Predicting patient satisfaction: a study of two emergency departments

J Behav Med. 1998 Dec;21(6):545-63. doi: 10.1023/a:1018796628917.


To identify perceptions that predict overall patient (dis)satisfaction with Emergency Department (ED) care, we studied responses to a survey mailed to all discharged patients over a 6-month period (Academic Hospital), and to a telephone interview of a random sample of discharged patients over a 1-year period (Community Hospital). The survey and interview both assessed overall satisfaction, as well as satisfaction with perceived waiting times, information delivery, and expressive quality of physicians, nurses, and staff. Data for 1176 patients (training sample) and 1101 patients (holdout sample) who rated overall satisfaction as either "very good" or "very poor" (Academic Hospital), and for 856 patients (training sample) and 431 patients (holdout sample) who rated overall satisfaction as either "excellent" or "poor" (Community Hospital), were retained for analysis. For both hospitals, nonlinear tree models efficiently achieved overall classification accuracy exceeding 98% in training analysis and 95% in holdout analysis (all p < .0001). The findings suggest that overall patient (dis)satisfaction with care received in the ED is nearly perfectly predictable on the basis of patient-rated expressive qualities of ED staff, particularly physicians and nurses. Interventions designed to reinforce positive (and extinguish negative) expressive health-care provider behaviors may cut the number of extremely dissatisfied patients in half.

Publication types

  • Comparative Study

MeSH terms

  • Chicago
  • Emergency Service, Hospital / organization & administration
  • Emergency Service, Hospital / standards*
  • Emergency Service, Hospital / statistics & numerical data
  • Female
  • Health Care Surveys
  • Hospitals, Community / standards*
  • Hospitals, University / standards*
  • Humans
  • Interviews as Topic
  • Logistic Models
  • Male
  • Observer Variation
  • Patient Satisfaction / statistics & numerical data*
  • Physician-Patient Relations
  • Quality Assurance, Health Care / methods*
  • Random Allocation
  • Surveys and Questionnaires