Journal Article

Risk Prediction of Emergency Department Revisit 30 Days Post Discharge: A Prospective Study

Zhao, Yifan, Stearns, Frank, Li, Zhen, Zhu, Chunqing, Ji, Jun, Culver, Devore S., Widen, Eric, Sylvester, Karl G., Rogow, Todd, Ling, Xuefeng B., Alfreds, Shaun T., Jin, Bo, Wang, Yong, Hao, Shiying, Dai, Dorothy, Shin, Andrew Young, Hu, Zhongkai, Zhao, Yingzhen, Fu, Changlin

2014

Overview

Among patients who are discharged from the Emergency Department (ED), about 3% return within 30 days. Revisits can be related to the nature of the disease, medical errors, and/or inadequate diagnoses and treatment during their initial ED visit. Identification of high-risk patient population can help device new strategies for improved ED care with reduced ED utilization. A decision tree based model with discriminant Electronic Medical Record (EMR) features was developed and validated, estimating patient ED 30 day revisit risk. A retrospective cohort of 293,461 ED encounters from HealthInfoNet (HIN), Maine's Health Information Exchange (HIE), between January 1, 2012 and December 31, 2012, was assembled with the associated patients' demographic information and one-year clinical histories before the discharge date as the inputs. To validate, a prospective cohort of 193,886 encounters between January 1, 2013 and June 30, 2013 was constructed. The c-statistics for the retrospective and prospective predictions were 0.710 and 0.704 respectively. Clinical resource utilization, including ED use, was analyzed as a function of the ED risk score. Cluster analysis of high-risk patients identified discrete sub-populations with distinctive demographic, clinical and resource utilization patterns. Our ED 30-day revisit model was prospectively validated on the Maine State HIN secure statewide data system. Future integration of our ED predictive analytics into the ED care work flow may lead to increased opportunities for targeted care intervention to reduce ED resource burden and overall healthcare expense, and improve outcomes.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Ambulatory care / Analytics / Biology and Life Sciences / Cluster analysis / Computer and Information Sciences / Data exchange / Data systems / Decision trees / Demographics / Disease / Electronic health records / Electronic medical records / Emergency Medical Services / Female / Gene expression / Health care / Health care expenditures / Health care policy / Hospitals / Humans / Maine / Male / Mathematical models / Medical diagnosis / Medical electronics / Medical errors / Medical Records Systems, Computerized / Medicine and Health Sciences / Models, Theoretical / Patients / Predictions / Principal components analysis / Prospective Studies / Resource utilization / Retrospective Studies / Risk analysis / Risk Factors / Risk groups / Statistical analysis / Studies / Surgery / Time Factors