Population Health Innovations and Payment to Address Social Needs Among Patients and Communities With Diabetes
Bibliographic record
Abstract
Policy Points Population health efforts to improve diabetes care and outcomes should identify social needs, support social needs referrals and coordination, and partner health care organizations with community social service agencies and resources. Current payment mechanisms for health care services do not adequately support critical up-front investments in infrastructure to address medical and social needs, nor provide sufficient incentives to make addressing social needs a priority. Alternative payment models and value-based payment should provide up-front funding for personnel and infrastructure to address social needs and should incentivize care that addresses social needs and outcomes sensitive to social risk. CONTEXT: Increasingly, health care organizations are implementing interventions to improve outcomes for patients with complex health and social needs, including diabetes, through cross-sector partnerships with nonmedical organizations. However, fee-for-service and many value-based payment systems constrain options to implement models of care that address social and medical needs in an integrated fashion. We present experiences of eight grantee organizations from the Bridging the Gap: Reducing Disparities in Diabetes Care initiative to improve diabetes outcomes by transforming primary care and addressing social needs within evolving payment models. METHODS: Analysis of eight grantees through site visits, technical assistance calls, grant applications, and publicly available data from US census data (2017) and from Health Resources and Services Administration Uniform Data System Resources data (2018). Organizations represent a range of payment models, health care settings, market factors, geographies, populations, and community resources. FINDINGS: Grantees are implementing strategies to address medical and social needs through augmented staffing models to support high-risk patients with diabetes (e.g., community health workers, behavioral health specialists), information technology innovations (e.g., software for social needs referrals), and system-wide protocols to identify high-risk populations with gaps in care. Sites identify and address social needs (e.g., food insecurity, housing), invest in human capital to support social needs referrals and coordination (e.g., embedding social service employees in clinics), and work with organizations to connect to community resources. Sites encounter challenges accessing flexible up-front funding to support infrastructure for interventions. Value-based payment mechanisms usually reward clinical performance metrics rather than measures of population health or social needs interventions. CONCLUSIONS: Federal, state, and private payers should support critical infrastructure to address social needs and incentivize care that addresses social needs and outcomes sensitive to social risk. Population health strategies that address medical and social needs for populations living with diabetes will need to be tailored to a range of health care organizations, geographies, populations, community partners, and market factors. Payment models should support and incentivize these strategies for sustainability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".