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A work force model to support the adoption of best practice care in chronic diseases – a missing piece in clinical guideline implementation

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Address: Health Economics and Policy Group, Division of Health Sciences, University of South Australia, GPO Box 2471, Adelaide, South Australia, 5001, Australia Email: Leonie Segal* - Leonie.Segal@unisa.edu.au; Kim Dalziel - Kim.Dalziel@unisa.edu.au; Tom Bolton - Tom.Bolton@unisa.edu.au * Corresponding author | Implementation Science BioMed Central Open Access Debate A work force model to support the adoption of best practice care in chronic diseases - a missing piece in clinical guideline implementation Leonie Segal Kim Dalziel and Tom Bolton Address Health Economics and Policy Group Division of Health Sciences University of South Australia GPO Box 2471 Adelaide South Australia 5001 Australia Email Leonie Segal - Leonie.Segal@unisa.edu.au Kim Dalziel - Kim.Dalziel@unisa.edu.au Tom Bolton - Tom.Bolton@unisa.edu.au Corresponding author Published 18 June 2008 Received 25 October 2007 Implementation Science 2008 3 35 doi l0.ll86 l748-5908-3-35 Accepted 18 June 2008 This article is available from http www.implementationscience.cOm content 3 1 35 2008 Segal et al licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License http creativecommons.org licenses by 2.0 which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Abstract The development and implementation of an evidence-based approach to health workforce planning is a necessary step to achieve access to best practice chronic disease management. In its absence the widely reported failure in implementation of clinical best practice guidelines is almost certain to continue. This paper describes a demand model to estimate the community-based primary care health workforce consistent with the delivery of best practice chronic disease management and prevention. The model takes a geographic region as the planning frame and combines data about the health status of the regional population by disease category and stage with best practice guidelines to estimate the clinical skill requirement or competencies for the region. The translation of the skill requirement into a service requirement can then be modelled incorporating various assumptions about the occupation group to deliver nominated .