The purpose for your population health program is to keep members healthy and prevent the onset and advancement of disease. Historically, programs have been disjointed because providers, employers and communities execute isolated efforts, with no collaboration or communication between them. Not surprisingly, the impact of silo programs has been unremarkable.
Despite valiant efforts, population health management remains a complex and daunting undertaking, and a topic far greater than anyone can cover in a single blog post. There are, however, very specific strategies that we have seen across hospital systems, medical groups and payers that are flourishing within the framework of health care legislation and the new payment structure. Only a few establishments have applied all of the strategies, and most have tackled and perfected only a scant handful of them in piecemeal fashion. Without fail however, every single organization we encounter that has applied one or more of these has improved total cost of care and quality of care. The strategies work because they align payment and measurement models with better health care delivery for the members.
The strategies are straightforward, but warrant detailed explanation. Over the coming weeks and months, we’ll explore them and share front-line accounts so you can apply them in your business.
No Excuses Health Care Organization: How will we know better care delivery when we see it?
Among all programs, there is a polar spectrum of interventions: the simple wellness program sits on one end, while personalized medicine falls on the opposite extreme,
with many possibilities in between. When we look at distinct programs, there is a direct correlation between program cost and overall population health impact
(Fig 1). Most generalized (and therefore inexpensive) programs produce meager results. Conversely, programs that offer a personal health coach have shown excellent results, with a corresponding price tag.
The problem that vexes population health program managers is figuring out the right combination of offerings, how to deliver them to maximize population state of health in relation to investment, and establishing a strong incentive for the population (community, families, patient group, or members) and clinicians for active, engaged participation.
Of course, long-term perspective is important when designing a population health program, and to fully understand state of health and disease impact. It’s also imperative to examine the medical cost and utilization in relation to the program over time. But, the breadth and depth of data available today enables program managers to frequently measure, analyze, and adjust in response to population transitions and health status.
Regardless of where you are with technology resources, the program optimization strategies of successful organizations form the blueprint to reach the one true goal: balance total cost of care with best quality of care (meaningful outcomes). Across the board, everyone agrees that to do this, organizations must align payment and measurement models to transform healthcare delivery.
As program managers strive to reduce the lifetime cost burden for each member, a strong benchmark of optimal and efficient care management is delayed onset of disease and constraining members along the lower progression line (Fig 2).
Population Health: A Multi-Dimension, Non-Intuitive Challenge
Regardless of size or notoriety, providers and payers typically experience a relative increase in per member per month (PMPM) cost in year one of a new population health program (primary care, up-front care management investment, technology and analytics). Organizations that apply the entire assortment of cost, clinical, and process data at their disposal to respond according to population needs and evolution, are able to predict which program adjustments will have a meaningful impact, and make timely changes. These are the organizations that achieve lower PMPM cost in years two and three, and mark a decrease in emergency room visits and total hospital admissions per n member years (Fig 3).
The cost/quality of care winners, regardless of if they are a PCMH, ACO, specialty hospital, or a payer, are those that understand program utilization, and put the existing data to work as they design and manage care management programs. They continue to win with a constant and consistent cycle of analyze, adjust and execute.
What does healthcare optimization look like?
Let’s revisit that “one true goal” once more: you want to improve outcomes. Everyone agrees it’s a pivotal issue that plagues the entire health care industry, and we know the key is hiding in plain sight with Triple Aim and population health objectives. There are a few (dare we say wildly?) successful payers and provider organizations that have tackled it scientifically, as a mathematical problem. Regardless of quantity and source, they resolutely brought to bear every piece of data available to them to improve population health and reduce overall program costs.
We call it the Population Health Optimization Function (Fig. 4), and it allows organizations to zero in on patient health outcomes, and organically improve quality, lower risk, and reduce PMPM. To improve overall state of health for the population for the period (t1-t2), optimize the care delivery chain for best outcomes (high quality of care, low cost of care, high patient satisfaction).
In other words, by focusing specifically on outcomes, payers and health care providers achieve a golden trifecta: better quality, less risk, and lower PMPM (Fig. 5).
Why do top population health analysts find this “mad scientist” approach so important? Using mathematical formulas, they’re able to shift their attention to objective reasoning and problem solving, rather than relying strictly on intuition and guesswork. The “right answer” is undisputable because of the science and math involved, and the object of attention then becomes execution – at the patient level, with clearly defined cohorts, or across the entire population.
The scientific approach also involves examining care management efficiency curves for the population, which we’ll discuss in a future post. In the meantime, if you want to learn more about optimization for healthcare, watch this webinar recording on using data and predictive analytics to help manage population health.