Last updated: July 28th, 2020
This time last year we discussed different Payer strategies for addressing non-clinical factors such as transportation, affordable housing, and nutritious food accessibility. Each of these have a direct impact both on patient health and healthcare spending.
Payer investments in community-based initiatives have shown encouraging results in improving the quality and experience of their beneficiaries. However, it still remains an industry challenge to identify and tackle these issues in a standardized and coordinated manner.
There are four key phases in implementing any successful program for tackling Social Determinants of Health (SDoH)– collecting data, analyzing data, taking actions, and assessing impact. Let’s discuss the two which are critical for creating successful community engagement programs:
One can collect SDoH data either directly (using risk based tools like the ones that the National Association of Community Health Centers does) or indirectly (analyzing credit reports, criminal history, etc.). Additionally, providers continue to look for ways to incorporate social needs into electronic health records (EHRs), but a pilot study conducted by Kaiser Permanente found numerous challenges with this. Challenges include lack of standard tools and resources to conduct assessments, burden in converting and storing paper assessments in electronic format, and difficulty in bringing together data housed in multiple places. Unfortunately there is no single point of access or common reference between clinical, financial, and socio-economic data collected.
Our healthcare data management and analytics platform Omni-HealthData (OHD) has helped customers address some of these problems. There are several subjects defined in OHD across different domains that capture key SDoH indicators – income, education, dual eligibility, race and ethnicity, marital status, language, family history, smoking status, functional status, etc. Then there are a set of subjects specifically designed to capture surveys and assessments, such as PHQ-9 or BIMS. The data elements in OHD’s subjects are standardized and have references to industry standard coding systems thereby reducing variability and improving efficiency in analyzing and benchmarking the data.
One example of data collected in OHD via a survey related to Housing Arrangement would be a LOINC code 89565-6 on housing status with a finding SNOMED code 609242005 – Lives in apartment without elevator access. With all the data on-boarded into a single platform, OHD’s proprietary domain-based mastering process seamlessly brings them together – financial, clinical, operational and survey data – all under a single golden entity that could point to a mastered patient or a mastered member (health plan beneficiary). A recent announcement from AMA and United Healthcare on their collaboration to create new ICD-10 codes related to SDoH further solidifies that OHD has the right data model and approach focused on standardizing the capture and processing of SDoH-related information.
A key goal in collecting SDoh data is to predict which communities are at risk due to various socio-economic factors, and then prevent them from suffering serious health issues at the hands of those risks. A consistent set of measures for monitoring nationally helps – the Centers for Disease Control and Prevention (CDC) provides tools to understand SDoH and help focus efforts to improve community health. For example, the CDC’s Social Vulnerability Index (SVI) is an assessment tool used to check the resilience of a community when confronted by stresses on human health such as natural disasters, or disease outbreaks.
Our analytics platform, powered by WebFOCUS, provides out-of-the-box capabilities to enrich the data collected in OHD with national demographic, psychographic, and socioeconomic data. Then it can be analyzed further looking at the impact of different SDoH factors.
For instance, by analyzing medication prescription order and fulfillment data and layering that data with income levels and education variables, analysts can predict which segments of the population would need assistance programs to improve their medication adherence rates. According to research from RTI International, this may save $100 to $289 billion in annual non-adherence costs.
Another potential use case would be analyzing patient encounter data to identify if a particular sector of elderly patients are unable to obtain timely treatment due to transportation issues. The outcome of analyses like these may lead to community support programs that offer rides through ride share services for patients needing urgent or critical care.
As you see, technology has significantly improved the ability to proactively identify and address the SDoH barriers for populations of patients. Omni-HealthData is integrating claims, clinical, and social determinants of data in real-time to provide powerful actionable analytics. Our software is helping care providers and care givers recognize and account for social determinants of health to create a more comprehensive approach for their patients while also helping payers identify opportunities to invest in community-based programs and member outreach initiatives.
Our solutions for population health management can provide more insight into the technology that is working towards providing safe, reliable quality care and service.