Prospective stratification of patients at risk for emergency department revisit: resource utilization and population management strategy implications. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Jin, Bo; Zhao, Yifan; Hao, Shiying; Shin, Andrew; Wang, Yue; Zhu, Chunqing; Hu, Zhongkai; Fu, Changlin; Ji, Jun; Wang, Yong; Zhao, Yingzhen; Jiang, Yunliang; Dai, Dorothy; Culver, Devore; Alfreds, Shaun; Rogow, Todd; Stearns, Frank; Sylvester, Karl; Widen, Eric; Ling, Xuefeng
• Is Part Of: BMC emergency medicine. Volume 16:Issue 1(2016)
• Page Start: 1
• Page End: 13
• Publication Date: 2016-12
• Subjects: ED revisit prediction -- Prospective validation -- Statistical modeling -- EMR; Emergency medicine -- Periodicals; 616.02505
• DOI: 10.1186/s12873-016-0074-5
• Languages: English
• ISSNs: 1471-227X

Abstract

Abstract Background Estimating patient risk of future emergency department (ED) revisits can guide the allocation of resources, e.g. local primary care and/or specialty, to better manage ED high utilization patient populations and thereby improve patient life qualities. Methods We set to develop and validate a method to estimate patient ED revisit risk in the subsequent 6 months from an ED discharge date. An ensemble decision-tree-based model with Electronic Medical Record (EMR) encounter data from HealthInfoNet (HIN), Maine's Health Information Exchange (HIE), was developed and validated, assessing patient risk for a subsequent 6 month return ED visit based on the ED encounter-associated demographic and EMR clinical history data. A retrospective cohort of 293, 461 ED encounters that occurred between January 1, 2012 and December 31, 2012, was assembled with the associated patients' 1-year clinical histories before the ED discharge date, for model training and calibration purposes. To validate, a prospective cohort of 193, 886 ED encounters that occurred between January 1, 2013 and June 30, 2013 was constructed. Results Statistical learning that was utilized to construct the prediction model identified 152 variables that included the following data domains: demographics groups (12), different encounter history (104), care facilities (12), primary and secondary diagnoses (10), primary and secondary procedures (2), chronic disease condition (1), laboratory test results (2), and outpatient prescription medications (9). Thec -statistics for the retrospective and prospective cohorts were 0.742 and 0.730 respectively. Total medical expense and ED utilization by risk score 6 months after the discharge were analyzed. Cluster analysis identified discrete subpopulations of high-risk patients with distinctive resource utilization patterns, suggesting the need for diversified care management strategies. Conclusions Integration of our method into the HIN secure statewide data system in real time prospectively validated its performance. It promises to provide increased opportunity for high ED utilization identification, and optimized resource and population management.