Clinical Review

Free Clinic Diagnosis Data Improvement Project Using International Classification of Diseases and Electronic Health Record



From Pacific Lutheran School of Nursing, Tacoma, WA.

Objective: This quality improvement project aimed to enhance The Olympia Free Clinic’s (TOFC) data availability using International Classification of Diseases (ICD) code entry into the electronic health record (EHR). Prior to this project, TOFC lacked quality diagnosis data. This project strived to answer questions like “How many TOFC patients have diabetes?”

Methods: A new system was implemented for inputting ICD codes into Practice Fusion, the clinic’s EHR. During the initial phase, TOFC’s 21 volunteer providers entered the codes associated with the appropriate diagnosis for each of 157 encounters using a simplified map of options, including a map of the 20 most common diagnoses and a more comprehensive 60-code map.

Results: An EHR report found that 128 new diagnoses were entered during project implementation, hypertension being the most common diagnosis, followed by depression, then posttraumatic stress disorder.

Conclusion: The knowledge of patient diagnoses enabled the clinic to make more-informed decisions.

Keywords: free clinic, data, quality improvement, electronic health record, International Classification of Diseases

Data creates a starting point, a goal, background, understanding of needs and context, and allows for tracking and improvement over time. This quality improvement (QI) project for The Olympia Free Clinic (TOFC) implemented a new system for tracking patient diagnoses. The 21 primary TOFC providers were encouraged to input mapped International Statistical Classification of Diseases and Related Health Problems (ICD) codes into the electronic health record (EHR). The clinic’s providers consisted of mostly retired, but some actively practicing, medical doctors, doctors of osteopathy, nurse practitioners, physician assistants, and psychiatrists.

Previous to this project, the clinic lacked any concrete data on patient demographics or diagnoses. For example, the clinic was unable to accurately answer the National Association of Free and Charitable Clinics’ questions about how many patients TOFC providers saw with diabetes, hypertension, asthma, and hyperlipidemia.1 Additionally, the needs of the clinic and its population were based on educated guesses.


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