- From bundled payment models to capitated healthcare payments, payers implement risk contracts to financially motivate providers to target high-cost and high-utilization patient populations to reduce overall healthcare costs. But increasing payer access to social determinant data is the key to creating more successful risk contracts, Jennifer Daley, MD, a Market Medical Executive at Cigna, said at HIMSS17.
In a session on social risk factors and risk contracting, Daley partnered with MediQuire’s CEO, Klaus Koenigshausen, to discuss how gathering social determinant data from EHR systems can lead to better risk contracts and clinical outcomes.
Social determinants of health heavily influence clinical outcomes, the session leaders elaborated. Factors such as homelessness, low income, race and ethnicity, and zip code may affect patient’s health.
According to the World Economic Forum, social risk factors and environment account for 20 percent of health outcomes, whereas healthcare represents 10 percent, genomics 30 percent, and individual behavior 40 percent.
A recent National Academies of Science, Engineering, and Medicine (NASEM) report also showed similar social risk influences on patient outcomes. In fact, the group called on Medicare to adjust value-based reimbursement models to better account for social risk factors because providers who disproportionately treat patients with the factors faced more financial penalties.
“More importantly, though, these social risk factors really increase the risk of future disease burden and that’s particularly true in the lower income populations, such as Medicaid, dual eligibles, and other vulnerable populations,” Koenigshausen said.
While many experts agree that social determinants impact clinical outcomes and healthcare spending, many payers and providers have struggled to develop methods for identifying social risk.
“Addressing these social determinants have until recently not systematically been incorporated into healthcare workflows,” he stated. “There is a legitimate debate whether addressing and screening for these social determinants should be added to the plate of an already busy healthcare provider. But the reality is, no matter where you sit on that debate, if you want to be successful in risk-based contracts with these populations you really have no choice but to address and modify some of these social components.”
Daley indicated that data source limitations may be exacerbating social determinant screening and workflow challenges. Based on a clinical outcomes prediction model with five components, she stated that only demographics, disease, severity and co-morbidities, and behavioral health have either readily or somewhat available data.
On the other hand, the last component, social determinants of health, has been the largest hurdle for data collection, Daley stated. Social determinant data is not commonly found in claims, medical record, and personalized medicine data sources. The information is typically found in coded and unstructured EHR data.
“To help healthcare providers and payers, we need to develop a social companion diagnostic and by that I mean a method that helps providers quickly identify at the point-of-care what the social risk factors for patients are and secondly, identify the non-medical interventions that these providers can actually facilitate,” Koenigshausen stated.
Developing a social companion diagnostic is like using personalized medicine to mitigate the genomic impact on health outcomes, he added. Mapping social determinants is key to developing personalized care just as mapping the genome was essential to creating personalized medicine.
Like how genomic diagnostics help providers prescribe the most effective drugs, a social companion diagnostic can help providers select the most appropriate personalized care program to mitigate social risk influences on health outcomes.
For example, session leaders discussed how using the companion can help payers reduce healthcare costs and encourage more effective interventions for their diabetic patient population. By identifying which diabetic patients are at risk for depression, payers can motivate providers to just use a mental health intervention on those patients rather than the whole diabetic patient group.
As a result, using the social determinant companion can help payers and providers lower healthcare costs through risk contracts, such as accountable care organizations, bundled payments, and other shared risk arrangements.
“A social companion diagnostic is relevant to the entire life cycle of risk-based contracting,” Koenigshausen stated. “First, it helps you better risk-stratify risk contracts in the first place. Current risk adjustment methodologies such as HCC [Hierarchical Condition Categories] or CDPS [Chronic Illness and Disability Payment System] may help you understand the current clinical risk of your population based on diagnosis codes and demographics.”
But two patient populations can have the same HCC or CDPS risk scores even though one group has higher social risk factor presence, he continued. Payers and providers should look at these populations differently for risk contracting.
Social companion diagnostics can also benefit payers and providers after a risk contract is implemented. The tool can help payers identify episode triggers and better develop prospective contracting and pricing.
“Once you already sign a risk contract, social companion can be helpful in identifying patients with emerging risks because many social determinants really happen before the patient deteriorates and the disease burden increases,” he added.
The social companion diagnostic should primarily draw social determinant data from EHR systems, the session leaders stated. Gathering social determinant data can be difficult for payers and providers, Daley explained, but EHRs actually contained more information than expected.
Session leaders found that about 25 percent of patients had at last one social determinant documented in the unstructured data feature of their EHRs. The unstructured data typically included information on incarceration, domestic abuse, substance abuse, emotional status, death and grief issues, stress, and anxiety.
Social determinant documentation also improved as more providers adopted EHR systems. At the start of the EHR adoption bubble in 2012, only 1.7 percent of patient EHRs contained some information on social determinants or behavioral health. That number jumped to 7.9 percent in 2014 and finally 25.2 percent in 2016.
While EHR data holds some social determinant information, session leaders agreed that many payers and providers cannot use the data as effectively because it is hidden in patient records.
However, using a social companion diagnostic tool could be helpful to better and more conveniently identify at-risk patients, especially as analytics solutions emerge that can capture social determinant information from other sources.