Clustering patient mobility patterns to assess effectiveness of health-service delivery

Abstract

BackgroundAnalysis of patient mobility in a country not only gives an idea of how the health-care system works, but also can be a guideline to determine the quality of health care and health disparity among regions. Even though determination of patient movement is important, it is not often realized that patient mobility could have a unique pattern beyond health-related endowments (e.g., facilities, medical staff). This study therefore addresses the following research question: Is there a way to identify regions with similar patterns using spatio-temporal distribution of patient mobility? The aim of the paper is to answer this question and improve a classification method that is useful for populous countries like Turkey that have many administrative areas.MethodsThe data used in the study consist of spatio-temporal information on patient mobility for the period between 2009 and 2013. Patient mobility patterns based on the number of patients attracted/escaping across 81 provinces of Turkey are illustrated graphically. The hierarchical clustering method is used to group provinces in terms of the mobility characteristics revealed by the patterns. Clustered groups of provinces are analyzed using non-parametric statistical tests to identify potential correlations between clustered groups and the selected basic health indicators.ResultsIneffective health-care delivery in certain regions of Turkey was determined through identifying patient mobility patterns. High escape values obtained for a large number of provinces suggest poor health-care accessibility. On the other hand, over the period of time studied, visualization of temporal mobility revealed a considerable decrease in the escape ratio for inadequately equipped provinces. Among four of twelve clusters created using the hierarchical clustering method, which include 64 of 81 Turkish provinces, there was a statistically significant relationship between the patterns and the selected basic health indicators of the clusters. The remaining eight clusters included 17 provinces and showed anomalies.ConclusionsThe most important contribution of this study is the development of a way to identify patient mobility patterns by analyzing patient movements across the clusters. These results are strong evidence that patient mobility patterns provide a useful tool for decisions concerning the distribution of health-care services and the provision of health care equipment to the provinces.

Risk of 30-day hospital readmission associated with medical conditions and drug regimens of polymedicated, older inpatients discharged home: a registry-based cohort study

“…46 These three clusters were selected because of their significant contributions to hospital readmissions. [48][49][50] The data set of medical information was composed of patients' principal medical diagnosis and four secondary medical diagnoses, based on ICD-10. Finally, the year of hospitalisation was introduced as a control variable, based on the fact that earlier admission to hospital during this period led to a higher probability of unplanned readmissions during the entire period covered.…”

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“…The program was aimed to improve preventative care through family physicians, and thus, decrease the number of unnecessary admissions to secondary and tertiary care institutions. As a result, citizens were assigned to a family physician, and a considerable number of physicians in different specialties were assigned to the disadvantaged provinces in this period [ 28 ]. Our findings indicate that the program resulted in different outcomes across the country.…”

“…After the health care reform, health care utilization has increased dramatically and patient mobility has continued to rise in Turkey. Main drivers of patient mobility are the search for better treatment or treatment affordability, and the availability of specialized health care services [ 28 ]. After the Social Security Institution (SSI) became the single buyer for health care services, different databases of providers and related insurance coverage were merged into a single database.…”

The Italian Patients’ Mobility as Hospital Policies Assessment

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“…As early as 1956 the economist Charles Mills Tiebout argued that "patients vote with their feet" [15]: they go to the health facilities that consider most suitable for the management of their pathology regardless of territorial boundaries and their choices reflect the perception of the quality of the health service [16,17].…”