As a social scientist, I did not start my career in the orbit of cardiologists, nor was it an orbit I had even contemplated entering. But for about eight years now, I have been working with leading heart doctors in this extraordinarily fast-paced, innovative, competitive specialty that often leads the healthcare field.
Another opportunity for cardiology to lead has presented itself — in the typically homeostatic arena of scientific methods. For the past decade, interest has been slowly building in the use of mixed methods in clinical and health-services research, first with a reluctant embrace and a fair amount of skepticism but more recently with growing awareness of its value.
What is mixed-methods research? How is it relevant to the everyday practice of medicine and to leadership in healthcare? For detailed answers to those questions, see an article in Circulation: Cardiovascular Quality and Outcomes that I coauthored with my colleagues. For the basics, here’s my quick overview:
Mixed-methods studies combine statistics and conversations (quantitative and qualitative methods) to elucidate not only “how many” and “how much” of something is happening or should happen, but also why and how things transpire in the complex world of healthcare. For example, we know it’s a problem that 14% of patients stop taking their clopidogrel after getting a heart stent. But there are two distinct dimensions to the dilemma:
- Quantitative: How big a problem is cessation of clopidogrel for the patient?
- Qualitative (and just as important): Why did the patient stop taking clopidogrel?
Both types of information are needed to deliver good care to patients and to design effective interventions that improve systems.
Take another example — a cardiologist, in a leadership role at a hospital, who’s responsible for reducing readmissions for patients with heart failure. To allocate resources, she can use evidence about strategies statistically associated with readmissions, such as close coordination with post-hospital care providers (quantitative information). Yet, to be effective, she also needs to understand the nuanced features of close coordination (qualitative information).
The momentum toward mixed-methods modeling is fueled in part by the recognition that large-scale statistical computations and sophisticated multilevel modeling, though useful, cannot alone address contemporary research challenges. Those challenges present themselves when, for example, we seek to understand highly complex systems of care; the intersection among healthcare financing, delivery, and quality; nuanced interactions between social and medical dimensions of health; how to foster authentically patient-centered care; and the critical role of context in all kinds of interventions. Such issues demand new forms of measurement and ways of understanding, including mixed methods.
Methods myopia can limit our understanding to mere P values and squelch the opportunity for discovery that motivates researchers of all stripes. Mixed methods offer great potential to move cardiovascular research and practice forward, if the field is ready to lead.
What are your thoughts about mixed-methods research and what role leading cardiologists should play in advancing it?