Linking Electronic Health Record-Extracted Psychosocial Data in Real-Time to Risk of Readmission for Heart Failure

• Published in: Psychosomatics (Washington, D.C.) D.C.), 2011-07, Vol.52 (4), p.319-327
• Main Authors: Watson, Alice J., M.D., M.P.H, O'Rourke, Julia, Ph.D., M.S, Jethwani, Kamal, M.D., M.P.H, Cami, Aurel, Ph.D, Stern, Theodore A., M.D, Kvedar, Joseph C., M.D, Chueh, Henry C., M.D., M.S, Zai, Adrian H., M.D., Ph.D., M.P.H
• Format: Article
• Language: English
• Subjects: Aged; Biological and medical sciences; Cardiology. Vascular system; Dementia - complications; Depression - complications; Electronic Health Records; Female; Heart; Heart Failure - etiology; Heart Failure - psychology; Heart Failure - therapy; Heart failure, cardiogenic pulmonary edema, cardiac enlargement; Humans; Internal Medicine; Logistic Models; Male; Medical Record Linkage; Medical sciences; Miscellaneous; Patient Compliance - statistics & numerical data; Patient Readmission - statistics & numerical data; Psychiatry; Psychology; Psychology. Psychoanalysis. Psychiatry; Psychopathology. Psychiatry; Risk Factors; Social psychiatry. Ethnopsychiatry; Time Factors; Treatment Refusal - statistics & numerical data
• ISSN: 0033-3182; 1545-7206
• DOI: 10.1016/j.psym.2011.02.007

Background Knowledge of psychosocial characteristics that helps to identify patients at increased risk for readmission for heart failure (HF) may facilitate timely and targeted care. Objective We hypothesized that certain psychosocial characteristics extracted from the electronic health record (EHR) would be associated with an increased risk for hospital readmission within the next 30 days. Methods We identified 15 psychosocial predictors of readmission. Eleven of these were extracted from the EHR (six from structured data sources and five from unstructured clinical notes). We then analyzed their association with the likelihood of hospital readmission within the next 30 days among 729 patients admitted for HF. Finally, we developed a multivariable predictive model to recognize individuals at high risk for readmission. Results We found five characteristics—dementia, depression, adherence, declining/refusal of services, and missed clinical appointments—that were associated with an increased risk for hospital readmission: the first four features were captured from unstructured clinical notes, while the last item was captured from a structured data source. Conclusions Unstructured clinical notes contain important knowledge on the relationship between psychosocial risk factors and an increased risk of readmission for HF that would otherwise have been missed if only structured data were considered. Gathering this EHR-based knowledge can be automated, thus enabling timely and targeted care.