Mixed integer nonlinear programming model for analyzing patient satisfaction data

Ning Liu, Soundar Rajan Tirupatikumara, Eric S. Reich

Research output: Contribution to conferencePaper

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

Fingerprint

Nonlinear programming
Communication

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering

Cite this

Liu, N., Tirupatikumara, S. R., & Reich, E. S. (2018). Mixed integer nonlinear programming model for analyzing patient satisfaction data. 1175-1180. Paper presented at 2018 Institute of Industrial and Systems Engineers Annual Conference and Expo, IISE 2018, Orlando, United States.
Liu, Ning ; Tirupatikumara, Soundar Rajan ; Reich, Eric S. / Mixed integer nonlinear programming model for analyzing patient satisfaction data. Paper presented at 2018 Institute of Industrial and Systems Engineers Annual Conference and Expo, IISE 2018, Orlando, United States.6 p.
@conference{ce8b023cffd543efa681069d004ed9a7,
title = "Mixed integer nonlinear programming model for analyzing patient satisfaction data",
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.",
author = "Ning Liu and Tirupatikumara, {Soundar Rajan} and Reich, {Eric S.}",
year = "2018",
month = "1",
day = "1",
language = "English (US)",
pages = "1175--1180",
note = "2018 Institute of Industrial and Systems Engineers Annual Conference and Expo, IISE 2018 ; Conference date: 19-05-2018 Through 22-05-2018",

}

Liu, N, Tirupatikumara, SR & Reich, ES 2018, 'Mixed integer nonlinear programming model for analyzing patient satisfaction data', Paper presented at 2018 Institute of Industrial and Systems Engineers Annual Conference and Expo, IISE 2018, Orlando, United States, 5/19/18 - 5/22/18 pp. 1175-1180.

Mixed integer nonlinear programming model for analyzing patient satisfaction data. / Liu, Ning; Tirupatikumara, Soundar Rajan; Reich, Eric S.

2018. 1175-1180 Paper presented at 2018 Institute of Industrial and Systems Engineers Annual Conference and Expo, IISE 2018, Orlando, United States.

Research output: Contribution to conferencePaper

TY - CONF

T1 - Mixed integer nonlinear programming model for analyzing patient satisfaction data

AU - Liu, Ning

AU - Tirupatikumara, Soundar Rajan

AU - Reich, Eric S.

PY - 2018/1/1

Y1 - 2018/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85054026279&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85054026279&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:85054026279

SP - 1175

EP - 1180

ER -

Liu N, Tirupatikumara SR, Reich ES. Mixed integer nonlinear programming model for analyzing patient satisfaction data. 2018. Paper presented at 2018 Institute of Industrial and Systems Engineers Annual Conference and Expo, IISE 2018, Orlando, United States.