Effects of a Standardized Community Health Worker Intervention on Health Care Utilization Within an Integrated Delivery System
Bibliographic record
Abstract
BACKGROUND: Community health worker (CHW) interventions can improve health outcomes and reduce acute care utilization. Few prior studies have examined the association of CHW interventions with health care utilization among patients within an integrated health system. OBJECTIVE: To evaluate the effects of Individualized Management for Patient Centered Targets (IMPaCT), a standardized CHW intervention originally developed within a single health system in Philadelphia, PA, on acute care utilization and primary care engagement among low-income patients at two clinics within an integrated health system in Portland, Oregon. DESIGN: Prospective randomized analysis using adjusted difference-in-differences regression. PARTICIPANTS: In total, 1230 adults living in low-income zip codes were randomized using a 2:1 allocation sequence to receive either IMPaCT (n = 820) or usual care (n = 410). INTERVENTIONS: IMPaCT is a standardized intervention in which CHWs use an in-depth interview to understand patients' strengths, social needs, and health-related goals and then collaboratively develop tailored action plans. Over 3 months, CHWs communicated with patients at least once weekly to provide coaching, social support, and navigation tailored to their goals. Due to the COVID- 19 pandemic, the intervention was predominantly delivered remotely. MAIN MEASURES: Primary outcome measures were hospital and emergency department (ED) utilization, both measured per 1000 members per month, and proportion of patients with 1+ primary care visits. Implementation fidelity and maintenance were also assessed. KEY RESULTS: Compared to usual care, patients who received IMPaCT had a relative reduction in total hospital days at 6 months (- 172.3 days per 1000 members per month, 95% CI - 320.05 to - 24.53, p= 0.022), and a greater proportion attended 1+ primary care visits (85.7% vs. 79.5%, p= 0.006). There were no differences in ED utilization. CONCLUSIONS: A standardized CHW intervention delivered remotely within an integrated health system during the COVID- 19 pandemic was associated with decreased hospital utilization and improved primary care engagement.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".