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Related Experiment Videos

Predicting prescribing costs in general practice using practice demography.

K Wilson-Davis1, W G Stevenson, H McGavock

  • 1Drug Utilisation Research Unit, The Queen's University of Belfast, Belfast, Northern Ireland.

Pharmacoepidemiology and Drug Safety
|May 1, 1997
PubMed
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A new model predicts 91.4% of primary healthcare prescribing costs using just three practice factors: young children, older adults, and partner numbers. This tool aids in understanding and managing rising NHS primary care expenditure.

Area of Science:

  • Health Economics
  • Primary Care Management
  • Health Services Research

Background:

  • Prescription and dispensing costs constitute a significant portion (approximately 56%) of UK National Health Service (NHS) primary care expenditure.
  • Existing predictive models often fail to capture a substantial amount of prescribing cost variation or adequately explain the influence of practice demographics.
  • There is a growing concern regarding the continuously increasing total expenditure on primary care prescribing.

Purpose of the Study:

  • To develop a highly accurate and parsimonious predictive model for primary healthcare prescribing costs.
  • To explain the variation in prescribing costs by incorporating practice list demography.
  • To provide a practical tool for healthcare professionals involved in cost auditing and containment.

Related Experiment Videos

Main Methods:

  • Utilized prescription data and practice demographic information from Northern Ireland.
  • Developed a predictive model incorporating three key independent variables: number of children aged 0-4 years, number of persons aged 60+ years, and number of practice partners.
  • Validated the model's ability to account for variations in prescribing costs, total monthly consultations, and prescription numbers.

Main Results:

  • The developed model explains 91.4% of the variation in primary healthcare prescribing costs.
  • The model demonstrates a high degree of parsimony, requiring only three easily understandable variables.
  • Significant correlations were found between the selected demographic variables and prescribing costs, consultations, and prescription numbers.

Conclusions:

  • The new predictive model offers a comprehensive and simple approach to understanding and managing primary care prescribing costs.
  • The model's identified variables (age groups and practice size) are readily comprehensible to General Practitioners (GPs) and negotiators.
  • This model can serve as a valuable tool for prescribing advisors and others focused on auditing and containing healthcare costs.