BACKGROUND Patient mobility can be defined as a patient's movement or utilization of a health care service located in a place or region other than the patient's place of residence. Mobility provides freedom to patients to obtain health care from providers across regions and even countries. It is essential to monitor patient choices in order to maintain the quality standards and responsiveness of the health system, otherwise, the health system may suffer from geographic disparities in the accessibility to quality and responsive health care. In this article, we study patient mobility in a national health care system to identify medical regions, spatio-temporal and service characteristics of health care utilization, and demands for patient mobility. METHODS We conducted a systematic analysis of province-to-province patient mobility in Turkey from December 2009 to December 2013, which was derived from 1.2 billion health service records. We first used a flow-based regionalization method to discover functional medical regions from the patient mobility network. We compare the results of data-driven regions to designated regions of the government in order to identify the areas of mismatch between planned regional service delivery and the observed utilization in the form of patient flows. Second, we used feature selection, and multivariate flow clustering to identify spatio-temporal characteristics and health care needs of patients on the move. RESULTS Medical regions we derived by analyzing the patient mobility data showed strong overlap with the designated regions of the Ministry of Health. We also identified a number of regions that the regional service utilization did not match the planned service delivery. Overall, our spatio-temporal and multivariate analysis of regional and long-distance patient flows revealed strong relationship with socio-demographic and cultural structure of the society and migration patterns. Also, patient flows exhibited seasonal patterns, and yearly trends which correlate with implemented policies throughout the period. We found that policies resulted in different outcomes across the country. We also identified characteristics of long-distance flows which could help inform policy-making by assessing the needs of patients in terms of medical specialization, service level and type. CONCLUSIONS Our approach helped identify (1) the mismatch between regional policy and practice in health care utilization (2) spatial, temporal, health service level characteristics and medical specialties that patients seek out by traveling longer distances. Our findings can help identify the imbalance between supply and demand, changes in mobility behaviors, and inform policy-making with insights.

中文翻译：

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分析大型患者流动性数据，以识别出行中的患者的医疗区域，时空特征和护理需求。

背景技术患者的移动性可以被定义为患者在位于患者居住地以外的地方或区域中的移动或对医疗服务的利用。移动性为患者提供了从地区甚至国家/地区获得医疗服务的自由。必须监测患者的选择，以维持卫生系统的质量标准和响应能力，否则，卫生系统可能会在获得质量和响应性医疗保健方面遭受地域差异的困扰。在本文中，我们研究了国家医疗保健系统中的患者流动性，以确定医疗区域，卫生保健利用的时空和服务特征以及患者流动性的需求。方法我们对土耳其从2009年12月至2013年12月省级到省级患者的流动性进行了系统分析，该数据来源于12亿份医疗服务记录。我们首先使用基于流的区域化方法从患者移动网络中发现功能性医疗区域。我们将数据驱动区域的结果与政府指定区域的结果进行比较，以便确定计划的区域服务交付与以患者流量的形式观察到的利用率之间的不匹配区域。其次，我们使用特征选择和多元流聚类来确定时空特征和移动患者的医疗需求。结果我们通过分析患者的流动性数据得出的医疗区域与卫生部指定的区域存在强烈的重叠。我们还确定了一些区域服务利用与计划的服务交付不匹配的区域。总体而言，我们对地区和长途患者流量的时空和多变量分析显示，其与社会的社会人口统计学和文化结构以及移民模式密切相关。此外，患者流量表现出季节性模式，并且在整个期间内与年度实施政策相关的年度趋势。我们发现政策在全国范围内产生了不同的结果。我们还确定了长途流量的特征，这些特征可以通过根据医疗专业，服务水平和类型评估患者的需求来帮助制定政策。结论我们的方法有助于确定（1）区域政策与医疗卫生利用实践之间的不匹配（2）患者通过长途旅行寻找的时空，卫生服务水平特征和医学专业。我们的发现可以帮助识别供求之间的不平衡，流动性行为的变化，并为决策提供见识。