Patient Satisfaction With Service Quality in an Oncology Setting

Implications for Prognosis in Non-small Cell Lung Cancer

Digant Gupta; Mark Rodeghier; Christopher G. Lis

Disclosures

Int J Qual Health Care. 2013;25(6):696-703. 

In This Article

Methods

Study Population

All NSCLC patients who were seen in consultation at one of three Cancer Treatment Centers of America (CTCA) hospitals between July 2007 and December 2010 and who elected to have treatment at CTCA were eligible for inclusion in this study. The three CTCA hospitals were CTCA Eastern, CTCA Midwestern and CTCA Southwestern. Patients included in this study were randomly selected from a population that had not responded to a service quality questionnaire within the preceding 60 days. The surveyed cohort included a total of 986 patients. The study was approved by the CTCA Institutional Review Board.

Questionnaire

The service quality questionnaire used in this study was first implemented at our institution in August 2006. The instrument was developed based on input obtained from patient focus groups, and survey dimensions were collated from several existing studies or questionnaires of oncology patients.[22–25] This service quality questionnaire covers the following dimensions of patient satisfaction: hospital operations and services, physicians and staff, and patient endorsements for others (friends and associates). The questionnaire was administered by trained survey associates at each CTCA hospital during a treating patient's visit. Eligible patients were typically contacted while they were waiting for various appointments. The survey was paper based and was completed by the patient and returned during that same visit at designated locations at each CTCA hospital.

The questionnaire included 13 individual service quality items: the ease of the registration process, the speed of the registration process, the timeliness with which care was delivered, the ease with which care was delivered, team helping you understand your medical condition, team explaining your treatment options, team involving you in decision-making, the amount of time spent with you, team calling you by your name, team genuinely caring for you as an individual, team providing you with a sense of well-being, 'whole person' approach to patient care and satisfaction with the treating medical oncologist (patient's primary physician). The questionnaire also contained one overall service quality item measured using the following question: 'considering everything, how satisfied are you with your overall experience with the institution?'

Statistical Analysis

Patient survival was the primary end point, and was defined as the time interval between the date a patient first returned the patient survey and the date of patient's death from any cause or the date of last contact/last known to be alive.

The 13 individual service quality items and 1 overall service quality item were used as independent variables in this study. The survey items were measured on a 7-point Likert-type scale ranging from 'completely dissatisfied' to 'completely satisfied'. Because of skewed data distributions, each service quality item was dichotomized into two categories for the purpose of this analysis: 'completely satisfied'[7] and 'not completely satisfied'.[1–6] Other control variables investigated for their relationship with survival were gender, prior treatment history, stage at diagnosis, age and CTCA hospital. The prior treatment history variable categorized patients into those who had received definitive cancer treatment elsewhere before coming to CTCA and those who were newly diagnosed at CTCA. The stage at diagnosis variable was dichotomized into metastatic (stage IV) and non-metastatic disease (stages I–III). For CTCA hospital, dummy variables were created with CTCA Southwestern as the reference category.

Descriptive statistics and frequencies were computed for each service quality item in the questionnaire. The overall survival was calculated using the Kaplan–Meier method. Service quality items were evaluated using univariate Cox proportional hazards models to determine which parameters showed individual prognostic value for survival. Multivariate Cox proportional hazards models were then performed to evaluate the joint prognostic significance of all service quality items significant on univariate analysis after controlling for relevant patient characteristics. We used both block entry method (all variables entered together at the same time in one block) as well as the forward stepwise method. Forward stepwise method was used because, as is common in service quality data, many of the individual items are highly correlated. Stepwise regression avoids the problem of multicollinearity because two highly correlated attributes will normally not both be entered in the model. Since 'overall patient satisfaction with service quality' is highly correlated with other individual service quality items, it was not included in multivariate Cox analyses when other service quality items were used, in order to achieve model stability. Instead, 'overall patient satisfaction with service quality' was analyzed separately after adjusting for clinical and demographic factors. The effect of perceived service quality on patient survival was expressed as hazard ratios (HRs) with 95% confidence intervals (CIs).

Cox regression with time-invariant covariates assumes that the ratio of hazards for any two groups remains constant in proportion over time. We checked this assumption by examining log-minus-log plots for categorical predictors. For continuous predictors, this assumption was checked using an extended Cox model with time-dependent covariates. Potential multicollinearity was assessed in two steps. Large values (>0.70) of Kendall's tau b correlation coefficient were used as an initial screen for pairs of service quality measures, with one member of the pair not entered into the multivariate model (the measure that was more meaningful or actionable was retained). Kendall's tau b is an appropriate measure of association for categorical variables and is commonly used when both variables have the same number of categories. As a second check, the variance inflation factor (VIF) was used with the final model to verify that multicollinearity was not significantly influencing model coefficients.[26,27]

All data were analyzed using IBM SPSS version 20.0 (IBM, Armonk, NY, USA). A difference was considered to be statistically significant if the P value was ≤0.05.

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