There are several research streams that I find fascinating, but I either have not found the time to explore them or, from sheer neglect, I forget that I am even interested in them. Considering 2013 is coming to a close, I’ve also included a few prophetic statements about where research is going or needs to go. We’ll see how 2014 handles these areas. So, without further ado, here is a small list of areas I either want to explore soon or I just simply do not want to forget:
Clinical Decision Support Systems (CDSS)
I am more interested in the development of CDSS than the outcomes of implementation. The idea that we can incorporate empirically-derived data into decision making is amazing. The fact that it has not been universally adopted in the areas of healthcare where we have truly figured out the role of CDSS I find to be academically disquieting. There are certainly areas which are more amenable to CDSS, but come on…who cannot get excited about Bayesian posterior probabilities and updated priors? All in the name of helping patients.
Clinical Prediction Rules
My interest in clinical prediction rules is similar to my interest in CDSS, in the development of prediction rules, which may be because I have more ready access to data than I would a health system that has implemented a system. The idea that we can take data and predict future events has intrigued me since graduate school, which might explain my dissertation focus (link to poster PDF) on improving risk-adjusted capitation payment methodologies that predict future expenditures.
Diffusion of Innovations
My masters thesis (link to poster PDF) used Everett Roger’s Diffusion of Innovations as a foundation to explore health-related technology acceptance. I am not sure if I want to conduct any more research in this area, but I still enjoy thinking about how innovations and information is diffused (and adopted). I may circle back to this in the future, but for now, I am just rereading bits of Diffusion of Innovations for sport.
Complex Adaptive Systems
There is something going on in health care that we haven’t been able to explain yet and I am convinced it is because we are taking a cross-sectional view of the world. Even our longitudinal attempts to explore the world scarcely allow interactions and the influence of time that would reflect reality. The property of emergence can be seen (and explained) in other fields that have applied the concepts of complex adaptive systems, but health care deals with human behavior, which makes explanation more difficult compared to scientific fields that follow laws and law-like generalities. We’ve got a long way to go to figure healthcare out.
One place I will not be going in my research:
Pharmacogenomics. It is incredibly interesting and with the emergence of true personalized medicine, knowing more about pharmacogenomics will springboard us into the next echelon of clinical care and will give us a new appreciation for the heterogeneity of treatment effects that we currently see in clinical trials and outcomes studies. Right now, we typically play the game of averages with drug approvals and pay little attention to individual treatment response, simply because we use statistical techniques that are slave to probability sampling, where large numbers dominate all and a huge tide of responders can wash out a tiny pool of non-responders – and vice versa. Pharmacogenomics is where the future is, People! I’m just not going to help you get there, but I will happily and excitedly watch from the sidelines.