While population health has become an increasingly popular term in medicine, it still lacks a commonly-accepted definition. David Kindig, who coined the term in 2003, defines population health as “the aggregate health outcome of health-adjusted life expectancy (quantity and quality) of a group of individuals, in an economic framework that balances the relative marginal returns from the multiple determinants of health” [1]. This definition has two important factors. First, it includes health outcomes of a group of individuals. In an updated definition, he states that population health includes “the distribution of such [health] outcomes within the group” [2]. The statement regarding distribution of outcomes is significant because it emphasizes the need to improve health outcomes of those who currently have the worst health status—typically those of low socioeconomic status (SES). Population health by definition places an emphasis on improving the health of low SES patients in order to improve the health of the population in aggregate. The second important factor of the Kindig definition of population health is the mention of an economic framework. Resources are limited—whether we are looking at the number of providers in a region, Medicare funding, or the supply of insulin. Thus, it is important that when caring for populations, we take into account the cost of any new policy intervention and the marginal improvement it has in health outcomes. There are alternatives to any intervention we implement. It is necessary to compare the cost and return of possible interventions in order to choose those with the greatest “health return.” In summary, the purpose of population health is to serve groups of patients, especially those of low SES, while using our limited health resources in the most cost-effective manner.
How is this population health delivered in a real-life clinical setting? While there are countless methods, I would like to share my personal account of how I participated in this process while doing a family medicine rotation in a federally-qualified health center (FQHC). Our clinic focused on identifying high-risk, poorly-controlled diabetics. The process began by using the clinic’s electronic health records (EHR) to identify all diabetic patients within the clinic. This list was then filtered to only include those with a significantly elevated hemoglobin A1C (our cutoff was anything greater than 9.0). Of these poorly-controlled diabetics, we filtered our list further to include only those patients who had not followed up with their physician in the last 6 months (a poorly controlled diabetic should be coming into clinic every 3 months). Patients on this final list were then called to set up an appointment with their doctor. This entire process was done by a nurse, sometimes with help from medical students like myself. The result is identification of high-risk patients that were previously lost to follow-up in order to manage their diabetes and other comorbidities.
Research studies are showing promising health and financial outcomes for population health interventions. Preliminary data from a 2018 study by David Dzieklak, PhD, demonstrate a cost savings of $9,700 per individual per year after implementing a population health approach for prevention of the progression of type 2 diabetes [3]. A 2017 study by Johnson et. al. [4] had equally exciting results. The authors analyzed data from a Denver-based academic hospital system serving 200,000 patients annually. The target population was Medicare and Medicaid beneficiaries over 19 years, specifically within the primary care setting. Their intervention included adapting a commercial predictive risk modeling software to identify patients who were at high risk of hospitalization based on age, gender, diagnosis, procedural history, medication history, and healthcare utilization patterns. Patients were given increasingly progressive team-based services based on their risk stratification. This required the hospital to increase primary care staffing with nurse care coordinators, pharmacists, behavioral health consultants, and patient navigators. Those in the highest risk group were given access to specific high-intensity clinics. At the end of the study, the authors reported that payers saved $15.8 million (1.7%) over the 26-month implementation period. The total cost of the program was $3.9 million of additional staffing. The cost savings were largely driven by reduced inpatient spending. However, quality metric results were mixed. The authors noted this was likely confounded by the 2014 Medicaid expansion.
Population health remains a term without any exact definition. However, by focusing on treating groups of patients and identifying those at highest-risk of poor health outcomes, it may be a method to improve health with significant cost savings to the healthcare system.
- Kindig D. What are we talking about when we talk about population health? Health Affairs. https://www.healthaffairs.org/do/10.1377/hblog20150406.046151/full. Accessed September 1, 2019.
- Kindig D, Stoddart G. What is population health?Am J Public Health. 2003 March; 93(3): 380–383.
- Dzielak D. The potential of a population health strategy to improve healthcare outcomes and reduce costs for Medicaid programs. American Journal of Managed Care. https://www.ajmc.com/journals/evidence-based-diabetes-management/2018/march-2018/the-potential-of-a-population-health-strategy-to-improve-healthcare-outcomes-and-reduce-costs-for-medicaid-programs. Accessed September 1, 2019.
- Johnson T. Population health in primary care: cost, quality, and experience impact. American Journal of Managed Care. https://www.ajmc.com/journals/ajac/2017/2017-vol5-n3/population-health-in-primary-care-cost-quality-and-experience-impact?p=1. Accessed September 1, 2019.
Jason Paul Singh is a student at The University of Arizona College of Medicine – Phoenix, class of 2020. He graduated summa cum laude from the University of Michigan – Ann Arbor with a BS in economics. His academic interests include alternative healthcare models and methods to improve efficiency in medicine. In his spare time, Jason enjoys traveling, reading and running. Please feel free to contact him at jpsingh[at]email.arizona.edu with any questions or comments.