Identifying the Uncertainty in Physician Practice Location through Spatial Analytics and Text Mining
AbstractIn 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. View Full-Text
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Shi, X.; Xue, B.; Xierali, I.M. Identifying the Uncertainty in Physician Practice Location through Spatial Analytics and Text Mining. Int. J. Environ. Res. Public Health 2016, 13, 930.
Shi X, Xue B, Xierali IM. Identifying the Uncertainty in Physician Practice Location through Spatial Analytics and Text Mining. International Journal of Environmental Research and Public Health. 2016; 13(9):930.Chicago/Turabian Style
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, no. 9: 930.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.