Reducing Physician Distribution Uncertainty in Spatial Accessibility Research

减少空间可达性研究中的医生分布不确定性

• 批准号: 8621390
• 负责人: Imam Xierali
• 金额: $ 14.39万
• 依托单位: ASSOCIATION OF AMERICAN MEDICAL COLLEGES
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-01-17 至 2015-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8621390
• 关键词: Address; Affect; Area; Classification; Clinic; Collection; County; Data; Data Quality; Data Set; Data Sources; Decision Making; Derivation procedure; Equipment and supply inventories; Face; Funding Opportunities; Future; Goals; Health; Health Personnel; Health Planning; Healthcare; Home environment; Intervention; Knowledge; Literature; Location; Measures; Medical center; Methods; Modeling; Nature; Needs Assessment; Pattern; Physicians; Policies; Primary Care Physician; Process; Provider; Public Health; Research; Research Personnel; Research Project Summaries; Services; Site; Solutions; Source; Statistical Methods; Surveys; Uncertainty; United States Public Health Service; Work; base; health care delivery; land use; novel; public health relevance; tool; underserved rural area

Imam Xierali Reducing Physician Distribution Uncertainty in Spatial Accessibility Research Project Summary In the wake of landmark health reform, there is widespread concern about the adequacy and distribution of our nation's health workforce. National estimates are insufficient for estimating the specific future workforce needs of state and local areas. For planners and policymakers, the correct identification of physicians' practice locations is critical, yet tremendous uncertainty endures in their use of existing national workforce datasets. The collection and geocoding of the health workforce data reveal three uncertainty issues that are of particular concern in the derivation of correct physician practice locations: 1) uncertainty in survey results, such as the accuracy of address information collected; 2) uncertainty in the road network data, which are the source for geocoding and deriving latitude and longitude from address information; and 3) uncertainty about whether the addresses are practice addresses or home addresses. Most of the literature has focused on the first two issues. Little effort has been made to reduce the impact of the third factor, which is the central theme of this research. The goal of this project is to explore potential solutions to reducing the uncertainty and understanding the probable patterns of physician distribution. Three methods will be used to reduce uncertainty related to physicians' addresses. First, spatial analytics will identify uncertainty about the practice locations of physicians by using a land use classification dataset which identifies physician addresses within residential areas. Second, physician practice sites will be inventoried using other data sources. Third, based on the hypothesis that physicians with unknown practice locations work in nearby medical centers or clinic clusters, a calibrated Huff model will be developed to allocate such physicians to given clinic sites. This model will be validated by using observed data and will permit an examination of the model's impact on spatial accessibility to primary care physicians, a group whose projected shortage is of particular concern to policymakers.

伊玛目谢拉利 减少空间可达性研究中医生分布的不确定性 项目摘要 在具有里程碑意义的卫生改革之后，人们普遍担心 以及我国卫生工作者的分布。国家估计数不足以 估计州和地方地区未来的具体劳动力需求。对于规划者和 政策制定者，正确识别医生的执业地点是至关重要的，但 它们在使用现有的国家劳动力数据集方面存在巨大的不确定性。的 卫生人力数据的收集和地理编码揭示了三个不确定性问题， 在正确的医生实践位置的推导中特别关注：1） 调查结果，如收集的地址信息的准确性; 2）道路的不确定性 网络数据，这些数据是地理编码以及从中导出纬度和经度的来源 地址信息;以及3）不确定地址是实践地址还是 家庭住址 大多数文献都集中在前两个问题上。几乎没有做出任何努力， 减少第三个因素的影响，这是本研究的中心主题。这个目标 项目旨在探索潜在的解决方案，以减少不确定性和了解 医生分布的可能模式。 将使用三种方法来减少与医生地址相关的不确定性。第一、 空间分析将通过使用 土地利用分类数据集，识别居民区内的医生地址。 第二，将使用其他数据源对医生执业地点进行清点。三是立足 假设执业地点未知的医生在附近的医疗中心工作 或诊所集群，将开发校准的哈夫模型，以将这些医生分配给给定的 诊所网站该模型将通过使用观测数据进行验证， 该模型对初级保健医生的空间可达性的影响， 预计的短缺是决策者特别关注的问题。