A Step Toward Identifying Sources of Medical Errors: Modeling Standards of Care Deviations for Different Disease States

• Published in: Military medicine 2018-03, Vol.183 (suppl_1), p.105-110
• Main Authors: Fidopiastis, Cali M, Venta, Kim E, Baker, Erin G, Stanney, Kay M
• Format: Article
• Language: English
• Subjects: Adult; Boolean; Computer Simulation - standards; Computer Simulation - statistics & numerical data; Decision making; Electronic health records; Electronic Health Records - instrumentation; Electronic Health Records - statistics & numerical data; Female; Guideline Adherence - standards; Humans; Male; Medical Errors; Middle Aged; Military medicine; Patient safety; Physicians - standards; Physicians - statistics & numerical data; Retrospective Studies; Standard of care; Standard of Care - trends
• ISSN: 0026-4075; 1930-613X
• DOI: 10.1093/milmed/usx203

Abstract Objective To examine the feasibility of utilizing electronic health records (EHR) to determine a metric for identifying physician diagnostic and treatment deviations in standards of care for different disease states. Methods A Boolean-rule-based model compared deviations in standards of care across four disease states: diabetes, cardiovascular disease, asthma, and rheumatoid arthritis. This metric was used to identify the relationship between physician deviations in standards of care procedures, before and after diagnosis, for 76 internal medicine physicians. Results The Boolean-rule-based model identified patterns of standards of care deviation for the physicians before diagnosis and during treatment. The deviations identified for each of the four disease states were then related to Continuing Medical Education courses that could support further training. The rule-based model was extended and improved by including system and process aspects of medical care that are not specifically related to the physician, yet potentially have an impact on the physician’s decision to deviate from the standards of care. Conclusion The Boolean-rule-based approach provided a means to systematically mine EHRs and use these data to assess deviations in standards of care that could identify quality of care issues stemming from system processes or the need for specific CME for a physician.