Automate Clinical Documentation Review with AI

Small primary care practices often rely on the built-in features of their EHR, like Athenahealth or eClinicalWorks. These platforms are excellent for data entry and claim submission but offer limited intelligence for documentation review. Their 'rules engines' can check for missing signatures or required fields, but they cannot interpret the unstructured text where the real clinical story lives.

Consider this common scenario: a physician's note mentions a 25-minute visit discussing three chronic conditions, but the submitted CPT code is for a standard 15-minute visit. The EHR sees a valid code and a signed note, so it passes the check. The practice loses revenue because no one had time to manually read the note and catch the under-coding. This happens dozens of times a week, costing the practice thousands of dollars monthly.

The structural problem is that EHRs are designed as databases, not language-processing engines. They treat the narrative note as a simple text blob. They lack the architecture to connect concepts within the text (e.g., 'discussed medication adherence for hypertension') to the structured data (e.g., CPT code 99213). This forces administrative staff to act as human middleware, manually reading every note to bridge the gap between clinical narrative and billing data.

The first step is a discovery audit of your current workflow and EHR system. Syntora would analyze 100-200 de-identified clinical notes to understand your documentation patterns and identify the most time-consuming review tasks. This audit determines the specific logic the AI needs, whether it's consistency checks for E/M coding, flagging missing MIPS measures, or drafting referral summaries.

The technical approach involves a HIPAA-compliant pipeline on AWS. A Python script running on AWS Lambda would retrieve daily chart exports from your EHR. Each note would be processed by the Claude API, which is adept at parsing clinical language. The AI would follow a set of custom instructions to extract key information, compare it against billing data, and output a structured summary of its findings. All processing is logged in a Supabase database to provide a complete audit trail.

The delivered system is a simple, secure dashboard that your practice manager or lead MA would use. It presents a queue of charts that require human attention, with clear explanations like, 'Note details support a Level 4 visit, but Level 3 was coded.' This allows your team to focus their 10 hours per week on high-judgment exceptions instead of routine checks, fitting directly into the existing review process without replacing your EHR.