What do measures of patient satisfaction with the doctor tell us?

Objective: To gain an understanding of how patient satisfaction (PS) with the doctor (PSD) is conceptualized through an empirical review of how it is currently being measured. The content of PS questionnaire items was examined to (a) determine the primary domains underlying PSD, and (b) summarize the specific doctor-related characteristics and behaviors, and patient-related perceptions, composing each domain. Methods: A scoping review of empirical articles that assessed PSD published from 2000 to November 2013. MEDLINE and PsycINFO databases were searched. Results: The literature search yielded 1726 articles, 316 of which fulfilled study inclusion criteria. PSD was realized in one of four health contexts, with questions being embedded in a larger questionnaire that assessed PS with either: (1) overall healthcare, (2) a specific medical encounter, or (3) the healthcare team. In the fourth context, PSD was the questionnaire's sole focus. Five broad domains underlying PSD were revealed: (1) Communication Attributes; (2) Relational Conduct; (3) Technical Skill/Knowledge; (4) Personal Qualities; and (5) Availability/Accessibility. Conclusions: Careful consideration of measurement goals and purposes is necessary when selecting a PSD measure. Practice implications: The five emergent domains underlying PSD point to potential key areas of physician training and foci for quality assessment

Organizational performance impacting patient satisfaction in Ontario hospitals: a multilevel analysis

Abstract Background Patient satisfaction in health care constitutes an important component of organizational performance in the hospital setting. Satisfaction measures have been developed and used to evaluate and improve hospital performance, quality of care and physician practice. In order to direct improvement strategies, it is necessary to evaluate both individual and organizational factors that can impact patients’ perception of care. The study aims were to determine the dimensions of patient satisfaction, and to analyze the individual and organizational determinants of satisfaction dimensions in hospitals. Methods We used patient and hospital survey data as well as administrative data collected for a 2008 public hospital report in Ontario, Canada. We evaluated the clustering of patient survey items with exploratory factor analysis and derived plausible dimensions of satisfaction. A two-level multivariate model was fitted to analyze the determinants of satisfaction. Results We found eight satisfaction factors, with acceptable to good level of loadings and good reliability. More than 95% of variation in patient satisfaction scores was attributable to patient-level variation, with less than 5% attributable to hospital-level variation. The hierarchical models explain 5 to 17% of variation at the patient level and up to 52% of variation between hospitals. Individual patient characteristics had the strongest association with all dimensions of satisfaction. Few organizational performance indicators are associated with patient satisfaction and significant determinants differ according to the satisfaction dimension. Conclusions The research findings highlight the importance of adjusting for both patient-level and organization-level characteristics when evaluating patient satisfaction. Better understanding and measurement of organization-level activities and processes associated with patient satisfaction could contribute to improved satisfaction ratings and care quality

Multimorbidity and Complexity Among Patients with Cancer in Ontario: A Retrospective Cohort Study Exploring the Clustering of 17 Chronic Conditions with Cancer

Background Multimorbidity is a concern for people living with cancer, as over 90% have at least one other condition. Multimorbidity complicates care coming from multiple providers who work within separate, siloed systems. Information describing high-risk and high-cost disease combinations has potential to improve the experience, outcome, and overall cost of care by informing comprehensive care management frameworks. This study aimed to identify disease combinations among people with cancer and other conditions, and to assess the health burden associated with those combinations to help healthcare providers more effectively prioritize and coordinate care. Methods We used a population-based retrospective cohort design including adults with a cancer diagnosis between March-2003 and April-2013, followed-up until March 2018. We used observed disease combinations defined by level of multimorbidity and partitive (k-means) clusters, ie groupings of similar diseases based on the prevalence of each condition. We assessed disease combination-associated health burden through health service utilization, including emergency department visits, primary care visits and hospital admissions during the follow-up period. Results 549,248 adults were included in the study. Anxiety, diabetes mellitus, hypertension, and osteoarthritis co-occurred with cancer 1.1 to 5.3 times more often than expected by chance. Disease combinations varied by cancer type and age but were similar between sexes. The largest partitive cluster included cancer and anxiety, with at least 25% of individuals also having osteoarthritis. Cancer also tended to co-occur with hypertension (8.0%) or osteoarthritis (6.2%). There were differences between clusters in healthcare utilization, regardless of the number of disease combinations or clustering approach used. Conclusion Researchers, clinicians, policymakers, and other stakeholders can use the clustering information presented here to improve the healthcare system for people with cancer multimorbidity by developing cluster-specific care management and clinical guidelines for common disease combinations