The New Patient Care Frontier

Social determinants of health (SDOH) include factors such as socioeconomic status, education, physical environment, employment, social support networks, and access to healthcare (KPN). Low income and indigent individuals with lower job security and less education are likely living in more dangerous neighborhoods with limited access to nutritional food sources and healthcare services. This population of patients is likely covered by Medicaid and lives in the urban or rural areas of the US. Social determinants are well-known factors in negatively impacting healthcare for these people.

The Problem: Socioeconomic, Environmental Factors Impact Care for Some Patient Populations

Currently, many healthcare networks have not established the communication and data-sharing partnerships that facilitate the efficient treatment of these patients. Most provider networks have few interactions with community health centers or social services, and when they do interact, these relationships are informal at best.

Improving care delivery for this population can be significantly impacted by understanding whether patients have reliable transportation for their care services, have access to nutritious food (e.g., grocery stores with fresh produce and Meals on Wheels), live in a high-stress and high-crime neighborhood, have access to digital services (e.g., smartphone, tablet, or PC), have stable jobs, have assistance from reliable family members, have access to community health clinics, or use social services.

Other challenges associated with implementing SDOH include the following:

• Staffing and workflow: Who gathers the data? Who analyzes the data? Who coordinates the care?
• Financial support: Beyond Medicaid managed care plans, how is this service funded?
• Who is the executive that is ultimately responsible for this service?

The Solution: Best-of-Breed versus Self-Developed Applications

Many healthcare organizations are evaluating the best approach for capturing and using SDOH data within their IT environments. In some cases, organizations are looking at self-developing data collection and analysis. The in-house solution is best suited for healthcare organizations that have robust application development, database administration, and data scientist skill sets. Healthcare organizations that do not have this level of IT capability are best suited for evaluating and implementing turnkey commercial solutions that are supplied by several vendors.

A key consideration for developing or buying SDOH solutions is creating a design to integrate data capture and analysis functions into the workflows that provide the benefits of care delivery outcomes without adding significant overhead to providers. The ability to capture and secure patient data and information and then present it in the care-delivery workflow where it can best be assessed, flagged for selected parameters, and shared, will likely generate the outcomes expected from these solutions.

SDOH data will become an important component for use in enterprise data warehouses and for helping clinical and business analytics track outcomes and service gaps. The challenge is that much of these data may not be standardized or codified, making manipulation and assessment more difficult for the informaticists.

The Justification: Pay Now or Pay More Later

By engaging in the collection and analytics of SDOH data, organizations will be able to better understand key factors that will enable indigent or low-income patients to be more compliant and participatory in their healthcare services.

As of 2017, 19 states require Medicaid-managed care plans to screen and provide referrals for social needs. The ability to gather SDOH data to better treat and support Medicaid patients will likely help reduce healthcare expenses for this population of patients. As payers drive reimbursements toward fee-for-value, expanding these capabilities across larger populations will help drive success with high-risk population health contracts.

The Players: Emerging SDOH Solutions

Analyses will be required to determine how SDOH solutions can be effectively implemented into the workflows and databases of organizations’ enterprise solutions. Many of these solutions are cloud based and have existing reference clients. Representative SDOH vendors include the following:

• Unite Us
• NowPow
• Aunt Bertha (an application for organizations to find social and health services to support SDOH operations)
• TAVHealth (just acquired by Signify Health)
• Innovaccer

Success Factors

1. Identify a Medicaid population that is high volume and high risk for the initial prototyping and testing of SDOH. Assess expected outcomes and the impacts and capabilities of enterprise-process workflows relative to social services and referral-integration requirements.
2. Identify a team of clinicians and operations managers to implement and manage the SDOH applications and integration services.
3. Engage informaticists to evaluate the expansion of the enterprise data-model effects for acquiring and analyzing SDOH data.

In summary, as healthcare moves to higher levels of fee-for-value reimbursement models, it’s becoming increasingly valuable to incorporate SDOH data into patient-care assessments and planning. SDOH data is likely to improve patient-care outcomes and care quality. The ability for SDOH data to assist with reducing care delivery costs should be realized with the elimination of unnecessary services, more timely care interventions, and improved patient care and medication compliance. Medicaid-managed care plans are early examples of how SDOH will be used to improve care quality and lower costs for high-risk patient populations. We expect SDOH applications to be a critical component for organizations that are moving to higher levels of at-risk or capitated contracts for present and future patient populations. Strategies for evaluating, implementing, and assessing SDOH applications in initial controlled environments will enable higher levels of success in this emerging environment.

[This piece was written by Mike Davis, Arch Collaborative Lead Analyst at KLAS. For more information about KLAS, click here. To follow KLAS on Twitter, click here.]