Healthcare Informatics & Health Information Technology (SIG Health)

Paper Type

Complete

Paper Number

1692

Description

Health organizations are diligently working to achieve the zenith in service outcome and furtherance of patient satisfaction by embracing patient-centric policies. Patient recommendation is a critical indicator of patient satisfaction and hospital service quality. Evidence suggests that patient recommendation is the most valuable form of marketing. However, hospitals often encounter patients' unwillingness to recommend them. Prior studies mainly rely on patient survey data to determine factors that impact patients’ willingness to recommend hospitals. Our study aims to identify factors that are not readily available in the patient surveys but have significant impact on hospital recommendation. Our proposed Machine Learning (ML) based model has incorporated multidimensional approach by identifying various affecting factors related to diverse hospital services for predicting the patient willingness to recommend the hospital. These factors will help providers to ameliorate quality of their services and implement more proactive measures that elevate hospital recommendations. Our results have shown that Random Forest (RF) to be the best technique for the prediction of hospital recommendation with a 0.08 RMSE and 0.59 adjusted R2. We have found that ED throughput, preventive care, and patient satisfaction related factors play a crucial role in influencing the patient's decision to recommend the hospital.

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Aug 9th, 12:00 AM

Prediction of Patient Willingness to Recommend Hospital: A Machine Learning-Based Exploratory Study

Health organizations are diligently working to achieve the zenith in service outcome and furtherance of patient satisfaction by embracing patient-centric policies. Patient recommendation is a critical indicator of patient satisfaction and hospital service quality. Evidence suggests that patient recommendation is the most valuable form of marketing. However, hospitals often encounter patients' unwillingness to recommend them. Prior studies mainly rely on patient survey data to determine factors that impact patients’ willingness to recommend hospitals. Our study aims to identify factors that are not readily available in the patient surveys but have significant impact on hospital recommendation. Our proposed Machine Learning (ML) based model has incorporated multidimensional approach by identifying various affecting factors related to diverse hospital services for predicting the patient willingness to recommend the hospital. These factors will help providers to ameliorate quality of their services and implement more proactive measures that elevate hospital recommendations. Our results have shown that Random Forest (RF) to be the best technique for the prediction of hospital recommendation with a 0.08 RMSE and 0.59 adjusted R2. We have found that ED throughput, preventive care, and patient satisfaction related factors play a crucial role in influencing the patient's decision to recommend the hospital.