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Identifying the active general practice workforce in one division of general practice: the utility of public domain databases
Author(s) -
Gill Gerard F,
Pilotto Louis S,
Thomson Alex N
Publication year - 1997
Publication title -
medical journal of australia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.904
H-Index - 131
eISSN - 1326-5377
pISSN - 0025-729X
DOI - 10.5694/j.1326-5377.1997.tb140079.x
Subject(s) - workforce , general practice , database , medicine , workforce planning , family medicine , medical practice , medical education , computer science , political science , law
Objective: To identify the non‐specialist medical practitioner workforce engaged in active general practice in the region served by the Division of General Practice — Northern Tasmania and to determine the usefulness of public domain databases for enumeration of individual non‐specialists providing general practice services. Methods: A masterlist of the active general practice workforce was compiled by obtaining the names and addresses/postcodes of all non‐specialist medical practitioners who were listed in at least one of nine public domain databases and who were confirmed by selected local medical practitioners to be in active general practice in the three months prior to 30 June 1994. This masterlist was used in calculating the sensitivity and positive predictive value (PPV) of each of the nine databases for enumerating non‐specialist practitioners in active general practice. Results: Combining the databases resulted in a list of 475 practitioners, which was refined to 139 practitioners who, by our criteria, were in active general practice. Databases had a range of sensitivities and PPVs, but those with high sensitivity tended to have low PPVs, and vice versa. The most useful database for enumerating these practitioners was the mailing list for Australian Family Physician (sensitivity, 94%; PPV, 0.79). Conclusions: When used alone, no single database had both high sensitivity and high positive predictive value for identifying the active general practice workforce. Combining multiple databases may improve precision. Developing methods to identify recent departures from local active practice has the potential to improve the PPV of existing highly sensitive databases.