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

被引：47

作者：

Hao, Shiying ^{[2
]}

Wang, Yue ^{[2
]}

Jin, Bo ^{[1
]}

Shin, Andrew Young ^{[3
]}

Zhu, Chunqing ^{[1
]}

Huang, Min ^{[4
]}

Zheng, Le ^{[2
]}

Luo, Jin ^{[2
]}

Hu, Zhongkai ^{[1
]}

Fu, Changlin ^{[1
]}

Dai, Dorothy ^{[1
]}

Wang, Yicheng ^{[2
]}

Culver, Devore S. ^{[5
]}

Alfreds, Shaun T. ^{[5
]}

Rogow, Todd ^{[5
]}

Stearns, Frank ^{[1
]}

Sylvester, Karl G. ^{[2
]}

Widen, Eric ^{[1
]}

Ling, Xuefeng B. ^{[2
]}

机构：

[1] HBI Solut Inc, Palo Alto, CA USA

[2] Stanford Univ, Dept Surg, Stanford, CA 94305 USA

[3] Stanford Univ, Dept Pediat, Stanford, CA 94305 USA

[4] Shanghai Jiao Tong Univ, Shanghai Childrens Hosp, Shanghai 200030, Peoples R China

[5] HealthInfoNet, Portland, ME USA

来源：

PLOS ONE | 2015年 / 10卷 / 10期

关键词：

HEART-FAILURE; 30-DAY READMISSION; TRANSITIONAL CARE; PREDICTION MODELS; RATES;

D O I：

10.1371/journal.pone.0140271

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

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.

引用

页数：15

共 40 条

[1] Prediction Model for 30-Day Hospital Readmissions Among Patients Discharged Receiving Outpatient Parenteral Antibiotic Therapy [J].