Commentary on “What Makes for Successful Registry Implementation: A Qualitative Comparative Analysis”
Commentary: When reviewing abstracts to feature in this month’s newsletter, a study authored by Dr. Jodi Summers Holtrop and colleagues caught my attention because of its use of an innovative method called qualitative comparative analysis (QCA). My colleague Heather Kane and I have been actively using QCA for several years in a variety of mixed methods research and evaluation projects and have come to appreciate its value, while also developing a deeper understanding of its limitations.1 QCA is a case-oriented method based on mathematical set theory and can accommodate qualitative or quantitative data (or both) to identify complex data patterns and relationships that variable-oriented and traditional qualitative methods may miss.2 This method is best designed to answer “configural” research questions, which are questions posed as “what combinations of features are found among cases that have a specific outcome.” QCA can identify necessary or sufficient features (or both) and combinations of features that are found among cases with a specific outcome.
Dr. Holtrop and colleague’s analysis using QCA is from a research study related to patient-centered medical home (PCMH) transformation within primary care practices in Colorado. A key feature of PCMH is the use of patient registries to support the provision of chronic illness care, with registry functionality built into many electronic health record (EHR) platforms. But implementing and maintaining patient registries can be complex. Thus, the study authors posed the research question “What makes for a successful registry implementation?”
The analysis used 13 practices as cases and the study authors selected several explanatory factors to evaluate based on emergent themes identified from a grounded theory analysis of data from practices they interviewed. These factors included whether the practice was part of a large health system, whether there was a key person in charge of registry implementation, the degree to which the practice had a quality improvement mindset, the leadership at the health system and practice-level to initiate and support changes, and three other factors relating to practice context (e.g. resources, incentives, EHR capability). The outcome was defined as the extent to which the practice had implemented a fully functioning registry within their EHR. The authors found that resources and leadership when combined with either a key person or being part of a large health system almost always resulted in successful registry implementation. Notably, EHR capability and incentives were not identified as part of any successful combination of factors. The authors come full circle with their analysis by demonstrating specific examples of how these combinations of factors manifested themselves within practices using interview data.
In summary, evaluating aspects of PCMH transformation offers an excellent perspective on the implementation of complex systems, processes, and interventions. QCA is an innovative method that can offer additional insights beyond traditional qualitative (or quantitative methods). If your implementation research involves asking configural questions and involves mixed data types, I encourage you to check it out! Additional resources are provided after the references.
Read the abstract.
1Kane H, Lewis MA, Williams PA, Kahwati LC. Using qualitative comparative analysis to understand and quantify translation and implementation. Transl Behav Med. 2014 Jun;4(2):201-8. doi: 10.1007/s13142-014-0251-6. PubMed PMID: 24904704; PubMed Central PMCID: PMC4041929.
2Ragin CC. Using qualitative comparative analysis to study causal complexity. Health Serv Res. 1999 Dec;34(5 Pt 2):1225-39. Review. PubMed PMID: 10591281; PubMed Central PMCID: PMC1089061.
Ragin, C.C. (2008). Redesigning social inquiry: Fuzzy sets and beyond. Chicago, IL: University of Chicago Press.
Rihoux, B. T., & Ragin, C. C. (2009). Configurational comparative methods: Qualitative comparative analysis (QCA) and related techniques. Thousand Oaks, CA: SAGE.
Schneider, C. Q. & Wagemann, C. (2012). Set-theoretic methods for the social sciences: A guide to qualitative comparative analysis. Cambridge, UK: Cambridge University Press.
Journal-length Overview Articles
Schneider, C. Q., & Wagemann C. (2010). Standards of good practice in qualitative comparative analysis (QCA) and fuzzy-sets. Comparative Sociology, 2010;9:397-418.
Thygeson, N. M., Piekes, D., & Zutshi, A. (2013). Fuzzy-Set Qualitative Comparative Analysis: A Configurational Comparative Method to Identify Multiple Pathways to Improve Patient-Centered Medical Home Models. AHRQ Publication No: 13-0026-EF. Rockville, MD: Agency for Healthcare Research and Quality, U.S. Department of Health and Human Services.
COMPASSS (COMPArative Methods for Systematic cross-caSe analysis) http://www.compasss.org/about.htm
Thomann, E., Wittwer, S. Guide Performing fuzzy- and crisp set QCA with R A user-oriented beginner’s guide. Version April 7, 2017. http://www.evathomann.com/links/qca-r-manual
Thiem, A., & Duşa, A. (2013). Boolean minimization in social science research: A review of current software for qualitative comparative analysis (QCA). Social Science Computer Review, 31(4), 505-521. doi: 10.1177/0894439313478999
Software Information from the COMPASSS website: http://www.compasss.org/software.htm
Duşa, A. The QCA with R Book. (2017) https://bookdown.org/dusadrian/QCAbook/