Introducing Co-Medic Research: Identify patient populations in primary care

From patient data to population research

Primary care holds an enormous amount of patient data, lab results, diagnoses, prescriptions, vital signs, specialist letters, but accessing it at population level has always been the bottleneck. Identifying a cohort like "patients with elevated NT-proBNP who are not on an ACE inhibitor" typically means weeks of manual chart review or custom database queries.

Co-Medic Research changes this. Describe the cohort you need, and Co-Medic evaluates the entire patient population, pulling from both coded EHR data and unstructured clinical text. The result is a structured, queryable population table ready for analysis.

How it works

Every data point in the practice, queryable

Each column in a population table maps to a specific type of clinical data. The system currently supports over 20 column types, including:

Enrichments: when structured data is not enough

Not every clinical fact lives in a neatly coded field. A diagnosis mentioned only in a discharge letter, a lab value buried in a PDF report, a medication change described in free text, this is the reality of primary care data.

Data coverage across a practice population

When the Research Tool encounters gaps in structured data, Co-Medic runs targeted enrichments to fill them. The system identifies exactly which patients are missing which data points, then searches and extracts from unstructured sources, specialist letters, discharge reports, clinical notes.

Every enriched value links back to the exact document and passage it was extracted from. Nothing is a black box, you can always verify where a data point came from.

A practical example: diabetes screening

A practice wants to get a complete picture of their diabetic population. They build a population query with five criteria, Type 2 diabetes diagnosis, HbA1c, eGFR, LDL cholesterol, and metformin use, and evaluate every patient in the practice in seconds.

Each column resolves data from the patient's clinical record, lab results by LOINC code, medications by ATC classification, diagnoses by ICPC-2 and SNOMED-CT. Where data is missing, Co-Medic can run targeted enrichments to extract values from unprocessed letters and reports.

From question to cohort

You do not need to configure every criterion by hand. Co-Medic Research includes a conversational AI agent that helps you build population queries through natural language. Describe your research question: "Find patients with CKD stage 3+ who are not on SGLT2 inhibitors" , and the agent translates that into the right criteria, enrichments, and thresholds. You stay in control; the AI handles the wiring.