By Abhinav Shashank, CEO and co-founder, Innovaccer.
Once while I was scrolling through the news feed on my phone, there was one specific line that really made me wonder: “There’s a 40 percent chance of gusty and blustery winds today.” Statements such as this one strongly influence people’s behavior, as they are based on evidence or data findings from years of surveying, studying, and analyzing past trends and occurrences. However, my question is “Why are we not able to make such claims in healthcare- even today?”
Can we predict the vulnerabilities a patient might face in the future or the current health risks a population segment faces?
Is risk scoring the answer we have been looking for?
Almost all kinds of care organizations have some risk scoring methodology to target care interventions. With quality, costs, and patient experience taking the center stage in healthcare, care organizations need to stratify patients based on their need for immediate intervention.
The need of the hour is to address high-risk issues that impact large groups of patients and ensure that these needs are met in a timely fashion. Often, frequent fliers among high-risk patients come into the emergency department as if it’s their second home.
What if we take the method of risk scoring to a whole new level?
Traditionally, providers and health systems have relied on claims-based risk models, such as CMS-HCC, ACG and DxCG, which were built to forecast the risk of populations/sub-populations but not for individual patients. Hence, these models give an accurate prediction of the average risk of the population but exhibit very poor accuracy if used to predict risk for individual patients.
Although risk scoring has turned out to be a key factor in addressing the needs of the patient population, this method cannot provide all the important insights that are needed to drive necessary interventions. Since healthcare already has the right data from sources such as EHRs, claims, labs, pharmacy, social determinants of health (SDoH) and others, can we predict the future cost of care instead of just stating the risk score of the patient?
The right machine learning-driven approach to predict the future cost of care for patients
It all starts with the right data. The first step is to integrate the data from multiple sources- whether it is clinical or non-clinical data, such as SDoH. The data from these sources can allow us to use the comprehensive patient’s data for multiple predictive models to predict future health cost with greater accuracy.
The reality is that very few people are doing great things with SDoH at this point. A lot of vendors and providers are thinking and talking about SDoH, but many of them don’t yet understand which social determinants are relevant or what to do about them. While the area is too new to boast a list of best practices, an introduction and discussion to the topic might be helpful for those considering a foray into SDoH.
What are SDoH?
The Office of Disease Prevention and Health Promotion, U.S. Department of Health and Human Services, defines SDoH as “conditions in the environments in which people are born, live, learn, work, play, worship, and age that affect a wide range of health, functioning, and quality-of-life outcomes and risks.”
If you think that sounds broad, you’re absolutely right. These determinants cover everything from how clean your water is to what your friends are like. The factors are innumerable. Stakeholders estimate that only 20 percent of one’s health is based on clinical care received from healthcare providers, with another 20 percent to 30 percent based on genetics and at least 50 percent based on SDoH.
With those assessments in mind, it seems unfair that almost everything related to health is pinned on provider organizations. The healthcare system cannot be the only player. We say that it takes a village to raise a child, and it would take a village to adequately deal with social determinants.
But those working in healthcare can’t just wait for villages to get involved. As the market continues to shift toward value-based reimbursement, health systems, payers, and vendors will be expected to incorporate SDoH into their tools and patient care. A few principles might help these stakeholders to get started.
The beginnings of a SDoH strategy
An organization’s first step in incorporating SDoH into their strategy should be to decide which data is the most important. For example, it probably wouldn’t help a physician to know which university a diabetic patient attended, but it could help a lot to know that the patient orders takeout almost daily because he doesn’t have a car and isn’t within walking distance of a grocery store with healthy options. These are aspects that, one day, may fall under the banner of SDoH.
Once an organization knows which data elements they want, they can determine how to get it. Unfortunately, the regional nature of SDoH data makes creating an excellent database very difficult. This is why we need vendors to keep SDoH on their minds. Providers need their vendor partners to incorporate SDoH data into their EMRs, population health tools, and other platforms. Healthcare organizations can also gather data by conducting assessments on-site or at patients’ homes.