Hire an LLM Engineer, Phoenix

Hire an LLM engineer in Phoenix.

Phoenix healthcare systems, Banner Health, Dignity Health, Valleywise, and insurance companies including Blue Cross Blue Shield of Arizona and State Farm's regional operations use large teams for medical record abstraction: pulling quality measures, clinical findings, and risk scores from patient charts for value-based care programs.

A health information specialist earns $50,000 to $60,000 per year and abstracts 20 to 30 records per day. An LLM abstraction pipeline processes 200 to 300 records per hour. For a 10-person abstraction team, replacing 60% of the volume with the pipeline saves $300,000 to $350,000 per year in labor.

How we work

Tell us your current abstraction team size and the measure set you are abstracting for.

The cost of a Phoenix healthcare abstraction team.

$55k

Average HIS salary per year

Fully loaded: $72-80k

25/day

Records abstracted per specialist

Clean inpatient records

$550k

Annual cost of a 10-person team

Salary alone, before management overhead

$300–350k

Annual savings replacing 60% of volume

Net of pipeline operating costs

What replacing 60% of abstraction volume requires.

The 60% figure comes from the distribution of record complexity. In a typical Banner Health or Dignity Health abstraction program, 60 to 70% of records are straightforward: the required data elements are present in expected locations, the chart is complete, and the measure criteria are unambiguous. These abstract in under 30 seconds with 95%+ accuracy using the LLM pipeline.

The remaining 30 to 40% have characteristics that reduce automation confidence: missing or inconsistent dates, split encounters, conflicting diagnoses, or incomplete documentation. These go to a human reviewer with the model's partial extraction pre-filled, reducing their time per record from 7 minutes to 2 to 3 minutes.

Running cost at 10,000 records per month: $600 to $1,500 in API costs depending on model selection and average chart length. Infrastructure (EHR integration, job queue, review interface) adds $300 to $600 per month. Total operating cost: $900 to $2,100/month versus $46,000/month for a 10-person team.

Accuracy against HEDIS and CMS quality measure specifications is measured before go-live against a 500-record labeled test set. Required accuracy levels are agreed before work starts.

Components of the Phoenix abstraction pipeline.

EHR chart retrieval

Pulls patient charts from Epic or Cerner via FHIR API, or from bulk export files. Structures the chart data before the LLM step.

Measure-specific extraction

Separate extraction prompts per measure type. HEDIS colonoscopy extraction differs from blood pressure extraction, they are not the same prompt.

Multi-source assembly

Combines data from claims, lab results, and clinical notes into a single measure-level record. Handles evidence split across multiple document types.

Confidence scoring and routing

Records above the confidence threshold process straight through. Records below threshold go to the human review queue with partial extraction pre-filled.

HIM reviewer interface

Review queue interface showing the chart alongside the model's extraction. Reviewers correct fields and approve records. Decision logged with reviewer ID.

Quality management system integration

Approved records write back to Epic, Cerner, or your quality management platform (Crimson, Quantros) via API. Field mapping documented.

Questions from Phoenix healthcare abstraction teams.

How does the LLM abstraction pipeline handle HEDIS measure extraction from Banner Health records?: HEDIS measures require extracting specific clinical events (colonoscopy date, HbA1c result, blood pressure reading) from a combination of claims data, lab results, and clinical notes. The pipeline has three extraction paths: one for structured claims data (direct field mapping), one for lab results PDFs (value extraction with reference range validation), and one for clinical notes (LLM extraction with HEDIS-specific prompts). The three outputs are assembled into a measure-level record. The pipeline flags records where the evidence for a measure is ambiguous or split across multiple document types.
What does the manual abstraction replacement actually look like at Valleywise Health?: A health information specialist at Valleywise abstracts a medical record by opening the chart, navigating to the relevant sections, finding the required data elements, and entering them into the abstraction tool. At 25 records per day, that is 5 to 7 minutes per record for clean inpatient records, longer for complex outpatient records with multiple visits. The LLM pipeline pulls the chart from the EHR API or file export, runs the extraction, and writes the result to the abstraction tool in under 30 seconds for 80 to 85% of records. The remaining 15 to 20% go to a human queue with the model's partial extraction pre-filled.
How do you ensure the pipeline meets BCBS Arizona or State Farm's accuracy requirements for value-based care reporting?: Accuracy requirements for value-based care abstraction typically run at 95% or above for primary clinical indicators, with lower tolerance for certain measure types. We set up a labeled test set of 500 records with gold-standard abstractions verified by a certified HIM professional. We measure precision and recall per measure type and per data element category. Before go-live, you receive an accuracy report showing the pipeline's performance against your specific measure set. For measure types that fall below threshold, we either improve the extraction prompt or set a confidence threshold that routes them to human review.
What integration options exist for writing abstraction results back into an Epic or Cerner system?: For Epic, we use the SMART on FHIR API for writing discrete data elements back to the patient chart, or a bulk import via the Epic Bulk Data API for large-volume programs. For Cerner (Oracle Health), the FHIR R4 API supports write operations for most clinical data types. For abstraction programs that use a separate quality management platform (like Crimson or Quantros), we build a direct API integration to that system rather than routing through the EHR. The integration path depends on which system holds the final abstraction record.

Ready to reduce your abstraction team's manual volume?

Tell us your team size, the measure set you are abstracting for, and your EHR system. We will send a labor savings estimate and scope within one business day.

Hire an LLM engineer in Phoenix.

What replacing 60% of abstraction volume requires.

Accuracy against HEDIS and CMS quality measure specifications is measured before go-live against a 500-record labeled test set. Required accuracy levels are agreed before work starts.

Components of the Phoenix abstraction pipeline.

EHR chart retrieval

Pulls patient charts from Epic or Cerner via FHIR API, or from bulk export files. Structures the chart data before the LLM step.

Measure-specific extraction

Separate extraction prompts per measure type. HEDIS colonoscopy extraction differs from blood pressure extraction, they are not the same prompt.

Multi-source assembly

Combines data from claims, lab results, and clinical notes into a single measure-level record. Handles evidence split across multiple document types.

Confidence scoring and routing

Records above the confidence threshold process straight through. Records below threshold go to the human review queue with partial extraction pre-filled.

HIM reviewer interface

Review queue interface showing the chart alongside the model's extraction. Reviewers correct fields and approve records. Decision logged with reviewer ID.

Quality management system integration

Approved records write back to Epic, Cerner, or your quality management platform (Crimson, Quantros) via API. Field mapping documented.

Questions from Phoenix healthcare abstraction teams.

How does the LLM abstraction pipeline handle HEDIS measure extraction from Banner Health records?

HEDIS measures require extracting specific clinical events (colonoscopy date, HbA1c result, blood pressure reading) from a combination of claims data, lab results, and clinical notes. The pipeline has three extraction paths: one for structured claims data (direct field mapping), one for lab results PDFs (value extraction with reference range validation), and one for clinical notes (LLM extraction with HEDIS-specific prompts). The three outputs are assembled into a measure-level record. The pipeline flags records where the evidence for a measure is ambiguous or split across multiple document types.

What does the manual abstraction replacement actually look like at Valleywise Health?

A health information specialist at Valleywise abstracts a medical record by opening the chart, navigating to the relevant sections, finding the required data elements, and entering them into the abstraction tool. At 25 records per day, that is 5 to 7 minutes per record for clean inpatient records, longer for complex outpatient records with multiple visits. The LLM pipeline pulls the chart from the EHR API or file export, runs the extraction, and writes the result to the abstraction tool in under 30 seconds for 80 to 85% of records. The remaining 15 to 20% go to a human queue with the model's partial extraction pre-filled.

How do you ensure the pipeline meets BCBS Arizona or State Farm's accuracy requirements for value-based care reporting?

Accuracy requirements for value-based care abstraction typically run at 95% or above for primary clinical indicators, with lower tolerance for certain measure types. We set up a labeled test set of 500 records with gold-standard abstractions verified by a certified HIM professional. We measure precision and recall per measure type and per data element category. Before go-live, you receive an accuracy report showing the pipeline's performance against your specific measure set. For measure types that fall below threshold, we either improve the extraction prompt or set a confidence threshold that routes them to human review.

What integration options exist for writing abstraction results back into an Epic or Cerner system?

For Epic, we use the SMART on FHIR API for writing discrete data elements back to the patient chart, or a bulk import via the Epic Bulk Data API for large-volume programs. For Cerner (Oracle Health), the FHIR R4 API supports write operations for most clinical data types. For abstraction programs that use a separate quality management platform (like Crimson or Quantros), we build a direct API integration to that system rather than routing through the EHR. The integration path depends on which system holds the final abstraction record.

Ready to reduce your abstraction team's manual volume?

Tell us your team size, the measure set you are abstracting for, and your EHR system. We will send a labor savings estimate and scope within one business day.