Machine Learning and Causal Approaches to Predict Readmissions and Its Economic Consequences Among Canadian Patients With Heart Disease: Retrospective Study.

Background: Unplanned patient readmissions within 30 days of discharge pose a substantial challenge in Canadian health care economics. To address this issue, risk stratification, machine learning, and linear regression paradigms have been proposed as potential predictive solutions. Ensemble machine learning methods, such as stacked ensemble models with boosted tree algorithms, have shown promise for early risk identification in specific patient groups.

Objective: This study aims to implement an ensemble model with submodels for structured data, compare metrics, evaluate the impact of optimized data manipulation with principal component analysis on shorter readmissions, and quantitatively verify the causal relationship between expected length of stay (ELOS) and resource intensity weight (RIW) value for a comprehensive economic perspective.

Methods: This retrospective study used Python 3.9 and streamlined libraries to analyze data obtained from the Discharge Abstract Database covering 2016 to 2021. The study used 2 sub-data sets, clinical and geographical data sets, to predict patient readmission and analyze its economic implications, respectively. A stacking classifier ensemble model was used after principal component analysis to predict patient readmission. Linear regression was performed to determine the relationship between RIW and ELOS.

Results: The ensemble model achieved precision and slightly higher recall (0.49 and 0.68), indicating a higher instance of false positives. The model was able to predict cases better than other models in the literature. Per the ensemble model, readmitted women and men aged 40 to 44 and 35 to 39 years, respectively, were more likely to use resources. The regression tables verified the causality of the model and confirmed the trend that patient readmission is much more costly than continued hospital stay without discharge for both the patient and health care system.

Conclusions: This study validates the use of hybrid ensemble models for predicting economic cost models in health care with the goal of reducing the bureaucratic and utility costs associated with hospital readmissions. The availability of robust and efficient predictive models, as demonstrated in this study, can help hospitals focus more on patient care while maintaining low economic costs. This study predicts the relationship between ELOS and RIW, which can indirectly impact patient outcomes by reducing administrative tasks and physicians' burden, thereby reducing the cost burdens placed on patients. It is recommended that changes to the general ensemble model and linear regressions be made to analyze new numerical data for predicting hospital costs. Ultimately, the proposed work hopes to emphasize the advantages of implementing hybrid ensemble models in forecasting health care economic cost models, empowering hospitals to prioritize patient care while simultaneously decreasing administrative and bureaucratic expenses.