Post-Acute Care Payment Reform Demonstration: Final Report Volume 1 of 4

This is the Final Report for the Post-Acute Care Payment Reform Demonstration (PAC-PRD), authorized by section 5008 of the Deficit Reduction Act of 2005, Public Law 109-171. The report has 12 sections, which are divided into four volumes: Volume 1: Executive Summary. Volume 2: Sections 1-4 (Section 1: Introduction; Section 2: Underlying Issues of the PAC-PRD Initiating Legislation; Section 3: Developing Standardized Measurement Approaches: The Continuity Assessment Record and Evaluation (CARE); Section 4: Demonstration Methods and Data Collection) Volume 3: Sections 5-6 (Section 5: Framework for Analysis; Section 6: Factors Associated with Hospital Discharge Destination) Volume 4: Sections 7-12; References (Section 7: Outcomes: Hospital Readmissions; Section 8: Outcomes: Functional Status; Section 9: Determinants of Resource Intensity: Methods and Analytic Sample Description; Section 10: Determinants of Resource Intensity: Lessons from the CART Analysis; Section 11: Determinants of Resource Intensity: Multivariate Regression Results; Section 12: Conclusions and Review of Findings; References

Real-time Prediction of the Risk of Hospital Readmissions

This study aims to identify predictors for patients likely to be readmitted to a hospital within 28 days of discharge and to develop and validate a prediction model for identifying patients at a high risk of readmission. Numerous attempts have been made to build similar predictive models. However, the majority of existing models suffer from at least one of the following shortcomings: the model is not based on Australian Health Data; the model uses insurance claim data, which would not be available in a real-time clinical setting; the model does not consider socio-demographic determinants of health, which have been demonstrated to be predictive of readmission risk; or the model is limited to a particular medical condition and is thus limited in scope. To address these shortcomings, we built several models to predict all-cause 28-day readmission risk and included Socio-economic Indexes for Areas (SEIFA) data as proxies for socio-demographic determinants of health. Additionally, instead of using insurance claims data, which could require several weeks to process, we built our models using data that is readily available during the inpatient stay or at the time of discharge. The set of default prediction models that were examined include logistic regression, elastic net, random forest and adaptive boosting (Ada Boost). This study examined A not for profit tertiary healthcare organisation from fiscal year 2012-2013 through fiscal year 2017-2018. The out-of-sample results show that all of the models performed similarly and adequately to predict readmission risk

A New Scalable, Portable, and Memory-Efficient Predictive Analytics Framework for Predicting Time-to-Event Outcomes in Healthcare

Time-to-event outcomes are prevalent in medical research. To handle these outcomes, as well as censored observations, statistical and survival regression methods are widely used based on the assumptions of linear association; however, clinicopathological features often exhibit nonlinear correlations. Machine learning (ML) algorithms have been recently adapted to effectively handle nonlinear correlations. One drawback of ML models is that they can model idiosyncratic features of a training dataset. Due to this overlearning, ML models perform well on the training data but are not so striking on test data. The features that we choose indirectly influence the performance of ML prediction models. With the expansion of big data in biomedical informatics, appropriate feature engineering and feature selection are vital to ML success. Also, an ensemble learning algorithm helps decrease bias and variance by combining the predictions of multiple models. In this study, we newly constructed a scalable, portable, and memory-efficient predictive analytics framework, fitting four components (feature engineering, survival analysis, feature selection, and ensemble learning) together. Our framework first employs feature engineering techniques, such as binarization, discretization, transformation, and normalization on raw dataset. The normalized feature set was applied to the Cox survival regression that produces highly correlated features relevant to the outcome.The resultant feature set was deployed to “eXtreme gradient boosting ensemble learning” (XGBoost) and Recursive Feature Elimination algorithms. XGBoost uses a gradient boosting decision tree algorithm in which new models are created sequentially that predict the residuals of prior models, which are then added together to make the final prediction. In our experiments, we analyzed a cohort of cardiac surgery patients drawn from a multi-hospital academic health system. The model evaluated 72 perioperative variables that impact an event of readmission within 30 days of discharge, derived 48 significant features, and demonstrated optimum predictive ability with feature sets ranging from 16 to 24. The area under the receiver operating characteristics observed for the feature set of 16 were 0.8816, and 0.9307 at the 35th, and 151st iteration respectively. Our model showed improved performance compared to state-of-the-art models and could be more useful for decision support in clinical settings