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.

Public Health Relevance

Reducing Physician Distribution Uncertainty in Spatial Accessibility Research Significance Many federal and state initiatives and policies have been created to address both insufficiency and maldistribution of the physician workforce, which are the most critical challenges facing the nation's health care workforce today. Spatial uncertainty about physicians'practice locations has significant implications to accessibility studies of physician services. This research will contribute to a more informed decision-making process by offering policymakers and planners better information about the distribution of the US physician workforce.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Exploratory/Developmental Grants (R21)
Project #
1R21CA182874-01
Application #
8621390
Study Section
Special Emphasis Panel (ZRG1-HSOD-J (09))
Program Officer
Lewis, Denise
Project Start
2014-01-17
Project End
2015-12-31
Budget Start
2014-01-17
Budget End
2014-12-31
Support Year
1
Fiscal Year
2014
Total Cost
$143,891
Indirect Cost
$24,723
Name
Association of American Medical Colleges
Department
Type
DUNS #
069287779
City
Washington
State
DC
Country
United States
Zip Code
20037
Xierali, Imam M (2018) Physician Multisite Practicing: Impact on Access to Care. J Am Board Fam Med 31:260-269
Xierali, Imam M; Nivet, Marc A (2018) The Racial and Ethnic Composition and Distribution of Primary Care Physicians. J Health Care Poor Underserved 29:556-570
Shi, Xuan (2017) Parallelizing Affinity Propagation Using Graphics Processing Units for Spatial Cluster Analysis over Big Geospatial Data. Proc Annu Conf GeoComput 2017:355-369
Shi, Xuan; Xue, Bowei; Xierali, Imam M (2016) Identifying the Uncertainty in Physician Practice Location through Spatial Analytics and Text Mining. Int J Environ Res Public Health 13:
Shi, Xuan; Xue, Bowei; Xierali, Imam (2015) Understanding the Clustering Patterns in Physician Distribution Through Affinity Propagation. Int Conf Geoinform 2015: