How Technology Can Unlock the Value of SDOH

Social determinants of health (SDOH) account for up to 80% of health outcomes, while medical care accounts for the balance, according to County Health Rankings. Our experiences with COVID-19 are a stark reminder that unaddressed social determinants and health inequities play significant roles in the comorbidities that have led to countless deaths attributed to the coronavirus since early 2020.

Rahul Sharma

Given the importance of SDOH in determining individual and population health outcomes, it’s clear that payers and providers can benefit from a comprehensive and secure medical and social longitudinal health record that captures SDOH data points to connect patients with community resources that address unmet social needs – reducing healthcare costs while improving lives. By having SDOH data available at the point of care, clinicians and other caregivers have a comprehensive view of the patient, enabling them to approach care holistically and take action.

Unfortunately, efforts to capture and integrate SDOH data across the healthcare continuum largely have been ineffective and plagued with interoperability issues, culture gaps, and lack of coordination. A 2019 Dartmouth study showed that only 24% of hospitals and 16% of physician practices in the U.S. reported screening for SDOH factors like food insecurity, housing instability, interpersonal violence, transportation needs, and utility needs.

Technology can make it possible to collect and synthesize data from different sources and in different forms (structured and unstructured). Screening tools can be augmented with external data sets, such as the 12 Dimensions of the Social Environment, for which the CDC has created a directory.

This data can be incorporated into longitudinal health records for individual patients. Digitization of the unstructured data sets using Artificial Intelligence (AI) algorithms can provide a wealth of information for enhancing the longitudinal health record of the patients.Machine learning (ML) can then be applied to build models for patient medical and social risk scores, cost prediction, and patient behavior.

The COVID-19 pandemic highlighted the importance of having quality, verified data accessible to care providers and public officials to help combat the spread of a communicable disease. Technology can then make it possible to synthesize data sources of different kinds – supply chain, disease registries, Internet of Medical Things (IoMT) and contact tracing data, for example – to address their inconsistencies, help identify errors or misreporting, and seamlessly integrate credible new feeds. Proper analysis done in near real-time can guide mitigation approaches and forecasting to optimize health resources. Similarly, accessible and verifiable SDOH data enables payers and providers to discern and act upon specific social factors impacting patient health.

The data challenge

There are several obstacles the healthcare industry must overcome to fully leverage SDOH.

One is disparate and siloed data from multiple sources across industries, agencies, and organizations in the public and private sectors, each with its own unique structures.

Second, roughly 80% of healthcare data is unstructured. This data can take the form of clinician electronic notes, patient-reported information such as portal messages, IoMT data and transcripts from telehealth visits that often are hard to access. Healthcare organizations need applied technology capabilities such as Natural Language Processing (NLP), multilingual support and translation service capabilities, to digitize this unstructured data.

Unstructured data such as handwritten notes, documents, free form text, images and audio and video recordings must be digitized and structured for use in applied analytics to provide valuable decision-making insights to healthcare professionals and other authorized care team stakeholders.

One way to ensure authorized disclosure and use of SDOH and other healthcare data is to leverage distributed ledger technology (DLT) for secure and permissioned sharing in near real-time, and at a granular level. This approach ensures the reliability of the data and its source(s), providing the transparency necessary for decision-making.It also ensures that the concerned parties are seeing the same data without any need for reconciliation.

Any strategy for sharing patient data across stakeholders must make protected health and other personally identifiable information a top priority. Safeguards can include biometrics data for proper patient identity verification and matching. Data should be encrypted at rest and in transit.

Third, in the realm of social determinants, there are stakeholders not subject to HIPAA regulations, so the need arises to obtain and manage patient consent for sharing data with and between stakeholders. As a result, the underlying technology must incorporate a consent management capability to ensure patient-permissioned data sharing with an immutable audit trail of disclosures.It also must support multi-channel communication approaches for obtaining and managing consent, such as mobile applications, secure text, audio, and video. If consent is being obtained through a caregiver, then Electronic Visit Verification (EVV) capture is another key data point that should be recorded.

Just as different data systems need a way to communicate with each other, different SDOH stakeholders must be able to securely exchange information. A next-generation communications engine that provides end-to-end secure communication capabilities for B2B and B2C using protocols like Email, SMS, MMS with extensions to widely used instant messaging software can eliminate communication barriers among health plans, hospitals, clinics, social service agencies, and patients.

Furthermore, technology supporting SDOH networks must have a simple user interface and an easy to navigate workflow from which data can be accessed, imported, or exported. Crucial to the creation of longitudinal health records is support for key patient data categories and information sources, including clinical data, administrative claims data, prescriptions, demographics, disease registries, immunity tests, employment, sensor data, SDOH data, and many more. While some datasets are standardized, others must be translated from unstructured data to be accessible.

If COVID-19 exposed the problems in healthcare caused by social factors, it also underscored the importance of the physical supply chain, especially in times of crisis. An effective healthcare approach features near real-time integration of supply chain data both from different medical/healthcare vendors as well as delivery companies like FedEx and UPS.

Conclusion

Improving individual and population health while reducing healthcare costs is a win for patients, providers, communities, and payers. To accomplish this goal, healthcare stakeholders need to capture and leverage social determinants of health data, which research shows accounts for the vast majority of patient outcomes and population health trends. By ensuring access to relevant and reliable SDOH data from verified sources, healthcare professionals can identify and mitigate health risks that overwhelm our system. This can be done by deploying multi-stakeholder collaboration technology with a longitudinal health record to optimize care coordination and population health measures.

Rahul Sharma is the CEO of HSBlox, which enables SDOH risk-stratification, care coordination and permissioned data sharing through its digital health platform.