Exploring the feasibility of Conjoint Analysis as a tool for prioritizing innovations for implementation

Implementation Science

Duncan Chambers²  Andria Hanbury¹  Carl Thompson¹  Katherine Farley¹ 

[1] Department of Health Sciences, The University of York, York YO10 5DD, England;Centre for Reviews and Dissemination, The University of York, York YO10 5DD, England

关键词: Preference;    Implementation;    Decision-making;    Innovation;    Healthcare;    Conjoint Analysis;   

Others  :  813634
DOI  :  10.1186/1748-5908-8-56

 received in 2012-10-18, accepted in 2013-05-21,  发布年份 2013

【 摘 要 】

Background

In an era of scarce and competing priorities for implementation, choosing what to implement is a key decision point for many behavioural change projects. The values and attitudes of the professionals and managers involved inevitably impact the priority attached to decision options. Reliably capturing such values is challenging.

Methods

This paper presents an approach for capturing and incorporating professional values into the prioritization of healthcare innovations being considered for adoption. Conjoint Analysis (CA) was used in a single UK Primary Care Trust to measure the priorities of healthcare professionals working with women with postnatal depression. Rating-based CA data was gathered using a questionnaire and then mapped onto 12 interventions being considered as a means of improving the management of postnatal depression.

Results

The ‘impact on patient care’ and the ‘quality of supporting evidence’ associated with the potential innovations were the most influential in shaping priorities. Professionals were least influenced by whether an innovation was an existing national or local priority, or whether current practice in the Trust was meeting minimum standards. Ranking the 12 innovations by the preferences of potential adopters revealed ‘guided self help’ was the top priority for implementation and ‘screening questions for post natal depression’ the least. When other factors were considered (such as the presence of routine data or planned implementation activity elsewhere in the Trust), the project team chose to combine the eight related treatments and implement these as a single innovation referred to as ‘psychological therapies’.

Conclusions

Using Conjoint Analysis to prioritise potential innovation implementation options is a feasible means of capturing the utility of stakeholders and thus increasing the chances of an innovation being adopted. There are some practical barriers to overcome such as increasing response rates to conjoint surveys before routine and unevaluated use of this technique should be considered.

【 授权许可】

   
2013 Farley et al.; licensee BioMed Central Ltd.

【 参考文献 】

• [1]Mason J: When is it cost-effective to change the behavior of health professionals? JAMA 2001, 286(23):2988-2992.
• [2]Sculpher M: Evaluating the cost-effectiveness of interventions designed to increase the utilisation of evidence-based guidelines. Fam Pract 2000, 17(Supp):S26-S31.
• [3]Jacques H: GPs face “tsunami” of guidance. BMJ Careers 2011.
• [4]Grimshaw JM: Effectiveness and efficiency of guideline dissemination and implementation strategies. Health Technol Assess 2004, 8(6):1-72. p. iii-iv
• [5]Mitton C, Donaldson C: Twenty-five years of programme budgeting and marginal analysis in the health sector, 1974–1999. J Health Serv Res Policy 2001, 6(4):9.
• [6]Greenhalgh T: Diffusion of innovations in service organizations: systematic review and recommendations. Milbank Q 2004, 82(4):581-629.
• [7]Rogers E: Diffusion of Innovations. London: Free Press; 2003.
• [8]Eccles M, Grimshaw J, Walker A: Changing the behaviour of healthcare professionals: the use of theory in promoting the uptake of research findings. J Clin Epidemiol 2005, 58(2):107-112.
• [9]Michie S: Specifying and reporting complex behaviour change interventions: the need for a scientific method. Implement Sci 2009, 4:40. BioMed Central Full Text
• [10]Foy R, MacLennan G, Grimshaw J, Penney G, Campbell M, Grol R: Attributes of clinical recommendations that influence change in practice following audit and feedback. J Clin Epidemiol 2002, 55:5.
• [11]Hanbury A, Thompson C, Wilson PM, Farley K, Chambers D, Warren E, Bibby J: Translating Research into Practice in Leeds and Bradford (TRiPLaB): Protocol for a Programme of Research. Implement Sci 2010., 5(37)
• [12]McFadden D: The Choice Theory Approach to Market Research. Mark Sci 1986, 5(4):22.
• [13]Luce D, Tukey J: Simultaneous conjoint measurement: a new type of fundamental measurement. J Math Psychol 1964, 1(1):27.
• [14]Farrar S: Using discrete choice modelling in priority setting: an application to clinical service developments. Soc Sci Med 2000, 50(1):63-75.
• [15]Ryan M, Farrar S: Using conjoint analysis to elicit preferences for health care. BMJ Careers 2000, 320:7248:1530-1533.
• [16]Cattin P, Wittink D: Commercial use of conjoint analysis: a survey. J Marketing 1982, 46:9.
• [17]Cave M, Burningham D, Buxton M, Hanney S, Pollitt C, Scanlan M, Shurmer M: The Valuation of Changes in Quality in the Public Services. MHSO 1993.
• [18]Ryan M: Using conjoint analysis to take account of patient preferences and go beyond health outcomes: an application to in vitro fertilisation. Soc Sci Med 1999, 48(4):11.
• [19]Ryan M, Farrar S: Briefing Paper for the NHS in Scotland, No. 6: Conjoint Analysis: a New Tool for Eliciting Patients' Preferences. In Health Economics Research Unit. University of Aberdeen; 1995.
• [20]Ratcliffe J, Buxton M: Patients' preferences regarding the process and outcomes of life-saving technology, An application of conjoint analysis to liver transplantation. Int J Technol Assess Health Care 1999, 15(2):340-51.
• [21]Ryan M: A role for conjoint analysis in technology assessment in health care? Int J Technol Assess Health Care 1999, 15:14.
• [22]Brusch M, Baier D, Treppa A: Conjoint Analysis and Stimulus Presentation - a Comparison of Alternative Methods. In Classification, Clustering, and Analysis. Edited by Jajuga EK. Berlin: Springer; 2002:203-210.
• [23]Melles T, Laumann R, Holling H: Proceedings of the Sawtooth Software Conference. WA: Sequim; 2000. [Validity and Reliability of Online Conjoint Analysis]
• [24]Orme BK, Alpert MI, Christensen E: Research Paper Series. Sawtooth Software Ltd; 1997. [Assessing the Validity of Conjoint Analysis - Continued]
• [25]De-Bekker-Grob E, Ryan M, Gerard K: Discrete choice Health experiments in health economics: a review of the literature. Health Econ 2012, 21:27.
• [26]Orme BK: Research Paper Series. Sawtooth Software Ltd; 2002. [Formulating Attributes and Levels in Conjoint Analysis]
• [27]Teijlingen AH: The importance of pilot studies. Social Research Update 2001., 35
• [28]Addelman S: Symmetrical and assmmetrical fractional factorial plans. Technometrics 1962, 4:11.
• [29]Nakash R, Hutton JL, Jørstad-Stein EC, Gates S, Lamb SE: Maximising response to postal questionnaires – A systematic review of randomised trials in health research. BMC Med Res Methodol 2006., 6(5)
• [30]Taylor R: Interpretation of the Correlation Coefficient: a Basic Review. Journal of Diagnostic Medical Sonography 1999, 6:35-39.
• [31]Marsh K, Dolan P, Kempster J, Lugon M: Prioritizing investments in public health: a multi-criteria decision analysis. J Public Health 2012.
• [32]Mauskopf JA: Principles of good practice for budget impact analysis: report of the ISPOR Task Force on good research practices–budget impact analysis. Value Health 2007, 10(5):336-47.
• [33]Mauskopf JA, Paul JE, Grant DM, Stergachis A: The role of cost-consequence analysis in healthcare decision-making. Pharmaco Economics 1998, 13(3):11.
• [34]McColl E, Jacoby A, Thomas L, Soutter J, Bamford C, Steen N, Thomas R, Harvey E, Garratt A, Bond J: Design and use of questionnaires: a review of best practice applicable to surveys in health service staff and patients. Health Technol Assess 2001., 5(31)
• [35]Ryan M, McIntosh E, Dean T, Old P: Trade –offs between location and waiting times in the provision of health care: the case of elective surgery on the Isle of Wight. 2000, 22(2):8.
• [36]Severin V, Louviere J, Finn A: The stability of shopping choices over time and across countries. J Retailing 2001, 77:17.
• [37]Mentzakis EAZJ: An investigation of individual preferences: consistency across incentives and stability over time. ECON Working paper: 0702012. University of Zurich;