Development, Validation and Deployment of a Real Time 30 Day Hospital Readmission Risk Assessment Tool in the Maine Healthcare Information Exchange

PLoS One

Andrew Young Shin¹  Karl G. Sylvester²  Jin Luo²  Le Zheng²  Shiying Hao²  Yicheng Wang²  Xuefeng B. Ling²  Yue Wang²  Chunqing Zhu³  Changlin Fu³  Frank Stearns³  Bo Jin³  Zhongkai Hu³  Eric Widen³  Dorothy Dai³  Shaun T. Alfreds⁴  Todd Rogow⁴  Devore S. Culver⁴  Min Huang⁵ 

[1] Departments of Pediatrics, Stanford University, Stanford, California, United States of America;Departments of Surgery, Stanford University, Stanford, California, United States of America;HBI Solutions Inc., Palo Alto, California, United States of America;HealthInfoNet, Portland, Maine, United States of America;Shanghai Children's Hospital, Shanghai Jiao Tong University, Shanghai, China

关键词: Inpatients;    Maine;    Hospitals;    Decision trees;    Principal component analysis;    Trees;    Geriatrics;    Machine learning;   

DOI  :  10.1371/journal.pone.0140271

学科分类：医学（综合）

来源: Public Library of Science

【 摘 要 】

Objectives Identifying patients at risk of a 30-day readmission can help providers design interventions, and provide targeted care to improve clinical effectiveness. This study developed a risk model to predict a 30-day inpatient hospital readmission for patients in Maine, across all payers, all diseases and all demographic groups. Methods Our objective was to develop a model to determine the risk for inpatient hospital readmission within 30 days post discharge. All patients within the Maine Health Information Exchange (HIE) system were included. The model was retrospectively developed on inpatient encounters between January 1, 2012 to December 31, 2012 from 24 randomly chosen hospitals, and then prospectively validated on inpatient encounters from January 1, 2013 to December 31, 2013 using all HIE patients. Results A risk assessment tool partitioned the entire HIE population into subgroups that corresponded to probability of hospital readmission as determined by a corresponding positive predictive value (PPV). An overall model c-statistic of 0.72 was achieved. The total 30-day readmission rates in low (score of 0–30), intermediate (score of 30–70) and high (score of 70–100) risk groupings were 8.67%, 24.10% and 74.10%, respectively. A time to event analysis revealed the higher risk groups readmitted to a hospital earlier than the lower risk groups. Six high-risk patient subgroup patterns were revealed through unsupervised clustering. Our model was successfully integrated into the statewide HIE to identify patient readmission risk upon admission and daily during hospitalization or for 30 days subsequently, providing daily risk score updates. Conclusions The risk model was validated as an effective tool for predicting 30-day readmissions for patients across all payer, disease and demographic groups within the Maine HIE. Exposing the key clinical, demographic and utilization profiles driving each patient’s risk of readmission score may be useful to providers in developing individualized post discharge care plans.

【 授权许可】

CC BY