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: 8792375
• 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 2016-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8792375
• 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; health data; land use; novel; tool; underserved rural area

DESCRIPTION (provided by applicant): 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 th 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.

描述（由申请人提供）：减少空间可达性研究项目摘要医生分布的不确定性在具有里程碑意义的卫生改革之后，人们普遍关注我国卫生人力的充足性和分布。国家估计数不足以估计各州和各州未来的具体劳动力需求， 地方。对于规划者和政策制定者来说，正确识别医生的执业地点至关重要，但他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。卫生工作人员数据的收集和地理编码揭示了三个不确定性问题，这些问题在推导正确的医生执业地点时特别令人关注：1）调查结果的不确定性，例如收集的地址信息的准确性; 2）道路网络数据的不确定性，这些数据是地理编码和从地址信息推导纬度和经度的来源;以及3）不确定地址是实践地址还是家庭地址。 大多数文献都集中在前两个问题上。几乎没有努力减少第三个因素的影响，这是本研究的中心主题。这个项目的目标是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。首先，空间分析将通过使用土地使用分类数据集来识别医生的执业地点的不确定性，该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。第三，基于具有未知实践位置的医生在附近的医疗中心或诊所集群工作的假设，将开发校准的哈夫模型以将这些医生分配到给定的诊所站点。该模型将通过使用观测数据进行验证，并将允许检查该模型对初级保健医生的空间可及性的影响，这一群体的预计短缺是决策者特别关注的问题。

项目成果

Parallelizing Affinity Propagation Using Graphics Processing Units for Spatial Cluster Analysis over Big Geospatial Data.

• DOI: 10.1007/978-3-319-22786-3_32
• 发表时间: 2017
• 期刊: Proceedings of the ... annual conference. GeoComputation
• 作者: Shi X

Identifying the Uncertainty in Physician Practice Location through Spatial Analytics and Text Mining.

• DOI: 10.3390/ijerph13090930
• 发表时间: 2016-09-21
• 期刊: International journal of environmental research and public health
• 作者: Shi X;Xue B;Xierali IM
• 通讯作者: Xierali IM

Application of Decomposition Analysis of Spatial Accessibility (DASA) in Health Services Research.

空间可达性分解分析（DASA）在卫生服务研究中的应用。

• DOI: 10.1353/hpu.2022.0015
• 发表时间: 2022
• 期刊: Journal of health care for the poor and underserved
• 影响因子: 1.4
• 作者: Xierali,ImamM;Nivet,MarcA
• 通讯作者: Nivet,MarcA

Understanding the Clustering Patterns in Physician Distribution Through Affinity Propagation.

通过亲和力传播了解医生分布的聚类模式。

• DOI: 10.1109/geoinformatics.2015.7378608
• 发表时间: 2015
• 期刊: International Conference on Geoinformatics : [proceedings]. International Conference on Geoinformatics
• 作者: Shi,Xuan;Xue,Bowei;Xierali,Imam
• 通讯作者: Xierali,Imam

Physician Multisite Practicing: Impact on Access to Care.

医生多地点执业：对获得护理的影响。

• DOI: 10.3122/jabfm.2018.02.170287
• 发表时间: 2018
• 期刊: Journal of the American Board of Family Medicine : JABFM
• 作者: Xierali,ImamM