Grant Number:	5R21CA182874-02 Interpret this number
Primary Investigator:	Xierali, Imam
Organization:	Association Of American Medical Colleges
Project Title:	Reducing Physician Distribution Uncertainty in Spatial Accessibility Research
Fiscal Year:	2015

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.




Application of Decomposition Analysis of Spatial Accessibility (DASA) in Health Services Research.
Authors: Xierali I.M. , Nivet M.A. .
Source: Journal Of Health Care For The Poor And Underserved, 2022; 33(1), p. 195-212.
PMID: 35153214
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Physician Multisite Practicing: Impact on Access to Care.
Authors: Xierali I.M. .
Source: Journal Of The American Board Of Family Medicine : Jabfm, 2018 Mar-Apr; 31(2), p. 260-269.
PMID: 29535243
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The Racial and Ethnic Composition and Distribution of Primary Care Physicians.
Authors: Xierali I.M. , Nivet M.A. .
Source: Journal Of Health Care For The Poor And Underserved, 2018; 29(1), p. 556-570.
PMID: 29503317
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Parallelizing Affinity Propagation Using Graphics Processing Units for Spatial Cluster Analysis over Big Geospatial Data.
Authors: Shi X. .
Source: Proceedings Of The ... Annual Conference. Geocomputation, 2017; 2017, p. 355-369.
EPub date: 2017-01-05 00:00:00.0.
PMID: 29057395
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Identifying The Uncertainty In Physician Practice Location Through Spatial Analytics And Text Mining
Authors: Shi X. , Xue B. , Xierali I.M. .
Source: International Journal Of Environmental Research And Public Health, 2016-09-21 00:00:00.0; 13(9), .
PMID: 27657100
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Understanding the Clustering Patterns in Physician Distribution Through Affinity Propagation.
Authors: Shi X. , Xue B. , Xierali I. .
Source: International Conference On Geoinformatics : [proceedings]. International Conference On Geoinformatics, 2015 Jun; 2015, .
EPub date: 2016-01-14 00:00:00.0.
PMID: 29399385
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