Biopsychosocial Networks: Non-Medical Services

Biopsychosocial Networks: Data Backbone

As part of an advanced biopsychosocial model of healthcare delivery, we are already rather developed in our efforts to integrate non-medical care into 'mainstream' healthcare and we look for a future where we will see these two elements as being part of the same integral care. To this point, we have been involved for many years in both data and care models designed to help community-based organizations and their health system partners plan sustainable financial arrangements to fund the delivery of social services to high-need, high-cost (HNHC) patients. HNHC patients, who account for a large share of overall health care spending, often have social needs, clinically complex conditions, cognitive or physical limitations, and/or behavioral health problems. Research shows that complex patients are likely to benefit from a holistic model of care that addresses the social determinants of health (SDOH) such as transportation, housing, and nutrition, in addition to medical needs.

Our SDoH strategy builds upon original work by Dr. Victor Tabbush (UCLA), The SCAN Foundation, and Carenodes -- affiliates of our network. We utilize a blended data strategy sourced from market-level utilization trends, historicals, the delivery patterns of social services to high-need, high-cost (HNHC) patients, along with the composite non-medical factors driving healthcare costs with HNHC patients. To probe this construct, we factor data including social needs, complex conditions, cognitive or physical limitations, and/or behavioral health problems and their prevalence.

We model our interventions and project our ROIs using The Commonwealth Fund, Health Foundation for Western & Central New York, and the Scan Foundation as our principal resources. We recognize that SDoH factors are, by definition, market/local driven constructs. While we have a fairly sophisticated approach to standardizing and measuring our value-add to health systems, payers, medical providers, social service providers, and community-based organizations seeking to address SDOH, we recognize that various factors -- such as senior isolation, malnutrition, and environmental hazards (barriers at home creating a high risk for falls) are not readily and systematically captured.

At a minimum, we contribute $0.14 for every $1.00 invested into our network (114% ROI to payer network). We have accomplished this not by chance but by design -- utilizing data on two main dimensions: utilization and cost.

Utilization Data

  • HCUP and State Emergency Department Databases

  • HCUP and National Inpatient Sample

  • MEPS

Cost Data

  • Agency for Healthcare Research and Quality

  • The Commonwealth Fund

  • Medicare FFS Claims (PUF)

For state-specific average costs and utilization information, see the Centers for Medicare and Medicaid (CMS) Medicare Mapping Tool. Relevant to the ROI Calculator, the mapping includes measures such as average total cost, emergency department visit rates, hospitalization rates, and readmissions. (TIP: Users can look at utilization information for beneficiaries having up to 3+ claims-based conditions). MEPS has a table for mean expenses per person with care for selected conditions by type of service.


The Mapping Medicare Disparities (MMD) Tool contains health outcome measures for disease prevalence, costs, hospitalization for 60 specific chronic conditions, emergency department utilization, readmissions rates, mortality, preventable hospitalizations, and preventive services.

The MMD Population View provides a user friendly way to explore and better understand disparities in chronic diseases, and allows users to:

1) visualize health outcome measures at a national, state, or county level;

2) explore health outcome measures by age, race and ethnicity, sex;

3) compare differences between two geographic locations (e.g., benchmark against the national average); and

4) compare differences between two racial and ethnic groups within the same geographic area.