Population health takes on the challenge of looking at whole groups (i.e., populations) with the goal of improving the health and preventing sickness in entire populations.1 Upon learning the term, the idea of population health made sense to me; yet, I still questioned how I, as a medical family therapist, could help intervene on the population level when my work has focused so much on families, couples, and individuals.
Even from my training as a psychological researcher, the term “population” tended to be theoretical and captured through a representative sample; thus, making claims about populations usually led to problems with generalizability. Are my interventions valid and effective for people beyond this patient? Do the results of my study really apply to whole populations?
Needless to say, it was a little intimidating to begin thinking in terms of population health.
As I sought answers to how I could begin contributing to population health, I stumbled upon its many parts:1,2,3
· Populations are defined by geographic regions
· Populations are defined by characteristics (i.e., determinants) of health (e.g., race, class)
· Populations are based on aggregates of individuals
· Populations are based on neighborhood-and-community-level factors (e.g., access to care)
· Population health discards individual identities for a broad understanding of health
· Population health narrows down to individual characteristics (e.g., socioeconomic status, race) to identify specific risk factors
· Population health addresses the absence of disease
· Population health addresses overall well-being
All these parts of population health sometimes left me confused… What is the definition for population health and who is responsible for it? From my research, it seems there is agreement that there are multiple definitions for population health and that one profession is not solely responsible for it. Many professions are called upon to promote population health together to address its many integral areas:3
· Health care
· Public health (e.g., clean water)
· Social environment (e.g., culture, income, education, employment, social support)
· Built and physical environments (e.g., street layout, land-use)
· Individual behavior (e.g., smoking, physical activity)
When seeking information on health care tools specifically, I found that many suggestions surrounded using the electronic health record (EHR). The recommendations started by suggesting we choose an EHR with population health capabilities, such as:
· Patient registries: an organized method used to collect and cluster information about a specific group (e.g., categorized by health condition or health determinant)
· Care pathways (aka clinical pathways): a tool that tracks all providers’ treatment plans per patient, identifies the most common and best practice interventions from the aggregated population, and sends back prompts in the EHR throughout the patients’ treatment to suggest particular interventions or remind providers to check-in on patients’ results
· Care analysis: tracking care and supplying pop-up messages for missed treatments or skipped standard-of-care protocol
· Risk stratification: process of identifying which patients are high risk, medium risk, and low risk for particular problems (e.g., recurrent depression or readmission)
· Coordinating care or referral tracking
A great deal of what I found, however, also stressed the importance of collaboration with big data analysts as our most important tool.4,5
Here in CFHA, we are well-acquainted with the term, collaboration, as we are physicians, patients, clinicians, educators, nurses, behavioral health professionals, family members, social workers, advocates and researchers all working together to promote quality and cost-effective models of healthcare delivery. Now, it seems, we are being called upon to invite new members to our team: big-data practitioners and data scientists.5
To answer questions/conduct research, big data analysis uses inductive research methodology to track patterns in data and then create models or find answers based on what the data are showing. Inductive methodology is in contrast with deductive methodology, which begins with an idea about what the patterns are (i.e., theory) and tests hypotheses based on that previous knowledge or assumptions to establish a model or find answers. While not often used in medical research, the inductive methodology of big data has the potential to help us piece together the vast amounts of information collected through EHRs. Big data is designed to deal with large quantities of data that have a variety of forms (i.e., text, integer, date) and that are being entered quickly yet possibly inconsistently due to human or machine error.5
To me, this sounds a lot like data being entered into EHRs.
After taking a class on databases, named Database Systems in Health Care, I can tell you, if you’re trained in traditional psychological statistics like I am, that it may not be easy to begin thinking in terms of big data and databases. It almost felt like learning a new language.
By the end of the class, however, I had gleaned enough to knowledge to know that finding a way to work through the language barrier is worthwhile because together we can harness the vast amounts of data in EHRs through the power of Big Data to help promote better health for entire populations.
As advocates for quality models of healthcare delivery, we can continue to petition to improve our ability to address population health through investing more time, money, and training in our information technology systems in order to:
1. Improve training in data science
2. Hire qualified big data practitioners
3. Capture and analyze our own data (e.g., ability to track patient adherence, cost of services)
4. Improve our ability to communicate/share information (e.g., with other healthcare providers, policy makers, media, institutional leaders1)
Technology has made astounding advances in the treatment of medical conditions within populations. Now, let us invest in our ability to manage, measure, and share that medical information for the sake of population health.
1Harris, D., Puskarz, K., & Golab, C. (2015). Population health: Curriculum framework for an emerging discipline. Population Health Management, 19, 39-45. doi:10.1089/pop.2015.0129
2Gourevitch, M. N., Cannell, T., Boufford, J. I. & Summer, C. (2012). The challenge of attribution: Responsibility for population health in the context of accountable care. American Journal of Public Health, 102, S322-S324.
3Kindig, D. & Stoddart, G. (2003). What is population health? American Journal of Public Health, 93, 380-383.
4Krumholz, H. M. (2014). Big data and new knowledge in medicine: The thinking, training, and tools needed for a learn health system. Health Affairs, 33, 1163-1170. doi:10.1377/hlthaff.2014.0053
5Attride, K. (2015). Big data enables population health. In M. M. G., Mayzell, Population Health: An Implementation Guide to Improve Outcomes and Lower Costs (1st Ed.). Boca Raton, US: Productivity Press. Retrieved from http://www.ebrary.com
Erin Sesemann is a current Ph.D. student in Medical Family Therapy at East Carolina University. She has experience working in community mental health agencies, private practice, and integrated behavioral health care in primary care. She graduated with her M.S. in Marriage and Family Therapy from Oklahoma State University in Stillwater, OK.