The promise of complementarity: Using the methods of foresight for health workforce planning. (May 2018)

• Record Type: Journal Article
• Authors: Rees, Gareth H; Crampton, Peter; Gauld, Robin; MacDonell, Stephen
• Is Part Of: Health services management research. Volume 31:Number 2(2018)
• Page Start: 97
• Page End: 105
• Publication Date: 2018-05
• Subjects: foresight -- health futures -- health workforce planning -- New Zealand -- workforce forecasting; Public health administration -- Research -- Periodicals; Public health administration -- Study and teaching (Higher) -- Periodicals; Health services administration -- Research -- Periodicals; Health services administration -- Study and teaching (Higher) -- Periodicals; 362.1072
• DOI: 10.1177/0951484818770408
• Languages: English
• ISSNs: 0951-4848

Abstract

Health workforce planning aims to meet a health system's needs with a sustainable and fit-for-purpose workforce, although its efficacy is reduced in conditions of uncertainty. This PhD breakthrough article offers foresight as a means of addressing this uncertainty and models its complementarity in the context of the health workforce planning problem. The article summarises the findings of a two-case multi-phase mixed method study that incorporates actor analysis, scenario development and policy Delphi. This reveals a few dominant actors of considerable influence who are in conflict over a few critical workforce issues. Using these to augment normative scenarios, developed from existing clinically developed model of care visions, a number of exploratory alternative descriptions of future workforce situations are produced for each case. Their analysis reveals that these scenarios are a reasonable facsimile of plausible futures, though some are favoured over others. Policy directions to support these favoured aspects can also be identified. This novel approach offers workforce planners and policy makers some guidance on the use of complimentary data, methods to overcome the limitations of conventional workforce forecasting and a framework for exploring the complexities and ambiguities of a health workforce's evolution.