converting it to lower-case gives me `"meloxicam"`.
For the `"referring_npi"`, I'm looking for the 10-digit NPI of the referring or ordering provider. The document explicitly states: "Referring: Priya Raghunathan, DO NPI 1730558812." This is precisely what I need. I've also noted the servicing NPI (1114477220) but correctly disregarded it as per the instructions. So, the value for this key will be `["1730558812"]`.
Then, I move to `"dates_of_service"`. The instruction is to extract dates of service that are either requested or performed, formatted as YYYY-MM-DD. The document mentions "Scheduled 2026-10-02". The key here is "Scheduled." This indicates a booked appointment, not a speculative or anticipated one. Therefore, this date should be included. The format is already correct: `"2026-10-02"`.
Finally, I consider `"allergies"`. I'm to extract allergies or intolerances, presented in lower-case, focusing solely on the substance. The document states: "ALLERGIES: none known." The instruction further clarifies that if the document doesn't supply a value, I should use an empty array. "None known" effectively signifies no recorded allergies. Therefore, an empty array `[]` is the most appropriate representation here, rather than including the phrase "none known" itself.
I've reviewed all the instructions and my extracted values. The final JSON structure looks correct:
```json
{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
```
I've confirmed that the prompt requires strictly one fenced JSON code block without any preamble or commentary. My thought process confirms each extraction and formatting decision.
json{
"diagnoses": [
"M23.221",
"M17.11"
],
"medications": [
"meloxicam"
],
"referring_npi": [
"1730558812"
],
"dates_of_service": [
"2026-10-02"
],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 1,021 tokens
**My Thought Process for Extracting Information into JSON**
Alright, the core task here is quite clear: I need to meticulously parse this prior authorization request document and distill specific pieces of information into a structured JSON format. The JSON object needs to contain five distinct arrays: "diagnoses", "medications", "referring_npi", "dates_of_service", and "allergies". My approach involves systematically identifying and extracting the relevant data for each of these keys.
First, I'll tackle the **"diagnoses"** array. I'm scanning the document for the "DIAGNOSES" section. I see two distinct codes listed: "M23.221" and "M17.11". My instruction is to extract these literal codes, so these will form the first two elements of my "diagnoses" array.
Next, I move on to **"medications"**. I locate the "MEDICATIONS" line, which specifies "meloxicam 15 mg daily." The requirement here is to extract only the generic name, in lower-case. So, "meloxicam" is what I'll capture, and it will be the sole element in the "medications" array.
The **"referring_npi"** is straightforward. I see "REFERRING: Priya Raghunathan, DO NPI 1730558812". I need to extract the 10-digit NPI number. That's clearly "1730558812", which will be the single item in the "referring_npi" array.
Now, for **"dates_of_service"**. The document states, "Procedure requested: diagnostic knee arthroscopy, right. Scheduled 2026-10-02." The instruction is to extract dates of service being requested or performed, in YYYY-MM-DD format, and importantly, *not* to include dates described as anticipated, not yet booked, or outside this request. "Scheduled" implies a confirmed date for the service itself, distinguishing it from something merely anticipated or unbooked. Therefore, "2026-10-02" is indeed the date of service requested, and it will be the only element in this array.
Finally, I consider the **"allergies"** array. The document states, "ALLERGIES: none known." The prompt specifies to return an empty array where the document doesn't supply a specific value and that allergies should be listed as lower-case substances only. Since "none known" explicitly indicates the absence of any known allergies, there are no substances to list. This translates directly to an empty array `[]` for "allergies".
Before finalizing, I'll do a quick mental review of each extraction against the requirements:
* **Diagnoses**: M23.221 and M17.11, as listed. Correct.
* **Medications**: "meloxicam" - generic, lower-case. Correct.
* **Referring NPI**: "1730558812" - 10-digit string. Correct.
* **Dates of Service**: "2026-10-02" - requested/scheduled date in YYYY-MM-DD. Correct, and I've double-checked that "scheduled" fits the criteria of a date of service for this request.
* **Allergies**: `[]` - "none known" means no specific substances to list, so an empty array is appropriate. Correct.
The resulting JSON structure appears to perfectly align with all the specified criteria.
json{
"diagnoses": [
"M23.221",
"M17.11"
],
"medications": [
"meloxicam"
],
"referring_npi": [
"1730558812"
],
"dates_of_service": [
"2026-10-02"
],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}100Claude Opus 5 · Anthropic2.4s · 158 out | 2.6s · 160 out | 2.7s · 146 out
json{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}json{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}json{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}100Gemini 3 Flash · Google8.6s · 899 out | 7.8s · 787 out | 8.2s · 838 out
Reasoning · 783 tokens
**My Thought Process for Extracting Information from the Prior Authorization Request**
Alright, let's break down this prior authorization request for a diagnostic knee arthroscopy. My primary objective here is to accurately extract specific pieces of information according to the predefined requirements: `diagnoses`, `medications`, `referring_npi`, `dates_of_service`, and `allergies`.
First, I'm looking at the core task: document extraction. The input is a request for a diagnostic knee arthroscopy for a member. I need to be meticulous in identifying and isolating the requested fields.
I see the **Referring Provider** is Priya Raghunathan, DO, with NPI 1730558812. This is crucial for the `referring_npi` field. The **Servicing Provider**, Mesquite Surgical Partners (NPI 1114477220), is noted, but for this specific extraction, I'm not interested in their NPI. The **Member DOB** (1985-08-30) and the **Procedure** itself (diagnostic knee arthroscopy, right) provide context, but aren't directly requested fields. The **Scheduled Date** is 2026-10-02; this is going to be my target for `dates_of_service`.
Now, let's zero in on each specific field.
For **`diagnoses`**, the requirement is to extract ICD-10-CM codes that appear literally. I scan the document and find "M23.221 Derangement of posterior horn of medial meniscus, right knee" and "M17.11 Unilateral primary osteoarthritis, right knee". So, the literal codes are "M23.221" and "M17.11". I'll verify these represent the member's own conditions, which they clearly do.
Next, **`medications`**. The requirement is for the member's current medications, specifically in lowercase generic names, without dose or frequency. I find "meloxicam 15 mg daily". The generic name is "meloxicam". It's lowercase, and I'll strip the dose and frequency.
For **`referring_npi`**, I need the 10-digit NPI of the referring or ordering provider. I've already identified Priya Raghunathan, DO, as the referring provider, and her NPI is indeed 1730558812. This is a 10-digit string, and it's the correct one, not the servicing provider's NPI.
The **`dates_of_service`** are the dates requested or performed. The document states "Scheduled Date: 2026-10-02". While it's scheduled, for a request like this, it functions as the date of service for the authorization. So, "2026-10-02" is what I'll extract. I'm not looking for anticipated dates, but rather the specific date tied to the procedure request.
Finally, **`allergies`**. The requirement is for allergies and intolerances, listed as lowercase substances only. The document explicitly states "none known". This means there are no specific substances listed as allergies. Therefore, the correct representation for this field, following the rule of listing substances, is an empty array.
So, to consolidate my findings based on these detailed considerations:
* `diagnoses`: ["M23.221", "M17.11"] - These are the literal ICD-10-CM codes for the member's conditions.
* `medications`: ["meloxicam"] - This is the lowercase generic name of the current medication.
* `referring_npi`: ["1730558812"] - This is the 10-digit NPI of the referring provider.
* `dates_of_service`: ["2026-10-02"] - This is the scheduled date of service for the procedure.
* `allergies`: [] - Since "none known" means no substances are listed, an empty array is the appropriate output.
My confidence in these extractions is high, as I've systematically addressed each requirement and cross-referenced the information within the document against the specified formats and criteria.
json{
"diagnoses": [
"M23.221",
"M17.11"
],
"medications": [
"meloxicam"
],
"referring_npi": [
"1730558812"
],
"dates_of_service": [
"2026-10-02"
],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 670 tokens
**My Thought Process in Extracting Prior Authorization Request Data**
As I processed this Prior Authorization Request document, my primary objective was to meticulously extract and structure key pieces of information. I approached this task systematically, treating each data point as a distinct element to be identified, validated, and formatted according to specific rules.
First, I recognized the **document type** as a "Prior Authorization Request." This set the context for all subsequent extractions.
Next, I identified the **referring provider** as Priya Raghunathan, DO, noting her NPI as 1730558812. Crucially, I understood that for the `referring_npi` field, I was only to capture the *referring or ordering provider's* 10-digit NPI, and therefore excluded the servicing provider's NPI (1114477220) from this specific extraction.
I then noted the **servicing provider's** information, Mesquite Surgical Partners, with NPI 1114477220. This was important for overall document understanding but didn't directly map to a structured output field I was tasked with populating.
The **member's date of birth** was straightforward: 1985-08-30.
The **procedure** was identified as a "diagnostic knee arthroscopy, right," scheduled for 2026-10-02. This date was specifically designated for the `dates_of_service` field, as it represented a requested date rather than an anticipated or unbooked one.
The **diagnoses** were presented as ICD-10-CM codes: M23.221 and M17.11. My internal validation confirmed that these were indeed ICD-10-CM codes, they appeared literally in the document, and they described the member's condition. Thus, I added them to the `diagnoses` array: ["M23.221", "M17.11"].
For **medications**, I found "meloxicam 15 mg daily." My processing rules dictated that I should extract current medications using only their lower-case generic names, omitting dose, route, and frequency, and converting any brand names to generic. Meloxicam is already a generic name, and it's presented here as current. Therefore, I extracted and formatted it as `meloxicam`, adding it to the `medications` array: ["meloxicam"].
Regarding **allergies**, the document stated "none known." My extraction logic for this field requires an empty array when "none" is indicated, as "none" itself is not a specific substance. Hence, the `allergies` array remained empty: [].
Finally, I consolidated the extracted data into the following structured format:
* `diagnoses`: ["M23.221", "M17.11"]
* `medications`: ["meloxicam"]
* `referring_npi`: ["1730558812"]
* `dates_of_service`: ["2026-10-02"]
* `allergies`: []
Throughout this process, I performed multiple checks:
* **Diagnoses:** Confirmed ICD-10-CM format, literal presence, and relevance to the member's condition.
* **Medications:** Verified current status, generic name, lower-case formatting, and absence of dose/frequency.
* **Referring NPI:** Ensured it was a 10-digit NPI and specifically belonged to the referring provider.
* **Dates of Service:** Distinguished between scheduled/requested dates and anticipated dates.
* **Allergies:** Applied the rule to return an empty array for "none known."
This systematic approach allowed me to accurately and efficiently transform the raw document information into the required structured data.
json{
"diagnoses": [
"M23.221",
"M17.11"
],
"medications": [
"meloxicam"
],
"referring_npi": [
"1730558812"
],
"dates_of_service": [
"2026-10-02"
],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 721 tokens
**My Thought Process for Extracting Prior Authorization Request Data**
Alright, the task at hand is document extraction specifically for a Prior Authorization Request, and the input is this document labeled ABS-003. My objective is to pull out specific pieces of information into predefined arrays of strings: `diagnoses`, `medications`, `referring_npi`, `dates_of_service`, and `allergies`. I've got some ground rules to follow: dates need to be in ISO 8601 format (YYYY-MM-DD), medications should be their generic names and in lower-case, NPIs must be exactly 10 digits, and diagnoses should be literal ICD-10-CM codes.
First, let's tackle `diagnoses`. These are the member's conditions, and I'm looking for those ICD-10-CM codes. I've scanned the document and identified "M23.221" and "M17.11". These are exactly what I need, so I'll list them out.
Next up, `medications`. The requirement here is for current medications, their generic names, and in lower-case. The document states "meloxicam 15 mg daily". I know meloxicam is already the generic name, and it's already in lower-case. So, that's straightforward.
Now for `referring_npi`. This is a crucial one; I *only* want the NPI of the referring or ordering provider, and it must be 10 digits. I see two NPIs mentioned: Priya Raghunathan, DO, with NPI 1730558812, is listed as the referring provider. Then there's Mesquite Surgical Partners with NPI 1114477220, which is the servicing provider. Per the instructions, I must exclude the servicing provider's NPI and only capture the referring one. So, my value for this field will be "1730558812".
Moving on to `dates_of_service`. I need the dates for which the service is being requested or performed, in YYYY-MM-DD format. The document indicates a scheduled date of 2026-10-02. This is already in the correct format, so that's what I'll capture.
Finally, `allergies`. This field needs to list allergies and intolerances, again in lower-case and just the substance itself. The document clearly states "none known". The ground rules also stipulate that if the document doesn't supply a value for allergies, I should leave it empty. Since "none known" indicates an absence of allergies, this means I should return an empty list for this field.
So, to recap my findings based on these rules:
* `diagnoses`: ["M23.221", "M17.11"] - These are the literal ICD-10-CM codes I found.
* `medications`: ["meloxicam"] - The generic name, lower-case, as required.
* `referring_npi`: ["1730558812"] - The 10-digit NPI of the referring provider only.
* `dates_of_service`: ["2026-10-02"] - The scheduled date in ISO 8601 format.
* `allergies`: [] - An empty list, as "none known" means no allergies to report and the instructions for missing values.
Putting it all together, the final JSON structure I've constructed reflects these extracted and processed pieces of information precisely according to the given constraints.
json{
"diagnoses": [
"M23.221",
"M17.11"
],
"medications": [
"meloxicam"
],
"referring_npi": [
"1730558812"
],
"dates_of_service": [
"2026-10-02"
],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}100Kimi K3 · Moonshot11.5s · 198 out | 7.6s · 216 out | 11.4s · 438 out
Reasoning · 120 tokens
Let me extract the fields.
Diagnoses: M23.221, M17.11 — both literal ICD-10-CM codes describing the member's conditions.
Medications: meloxicam (current, lower-case generic, no dose).
Referring NPI: 1730558812 (Priya Raghunathan, DO). Servicing NPI 1114477220 should not be included.
Dates of service: 2026-10-02 (scheduled procedure).
Allergies: "none known" — empty array.
Output exactly one fenced JSON block.json{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 138 tokens
Let me extract the fields:
- diagnoses: M23.221, M17.11 — both describe member's own conditions, literal ICD-10-CM codes.
- medications: meloxicam (generic, lower-case, no dose).
- referring_npi: 1730558812 (Priya Raghunathan, referring). Servicing NPI 1114477220 excluded.
- dates_of_service: 2026-10-02 (scheduled). The document says "Scheduled 2026-10-02" — that's a booked date of service, not "anticipated/not yet booked". Include it.
- allergies: none known — empty array.json{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 355 tokens
Let me carefully extract the fields from this prior authorisation request.
1. "diagnoses" — ICD-10-CM diagnosis codes that appear LITERALLY in the document describing the member's own conditions:
- M23.221 Derangement of posterior horn of medial meniscus, right knee
- M17.11 Unilateral primary osteoarthritis, right knee
Both are literal codes in the document, both are member's own conditions. Include both.
2. "medications" — current medications, lower-case generic names, no dose/route/frequency:
- meloxicam 15 mg daily → "meloxicam"
Not discontinued. Include ["meloxicam"].
3. "referring_npi" — NPI of referring/ordering provider only:
- REFERRING: Priya Raghunathan, DO NPI 1730558812 — that's 10 digits. Include "1730558812".
- SERVICING NPI 1114477220 — exclude.
4. "dates_of_service" — dates of service requested/performed, YYYY-MM-DD:
- Scheduled 2026-10-02. The procedure requested is scheduled for 2026-10-02. The instruction says "Do not include dates that the document describes as anticipated, not yet booked, or outside this request." Scheduled means booked — it's the date of service being requested. Include "2026-10-02".
- MEMBER DOB 1985-08-30 — not a date of service. Exclude.
5. "allergies" — allergies and intolerances, lower-case, substance only:
- "ALLERGIES: none known." → empty array.
Output exactly one JSON code block.json{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}100Gemini 2.5 Pro · Google8.7s · 740 out | 6.8s · 585 out | 9.1s · 637 out
Reasoning · 623 tokens
Alright, I've been tasked with extracting some very specific pieces of information from this prior authorization request, and as an expert in this domain, I understand precisely what's needed. My goal is to identify and isolate the diagnoses, current medications, the referring provider's NPI, the requested dates of service, and any documented allergies. I need to be meticulous and ensure I'm pulling the *exact* data points requested, in the correct format.
Let's break down how I approached this, field by field:
First, for **`diagnoses`**, I scanned the document for the ICD-10-CM codes that describe the member's medical conditions. I found two directly listed: `M23.221 Derangement of posterior horn of medial meniscus, right knee` and `M17.11 Unilateral primary osteoarthritis, right knee`. These are precisely what's required, so I've captured `M23.221` and `M17.11` for this field.
Next, the **`medications`**. I was looking for the *current* generic names of any medications the member is taking, and importantly, these need to be in lowercase. The document clearly states `meloxicam 15 mg daily`. The generic name is indeed `meloxicam`, it's a current medication, and I'll ensure it's entered as `meloxicam` (lowercase).
Then, I focused on the **`referring_npi`**. This is straightforward; I just needed to locate the NPI associated with the *referring* provider, not the servicing provider. The document explicitly states `REFERRING: Priya Raghunathan, DO NPI 1730558812`. So, the NPI I need is `1730558812`. I've deliberately ignored the servicing NPI (`1114477220`) as per the instructions.
For **`dates_of_service`**, the request is for the date the service is scheduled. The document mentions `Scheduled 2026-10-02`. This is the date I'll use.
Finally, the **`allergies`**. I've checked the document for any listed allergies. It clearly states `ALLERGIES: none known`. This means that for this field, there are no entries to make. The list will be empty.
So, to summarize my findings and construct the final output object:
* **`diagnoses`**: I have collected `["M23.221", "M17.11"]`.
* **`medications`**: I have collected `["meloxicam"]`.
* **`referring_npi`**: I have collected `["1730558812"]`.
* **`dates_of_service`**: I have collected `["2026-10-02"]`.
* **`allergies`**: I have collected `[]`.
I’ve cross-checked each item against the requirements and the source document. Everything looks to be in order and precisely as requested. I am now ready to format this into the final JSON output.json{
"diagnoses": [
"M23.221",
"M17.11"
],
"medications": [
"meloxicam"
],
"referring_npi": [
"1730558812"
],
"dates_of_service": [
"2026-10-02"
],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 468 tokens
Alright, I've been tasked with parsing this prior authorization request to extract specific pieces of information that are crucial for processing. My goal is to meticulously go through the provided document and pull out the relevant details, structuring them as requested.
Here's my thought process for each data point:
First, I need to identify the **diagnoses**. I'll be looking for ICD-10-CM codes. Scanning the document, I see a section clearly labeled "DIAGNOSES." Within that, two codes are listed: `M23.221` and `M17.11`. These are exactly what I need, so I'll add them directly to my `diagnoses` array.
Next, I need to extract the **medications**. The request specifies that I should list them by their generic names. I've found a mention of "meloxicam 15 mg daily." The generic name here is "meloxicam." Since it's listed as a current medication, I'll add "meloxicam" to my `medications` array.
Then, I have to locate the **referring NPI**. The instructions are clear: I only need the NPI of the *referring* provider. I see a line that says "REFERRING: Priya Raghunathan, DO NPI 1730558812". The NPI is explicitly provided as `1730558812`. I'll add this to my `referring_npi` array. I note there's also a servicing NPI (`1114477220`), but my instructions specifically tell me to omit that, so I'll stick to just the referring one.
The **dates of service** are also a key piece of information. I'm looking for the scheduled date of the procedure. The document states "Scheduled 2026-10-02." This is the date I need, so I will add `2026-10-02` to my `dates_of_service` array.
Finally, I need to capture any **allergies**. I've scanned the document and found the "ALLERGIES" section. It explicitly states "none known." This means there are no allergies to report, so my `allergies` array will remain empty.
With all these pieces of information gathered and categorized according to the requirements, I will now assemble them into the final JSON object.json{
"diagnoses": [
"M23.221",
"M17.11"
],
"medications": [
"meloxicam"
],
"referring_npi": [
"1730558812"
],
"dates_of_service": [
"2026-10-02"
],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 520 tokens
Alright, I'm tackling this prior authorization request. My primary goal is to extract specific pieces of information from it, and I've got a clear list of what I need to find: diagnosis codes (ICD-10-CM), current medications (as generic names), the referring provider's NPI, the dates of service for the requested procedure, and the member's known allergies.
I'm going to methodically go through the document section by section to pull out these details.
First, I'll look for the referring provider information. Ah, I see "REFERRING: Priya Raghunathan, DO NPI 1730558812". Perfect, that gives me the `referring_npi`: `1730558812`. I'll make a note of that. I also see "SERVICING: Mesquite Surgical Partners NPI 1114477220". That's important for context, but it's the *servicing* NPI, not the *referring* one, so I'll set that aside and ignore it for the specific `referring_npi` field I need. The member's DOB is also present, "MEMBER DOB: 1985-08-30", but that's not one of the fields I'm tasked with extracting, so I'll skip over it.
Next, the procedure itself. It says "Procedure requested: diagnostic knee arthroscopy, right. Scheduled 2026-10-02." The scheduling date is exactly what I need for `dates_of_service`, so I'll record `2026-10-02`.
Now, for the diagnoses. The document clearly labels this section: "DIAGNOSES". Underneath, I see:
* "M23.221 Derangement of posterior horn of medial meniscus, right knee"
* "M17.11 Unilateral primary osteoarthritis, right knee"
These are the ICD-10-CM codes I'm looking for. I'll add both `M23.221` and `M17.11` to my list of `diagnoses`.
Moving on to medications. The "MEDICATIONS" section lists: "meloxicam 15 mg daily." The request asks for generic names, and "meloxicam" is indeed the generic name. I'll add `meloxicam` (lowercased, as per usual practice) to my `medications` list.
Finally, allergies. Under "ALLERGIES", it states: "none known." This means there are no allergies to report, so my `allergies` array will be empty.
Now, I'll consolidate everything into the final JSON object.
* For `diagnoses`, I have `["M23.221", "M17.11"]`.
* For `medications`, I have `["meloxicam"]`.
* For `referring_npi`, I have `["1730558812"]`.
* For `dates_of_service`, I have `["2026-10-02"]`.
* And for `allergies`, I have `[]`.
It looks like all the required fields have been successfully extracted and formatted. The process was quite straightforward for this particular request. I'm ready to construct the JSON object.json{
"diagnoses": [
"M23.221",
"M17.11"
],
"medications": [
"meloxicam"
],
"referring_npi": [
"1730558812"
],
"dates_of_service": [
"2026-10-02"
],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}100GLM-5.2 · Z.ai1.9s · 81 out | 1.5s · 79 out | 4.5s · 316 out
Reasoning · 8 tokens
Extract fields from the PA request document.json{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 6 tokens
Extract fields from the document.json{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 240 tokens
We need to answer as analyst/reviewer extracting required fields from a document. Need output exactly one JSON block. Need arrays. Diagnoses: literally in document: M23.221, M17.11. Medications current: meloxicam. Referring NPI: 1730558812 (Priya Raghunathan). Dates of service: procedure scheduled 2026-10-02. Is that "dates of service being requested or performed" and document says "Scheduled" - do we include? Need carefully parse instructions: dates_of_service: dates of service being requested or performed, in YYYY-MM-DD. Do not include dates the document describes as anticipated, not yet booked, or outside this request. Here procedure requested and scheduled 2026-10-02, not just anticipated; it is the scheduled DOS. Include. Allergies: none known. But "allergies" array? If document says none known, return empty array likely. Need no "none known". Medications lower-case generic no dose route freq: meloxicam. Diagnoses as literal codes. Referring NPI array string. Need include nothing else. Ensure valid JSON.json{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}100Claude Opus 4.8 · Anthropic2.0s · 93 out | 1.8s · 93 out | 2.1s · 93 out
json{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}json{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}json{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}100Claude Sonnet 5 · Anthropic2.4s · 216 out | 2.3s · 93 out | 3.2s · 230 out
json{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}json{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}json{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}100Claude Sonnet 4.6 · Anthropic1.8s · 78 out | 1.9s · 78 out | 1.8s · 78 out
json{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}json{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}json{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}100Qwen3.7 Max · Alibaba14.9s · 794 out | 10.5s · 757 out | 10.2s · 828 out
Reasoning · 675 tokens
The task requires extracting specific fields from a prior authorization request document.
Fields to extract:
1. `diagnoses`: ICD-10-CM diagnosis codes literally in the document describing the member's conditions.
2. `medications`: Current medications, lower-case generic names, no dose/route/frequency.
3. `referring_npi`: NPI of the referring/ordering provider only, 10-digit string.
4. `dates_of_service`: Dates of service requested/performed, YYYY-MM-DD.
5. `allergies`: Allergies/intolerances, lower-case, substance only.
Let's analyze the document:
REFERRING: Priya Raghunathan, DO NPI 1730558812
SERVICING: Mesquite Surgical Partners NPI 1114477220
MEMBER DOB 1985-08-30
Procedure requested: diagnostic knee arthroscopy, right. Scheduled 2026-10-02.
DIAGNOSES
M23.221 Derangement of posterior horn of medial meniscus, right knee
M17.11 Unilateral primary osteoarthritis, right knee
MEDICATIONS: meloxicam 15 mg daily.
ALLERGIES: none known.
Extraction:
1. `diagnoses`: ["M23.221", "M17.11"]
2. `medications`: ["meloxicam"]
3. `referring_npi`: ["1730558812"]
4. `dates_of_service`: ["2026-10-02"]
5. `allergies`: [] (none known)
Let's double check.
Diagnoses: M23.221, M17.11. Both are ICD-10 codes.
Medications: meloxicam. Lower-case, generic.
Referring NPI: 1730558812. (Servicing is 1114477220, ignore).
Dates of service: 2026-10-02. (Scheduled date).
Allergies: none known -> empty array.
Format requirements:
Exactly one fenced JSON code block.
Keys: "diagnoses", "medications", "referring_npi", "dates_of_service", "allergies".
All arrays of strings.
```json
{
"diagnoses": [
"M23.221",
"M17.11"
],
"medications": [
"meloxicam"
],
"referring_npi": [
"1730558812"
],
"dates_of_service": [
"2026-10-02"
],
"allergies": []
}
```
All good.json{
"diagnoses": [
"M23.221",
"M17.11"
],
"medications": [
"meloxicam"
],
"referring_npi": [
"1730558812"
],
"dates_of_service": [
"2026-10-02"
],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 638 tokens
The user wants me to extract specific fields from the provided prior authorization request document.
Fields to extract:
1. `diagnoses`: ICD-10-CM diagnosis codes literally in the document describing the member's conditions.
* M23.221
* M17.11
2. `medications`: Current medications, lower-case generic names, no dose/route/frequency.
* meloxicam
3. `referring_npi`: NPI of the referring/ordering provider (10-digit string).
* Referring: Priya Raghunathan, DO NPI 1730558812 -> "1730558812"
4. `dates_of_service`: Dates of service requested or performed (YYYY-MM-DD).
* Scheduled 2026-10-02 -> "2026-10-02"
5. `allergies`: Allergies and intolerances, lower-case, substance only.
* "none known" -> empty array []
Let's double check the requirements:
- `diagnoses`: ["M23.221", "M17.11"]
- `medications`: ["meloxicam"]
- `referring_npi`: ["1730558812"]
- `dates_of_service`: ["2026-10-02"]
- `allergies`: []
Wait, the referring NPI is 1730558812.
Servicing NPI is 1114477220 (do not include).
Let's format the JSON correctly.
```json
{
"diagnoses": [
"M23.221",
"M17.11"
],
"medications": [
"meloxicam"
],
"referring_npi": [
"1730558812"
],
"dates_of_service": [
"2026-10-02"
],
"allergies": []
}
```
All keys are arrays of strings.
"diagnoses": ["M23.221", "M17.11"]
"medications": ["meloxicam"]
"referring_npi": ["1730558812"]
"dates_of_service": ["2026-10-02"]
"allergies": []
Check for any other dates: DOB 1985-08-30 (not DOS).
Check for any other medications: none.
Check for any other allergies: none known -> [].
Looks good.
json{
"diagnoses": [
"M23.221",
"M17.11"
],
"medications": [
"meloxicam"
],
"referring_npi": [
"1730558812"
],
"dates_of_service": [
"2026-10-02"
],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 709 tokens
The user wants to extract specific fields from the provided text document.
The required fields are:
- `diagnoses`: ICD-10-CM diagnosis codes literally in the document describing the member's conditions.
- `medications`: Current medications, lower-case generic names, no dose/route/frequency.
- `referring_npi`: NPI of the referring/ordering provider (10-digit string).
- `dates_of_service`: Dates of service requested/performed (YYYY-MM-DD).
- `allergies`: Allergies/intolerances, lower-case, substance only.
Let's analyze the document:
--- PRIOR AUTHORISATION REQUEST ---
REFERRING: Priya Raghunathan, DO NPI 1730558812
SERVICING: Mesquite Surgical Partners NPI 1114477220
MEMBER DOB 1985-08-30
Procedure requested: diagnostic knee arthroscopy, right. Scheduled 2026-10-02.
DIAGNOSES
M23.221 Derangement of posterior horn of medial meniscus, right knee
M17.11 Unilateral primary osteoarthritis, right knee
MEDICATIONS: meloxicam 15 mg daily.
ALLERGIES: none known.
Extraction:
- `diagnoses`: ["M23.221", "M17.11"]
- `medications`: ["meloxicam"]
- `referring_npi`: ["1730558812"]
- `dates_of_service`: ["2026-10-02"]
- `allergies`: [] (none known)
Check rules:
- diagnoses: ICD-10-CM codes literally in the doc. M23.221, M17.11.
- medications: lower-case generic, no dose. "meloxicam".
- referring_npi: 10-digit string. "1730558812".
- dates_of_service: YYYY-MM-DD. "2026-10-02".
- allergies: lower-case, substance only. None known -> empty array.
Let's format the JSON output.
```json
{
"diagnoses": [
"M23.221",
"M17.11"
],
"medications": [
"meloxicam"
],
"referring_npi": [
"1730558812"
],
"dates_of_service": [
"2026-10-02"
],
"allergies": []
}
```
Double check everything.
Diagnoses: M23.221, M17.11
Medications: meloxicam
Referring NPI: 1730558812
Dates of service: 2026-10-02
Allergies: none -> []
Looks perfect.
End of thought.
json{
"diagnoses": [
"M23.221",
"M17.11"
],
"medications": [
"meloxicam"
],
"referring_npi": [
"1730558812"
],
"dates_of_service": [
"2026-10-02"
],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}100GLM-5.3 · Z.ai5.3s · 502 out | 2.8s · 607 out | 3.3s · 339 out
Reasoning · 396 tokens
Let me extract the fields from this prior authorization request document.
Required keys:
- "diagnoses": ICD-10-CM diagnosis codes that appear LITERALLY in the document describing the member's own conditions. The document shows:
- M23.221 Derangement of posterior horn of medial meniscus, right knee
- M17.11 Unilateral primary osteoarthritis, right knee
These are both codes appearing literally. So diagnoses = ["M23.221", "M17.11"]
- "medications": current medications as lower-case generic names with no dose/route/frequency. Document: meloxicam 15 mg daily. → ["meloxicam"]
- "referring_npi": NPI of the REFERRING or ORDERING provider only, as 10-digit string. The referring is Priya Raghunathan, DO NPI 1730558812. The servicing NPI is 1114477220 — do not include. So ["1730558812"]
- "dates_of_service": dates of service being requested or performed, YYYY-MM-DD. The procedure is scheduled 2026-10-02. Is a scheduled date considered "anticipated, not yet booked"? It says "Scheduled 2026-10-02" — it is scheduled, so it's booked. The instruction says "Do not include dates that the document describes as anticipated, not yet booked, or outside this request." A scheduled date is booked. So dates_of_service = ["2026-10-02"].
- "allergies": allergies and intolerances, lower-case, substance only. "none known" — no allergies. Empty array.
So the answer:
```json
{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}json{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 496 tokens
Let me extract the fields.
"diagnoses": ICD-10-CM diagnosis codes that appear LITERALLY in the document: M23.221 and M17.11. Both describe the member's own conditions. So ["M23.221", "M17.11"].
"medications": current medications, lower-case generic names: meloxicam. So ["meloxicam"].
"referring_npi": NPI of the REFERRING provider only: 1730558812. So ["1730558812"]. The servicing NPI 1114477220 should not be included.
"dates_of_service": dates of service being requested or performed. The procedure is scheduled 2026-10-02. Hmm — "Do not include dates that the document describes as anticipated, not yet booked, or outside this request." Scheduled 2026-10-02 — this is a prior authorization request for a procedure scheduled on that date. It is booked (scheduled). So include "2026-10-02". The date the document was written isn't given. DOB is not a date of service.
"allergies": "none known" — so empty array. The document says allergies: none known. So allergies = [].
Now format: referring_npi is "as a 10-digit string" — but the required JSON keys are "every one an array of strings"? Let me re-read: "Required JSON keys, every one an array of strings, empty where the document does not supply the value". Hmm, but referring_npi says "as a 10-digit string. If the document shows a servicing or rendering NPI as well, do not include it. If no complete 10-digit NPI is present, return an empty array."
The phrase "return an empty array" suggests referring_npi is also an array. The header says every one an array of strings. So referring_npi: ["1730558812"].
All keys are arrays of strings. So:
{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
Diagnosis codes: should they be as they appear literally — M23.221 and M17.11. Yes.
Allergies: "none known" — empty array, since no allergies documented. Yes.json{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 250 tokens
Let me extract the required fields from this prior authorisation request.
"diagnoses": ICD-10-CM codes that appear literally: M23.221 and M17.11. Both describe the member's own conditions. No family history. No procedure codes. So ["M23.221", "M17.11"].
"medications": meloxicam 15 mg daily → "meloxicam" (lower-case generic, no dose/route/frequency). Current — no discontinuation noted. So ["meloxicam"].
"referring_npi": referring provider Priya Raghunathan, DO NPI 1730558812. Servicing NPI 1114477220 should NOT be included. So ["1730558812"].
"dates_of_service": dates of service being requested or performed. The procedure is "Scheduled 2026-10-02." Hmm — the instruction says "Do not include dates that the document describes as anticipated, not yet booked, or outside this request." Scheduled means booked, so it's a date of service being requested. So ["2026-10-02"].
"allergies": "none known" — no allergies. Empty array.
All arrays. Output JSON block only.json{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}100Qwen3.8 Max · Alibaba15.0s · 625 out | 18.5s · 903 out | 10.9s · 453 out
Reasoning · 506 tokens
We need answer user's task: document extraction. Need produce exactly one fenced JSON block. Need analyze fields.
Document:
PRIOR AUTHORISATION REQUEST
REFERRING: Priya Raghunathan, DO NPI 1730558812
SERVICING: Mesquite Surgical Partners NPI 1114477220
MEMBER DOB 1985-08-30
Procedure requested: diagnostic knee arthroscopy, right. Scheduled 2026-10-02.
DIAGNOSES
M23.221 Derangement of posterior horn of medial meniscus, right knee
M17.11 Unilateral primary osteoarthritis, right knee
MEDICATIONS: meloxicam 15 mg daily.
ALLERGIES: none known.
Required keys arrays strings empty if not supplied:
diagnoses: ICD-10-CM codes literal, member's own conditions. Include M23.221, M17.11. Not family history. OK.
medications: current meds lower-case generic names no dose route frequency. meloxicam. Brand? meloxicam generic already. Include.
referring_npi: referring/ordering only 10-digit string. NPI 1730558812. Servicing NPI exclude. Need check complete 10 digits: 1730558812 is 10. Include as string.
dates_of_service: dates requested/performed. Scheduled 2026-10-02. Not anticipated? It says scheduled, requested. Include 2026-10-02. DOB not service.
allergies: allergies/intolerances lower-case substance only. none known. Document says allergies: none known. Should array be empty? It says empty where document does not supply value. Does "none known" supply no allergies? The gold likely expects empty array, not "none known" because allergies are substances. Need decide. Task says allergies and intolerances, lower-case, substance only. If none known, probably empty array. Because no substance. Could include "none known"? It says allergies and intolerances; if no allergies, empty. Use empty.
Need final JSON only. Ensure arrays.json{
"diagnoses": [
"m23.221",
"m17.11"
],
"medications": [
"meloxicam"
],
"referring_npi": [
"1730558812"
],
"dates_of_service": [
"2026-10-02"
],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"m23.221",
"m17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 784 tokens
We need answer user's task. Need extract fields from prior auth request. Need output exactly one fenced JSON block. Need analyze carefully.
Input document:
--- PRIOR AUTHORISATION REQUEST ---
REFERRING: Priya Raghunathan, DO NPI 1730558812
SERVICING: Mesquite Surgical Partners NPI 1114477220
MEMBER DOB 1985-08-30
Procedure requested: diagnostic knee arthroscopy, right. Scheduled 2026-10-02.
DIAGNOSES
M23.221 Derangement of posterior horn of medial meniscus, right knee
M17.11 Unilateral primary osteoarthritis, right knee
MEDICATIONS: meloxicam 15 mg daily.
ALLERGIES: none known.
Required keys arrays strings: diagnoses, medications, referring_npi, dates_of_service, allergies.
diagnoses: ICD-10-CM codes appear literally and describe member's own conditions. Here M23.221 and M17.11. Include exactly? likely ["M23.221","M17.11"]. Case? probably preserve? Scoring lower-case trim? It says set comparison after lower-casing and trimming. So can output as given. Do not include procedure code. No family history.
medications: current medications lower-case generic names no dose/route/frequency. meloxicam. Brand? meloxicam generic already. ["meloxicam"].
referring_npi: referring/ordering provider only 10-digit string. Referring NPI 1730558812. Servicing NPI exclude. ["1730558812"].
dates_of_service: dates requested or performed. Scheduled 2026-10-02. Include. DOB not service. ["2026-10-02"].
allergies: allergies/intolerances lower-case substance only. Document says none known. The value? It says allergies: none known. Should we include empty array or string? Required array of strings, empty where document does not supply value. "none known" is not an allergy substance. Gold likely empty array? Need decide. Task says allergies and intolerances, lower-case, substance only. If no allergies, likely empty array, not "none known" because not substance. But could gold expect "none known"? In extraction tasks, allergies field often include "none known"? Let's parse instruction: "allergies allergies and intolerances, lower-case, substance only, with no reaction description." If document says none known, no allergy substance supplied. Empty where document does not supply the value. So empty array. However if gold set includes "none known"? It says substance only; none known is not substance. Also scoring set comparison lower-case. Many similar tasks expect [] for no known allergies. We'll output [].
Need ensure JSON only. No extra keys. Arrays. final:
```json
{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
```
Double-check: "diagnoses" maybe include only literal codes. Yes.
Need no preamble. final only fenced JSON. Ensure valid.json{
"diagnoses": [
"M23.221",
"M17.11"
],
"medications": [
"meloxicam"
],
"referring_npi": [
"1730558812"
],
"dates_of_service": [
"2026-10-02"
],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 334 tokens
We need answer user's request. Need produce JSON only. Need extract fields from document.
User: task family document extraction. Need required JSON keys arrays of strings:
diagnoses: ICD-10-CM codes literally in document and describe member's own conditions. Include M23.221, M17.11. Need not include family history none.
medications: current medications lower-case generic names no dose/route/freq. meloxicam.
referring_npi: referring/ordering provider only 10-digit string. Referring: Priya Raghunathan DO NPI 1730558812. Servicing NPI 1114477220 excluded.
dates_of_service: dates requested or performed. Scheduled 2026-10-02. Include.
allergies: lower-case substance only. Document says none known. So empty array? They ask allergies and intolerances. If none known, empty. Should not include "none known" probably empty array because no substance. Need output JSON only fenced. Ensure no extra keys.
Check meds: brand? meloxicam generic already. Lower-case.
Diagnoses strings maybe as exact codes no descriptions. Arrays of strings.
Date ISO.
NPI as string 10 digits. Validate 10 digits: 1730558812 length 10? 1 7 3 0 5 5 8 8 1 2 =10 yes.
Allergies empty array.json{
"diagnoses": [
"M23.221",
"M17.11"
],
"medications": [
"meloxicam"
],
"referring_npi": [
"1730558812"
],
"dates_of_service": [
"2026-10-02"
],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}100GPT-5.6 Terra · OpenAI1.4s · 88 out | 1.2s · 59 out | 1.1s · 88 out
json{
"diagnoses": [
"M23.221",
"M17.11"
],
"medications": [
"meloxicam"
],
"referring_npi": [
"1730558812"
],
"dates_of_service": [
"2026-10-02"
],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}json{"diagnoses":["M23.221","M17.11"],"medications":["meloxicam"],"referring_npi":["1730558812"],"dates_of_service":["2026-10-02"],"allergies":[]}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}json{
"diagnoses": [
"M23.221",
"M17.11"
],
"medications": [
"meloxicam"
],
"referring_npi": [
"1730558812"
],
"dates_of_service": [
"2026-10-02"
],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}100GLM-5.3 Flash · Z.ai5.9s · 304 out | 13.5s · 608 out | 9.1s · 314 out
Reasoning · 217 tokens
Let me parse this document.
Diagnoses: M23.221 and M17.11 appear literally and describe member's own conditions. Include both.
Medications: meloxicam 15 mg daily → "meloxicam" (lower-case generic, no dose/frequency).
Referring NPI: Priya Raghunathan, NPI 1730558812. Servicing NPI 1114477220 should be excluded. So ["1730558812"].
Dates of service: Scheduled 2026-10-02. It says "Scheduled 2026-10-02" — is that "anticipated, not yet booked"? The instruction says do not include dates described as anticipated, not yet booked, or outside this request. "Scheduled" means it's booked — it's the date of service being requested/performed. A prior auth request for a scheduled procedure: the date is the date of service. I'd include 2026-10-02. It's scheduled, not "anticipated". Include it.
Allergies: none known → empty array.
Output JSON.json{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 535 tokens
Let me work through this task carefully.
The task family is DOCUMENT EXTRACTION. I'm given one document (a prior authorization request) and must extract specific fields.
Required keys:
1. "diagnoses" - ICD-10-CM codes that appear LITERALLY in the document describing the member's own conditions.
- M23.221 — appears literally. Include.
- M17.11 — appears literally. Include.
Both describe the member's conditions. Include both.
2. "medications" - current medications as lower-case generic names with no dose, route, frequency.
- "meloxicam 15 mg daily" → meloxicam. Include as "meloxicam".
3. "referring_npi" - NPI of referring/ordering provider only, 10-digit string.
- Priya Raghunathan, DO, NPI 1730558812 — referring. Include "1730558812".
- Servicing NPI 1114477220 — exclude per instructions.
4. "dates_of_service" - dates of service being requested or performed, YYYY-MM-DD.
- "Scheduled 2026-10-02" — the procedure is scheduled for that date. Is "scheduled" the same as "anticipated, not yet booked"? The instruction says do not include dates described as "anticipated, not yet booked, or outside this request." Scheduled means it's booked. So include 2026-10-02.
- The document itself has no written date given, so no issue there.
5. "allergies" - allergies and intolerances, lower-case, substance only.
- "none known" — no allergies. So empty array.
Format: exactly one fenced JSON code block, keys as arrays of strings, empty where not supplied.
```json
{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
```
Wait — referring_npi: the spec says "as a 10-digit string" but the required keys section says "every one an array of strings". So it's an array containing the string. Yes, arrays for all.
Check diagnoses formatting: should I include them exactly as written? "M23.221" and "M17.11". Yes, literally as they appear.
All good.json{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 241 tokens
Let me extract from this prior authorization request.
Diagnoses: M23.221 and M17.11 appear literally. Both describe the member's own conditions. Include both.
Medications: meloxicam (lower-case generic, no dose). Current.
Referring NPI: 1730558812 (Priya Raghunathan, referring). Servicing NPI 1114477220 — exclude.
Dates of service: scheduled 2026-10-02. It's scheduled — is "scheduled" the same as booked? The instruction says "Do not include dates that the document describes as anticipated, not yet booked, or outside this request." Scheduled 2026-10-02 for the procedure requested. "Scheduled" implies booked/being requested as part of this request. The procedure requested is the arthroscopy, and the date scheduled is 2026-10-02. I'd include it. It's not described as anticipated or not yet booked; "scheduled" is the date of service being requested. Include 2026-10-02.
Allergies: none known — empty array.
Output JSON with the five keys.json{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}100Hunyuan 4 · Tencent34.1s · 1,998 out | 43.0s · 2,721 out | 31.8s · 1,943 out
Reasoning · 1,927 tokens
We need answer JSON only. Need extract fields from given document. Let's parse.
Document: PRIOR AUTHORISATION REQUEST
REFERRING: Priya Raghunathan, DO NPI 1730558812
SERVICING: Mesquite Surgical Partners NPI 1114477220
MEMBER DOB 1985-08-30
Procedure requested: diagnostic knee arthroscopy, right. Scheduled 2026-10-02.
DIAGNOSES
M23.221 Derangement of posterior horn of medial meniscus, right knee
M17.11 Unilateral primary osteoarthritis, right knee
MEDICATIONS: meloxicam 15 mg daily.
ALLERGIES: none known.
Required keys:
- diagnoses: ICD-10-CM diagnosis codes that appear LITERALLY in the document and describe member's own conditions. Do not assign code for condition named words only. Do not include procedure/supply/HCPCS. Do not include family history codes. Here we have literal ICD-10-CM codes: M23.221, M17.11. They describe member's own conditions? Yes, diagnoses for right knee derangement and osteoarthritis. No family history. Include both as strings: "m23.221"? Wait set comparison after lower-casing and trimming. Output strings should we emit exactly as appear? The instruction: "every one an array of strings". It says scoring is a set comparison after lower-casing and trimming. For codes, should we output lower-case? It says lower-casing applied for scoring, so we can output original case or lower-case; to be safe, maybe output as they appear? They said "after lower-casing and trimming" meaning they will lower-case our strings and gold strings before compare; so case doesn't matter. But medication: "lower-case generic names". For diagnoses, they didn't explicitly say lower-case, but they say every one an array of strings empty where not supplied. They said scoring lower-cases. I can output codes as they appear, "M23.221", "M17.11". Maybe better to output exactly literal code? The gold likely "M23.221" and "M17.11". If I lower-case to "m23.221", scoring lower-cases anyway, same. But order doesn't matter. We'll output as literal: "M23.221", "M17.11". Or maybe lower-case? Let's think: Medications explicitly lower-case. Allergies lower-case. Diagnoses no case instruction but lower-casing scoring means both pass. To be consistent with "lower-casing and trimming" I might output lower-case? However, if gold is "M23.221", lower-casing gold yields "m23.221". Our lower-case yields "m23.221". If we output "M23.221", lower-casing yields "m23.221". Both match. So either is fine. I'll output as in document: "M23.221", "M17.11" maybe. But if they compare exact before lower-casing? They said after lower-casing and trimming. So no issue.
- medications: CURRENT medications, lower-case generic names with no dose, route, frequency. Convert brand to generic. Omit discontinued/stopped. Document: "MEDICATIONS: meloxicam 15 mg daily." Meloxicam is generic (brand Mobic). So medication is "meloxicam". Lower-case already. Output ["meloxicam"].
- referring_npi: NPI of REFERRING or ORDERING provider only, as 10-digit string. Document: REFERRING: Priya Raghunathan, DO NPI 1730558812. SERVICING NPI 1114477220. So referring_npi is ["1730558812"] (10 digits). Check: 1730558812 length 10. Yes.
- dates_of_service: dates of service being requested or performed, YYYY-MM-DD. Do not include dates anticipated, not yet booked, or outside request. Do not include date document written unless also date of service. Document: "Procedure requested: diagnostic knee arthroscopy, right. Scheduled 2026-10-02." Is a scheduled future date "anticipated, not yet booked"? It says Scheduled 2026-10-02. In prior auth, the date of service being requested is the scheduled procedure date. The instruction: "dates of service being requested or performed... Do not include dates that the document describes as anticipated, not yet booked, or outside this request." It says Scheduled, not "anticipated" or "not yet booked". It's scheduled, so it's booked; it's the date of service being requested. So include "2026-10-02". There's also MEMBER DOB 1985-08-30, not a date of service, don't include. Does the document have any other dates? No. So ["2026-10-02"].
Check if procedure is "diagnostic knee arthroscopy, right" scheduled 2026-10-02. It's a prior auth request, so date of service being requested is scheduled date. Include.
- allergies: allergies and intolerances, lower-case, substance only, with no reaction description. Document: "ALLERGIES: none known." None known means no allergies. Should we output empty array [] or maybe ["none known"]? The instruction: "allergies and intolerances, lower-case, substance only, with no reaction description." If document says "none known", there is no substance. The field is an array of strings, empty where the document does not supply the value. Does "none known" supply an allergy? No, it indicates no known allergies. Gold likely empty array. We must output empty array []. Not ["none known"] because that's not a substance; also instruction: substance only, no reaction description. "none known" is not a substance. So [].
Need ensure output exactly one fenced JSON code block and nothing else. Keys: diagnoses, medications, referring_npi, dates_of_service, allergies. All arrays of strings.
Let's craft final:
```json
{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
```
Wait: Should diagnoses be lower-case? Let's examine instructions: "Required JSON keys, every one an array of strings, empty where the document does not supply the value:
"diagnoses" ICD-10-CM diagnosis codes that appear LITERALLY in the
document and describe the member's own conditions. ..."
"appear LITERALLY" suggests keep the literal string from document, which includes the code as written: "M23.221" and "M17.11". They are capitalized letters. Literal appearance includes the code, not the description. The code itself is alphanumeric with uppercase letters. If I lower-case it, it wouldn't appear literally. But they say scoring lower-cases and trims. However, "appear LITERALLY in the document" means the code must be present in document as substring; the document contains "M23.221" and "M17.11". If I output "m23.221", the literal document contains "M23.221", not "m23.221" (case-sensitive?). Usually ICD-10 codes are uppercase; the document has uppercase M. But scoring lower-cases both, so it's fine. But to follow "literal", I should output "M23.221". I'll keep uppercase.
Medication: "meloxicam" lower-case as required.
Referring NPI: string of digits. They said "as a 10-digit string." Keep string "1730558812". Good.
Dates ISO: "2026-10-02". Good.
Allergies empty array. Good.
Double-check no other NPI? Referring NPI is 1730558812. Servicing NPI 1114477220 not included. Good.
Double-check family history: none. Good.
Double-check procedure codes: none. Good.
Check member DOB not included. Good.
Check medication discontinued: none said. Good.
Check "meloxicam 15 mg daily." generic meloxicam. No brand. Good.
Check if "diagnostic knee arthroscopy, right. Scheduled 2026-10-02." Could the scheduled date be considered "anticipated"? They said "Do not include dates that the document describes as anticipated, not yet booked, or outside this request." Here it's "Scheduled", which implies booked, not "anticipated". Prior auth requests are for future scheduled services; date of service requested is the scheduled date. So include. If it were "anticipated 2026-10-02" maybe exclude? But it's scheduled. Include.
Now final answer with exactly one fenced JSON code block and nothing else. No preamble.
Let's produce.json{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 2,650 tokens
We need to extract fields from document. Need output JSON only with required keys: diagnoses, medications, referring_npi, dates_of_service, allergies. Each an array of strings, empty where document does not supply value.
Let's parse document.
--- PRIOR AUTHORISATION REQUEST ---
REFERRING: Priya Raghunathan, DO NPI 1730558812
SERVICING: Mesquite Surgical Partners NPI 1114477220
MEMBER DOB 1985-08-30
Procedure requested: diagnostic knee arthroscopy, right. Scheduled 2026-10-02.
DIAGNOSES
M23.221 Derangement of posterior horn of medial meniscus, right knee
M17.11 Unilateral primary osteoarthritis, right knee
MEDICATIONS: meloxicam 15 mg daily.
ALLERGIES: none known.
We need:
"diagnoses": ICD-10-CM diagnosis codes that appear LITERALLY in the document and describe member's own conditions. Do not assign code for condition named in words only. Do not include procedure/supply/HCPCS codes. Do not include family history codes. The document lists under DIAGNOSES:
M23.221 Derangement... right knee
M17.11 Unilateral primary osteoarthritis, right knee
Both are ICD-10-CM codes and appear literally. They describe member's own conditions? Yes, it's prior authorization for procedure, diagnoses listed. No family history. So include both: ["M23.221", "M17.11"]? Need check wording: "ICD-10-CM diagnosis codes that appear LITERALLY in the document and describe the member's own conditions." They appear literally. Both are diagnoses. So array of strings: ["M23.221", "M17.11"].
"medications": the member's CURRENT medications, as lower-case generic names with no dose, route, or frequency. Convert brand name to generic. Omit discontinued/stopped. Document: MEDICATIONS: meloxicam 15 mg daily. Meloxicam is generic (brand Mobic). It's current? Just listed, not discontinued. Need lower-case generic name with no dose, route, frequency. So "meloxicam". Array: ["meloxicam"].
"referring_npi": NPI of REFERRING or ORDERING provider only, as 10-digit string. If shows servicing or rendering NPI as well, don't include. REFERRING: Priya Raghunathan, DO NPI 1730558812. That's 10-digit string. Document shows servicing NPI 1114477220, but we exclude. So array: ["1730558812"]. Wait, is NPI exactly 10 digits? 1730558812 has 10 digits? Let's count: 1 7 3 0 5 5 8 8 1 2 = 10. Yes. Servicing 1114477220 is 10 digits. So referring_npi: ["1730558812"].
"dates_of_service": dates of service being requested or performed, in YYYY-MM-DD. Do not include dates that document describes as anticipated, not yet booked, or outside this request. Do not include date document written unless also date of service. Document: "Procedure requested: diagnostic knee arthroscopy, right. Scheduled 2026-10-02." The procedure is scheduled for 2026-10-02. Is that a date of service being requested or performed? It says Scheduled 2026-10-02. Prior authorization request for procedure scheduled 2026-10-02. That is the date of service being requested? Wait rule says do not include dates that document describes as anticipated, not yet booked, or outside this request. Here it says "Scheduled", which is booked? The word "Scheduled" implies it is booked, not merely anticipated? The instruction: "dates of service being requested or performed, in YYYY-MM-DD. Do not include dates that the document describes as anticipated, not yet booked, or outside this request." The procedure is scheduled, so it is booked. But is it being requested? It's a prior authorization request for a procedure scheduled on 2026-10-02. That is the date of service for the requested procedure. Should we include 2026-10-02? Let's examine: It is not yet performed (future date 2026-10-02). The document is a prior authorization request. The date of service being requested is the scheduled date. The instruction says dates of service being requested or performed. This is requested (the provider requests authorization for procedure scheduled). It is not "anticipated, not yet booked" because it says "Scheduled", which indicates booked. In prior auth, the requested date of service is often the scheduled date. So include ["2026-10-02"].
But wait: They say "Do not include dates that the document describes as anticipated, not yet booked, or outside this request." If a date is "Scheduled" that is booked. So include. If it said "anticipated" or "proposed" or "to be scheduled", exclude. Here "Scheduled 2026-10-02." So include.
Check member DOB 1985-08-30. That's date of birth, not date of service. Exclude. No document written date given. So dates_of_service: ["2026-10-02"].
"allergies": allergies and intolerances, lower-case, substance only, with no reaction description. Document: ALLERGIES: none known. That means no known allergies. The field should be array of strings, empty where document does not supply value. Since document says none known, does that supply a value? It says allergies: none known. For the array of allergies and intolerances, if none known, should we output empty array? The instruction: "Required JSON keys, every one an array of strings, empty where the document does not supply the value". Document supplies "none known", which means there are no allergies. The set of allergies is empty. In similar extraction tasks, "allergies" should be [] because there are no allergy substances. If a document says "NKDA" or "none known", the list of allergy substances is empty. Do we put ["none known"]? No, because it's not a substance; it's a statement of no known allergies. The instruction: "allergies and intolerances, lower-case, substance only, with no reaction description." Since there is no substance, empty array. Yes, empty array.
Double-check medications: "meloxicam 15 mg daily." lower-case generic: meloxicam. Array: ["meloxicam"].
Diagnoses: Are there any other codes? The procedure requested is "diagnostic knee arthroscopy, right" - that's a procedure, not a diagnosis code. No CPT code given anyway. The diagnoses list includes M23.221 and M17.11. Should we include the descriptions? The key says "diagnoses: ICD-10-CM diagnosis codes that appear LITERALLY in the document and describe the member's own conditions." So just the codes, not descriptions. So ["M23.221", "M17.11"].
Need to ensure JSON formatting exactly one fenced JSON code block, nothing else. Keys: diagnoses, medications, referring_npi, dates_of_service, allergies. All arrays of strings.
Let's draft:
```json
{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
```
Check NPI: Should it be just string "1730558812"? Yes, 10-digit string. No spaces.
Check dates: ISO 8601 YYYY-MM-DD. "2026-10-02" is correct.
Check medication generic: Meloxicam is generic. Brand name Mobic. The document says meloxicam, lower-case generic already. So "meloxicam". No dose. Good.
Check allergies: The document says "none known." The gold set likely is []. If they expected ["none known"]? Let's think. The instruction: "allergies and intolerances, lower-case, substance only, with no reaction description." If the document says "none known", there is no substance. So the array of substances is empty. The instruction also says "empty where the document does not supply the value". Does "none known" supply a value? It supplies the information that there are no allergies, but not a value for an allergy substance. In previous similar tasks, "allergies" with "NKDA" yields empty array. Yes.
Need to ensure no extra keys. The task says "Extract the fields below and nothing else." Required JSON keys, every one an array of strings. So output exactly those five keys.
Double-check if "dates_of_service" should include the scheduled date. Let's re-read: "dates of service being requested or performed, in YYYY-MM-DD. Do not include dates that the document describes as anticipated, not yet booked, or outside this request. Do not include the date the document was written unless it is also the date of service."
The procedure requested: diagnostic knee arthroscopy, right. Scheduled 2026-10-02.
In a prior authorization request, the provider is requesting authorization for a procedure that is scheduled for 2026-10-02. This is a date of service being requested (the service is scheduled, i.e., booked). It is not "anticipated" (they didn't say "anticipated 2026-10-02" or "plan to schedule"). "Scheduled" means an appointment is made/book. The exclusion is for "anticipated, not yet booked". Since it's scheduled, it is booked. So include.
What about the member DOB? Not a date of service. Exclude.
What about any date the document was written? Not present. So nothing.
Thus final answer.
Wait, one nuance: The document says "Procedure requested: diagnostic knee arthroscopy, right. Scheduled 2026-10-02." The schedule date is in 2026, perhaps future relative to document? No document date given. It's a future date maybe. But prior auth is for that scheduled procedure. Yes.
Now verify the NPI: "REFERRING: Priya Raghunathan, DO NPI 1730558812". The NPI is 10 digits. Good.
One more check: "diagnoses" should not include codes listed under family history. There's no family history. Both are the member's own conditions: derangement of medial meniscus right knee, osteoarthritis right knee, both for the right knee matching procedure. So yes.
Now produce final answer with exactly one fenced JSON code block. No preamble. Use json language? The contract says "Reply with exactly one fenced JSON code block and nothing else." In examples, they show:
```json
{ ... }
```
So I'll use ```json ... ```.
Make sure arrays have strings. Yes.
Let's ensure no trailing commas.
Final:
```json
{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
```
Wait, should "medications" be ["meloxicam"] or ["meloxicam 15 mg daily"]? The instructions: "lower-case generic names with no dose, route, or frequency." So just "meloxicam". Good.
Should "diagnoses" include the trailing spaces or exactly code? The document shows "M23.221" and "M17.11" (with two spaces after M17.11? Actually "M17.11 Unilateral..."). The code is M17.11. There's a space and description. So code string is "M17.11". Good.
Is there any possibility that "M23.221" is not a valid ICD-10-CM code? It is. Derangement of posterior horn of medial meniscus, right knee. Valid. M17.11 is unilateral primary osteoarthritis, right knee. Valid. They appear literally. Good.
All set.json{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 1,872 tokens
We need answer with JSON only. Need extract fields from document. Let's parse carefully.
Document: PRIOR AUTHORISATION REQUEST
REFERRING: Priya Raghunathan, DO NPI 1730558812
SERVICING: Mesquite Surgical Partners NPI 1114477220
MEMBER DOB 1985-08-30
Procedure requested: diagnostic knee arthroscopy, right. Scheduled 2026-10-02.
DIAGNOSES
M23.221 Derangement of posterior horn of medial meniscus, right knee
M17.11 Unilateral primary osteoarthritis, right knee
MEDICATIONS: meloxicam 15 mg daily.
ALLERGIES: none known.
Required keys: all arrays of strings.
- "diagnoses": ICD-10-CM diagnosis codes that appear LITERALLY in the document and describe the member's own conditions. Do not assign code for condition named words only. Do not include procedure/supply/HCPCS. Do not include family history. Here diagnoses section lists codes literally: M23.221 and M17.11. Both are ICD-10-CM, appear literally, describe member's own conditions (right knee derangement and OA). Are these the member's conditions? Yes, in DIAGNOSES. Include codes as strings. Should we include the text or just codes? Key says "ICD-10-CM diagnosis codes". So just code strings: ["M23.221", "M17.11"]. Need maybe include with no descriptions, just codes. Since array of strings. Use exactly codes. Should I include leading/trailing spaces? No.
- "medications": the member's CURRENT medications, as lower-case generic names with no dose, route, or frequency. Convert brand name to generic. Omit discontinued/stopped. Document: MEDICATIONS: meloxicam 15 mg daily. meloxicam is generic? Meloxicam is generic name (brand Mobic). It is lower-case generic. So ["meloxicam"]. Any other meds? No. Include only current, not discontinued. Done.
- "referring_npi": the NPI of REFERRING or ORDERING provider only, as a 10-digit string. If document shows a servicing or rendering NPI as well, do not include it. If no complete 10-digit NPI present, empty array. Referring: Priya Raghunathan, DO NPI 1730558812. That's 10 digits? 1 7 3 0 5 5 8 8 1 2 = 10 digits. Yes. Servicing NPI 1114477220 also 10 digits, but exclude. So ["1730558812"].
- "dates_of_service": dates of service being requested or performed, in YYYY-MM-DD. Do not include dates described as anticipated, not yet booked, or outside this request. Do not include date document was written unless it's also date of service. Document: "Procedure requested: diagnostic knee arthroscopy, right. Scheduled 2026-10-02." Scheduled is a date of service being requested? It says "dates of service being requested or performed". The procedure is scheduled 2026-10-02. Is "Scheduled" considered anticipated/not yet booked? The instruction: "Do not include dates that the document describes as anticipated, not yet booked, or outside this request." Wait careful: It says Do not include dates that the document describes as anticipated, not yet booked, or outside this request. Here it says "Scheduled 2026-10-02". Is that "anticipated" or "not yet booked"? Scheduled implies booked/appointment made. It is a scheduled date, not "anticipated" maybe? They say "dates of service being requested or performed". A prior auth request for a scheduled procedure: date of service is the scheduled procedure date. The exclusion is "anticipated, not yet booked". Scheduled is booked. But wait, sometimes "scheduled" might be future date. But they didn't say exclude future dates, only anticipated/not yet booked/outside request. It is the service requested. So include 2026-10-02. What about member DOB 1985-08-30? That's not date of service. Exclude. Document date? None. So ["2026-10-02"].
Let's double-check if "Scheduled" might be considered "anticipated"? The wording: "Procedure requested: diagnostic knee arthroscopy, right. Scheduled 2026-10-02." In prior auth, the date of service is the scheduled/requested date. The exclusion says "dates that the document describes as anticipated, not yet booked". Since it says "Scheduled" rather than "anticipated" or "to be scheduled", it is booked. However, some interpretations: "Scheduled" is future and not yet performed, but it's not "anticipated" in the sense of not yet booked. It is a date of service being requested. Include.
But let's think: The task family: "dates_of_service: dates of service being requested or performed, in YYYY-MM-DD. Do not include dates that the document describes as anticipated, not yet booked, or outside this request. Do not include the date the document was written unless it is also the date of service." In a prior auth request, the service is "requested" and "Scheduled 2026-10-02". It's the date of service requested. It's not just the date the document was written. So yes include. If the doc said "anticipated date of service: ..." maybe exclude? Here it says Scheduled. Good.
- "allergies": allergies and intolerances, lower-case, substance only, with no reaction description. Document: ALLERGIES: none known. That means no known allergies. Should we output empty array [] or include "none known"? The key says allergies and intolerances. If none known, there are no allergies/intolerances. The required array empty where document does not supply the value. Does "none known" supply a value? It supplies a statement of no known allergies, not an allergy substance. The instruction: "allergies: allergies and intolerances, lower-case, substance only, with no reaction description." Since no substance, the array should be empty, not ["none known"] because "none known" is not a substance. Also says empty where the document does not supply the value. The document says none known, meaning no allergies supplied. So [].
Now ensure JSON formatting. Exactly one fenced JSON code block, nothing else. Keys: diagnoses, medications, referring_npi, dates_of_service, allergies. All arrays of strings.
Let's produce:
```json
{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
```
Check diagnoses: Should we include exactly as they appear? "M23.221" and "M17.11". The document: "M23.221 Derangement..." and "M17.11 Unilateral...". M17.11 has two spaces before description. Code is M17.11. Good. M23.221. Good.
Meds: "meloxicam" is generic. Lower-case. No dose. Good.
Referring NPI: 10-digit string. Good.
Dates: "2026-10-02". Good.
Allergies: empty array.
One nuance: Are there any other diagnoses? The procedure requested is diagnostic knee arthroscopy, right. Not a diagnosis code. No HCPCS. No family history. Fine.
Another nuance: Is "M17.11" unilateral primary osteoarthritis, right knee. Is it an ICD-10-CM code? Yes, M17.11 is "Unilateral primary osteoarthritis, right knee". M23.221 is "Derangement of posterior horn of medial meniscus due to old tear or injury, right knee"? Actually M23.221 is "Derangement of posterior horn of medial meniscus due to old tear or injury, right knee". But document says "Derangement of posterior horn of medial meniscus, right knee". Code appears literally. Include.
Check "diagnoses" key: "ICD-10-CM diagnosis codes that appear LITERALLY in the document and describe the member's own conditions. Do not assign a code for a condition the document names in words only." The document lists codes literally in DIAGNOSES. So include.
Now final answer: exactly one fenced JSON code block. No preamble/commentary. Ensure JSON null not needed. Use JSON arrays. Good.
Let's produce final.json{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}100Qwen3.8 Flash · Alibaba8.2s · 404 out | 4.1s · 287 out | 16.9s · 511 out
Reasoning · 285 tokens
We need answer user's task. Need produce final JSON only. Need analyze in English? User request English. Need extract fields.
Document:
PRIOR AUTH REQUEST
Referring Priya NPI 1730558812
Servicing NPI 1114477220
Member DOB not DOS
Procedure requested diagnostic knee arthroscopy right scheduled 2026-10-02.
Diagnoses literal ICD-10-CM codes: M23.221, M17.11. Both describe member conditions. Procedure code not supplied. Medications current: meloxicam (lower-case generic, dose/freq omitted). Allergies none known -> empty. Referring NPI -> ["1730558812"] exclude servicing. Dates of service requested/performed: scheduled 2026-10-02 -> ["2026-10-02"].
Need JSON arrays of strings. Ensure lowercase? diagnoses codes maybe case? Gold likely upper? Requirement says after lower-casing; but output exact strings? It says ICD codes appear literally. We can output as shown maybe uppercase; scoring lowercases. Medications lower-case generic. allergies lower-case. Use empty arrays.
Final only fenced JSON code block.json{
"diagnoses": [
"M23.221",
"M17.11"
],
"medications": [
"meloxicam"
],
"referring_npi": [
"1730558812"
],
"dates_of_service": [
"2026-10-02"
],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 168 tokens
We need answer user's request. Need produce final JSON code block. Need parse document. Task: extract fields. Document has prior authorisation request. diagnoses ICD-10-CM that appear literally: M23.221, M17.11. Need maybe lower-case? Gold lower-case? keys arrays strings. Should we include code descriptions? The instructions: ICD-10-CM diagnosis codes that appear LITERALLY. So just codes likely. Medications current generic lower-case no dose/route: meloxicam. Referring NPI only 1730558812. Servicing not include. Dates of service requested: 2026-10-02 scheduled. Allergies: none known -> empty array. Ensure JSON only.
json{
"diagnoses": [
"M23.221",
"M17.11"
],
"medications": [
"meloxicam"
],
"referring_npi": [
"1730558812"
],
"dates_of_service": [
"2026-10-02"
],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 392 tokens
We need answer with JSON only. Need extract fields.
User document:
PRIOR AUTHORISATION REQUEST
REFERRING: Priya Raghunathan, DO NPI 1730558812
SERVICING: Mesquite Surgical Partners NPI 1114477220
MEMBER DOB 1985-08-30
Procedure requested: diagnostic knee arthroscopy, right. Scheduled 2026-10-02.
DIAGNOSES
M23.221 ...
M17.11 ...
MEDICATIONS: meloxicam 15 mg daily.
ALLERGIES: none known.
Required keys arrays of strings:
diagnoses: ICD-10-CM codes literally in doc describing member's conditions: M23.221, M17.11. Need lower-case? Scoring lower-cases and trims, but output can exact? Maybe use lower-case? ICD10 codes typically uppercase? Since lower-casing scoring, either ok. Better exact from document? It says ICD-10-CM diagnosis codes that appear literally. If scoring lowercases, maybe exact. Use ["M23.221","M17.11"].
medications: current meds lower-case generic names no dose. meloxicam.
referring_npi: 1730558812. Exclude servicing 1114477220. Complete 10 digits yes.
dates_of_service: date requested/performed. Scheduled 2026-10-02. Include. DOB not include.
allergies: none known => empty array.
JSON only. Ensure no extra keys.
json{
"diagnoses": [
"M23.221",
"M17.11"
],
"medications": [
"meloxicam"
],
"referring_npi": [
"1730558812"
],
"dates_of_service": [
"2026-10-02"
],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}100Kimi K2.6 · Moonshot7.3s · 1,299 out | 22.6s · 1,430 out | 19.2s · 975 out
Reasoning · 1,293 tokens
The user wants me to extract specific fields from a prior authorization request document into a JSON format.
Let me identify each required field:
1. "diagnoses": ICD-10-CM diagnosis codes that appear literally in the document and describe the member's own conditions.
- M23.221
- M17.11
These appear literally. No family history mentioned. No procedure/HCPCS codes listed under diagnoses.
2. "medications": Current medications as lower-case generic names with no dose/route/frequency.
- meloxicam (document says "meloxicam 15 mg daily" - current medication, not discontinued)
Lower-case generic name: meloxicam
3. "referring_npi": NPI of the referring or ordering provider only, as a 10-digit string.
- Referring: Priya Raghunathan, DO NPI 1730558812
- Servicing: Mesquite Surgical Partners NPI 1114477220
I should include only the referring NPI: "1730558812"
It's a 10-digit string.
4. "dates_of_service": Dates of service being requested or performed.
- Scheduled 2026-10-02.
This is the procedure date. The document date is not provided (the header says prior authorization request but no document date). Even if there was a document date, it says not to include it unless it is also the date of service.
So: 2026-10-02
5. "allergies": allergies and intolerances, lower-case, substance only.
- "none known"
Wait, the instruction says "empty where the document does not supply the value". Does "none known" count as supplying a value? It indicates no allergies are known. But usually "none known" means there are no allergies listed. However, the field asks for allergies and intolerances. If the document says "none known", there are literally no allergies to list. But is the document "supplying the value" that there are no allergies? The instruction says empty where the document does not supply the value. If it supplies "none known", some might interpret that as no allergies. But for set comparison, if gold set is empty, then [] would match. If gold set includes something like "none known"... wait, the instruction says "allergies and intolerances, lower-case, substance only, with no reaction description." "none known" is not a substance. So I think the array should be empty [] because there are no substances listed. But let me reconsider: sometimes these extraction tasks want "none known" captured? No, it says "substance only". "none known" is not a substance. So empty array.
Wait, let me double check: "empty where the document does not supply the value". The document says "ALLERGIES: none known." This is a statement about allergies. But since it's not a substance, and there are no substances listed, I should return an empty array.
Let me verify all rules:
- JSON block only, no preamble.
- Keys: diagnoses, medications, referring_npi, dates_of_service, allergies. All arrays of strings.
diagnoses: ["M23.221", "M17.11"]
Wait, should I include them exactly as they appear? The document shows:
M23.221 Derangement of posterior horn of medial meniscus, right knee
M17.11 Unilateral primary osteoarthritis, right knee
Codes appear literally. Yes.
medications: ["meloxicam"]
Lower-case generic name. No dose/route/freq.
referring_npi: ["1730558812"]
Referring provider NPI only.
dates_of_service: ["2026-10-02"]
ISO 8601.
allergies: []
"none known" is not a substance.
Wait, let me double-check the NPI. The referring is Priya Raghunathan, DO NPI 1730558812. That's 10 digits. Servicing is 1114477220. I should not include servicing. So only ["1730558812"].
Let me make sure about the medication: meloxicam is generic. Brand name is Mobic. Document says meloxicam, so that's already generic.
Dates: "Scheduled 2026-10-02." This is the date of service for the requested procedure. Include it.
Diagnoses: Are there any other codes? No, just those two.
Allergies: Could "none known" be interpreted as a value to include? The instruction says "allergies and intolerances, lower-case, substance only". If there are no substances, the array should be empty. "none known" is not a substance. I'll return [].
Final JSON:
```json
{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
```
Wait, order doesn't matter for arrays but I'll list them.
One more check: "Do not include dates that the document describes as anticipated, not yet booked, or outside this request." The procedure is scheduled for 2026-10-02. It's a prior auth request, so it's being requested. The date is scheduled. Should I include it? Yes, it's the date of service being requested. "Dates of service being requested or performed". This is requested/scheduled. The exclusion is for "anticipated, not yet booked, or outside this request". Scheduled seems fine. The date is part of the request. So include.
Also "Do not include the date the document was written unless it is also the date of service." No document date is shown.
Everything looks good.json{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 1,405 tokens
The user wants me to extract specific fields from a prior authorization request document.
Required JSON keys (all arrays of strings):
1. "diagnoses" - ICD-10-CM diagnosis codes that appear literally in the document describing member's own conditions. Do not assign codes for conditions named in words only. Do not include procedure/supply/HCPCS codes. Do not include family history codes.
- The document lists: M23.221, M17.11
- These appear literally and describe the member's conditions.
2. "medications" - member's current medications, lower-case generic names, no dose/route/frequency. Convert brand to generic. Omit discontinued/stopped.
- Document says: "meloxicam 15 mg daily."
- Generic name: meloxicam
- Remove dose: "meloxicam"
- It's current (listed under medications, no indication discontinued).
3. "referring_npi" - NPI of REFERRING or ORDERING provider only, as 10-digit string. Do not include servicing/rendering NPI.
- REFERRING: Priya Raghunathan, DO NPI 1730558812
- SERVICING: Mesquite Surgical Partners NPI 1114477220
- So only include: 1730558812
4. "dates_of_service" - dates of service requested/performed, YYYY-MM-DD. Do not include anticipated/not yet booked/outside request dates. Do not include document date unless also DOS.
- Scheduled 2026-10-02. This is the date of the requested procedure. It is being requested for this date. That counts as date of service requested.
- Format: 2026-10-02
5. "allergies" - allergies and intolerances, lower-case, substance only, no reaction description.
- Document says: "ALLERGIES: none known."
- So empty array.
Let me double-check rules:
- "dates_of_service" - "Do not include dates that the document describes as anticipated, not yet booked, or outside this request." The date is scheduled for the procedure requested. Is it considered anticipated? The instruction says "Do not include dates that the document describes as anticipated, not yet booked, or outside this request." The document says "Scheduled 2026-10-02." This is the date the procedure is planned. Is that a date of service being requested? Yes, it's the date of the requested procedure. But wait, "anticipated, not yet booked" - scheduled seems booked. However, is it considered a service being requested or performed? It's requested for that date. The prompt says "dates of service being requested or performed". So 2026-10-02 fits.
- Actually, let me reconsider: scheduled dates in prior auth requests can be tricky. But the instruction explicitly says include dates of service being requested. The procedure is requested for 2026-10-02. So include it.
Wait, "Do not include dates that the document describes as anticipated, not yet booked, or outside this request." The document says "Scheduled 2026-10-02." That sounds booked. So include.
Also, the patient DOB is 1985-08-30, but that's not a date of service.
JSON required:
- diagnoses: ["M23.221", "M17.11"] - wait, should I include the literal string as appears? Yes. The codes appear literally.
- medications: ["meloxicam"]
- referring_npi: ["1730558812"]
- dates_of_service: ["2026-10-02"]
- allergies: []
Wait, should I include the period in the diagnosis code? The document says "M23.221" and "M17.11". But ICD-10-CM codes sometimes include a period after the first three characters. However, the instruction says "ICD-10-CM diagnosis codes that appear LITERALLY in the document". The document shows them with spaces: "M23.221" and "M17.11". Actually, standard ICD-10-CM formatting includes a decimal point after the 3rd character: M23.221 and M17.11. The document shows "M23.221" and "M17.11". So literally they appear with the decimal (dot) already. Yes, M23.221 includes the dot. M17.11 includes the dot. So extract as shown.
Wait, looking carefully: "M23.221" and "M17.11". Yes.
Medications lower-case generic names: "meloxicam". Correct.
Referring NPI: 1730558812. Correct.
Dates: 2026-10-02.
Allergies: none known -> empty array.
All fields must be arrays of strings.
Check output contract: exactly one fenced JSON code block and nothing else.
Format:
```json
{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
```
Double check if any other medications? No.
Any other dates? DOB is 1985-08-30 but that's not DOS.
Any allergies? none known -> empty array.
Referring vs servicing: Only referring. Yes.
One thing: "Do not include dates that the document describes as anticipated, not yet booked, or outside this request." The procedure is "Scheduled 2026-10-02." Is a scheduled date considered "anticipated, not yet booked"? Usually scheduled means booked. But sometimes in these tasks, a future scheduled date might be excluded if it's described as anticipated? Let's see. "dates of service being requested or performed". This is the date of service for the requested procedure. It is being requested. So it's valid.
Wait, could "Scheduled 2026-10-02" mean it's not yet performed, but it is the date being requested. The phrasing "anticipated, not yet booked" refers to dates like "planned for sometime next year" or "tentatively scheduled"? No, the instruction is to exclude anticipated/not yet booked/outside request. A specific scheduled date within the request is exactly what the date of service is. So keep it.
Another check: "Do not include the date the document was written unless it is also the date of service." No document date is given.
Looks good.json{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 905 tokens
The user wants me to extract specific fields from a prior authorization request document and output them as a JSON object with exactly the required keys.
Let me analyze the document:
1. **diagnoses**: ICD-10-CM diagnosis codes that appear literally in the document and describe the member's own conditions.
- M23.221 - appears literally
- M17.11 - appears literally
- Both describe the member's own conditions.
- No family history codes listed.
- Result: ["M23.221", "M17.11"]
2. **medications**: Current medications, lower-case generic names with no dose, route, or frequency.
- "meloxicam 15 mg daily" -> generic name is meloxicam. Dose and frequency should be removed.
- Document doesn't say it was discontinued.
- Result: ["meloxicam"]
3. **referring_npi**: NPI of the referring or ordering provider only, as a 10-digit string.
- REFERRING: Priya Raghunathan, DO NPI 1730558812
- SERVICING: Mesquite Surgical Partners NPI 1114477220
- Only include referring NPI. 1730558812 is 10 digits.
- Result: ["1730558812"]
4. **dates_of_service**: Dates of service being requested or performed.
- "Scheduled 2026-10-02." This is the date of the procedure being requested.
- Do not include document written date (not given anyway).
- Result: ["2026-10-02"]
5. **allergies**: allergies and intolerances, lower-case, substance only, no reaction description.
- "none known." -> This means no allergies are documented. The document does not supply any specific allergy substance. So empty array.
- Wait, let me re-read: "ALLERGIES: none known." This indicates the absence of allergies, but the instruction says "empty where the document does not supply the value." Since there are no allergies listed (only "none known"), the correct extraction is an empty array [].
- Actually, looking at the requirement: "allergies and intolerances, lower-case, substance only". If the document says "none known", there are no substances to extract. So empty array.
- Result: []
Let me double check everything:
- diagnoses: ["M23.221", "M17.11"] - both literal, member's own conditions, not family history. Check.
- medications: ["meloxicam"] - current, generic, no dose/route/freq, lower-case. Check.
- referring_npi: ["1730558812"] - referring provider only, 10-digit string. Check.
- dates_of_service: ["2026-10-02"] - requested/performed date, ISO format. Check.
- allergies: [] - none known means empty array. Check.
Output must be exactly one fenced JSON code block with nothing else.
```json
{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
```
Wait, I need to make sure about the order. Order does not matter according to instructions.
Also check: "Do not include procedure, supply, or HCPCS codes." - these are diagnosis codes, so fine.
"Convert a brand name to its generic name." - meloxicam is already generic.
"If no complete 10-digit NPI is present, return an empty array." - It is present.
"Do not include the date the document was written unless it is also the date of service." - Not present.
"Do not include dates that the document describes as anticipated, not yet booked, or outside this request." - 2026-10-02 is scheduled, so it is being requested. This should be included.
"allergies: none known." - This means no allergies. Empty array is correct.
One final check: The JSON must include every required key and no others. The keys are: diagnoses, medications, referring_npi, dates_of_service, allergies. All present.
Looks good.json{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}100DeepSeek V4 Pro · DeepSeek5.3s · 311 out | 6.8s · 415 out | 2.7s · 272 out
Reasoning · 243 tokens
We are asked to extract specific fields from the given document. The document is a prior authorization request.
Let's extract each field:
- diagnoses: ICD-10-CM codes that appear literally. We see "M23.221" and "M17.11". Both are diagnosis codes. So array: ["M23.221", "M17.11"]
- medications: current medications, lower-case generic names. "meloxicam" is a generic name. So array: ["meloxicam"]
- referring_npi: The referring provider's NPI, as a 10-digit string. The document says "REFERRING: Priya Raghunathan, DO NPI 1730558812". That's a 10-digit number. So array: ["1730558812"]. The servicing NPI is not included.
- dates_of_service: The procedure is scheduled 2026-10-02. That's a date of service. So array: ["2026-10-02"]
- allergies: "none known" so empty array: []
Check: no other codes, medications discontinued, etc. So the JSON should be.
Return exactly one fenced JSON block.json{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 347 tokens
We are asked to extract from the given document. Let's read the prompt.
The document is a prior authorization request. We need arrays for:
- diagnoses: ICD-10-CM codes that appear literally.
- medications: current medications, lower-case generic names, no dose/route/frequency. The document lists "meloxicam 15 mg daily" – generic name meloxicam, so "meloxicam" (lowercase).
- referring_npi: the NPI of the referring/ordering provider only. It says "REFERRING: Priya Raghunathan, DO NPI 1730558812". So that's the referring NPI. The servicing NPI is also shown but we only include the referring. So array with string "1730558812".
- dates_of_service: dates being requested or performed. The procedure is "diagnostic knee arthroscopy, right. Scheduled 2026-10-02." So date of service is 2026-10-02. No other dates. So array with "2026-10-02".
- allergies: "none known." So empty array [].
Diagnoses: The document lists "DIAGNOSES" with M23.221 and M17.11. Those are ICD-10-CM codes. We include them as strings.
So the JSON:
{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
All fields are arrays as required. No extra keys.json{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 204 tokens
We are asked to extract:
- "diagnoses": ICD-10-CM diagnosis codes from the document describing member's conditions. Document has: M23.221, M17.11. So array: ["M23.221", "M17.11"]
- "medications": current medications, generic name, lower-case. Document says "meloxicam 15 mg daily". So generic is meloxicam, lower-case. So array: ["meloxicam"]
- "referring_npi": NPI of referring provider. Referring: Priya Raghunathan, DO, NPI 1730558812. So array: ["1730558812"]
- "dates_of_service": dates of service requested/performed. Scheduled 2026-10-02. So array: ["2026-10-02"]
- "allergies": allergies listed. Document says "none known". So empty array [].
No other codes. Ensure no extraneous info. Output JSON.json{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}100GPT-5.6 Luna · OpenAI2.7s · 135 out | 30.5s · 142 out | 2.5s · 115 out
Reasoning · 74 tokens
**Clarifying JSON requirements**
I need to ensure the output matches the exact JSON format with the appropriate keys and values. It looks like I should include scheduled dates, like "2026-10-02," which possibly indicates a service request. I’m also noting that for diagnoses, literal codes are required, and medications need to be listed by their lower generic names. Additionally, since there are no known allergies, I should specify that as an empty array for "none known." I might also need to reference NPI.json{"diagnoses":["M23.221","M17.11"],"medications":["meloxicam"],"referring_npi":["1730558812"],"dates_of_service":["2026-10-02"],"allergies":[]}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 81 tokens
**Clarifying medical output requirements**
I need to focus on providing the exact keys in the arrays for the output. Using meloxicam as the current medication, I should reference the NPI. I wonder if the date of service (DOS) is scheduled or if the request has been performed. The wording indicates that "scheduled" shouldn’t include anticipated dates—so if it’s not booked yet, I might need to clarify that. For allergies, it seems none are known, so I can likely keep that array empty.json{"diagnoses":["M23.221","M17.11"],"medications":["meloxicam"],"referring_npi":["1730558812"],"dates_of_service":["2026-10-02"],"allergies":[]}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 54 tokens
**Clarifying JSON output details**
I need to ensure that the output follows the exact JSON format with the required keys and arrays. The current medication is meloxicam, and I’m referring to the NPI. I need to clarify whether the date of service (DOS) is scheduled and not just anticipated or not yet booked. The wording indicates it's scheduled, so I will include that. Lastly, since there are no known allergies, I'll represent that as an empty array, and I'll use "diagnoses" literally.json{"diagnoses":["M23.221","M17.11"],"medications":["meloxicam"],"referring_npi":["1730558812"],"dates_of_service":["2026-10-02"],"allergies":[]}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}100Claude Haiku 4.5 · Anthropic12.9s · 835 out | 11.6s · 993 out | 12.9s · 751 out
Reasoning · 754 tokens
The user is asking me to extract information from a prior authorization request document. Let me go through each required field:
1. **diagnoses** - ICD-10-CM diagnosis codes that appear LITERALLY in the document describing the member's own conditions.
- M23.221 Derangement of posterior horn of medial meniscus, right knee
- M17.11 Unilateral primary osteoarthritis, right knee
Both are present literally in the document and describe the member's conditions.
2. **medications** - Current medications as lower-case generic names with no dose, route, or frequency.
- meloxicam 15 mg daily → generic name is meloxicam, remove dose/frequency → "meloxicam"
The document doesn't say it was discontinued, so include it.
3. **referring_npi** - The National Provider Identifier of the REFERRING or ORDERING provider only, as a 10-digit string.
- Referring provider: Priya Raghunathan, DO with NPI 1730558812 (10 digits)
- Servicing provider: Mesquite Surgical Partners with NPI 1114477220 (10 digits)
I need only the referring NPI, not the servicing/rendering NPI.
So: 1730558812
4. **dates_of_service** - Dates of service being requested or performed.
- Scheduled 2026-10-02 - this is the date the procedure is scheduled, so it's a date of service being requested.
- The document doesn't have a date it was written (or if it does, it's not explicitly stated), so I shouldn't include that.
- 2026-10-02
5. **allergies** - Allergies and intolerances, lower-case, substance only.
- "none known" - so there are no allergies listed
- Empty array
Compiling this into the final JSON structure now. Double-checking each field matches the instructions: diagnoses codes appear verbatim, medication reduced to generic name only, referring NPI (not the servicing provider's) correctly identified as 1730558812, and the scheduled procedure date captured as the sole date of service.
No documented allergies means that array stays empty. Everything checks out against the requirements.
json{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 913 tokens
This is a document extraction task. I need to extract the following fields from the given prior authorization request:
1. **diagnoses**: ICD-10-CM diagnosis codes that appear LITERALLY in the document describing the member's own conditions. Should not assign codes for conditions named in words only, exclude procedure/supply/HCPCS codes, and exclude codes under family history.
Looking at the document:
- M23.221 Derangement of posterior horn of medial meniscus, right knee
- M17.11 Unilateral primary osteoarthritis, right knee
Both are listed under "DIAGNOSES" and are literally present in the document. These are both diagnosis codes describing the member's conditions.
2. **medications**: Current medications as lower-case generic names with no dose, route, or frequency. Convert brand names to generic. Omit discontinued/stopped medications.
The document states: "MEDICATIONS: meloxicam 15 mg daily."
Meloxicam is already a generic name. I should list it as "meloxicam" (no dose information).
3. **referring_npi**: The 10-digit NPI of the REFERRING or ORDERING provider only. The document shows:
- REFERRING: Priya Raghunathan, DO NPI 1730558812
- SERVICING: Mesquite Surgical Partners NPI 1114477220
The referring NPI is 1730558812, which is 10 digits. I should include only this one.
4. **dates_of_service**: Dates of service being requested or performed. Do not include anticipated dates not yet booked, or dates outside this request. Do not include the date the document was written unless it's also the date of service.
The document states: "Scheduled 2026-10-02"
This is the date of service for the diagnostic knee arthroscopy. I should include this as 2026-10-02.
Checking allergies next - the document notes "ALLERGIES: none known," so this field should be an empty array.
Pulling everything together: diagnoses ["M23.221", "M17.11"], medications ["meloxicam"], referring_npi ["1730558812"], dates_of_service ["2026-10-02"], and no allergies.
For dates_of_service, "Scheduled 2026-10-02" indicates a booked appointment, not merely anticipated, so it qualifies for inclusion. No document date is given, so that exclusion rule doesn't apply here either.
Finalizing: diagnoses ["M23.221", "M17.11"], medications ["meloxicam"], referring_npi ["1730558812"], dates_of_service ["2026-10-02"], allergies [].
json{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 644 tokens
I need to extract the fields requested in this document extraction task. Let me go through each required key:
1. **diagnoses**: ICD-10-CM diagnosis codes that appear LITERALLY in the document describing the member's own conditions.
- M23.221 Derangement of posterior horn of medial meniscus, right knee
- M17.11 Unilateral primary osteoarthritis, right knee
Both are explicitly listed and are diagnoses of the member's conditions. These should be included.
2. **medications**: Current medications as lower-case generic names with no dose, route, or frequency. Convert brand names to generic names. Omit discontinued medications.
- meloxicam 15 mg daily - this is a current medication. The generic name is "meloxicam". I need to remove the dose (15 mg) and frequency (daily) and make it lower case.
- Result: "meloxicam"
3. **referring_npi**: The National Provider Identifier of the REFERRING or ORDERING provider only, as a 10-digit string. If no complete 10-digit NPI is present, return empty array.
- REFERRING: Priya Raghunathan, DO NPI 1730558812
- This is 10 digits. It should be included.
- SERVICING: Mesquite Surgical Partners NPI 1114477220
- This is the servicing/rendering provider, not the referring provider, so it should NOT be included.
- Result: ["1730558812"]
4. **dates_of_service**: Dates of service being requested or performed, in YYYY-MM-DD format. Do not include anticipated dates, not yet booked, or outside this request. Do not include the date the document was written unless it is also the date of service.
- "Scheduled 2026-10-02" - this is the scheduled date for the procedure. This appears to be the date of service being requested.
- Result: ["2026-10-02"]
5. **allergies**: Allergies and intolerances, lower-case, substance only, with no reaction description.
- ALLERGIES: none known.
- Nothing to list here.
- Result: []
Checking everything once more:
- diagnoses: ["M23.221", "M17.11"] - both codes explicitly stated
- medications: ["meloxicam"] - generic name, stripped of dose/frequency
- referring_npi: ["1730558812"] - correctly excludes the servicing provider's NPI
- dates_of_service: ["2026-10-02"] - the scheduled procedure date
- allergies: [] - none known documented
This all checks out.
json{
"diagnoses": [
"M23.221",
"M17.11"
],
"medications": [
"meloxicam"
],
"referring_npi": [
"1730558812"
],
"dates_of_service": [
"2026-10-02"
],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}100DeepSeek V3.2 · DeepSeek3.0s · 67 out | 2.3s · 67 out | 2.0s · 67 out
json{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}json{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}json{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}93DeepSeek V4 Flash · DeepSeek83.0s · 2,939 out | 6.0s · 460 out | 4.4s · 473 out
Reasoning · 2,871 tokens
We need answer JSON extraction. Need fields arrays. Need parse document. Need include ICD-10 codes literally appearing and describe member conditions. Diagnoses: M23.221 and M17.11. Medications current: meloxicam (lowercase generic) no dose. Referral NPI? "REFERRING: Priya Raghunathan, DO NPI 1730558212"? Wait document says NPI 1730558212? Let's read: "REFERRING: Priya Raghunathan, DO NPI 1730558212"? Actually text: "NPI 1730558212"? The prompt: "REFERRING: Priya Raghunathan, DO NPI 1730558212"? Let me copy: "REFERRING: Priya Raghunathan, DO NPI 1730558212"? It says "NPI 1730558212"? Need exact. Let's parse: "REFERRING: Priya Raghunathan, DO NPI 1730558212"? It may be 1730558212? But in the prompt above: "REFERRING: Priya Raghunathan, DO NPI 1730558212"? Wait original user message:
"--- PRIOR AUTHORISATION REQUEST ---
REFERRING: Priya Raghunathan, DO NPI 1730558212
SERVICING: Mesquite Surgical Partners NPI 1114477220
MEMBER DOB 1985-08-30
Procedure requested: diagnostic knee arthroscopy, right. Scheduled 2026-10-02.
DIAGNOSES
M23.221 Derangement of posterior horn of medial meniscus, right knee
M17.11 Unilateral primary osteoarthritis, right knee
MEDICATIONS: meloxicam 15 mg daily.
ALLERGIES: none known."
Need referring NPI. The referring NPI is 1730558212? Actually format: "NPI 1730558212" maybe 10 digits: 1730558212? Let's count: 1-7-3-0-5-5-8-2-1-2 = 10? 1730558212 is 10 digits? 1 7 3 0 5 5 8 2 1 2 yes 10. Wait the prompt says "NPI 1730558212" because after "NPI" there's "1730558212". Yes. But the user message in first line: "REFERRING: Priya Raghunathan, DO NPI 1730558212" Actually I see "NPI 1730558212" not "1730558212". The prompt line: "REFERRING: Priya Raghunathan, DO NPI 1730558212" yes. But in the text above in this "system" it says "NPI 1730558212"? Let me check the original:
"REFERRING: Priya Raghunathan, DO NPI 1730558212
SERVICING: Mesquite Surgical Partners NPI 1114477220"
Actually "NPI 1730558212" maybe due to "NPI 1730558212" because the number is 1730558212. However I need not include servicing NPI, only referring/ordering provider. The referring NPI is 1730558212. Need ensure if exact includes maybe "1730558212" not "1730558212"? Let's consider if the number could be "1730558212" vs "1730558212": The displayed "NPI 1730558212" has digits: 1 7 3 0 5 5 8 2 1 2? Wait the string "1730558212" vs "1730558212": I need see from prompt: "NPI 1730558212" because it's "NPI 1730558212"? The text above: "REFERRING: Priya Raghunathan, DO NPI 1730558212" It appears "NPI 1730558212" (after 1730? maybe 558212). Let's parse: "NPI 1730558212" -> "1730558212" (10 digits: 1 7 3 0 5 5 8 2 1 2? Wait "1730558212" length 10: 1,7,3,0,5,5,8,2,1,2 yes). But if there were "1730558212" it would be 10 digits: 1,7,3,0,5,5,8,2,1,2? Hmm no "1730558212" includes after 1730: 558212 = 6 digits => total 4+6=10. "1730558212" after 1730: 558212? Actually "1730558212" sequence: 1 7 3 0 5 5 8 2 1 2? Let's write: 1730558212 = 1730 558212. Yes. The prompt: "NPI 1730558212" maybe due to font: "1730558212" not "1730558212". Wait the user message: "REFERRING: Priya Raghunathan, DO NPI 1730558212" I can see "NPI 1730558212" with maybe "NPI 1730558212"? The exact after NPI: "1730558212" vs "1730558212" impossible from text? Let's copy from prompt at top:
"REFERRING: Priya Raghunathan, DO NPI 1730558212
SERVICING: Mesquite Surgical Partners NPI 1114477220"
Actually in the visible message above in this interface: "REFERRING: Priya Raghunathan, DO NPI 1730558212" I see "NPI 1730558212" (number is 1730558212? Wait "1730558212" has "1730 558212"; the displayed "NPI 1730558212" maybe exactly "1730558212". Let me not overcomplicate; I'll output the string as appears: "1730558212"? Need determine if leading digits might be "1730558212" vs "1730558212"? The document line: "REFERRING: Priya Raghunathan, DO NPI 1730558212" The substring after NPI is "1730558212"? Let's count characters in "1730558212": 1 7 3 0 5 5 8 2 1 2 = 10. If the displayed is "1730558212", yes. But I see "1730558212" maybe missing a "5"? Let's compare with servicing NPI: "1114477220" 10 digits. The referring NPI likely "1730558212"? Wait the prompt first line: "REFERRING: Priya Raghunathan, DO NPI 1730558212" Could be "NPI 1730558212" where after "1730" there is "558212". Yes.
Need dates_of_service: "Scheduled 2026-10-02" is requested/planned, is that a date of service being requested? The field says dates of service being requested or performed. This is a prior authorization request for scheduled procedure 2026-10-02. It's a requested date of service. Include? "Do not include dates that the document describes as anticipated, not yet booked, or outside this request." The date is scheduled, requested. It's in future but part of prior auth. It says "Procedure requested ... Scheduled 2026-10-02." So yes include "2026-10-02". Need not include written date none. Allergies: "none known" => no allergies, empty array. Medications: meloxicam 15 mg daily -> include "meloxicam"; omit dose. Maybe "meloxicam" generic. Need output arrays of strings. "referring_npi" array? The keys require every one an array of strings. For referring_npi, if present return array with "1730558212". Need be careful: "referring_npi" should be "National Provider Identifier of the REFERRING or ORDERING provider only". It's one. Include array with string.
Need "diagnoses" include codes that appear literally and describe member conditions. M23.221 and M17.11. Should include codes only, not descriptions. Yes.
Need "medications" current medications. Convert brand to generic. No brand. Include "meloxicam" lower-case. Should include exact as lower-case. Maybe "meloxicam" no dosage. Good.
Need "allergies" empty array because none known. Should we include "none known"? No, allergies field should list allergies or intolerances, substance only. None known => no allergies. Empty array.
Need "dates_of_service" include scheduled date "2026-10-02". Maybe include only if "performed"? The instruction says "dates of service being requested or performed" includes requested. So yes.
Need final JSON with keys in order? The required keys: diagnoses, medications, referring_npi, dates_of_service, allergies? The prompt says keys every one an array of strings. Order doesn't matter but can use same. Need output JSON code block only.
Let's produce:
```json
{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558212"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
```
Need ensure no extra keys. Good.
But let's double-check if referring NPI should be "1730558212" or "1730558212"? The line in prompt: "REFERRING: Priya Raghunathan, DO NPI 1730558212" Let me examine: The substring "1730558212" maybe I can identify from the prompt text in the user message:
"REFERRING: Priya Raghunathan, DO NPI 1730558212
SERVICING: Mesquite Surgical Partners NPI 1114477220"
Maybe the number "1730558212" is 10 digits, but I might be seeing "1730558212" because the font uses "0" after 3? Wait "NPI 1730558212" includes "1730 558212". The first six? Let's map: Pos1 1, pos2 7, pos3 3, pos4 0, pos5 5, pos6 5, pos7 8, pos8 2, pos9 1, pos10 2. So yes 1730558212. I can output. If actual was "1730558212" (i.e., 1 7 3 0 5 5 8 2 1 2? same? Wait "1730558212" has after 1730 "558212"; "1730558212" would after 1730 "558212"? no "1730558212" vs "1730558212": Let's compare:
- 1730558212: digits: 1 7 3 0 5 5 8 2 1 2
- 1730558212: digits: 1 7 3 0 5 5 8 2 1 2? Actually both same? Wait I'm confused. "1730558212" has 8 digits after 1730? Let's write "1730558212" as groups: 173 055 8212? No, "1730558212" length? Count "1730558212": 1(1) 7(2) 3(3) 0(4) 5(5) 5(6) 8(7) 2(8) 1(9) 2(10). Yes. "1730558212" also same? The difference is "1730558212" vs "1730558212": There is "55" after "0" in both? Let's look: "1730558212" has a "0" at position 4, "5" at position 5, "5" at position 6, "8" at pos 7, "2" pos 8, "1" pos 9, "2" pos 10. So it's exactly 1730558212. If someone typed "1730558212" maybe also? no.
Need maybe include "M23.221" and "M17.11" with no period? ICD-10 codes include decimal? The document has "M23.221" and "M17.11" with periods. Should include exactly with period? Typically codes include decimal for subcategory, but ICD-10-CM codes can be stored without decimal. The task says "ICD-10-CM diagnosis codes that appear LITERALLY in the document", so include as they appear with period? It says "M23.221" and "M17.11". Include with decimal. Good.
Need all good.json{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558212"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 0,
"precision": 0,
"recall": 0,
"tp": 0,
"fp": 1,
"fn": 1,
"gold": [
"1730558812"
],
"got": [
"1730558212"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 0.8,
"microRecall": 0.8,
"hallucinated": 1
}Reasoning · 392 tokens
We need answer extraction task. Need produce JSON with arrays strings. Need analyze document.
We have prior auth request. Referring NPI 1730558812 -> referring provider only. Servicing NPI 1114477220 should not include. Need array strings? "referring_npi" required array of strings. So ["1730558812"].
Diagnoses: ICD-10-CM codes appearing literally and describe member's own conditions. Codes: M23.221, M17.11. Include both. Do not include family history. None. Format? As strings likely "M23.221", "M17.11". Need maybe standard formatting no decimals? They appear literally with decimals. Gold likely "M23.221", "M17.11". Use exactly literal? "M23.221" includes decimal. ICD codes appear literally with dot. Should include as written? Usually extraction expects codes maybe with decimal? The prompt says codes that appear LITERALLY. So include with decimal. We'll include "M23.221" and "M17.11".
Medications: current medications lower-case generic names no dose route frequency. "meloxicam 15 mg daily." -> generic "meloxicam". Include. Convert brand none. Omit discontinued none. ["meloxicam"].
Dates_of_service: procedure scheduled 2026-10-02. Do not include anticipated, not yet booked, outside this request. "Scheduled" is a requested service date. Include "2026-10-02". Need only dates of service being requested/performed. Yes.
Allergies: none known -> likely empty array? The document says "ALLERGIES: none known." Allergies field asks allergies and intolerances, lower-case, substance only. None known -> no allergies. Return empty array [].
Need exactly JSON code block. Ensure keys all arrays of strings. No extra keys. Output.json{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}Reasoning · 424 tokens
We need to extract fields per instructions. The document is a prior authorization request. Required JSON keys: diagnoses, medications, referring_npi, dates_of_service, allergies.
Diagnoses: ICD-10 codes that appear literally. The document lists M23.221 and M17.11. Also the words "Derangement of posterior horn of medial meniscus, right knee" and "Unilateral primary osteoarthritis, right knee" but those are descriptions, not codes. The codes are literally present. So diagnoses: ["M23.221", "M17.11"].
Medications: current medications, lower-case generic names, no dose. The document says "meloxicam 15 mg daily." So generic name is meloxicam. No indication it's discontinued. So medications: ["meloxicam"].
Referring NPI: the referring provider's NPI. The referring is Priya Raghunathan, DO, NPI 1730558812. The servicing NPI is 1114477220 but we exclude that. So referring_npi: ["1730558812"].
Dates of service: the procedure scheduled 2026-10-02. That is the requested/performance date. Do not include anticipated? The instruction says "Do not include dates that the document describes as anticipated, not yet booked, or outside this request." The date is scheduled, so it is the date of service? The prompt says "Procedure requested: diagnostic knee arthroscopy, right. Scheduled 2026-10-02." That is the date of the procedure being requested/performance. So we include it. So dates_of_service: ["2026-10-02"].
Allergies: "ALLERGIES: none known." So no allergies. Return empty array.
Thus the JSON. Ensure all keys are arrays of strings.
Also note: "referring_npi" is an array of strings per required keys. So we put a 10-digit string as an element.
Final answer.json{
"diagnoses": ["M23.221", "M17.11"],
"medications": ["meloxicam"],
"referring_npi": ["1730558812"],
"dates_of_service": ["2026-10-02"],
"allergies": []
}
Grader detail
json{
"perField": [
{
"field": "diagnoses",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 2,
"fp": 0,
"fn": 0,
"gold": [
"M23.221",
"M17.11"
],
"got": [
"M23.221",
"M17.11"
]
},
{
"field": "medications",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"meloxicam"
],
"got": [
"meloxicam"
]
},
{
"field": "referring_npi",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"1730558812"
],
"got": [
"1730558812"
]
},
{
"field": "dates_of_service",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 1,
"fp": 0,
"fn": 0,
"gold": [
"2026-10-02"
],
"got": [
"2026-10-02"
]
},
{
"field": "allergies",
"f1": 1,
"precision": 1,
"recall": 1,
"tp": 0,
"fp": 0,
"fn": 0,
"gold": [],
"got": []
}
],
"microPrecision": 1,
"microRecall": 1,
"hallucinated": 0
}