Healthcare organizations (HCOs) across the country are finding themselves at a turning point. For years, there have been warnings of the imminent transition from a fee-for-service to a fee-for-value world where reimbursement models would focus on the quality of care provided, rather than the quantity of tests and treatments administered.
Thankfully, to assist with this major industry overhaul came advances in care methodologies and tools that would enable these quality-based clinical and financial outcomes, one of which was the notion of population health management.
Understanding the Difference Between Patient and Population Care
HCOs, Accountable Care Organizations (ACOs) and risk-bearing organizations are responsible for caring for an attributable patient population. Within that population, each patient is unique and requires different health and care interventions. Under value-based care models, organizations are responsible for improving the overall outcome of their patient populations, while driving down total costs. However, understanding the full scope of patient care needs has historically been daunting, if not impossible, due to the lack of digitized health data. Providers have made care decisions on a case-by-case basis, with little visibility to which care decisions might have the greatest impact on the population as a whole.
If we want to improve population health, interventions need to happen one patient at a time. The crux is knowing what interventions will be most effective for each patient. This can only happen by aggregating and normalizing data across all settings of care for all patients under management. Analysis of this comprehensive data set allows caregivers to have a targeted approach, placing patients in cohorts based on their current health status and the likelihood they will have future adverse events.
Individual patients are not only members of multiple populations, but move among different cohorts as circumstances change. Take, for example, a patient who may initially be in a high-risk population for developing coronary heart disease, only to then improve her health status and move to a moderate-risk category. Because of this, we need both the close-up view of individuals and the wide-angle perspective of entire populations to see and manage these cohorts clearly and effectively.
Using Population-based Tools for Individual Care
Population health efforts require both data analytics and interoperability. Today's analytics tools, for example, can examine and quantify infinite amounts of patient data faster than ever before. However, unless that data includes the entire care history across all settings of care, the tools might not place a patient in the appropriate risk cohort, and hence may miss an opportunity for intervention.
Interoperable systems underpin successful population health initiatives aggregating disparate data from multiple clinical systems, which represents the patient's entire health status and their full care team. No single care setting, or single EHR, for that matter, has all the information required to monitor and manage population health. This is especially true given the increase in life expectancy and the rise of complex patient populations that receive care in many settings. To overcome this, we must start by compiling an individual patient's longitudinal health and social care record, and offering a comprehensive clinical summary that draws data from multiple information sources, regardless of care facility or EHR system. This is, of course, where the need for interoperability comes into play. In order to optimize the health of populations, the healthcare industry needs access to shared information coupled with intelligent logic and active interventions.
For example, consider the diabetes population, a common target for population health management undergirded by performance metrics. For any given patient with type 2 diabetes, an analytics tool may pull data from the EHR, which includes information from the PCP, endocrinologist and nutritionist, but what about the information from the health insurer or from the podiatrist? Without that larger information set, it will be impossible to determine whether metrics for preventive foot exams have been met at either the individual or the population level.
Before healthcare providers analyze data for a greater patient population, they need to make sure that they account for every patient's touch points across the care continuum. The resulting risk stratification models can then identify those patients in greatest need of care management and intervention. Armed with this analysis, the care teams can drill down to more effectively target interventions to individuals within the population, thus improving outcomes for the population as a whole.
As healthcare organizations move from a volume to value-based care model and start to incorporate population health management initiatives into their methodologies for success, certain healthcare technologies are critical in the transition. However, while many of these technologies are tailored for population-based initiatives, organizations may be better served by first focusing on the individual patient level. Optimal population health management requires safe, high-quality care for every individual. By taking a detailed, comprehensive look at each patient within a cohort, healthcare institutions will find themselves in the best position possible to succeed in their population and value-based care initiatives.
By Lynda Rowe, Sr. Advisor, Value Based Markets, InterSystems
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