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

View Article: PubMed Central - PubMed

ABSTRACT

In response to the widespread concern about the adequacy, distribution, and disparity of access to a health care workforce, the correct identification of physicians’ practice locations is critical to access public health services. In prior literature, little effort has been made to detect and resolve the uncertainty about whether the address provided by a physician in the survey is a practice address or a home address. This paper introduces how to identify the uncertainty in a physician’s practice location through spatial analytics, text mining, and visual examination. While land use and zoning code, embedded within the parcel datasets, help to differentiate resident areas from other types, spatial analytics may have certain limitations in matching and comparing physician and parcel datasets with different uncertainty issues, which may lead to unforeseen results. Handling and matching the string components between physicians’ addresses and the addresses of the parcels could identify the spatial uncertainty and instability to derive a more reasonable relationship between different datasets. Visual analytics and examination further help to clarify the undetectable patterns. This research will have a broader impact over federal and state initiatives and policies to address both insufficiency and maldistribution of a health care workforce to improve the accessibility to public health services.

No MeSH data available.


Misspelling error in physician data.
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ijerph-13-00930-f006: Misspelling error in physician data.

Mentions: In another example, Figure 6 displays three clusters of 50 physicians’ addresses that cannot be matched to nearby parcel addresses. Eventually, the difference in the street name was identified as the cause of this problem. The street name of physicians’ addresses is recorded as “MERIDIAN MARKS RD”, while in the parcel data, the street name is “MERIDIAN MARK RD”. While the parcel datasets are used as the reference data to compare and match addresses in physicians’ data, regrettably, the parcel datasets also contain potential errors. Figure 7 displays a mismatch, in which the physician’s address is “502 VALLEYBROOK XING”, while the only address for parcels along “VALLEYBROOK XING” could be wrongly documented as “777 VALLEY BROOK RD”. As a conclusion, errors in the addresses of both physicians’ and parcel datasets could result in unmatched cases.


Identifying the Uncertainty in Physician Practice Location through Spatial Analytics and Text Mining
Misspelling error in physician data.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5036762&req=5

ijerph-13-00930-f006: Misspelling error in physician data.
Mentions: In another example, Figure 6 displays three clusters of 50 physicians’ addresses that cannot be matched to nearby parcel addresses. Eventually, the difference in the street name was identified as the cause of this problem. The street name of physicians’ addresses is recorded as “MERIDIAN MARKS RD”, while in the parcel data, the street name is “MERIDIAN MARK RD”. While the parcel datasets are used as the reference data to compare and match addresses in physicians’ data, regrettably, the parcel datasets also contain potential errors. Figure 7 displays a mismatch, in which the physician’s address is “502 VALLEYBROOK XING”, while the only address for parcels along “VALLEYBROOK XING” could be wrongly documented as “777 VALLEY BROOK RD”. As a conclusion, errors in the addresses of both physicians’ and parcel datasets could result in unmatched cases.

View Article: PubMed Central - PubMed

ABSTRACT

In response to the widespread concern about the adequacy, distribution, and disparity of access to a health care workforce, the correct identification of physicians’ practice locations is critical to access public health services. In prior literature, little effort has been made to detect and resolve the uncertainty about whether the address provided by a physician in the survey is a practice address or a home address. This paper introduces how to identify the uncertainty in a physician’s practice location through spatial analytics, text mining, and visual examination. While land use and zoning code, embedded within the parcel datasets, help to differentiate resident areas from other types, spatial analytics may have certain limitations in matching and comparing physician and parcel datasets with different uncertainty issues, which may lead to unforeseen results. Handling and matching the string components between physicians’ addresses and the addresses of the parcels could identify the spatial uncertainty and instability to derive a more reasonable relationship between different datasets. Visual analytics and examination further help to clarify the undetectable patterns. This research will have a broader impact over federal and state initiatives and policies to address both insufficiency and maldistribution of a health care workforce to improve the accessibility to public health services.

No MeSH data available.