Profiling the different needs and expectations of patients for population-based medicine: a case study using segmentation analysis.

BACKGROUND: This study illustrates an evidence-based method for the segmentation analysis of patients that could greatly improve the approach to population-based medicine, by filling a gap in the empirical analysis of this topic. Segmentation facilitates individual patient care in the context of the culture, health status, and the health needs of the entire population to which that patient belongs. Because many health systems are engaged in developing better chronic care management initiatives, patient profiles are critical to understanding whether some patients can move toward effective self-management and can play a central role in determining their own care, which fosters a sense of responsibility for their own health. A review of the literature on patient segmentation provided the background for this research. METHOD: First, we conducted a literature review on patient satisfaction and segmentation to build a survey. Then, we performed 3,461 surveys of outpatient services users. The key structures on which the subjects' perception of outpatient services was based were extrapolated using principal component factor analysis with varimax rotation. After the factor analysis, segmentation was performed through cluster analysis to better analyze the influence of individual attitudes on the results. RESULTS: Four segments were identified through factor and cluster analysis: the "unpretentious," the "informed and supported," the "experts" and the "advanced" patients. Their policies and managerial implications are outlined. CONCLUSIONS: With this research, we provide the following: - a method for profiling patients based on common patient satisfaction surveys that is easily replicable in all health systems and contexts;- a proposal for segments based on the results of a broad-based analysis conducted in the Italian National Health System (INHS).Segments represent profiles of patients requiring different strategies for delivering health services. Their knowledge and analysis might support an effort to build an effective population-based medicine approach.

Why non-urgent patients choose emergency over primary care services? Empirical evidence and managerial implications.

OBJECTIVE: To investigate structural and psychological factors that lead non-urgent patients to choose the Accidents & Emergency Department (A&ED) rather than primary care services. DATA SOURCES: Data were collected through interviews by means of a structured questionnaire. Data regarding the A&ED sample were also drawn from the database of the department. STUDY DESIGN: Hypotheses were tested in a survey comparing A&ED non-urgent patients and patients using GP surgeries. Different perceptions of the characteristics of A&ED and primary care services were measured and a perceptual map was created using the linear discriminant analysis (LDA). DATA COLLECTION: Emergency services users were interviewed in the A&ED of the General Hospital of the Province of Macerata (Italy). Primary care users were interviewed in four GP surgeries. 527 patients were interviewed between December 2006 and February 2007. PRINCIPAL FINDINGS: A&ED and primary care patients look for different characteristics as diagnostic and therapeutic potentialities, empathy and competence, quick access or long-lasting relationship. Information asymmetry explains part of the behaviour. CONCLUSIONS: Use of A&ED services for non-urgent care can be reduced. The understanding of reasons underlying the choice and a change in access, timing and contents of care/services provided by general practitioners (GPs) might provide incentives for shifting from A&ED to GPs surgeries.

Applying discrete choice modelling in a priority setting: an investigation of public preferences for primary care models.

OBJECTIVES: The shift toward more innovative and sustainable primary care models in Italy leads policy makers and clinicians to face difficult decisions between options that are all regarded as potentially beneficial. In this study, patient preferences for different primary care models in the Tuscany region of Italy were elicited. The relative importance of different attributes to the surveyed respondents was then examined, as well as the rate at which individuals trade between attributes and the relative value of different service configurations. METHODS: A discrete choice experiment survey explored the following attributes in a stratified random sample of 6,970 adults: primary care provider, diagnostic facilities and waiting time for the visit. RESULTS: Respondents (3,263) were likely to prefer a consultation by their own general practitioner (GP) and a practice with many diagnostic facilities. The predicted utilities of different service configurations have shown that a "primary care centre" with many diagnostic facilities was preferable to a "solo GP" model or a "group general practice". CONCLUSIONS: The study demonstrated how a patient choice model could be used by decision makers for developing successful policies that takes into account different healthcare needs, balancing responsiveness with care continuity, equity and appropriateness. Considering that a primary care centre would perform better than a "solo GP", especially for younger respondents and for those with minor healthcare needs, for a more rapid diffusion of this model policymakers and managers could direct the care of primary care centres towards these targeted subgroups, at least in the first phase.