Mixed integer nonlinear programming model for analyzing patient satisfaction data

Ning Liu, Soundar Kumara, Eric S. Reich

Research output: Contribution to conferencePaperpeer-review

Abstract

Patient satisfaction is one of the most critical indicators of healthcare quality. In the era of patient-centered healthcare, it is considerably desired to investigate and identify the main factors that affect patient satisfaction. Self-reported patient satisfaction survey data plays a vital role since it provides abundant information on the performance of the hospital care delivered to the patients. Given the survey data, it is necessary to find the key reasons that drive patient satisfaction. The findings can be used in the future for corrective actions and quality improvements. In the paper, we propose a mixed integer nonlinear programming model for identifying the features that affect patient satisfaction. Results show that variables related to courtesy and respect from nurses and doctors, communication between doctors and patients, room cleanliness and quietness significantly impact overall satisfaction of patients. The model can be flexibly extended to various healthcare settings. Our approach and findings will help establish guidelines for quality healthcare.

Original languageEnglish (US)
Pages1175-1180
Number of pages6
StatePublished - Jan 1 2018
Event2018 Institute of Industrial and Systems Engineers Annual Conference and Expo, IISE 2018 - Orlando, United States
Duration: May 19 2018May 22 2018

Other

Other2018 Institute of Industrial and Systems Engineers Annual Conference and Expo, IISE 2018
CountryUnited States
CityOrlando
Period5/19/185/22/18

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering

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