Gaining Insights into Patient Satisfaction Through Interpretable Machine Learning

Ning Liu, Soundar Kumara, Eric Reich

Research output: Contribution to journalArticlepeer-review

Abstract

Patient satisfaction is a key performance indicator of patient-centered care and hospital reimbursement. To discover the major factors that affect patient experiences is considered as an effective way to formulate corrective actions. A patient during his/her healthcare journey interacts with multiple health professionals across different service units. The health-related data generated at each step of the journey is a valuable resource for extracting actionable insights. In particular, self-reported satisfaction survey and the associated patient electronic health records play an important role in the hospital-patient interaction analysis. In this paper, we propose an interpretable machine learning framework to formulate the patient satisfaction problem as a supervised learning task and utilize a mixed-integer programming model to identify the most influential factors. The proposed framework transforms heterogeneous data into human-understandable features and integrates feature transformation, variable selection, and coefficient learning into the optimization process. Therefore, it can achieve desirable model performance while maintaining excellent model interpretability, which paves the way for successful real-world applications.

Original languageEnglish (US)
JournalIEEE Journal of Biomedical and Health Informatics
DOIs
StateAccepted/In press - 2020

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Health Information Management

Fingerprint Dive into the research topics of 'Gaining Insights into Patient Satisfaction Through Interpretable Machine Learning'. Together they form a unique fingerprint.

Cite this