By utilizing Design Science Research Methodology (DSRM), this work aims to provide an artifact for predicting patient’s responses for future hospital recommendations. We used hospital consumer assessment of healthcare providers and systems (HCAHPS) survey data, Timely and effective care (TEC) data, and Hospital general information (HGI) data to build a three-layered machine learning (ML) based recommendation model. Our preliminary results show that the ensemble based AdaBoost ML model can predict the patient’s response with 86.77% accuracy and 86.82% recall score. The proposed artifact provides a potentially viable solution to solve the raising problem related to patient satisfaction and hospital recommendation.
Vyas, Piyush; Ambati, Loknath Sai; Bojja, Giridhar Reddy; and Vyas, Gitika, "Utilizing Timely and Preventive Care Measures to Predict Patient’s Response for Hospital Recommendation" (2021). MWAIS 2021 Proceedings. 25.