Early Prediction of Unplanned 30-Day Hospital Readmission: Model Development and Retrospective Data Analysis

Abstract

Background Existing readmission reduction solutions tend to focus on complementing inpatient care with enhanced care transition and postdischarge interventions. These solutions are initiated near or after discharge, when clinicians’ impact on inpatient care is ending. Preventive intervention during hospitalization is an underexplored area that holds potential for reducing readmission risk. However, it is challenging to predict readmission risk at the early stage of hospitalization because few data are available. Objective The objective of this study was to build an early prediction model of unplanned 30-day hospital readmission using a large and diverse sample. We were also interested in identifying novel readmission risk factors and protective factors. Methods We extracted the medical records of 96,550 patients in 205 participating Cerner client hospitals across four US census regions in 2016 from the Health Facts database. The model was built with index admission data that can become available within 24 hours and data from previous encounters up to 1 year before the index admission. The candidate models were evaluated for performance, timeliness, and generalizability. Multivariate logistic regression analysis was used to identify readmission risk factors and protective factors. Results We developed six candidate readmission models with different machine learning algorithms. The best performing model of extreme gradient boosting (XGBoost) achieved an area under the receiver operating characteristic curve of 0.753 on the development data set and 0.742 on the validation data set. By multivariate logistic regression analysis, we identified 14 risk factors and 2 protective factors of readmission that have never been reported. Conclusions The performance of our model is better than that of the most widely used models in US health care settings. This model can help clinicians identify readmission risk at the early stage of hospitalization so that they can pay extra attention during the care process of high-risk patients. The 14 novel risk factors and 2 novel protective factors can aid understanding of the factors associated with readmission.

Forecasting Hospital Readmissions with Machine Learning

“…In Greece, a member of the OECD, this figure peaks by reaching 93%. The definition of the term “readmission” ranges in the relevant literature from 1 [ 2 , 3 , 4 ], 2 [ 5 ], 3 [ 6 ] to 12 months [ 7 ] after first initial admission. Almost 20% of the patients in the U.S. covered by Medicare are readmitted within 30 days of their initial discharge with an estimated annual cost of $17 billion [ 8 ].…”

Predicting hospital readmission risk in patients with COVID-19: A machine learning approach

“…Ryu (2021) [ 48 ] showed Gradient Boosting Machine (GBM) and Lo (2021) [ 49 ] concluded Categorical boosting (Catboost) had the highest AUC performance (= %75.1 and %75.15 respectively) in prediction readmission. Besides in recent studies (performed in 2021) by Zhao [ 50 ], Darabi [ 51 ], Chen [ 52 ], Shah [ 53 ], the results showed Boosting algorithms gained better performance in predicting patient readmission.…”

Predictive modeling for COVID-19 readmission risk using machine learning algorithms

“…Koteswari et al utilized ML techniques to predict the readmission probability of various COVID-19 cases [15]. In other studies by Ryu [90] Zhao [91], Darabi [92], Chen [93], and Shah [94], ML algorithms were applied to predict the likelihood of readmission of COVID-19 patients.…”