IDENTIFYING THE ACTIVE GENERAL-PRACTICE WORKFORCE IN ONE DIVISION OF GENERAL-PRACTICE - THE UTILITY OF PUBLIC DOMAIN DATABASES

Citation
Gf. Gill et al., IDENTIFYING THE ACTIVE GENERAL-PRACTICE WORKFORCE IN ONE DIVISION OF GENERAL-PRACTICE - THE UTILITY OF PUBLIC DOMAIN DATABASES, Medical journal of Australia, 166(4), 1997, pp. 208-210
Citations number
6
Categorie Soggetti
Medicine, General & Internal
ISSN journal
0025729X
Volume
166
Issue
4
Year of publication
1997
Pages
208 - 210
Database
ISI
SICI code
0025-729X(1997)166:4<208:ITAGWI>2.0.ZU;2-8
Abstract
Objective: To identify the non-specialist medical practitioner workfor ce engaged in active general practice in the region served by the Divi sion of General Practice - Northern Tasmania and to determine the usef ulness of public domain databases for enumeration of individual non-sp ecialists providing general practice services. Methods: A masterlist o f the active general practice workforce was compiled by obtaining the names and addresses/postcodes of all non-specialist medical practition ers who were listed in at least one of nine public domain databases an d who were confirmed by selected local medical practitioners to be in active general practice in the three months prior to 30 June 1994. Thi s masterlist was used in calculating the sensitivity and positive pred ictive value (PPV) of each of the nine databases for enumerating non-s pecialist practitioners in active general practice. Results: Combining the databases resulted in a list of 475 practitioners, which was refi ned to 139 practitioners who, by our criteria, were in active general practice. Databases had a range of sensitivities and PPVs, but those w ith high sensitivity tended to have low PPVs, and vice versa. The most useful database for enumerating these practitioners was the mailing l ist for Australian Family Physician (sensitivity, 94%; PPV, 0.79). Con clusions: When used alone, no single database had both high sensitivit y and high positive predictive value for identifying the active genera l practice workforce. Combining multiple databases may improve precisi on. Developing methods to identify recent departures from local active practice has the potential to improve the PPV of existing highly sens itive databases.