Predictive Analysis of Interventions and Value-Based Scorecards Through Machine Learning in Healthcare

Abstract

Aim: In the changing healthcare landscape, value-based care has been a key framework to maximize patient outcomes against cost. However, to effectively assess the impact of the intervention and align healthcare delivery with value, those solutions must be innovative and data-driven. This study aims to evaluate how machine learning (ML) techniques can enhance the predictive accuracy of healthcare intervention assessments and improve value-based care scorecard systems. Methods: XGBoost, Random Forest, and Neural Network models were trained and tested on a curated dataset of electronic health records comprising approximately 54 million patient records. Standard preprocessing techniques such as feature scaling and missing-value imputation were applied. To determine the effectiveness in forecasting intervention success and trajectories of patient outcomes, these models were evaluated using precision, recall, F1 score, and ROC-AUC. Results: The ML models outperformed logistic regression, with XGBoost achieving the highest ROC-AUC score of 0.912 compared to 0.781 for logistic regression. SHAP analysis identified key contributors such as acuity of a patient’s condition, intervention hours, and age. The ML models were better suited to dealing with high-dimensional data, improving classification accuracy and model generality. Conclusion: The results indicate that machine learning algorithms, particularly XGBoost, can substantially improve clinical decision support by increasing predictive accuracy for patient outcomes, thus facilitating value-based care programs. Recommendation: Future efforts should involve integrating the prediction algorithm into clinical decision support systems for real-time, point-of-care use and evaluating their integration into electronic health record systems.

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