Zoller J, Lackland DT, Arana G, Dunbar J, evert H, Gross A, Johnson J, Silverstein M; Association for Health Services Research. Meeting.

Abstr Book Assoc Health Serv Res Meet. 1998; 15: 215-6.

Center for Health Care Research, Medical University of South Carolina, Charleston 29425-8060, USA.

OBJECTIVE: Patient satisfaction exerts an influence on how a patient views and uses a medical facility, and examination of these determinants of satisfaction can provide insight to how patients decide to return to providers for care. Patient satisfaction is routinely assessed in many ambulatory medical facilities for practice management, marketing and outcomes research and the effectiveness of evaluating interventions is based on the quality of the data collected. The instruments used for assessing satisfaction range from 'home-grown' informal surveys to elaborate, scientifically developed ones. There is limited data on the association between patient satisfaction and future use of services except to demonstrate the dissatisfaction leads to non-return. This study developed and evaluated a satisfaction assessment survey and reports the effect of components of satisfaction on the patients' intention to return. The survey generated a large volume of data on a routine basis which was used primarily for various institutional quality improvement purposes. STUDY DESIGN: A Likbert-type scale survey instrument with questions about 14 aspects of service - making appointments, appointment times, parking, registration, waiting room, finding seating, finding location, office staff, waiting time, doctor's explanation, time with doctor, nurse's courtesy, nurse's explanation, and overall care - was designed for use in all of the 55 MUSC outpatient clinics in 1995. A dichotomous response question expressing the patient's intent for future return to the clinics was also included. The instrument was distributed at the time of the visit, to be returned atthe office or by mail. The instrument was administered to a systematic sample of patients continuously for the past 4 years with an accumulation of over 23,000 responses, which have been entered into a computerized database. A multiple logistic regression analysis of the FYE 1997 data with the 14 satisfaction questions as the independent variables is used to identify and estimate significant components predicting patient intent to return. Odds ratios and 95% confidence intervals (CI) for the likelihood that a patient will not return to the clinic should he/she need service when the mean satisfaction score increases one point are calculated. Odds ratios less than 1 imply lower probability of returning to the clinic. PRINCIPAL FINDINGS: The response rate for return in FYE 1997 was 24%, n=10,125. A univariate analysis eliminated 1 item (c. finding parking) as not significant. A stepwise analysis of the remaining 13 variables identified 2 independent predictors of patient's decision to return: i. waiting time to see the doctor, Odds Ratio = 0.609, 95% CI 0.483 to 0.768 and j. understanding the doctor's explanation, Odds Ratio = 0.467, 95% CI 0.351 to 0.623. The interaction between the components was not significant. Based on 320,000 annual clinic visits, at current satisfaction levels, the model predicts a loss of approximately 1,464 visits a year due to dissatisfaction. If mean satisfaction scores were to increase by 10%, 634 fewer visits would be lost; by 20%, 993 fewer visits would be lost. If mean satisfaction scores were to decrease by 10% an additional 1,119 visits would be lost; with a 20% decrease, 3,092 additional visits would be lost. CONCLUSIONS: Patient's waiting time and understanding of the doctor's explanation are significant predictors of the patient's intent to return to the clinic for service in the future. The logistic regression model allows an estimation of the impact of changes in satisfaction levels on clinic visits. The model also facilitates a representation of patient loss due to varying levels of satisfaction. The results indicate that this model can identify behaviors associated with patient satisfaction and the components of the model make it possible to transfer to other settings. The analysis also helps identify patient satisfaction enhancement strategies that will maximize the probability of patient return, e.g. adminsitrative intervention for waiting time and physician education for doctor's explanation.

Publication Types:

• Meeting Abstracts

Keywords:

• Ambulatory Care Facilities
• Appointments and Schedules
• Data Collection
• Evaluation Studies
• Humans
• Interviews as Topic
• Logistic Models
• Patient Satisfaction
• hsrmtgs

Other ID:

• HTX/98619821

UI: 102234385

From Meeting Abstracts