Determinants of consultation length in Australian general practice

Med J Aust. 2005 Jul 18;183(2):68-71. doi: 10.5694/j.1326-5377.2005.tb06924.x.


Objective: To measure the independent effect on length of general-practice consultations of a range of characteristics of the general practitioner (GP), practice, patient and consultation, as a basis for considering future GP workforce needs.

Design: Secondary analysis of data from the BEACH (Bettering the Evaluation and Care of Health) study.

Setting and participants: Data were obtained from 1904 GPs Australia-wide on 70,758 consultations between 1 January 2001 and 31 December 2002; all consultations that were claimable from the Australian Government's Medicare system as General Practice Attendances and had recorded start and finish times were included.

Main outcome variables: Characteristics of the GP, practice, patient and consultation that were significantly related to consultation length, determined by multiple regression analysis.

Results: The following variables had an independent positive effect on consultation length: GP female, older, graduated in Australia, FRACGP-qualified, and in rural practice; patient female, older, new to practice, with higher socioeconomic status, no health concession card, more reasons for encounter, and more problems managed; and management of specific problem types (social, psychological and female genital problems), management of chronic disease, and provision of clinical treatments.

Conclusion: The independent relationship of some GP, practice, patient and consultation characteristics with length of consultation may affect future GP supply. These factors should be considered in modelling future general practice workforce needs.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Adult
  • Age Factors
  • Aged
  • Australia
  • Child
  • Family Practice / statistics & numerical data*
  • Female
  • Humans
  • Insurance, Health / statistics & numerical data
  • Male
  • Middle Aged
  • Multivariate Analysis
  • Office Visits / statistics & numerical data*
  • Practice Patterns, Physicians' / statistics & numerical data
  • Regression Analysis
  • Rural Health Services / statistics & numerical data
  • Sex Factors
  • Socioeconomic Factors
  • Time*