Evidence map›Paper›PMID 41857842›Full record

ArticleBMJ open2026

Integrating healthcare system context to improve risk prediction and assess racial disparities among dual-eligible Medicare-Medicaid beneficiaries: a retrospective cohort study using national fee-for-service claims.

Sadiq Y Patel, Sanjay Basu

Abstract read
In one paragraph

Article in BMJ open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Sadiq Y PatelClinical Product Development, Waymark, San Francisco, California, USA sadiq.patel@waymarkcare.com.ORCID http://orcid.org/0000-0003-4042-1515
Sanjay BasuClinical Product Development, Waymark, San Francisco, California, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesMedicare-Medicaid dual-eligible beneficiaries comprise <15% of enrollees but account for disproportionate spending. Current models used to predict future spending rely on patient demographics and diagnoses, potentially missing healthcare system context (provider availability, care fragmentation, facility characteristics). We estimated the degree to which healthcare context data improve risk prediction and what portion of risk and risk disparities among racial/ethnic groups is predicted by such data.

designRetrospective cohort study with 6-month feature window (January-June 2023) and 6-month outcome window (July-December 2023).

settingNational fee-for-service Medicare programme across 50 US states, District of Columbia and Puerto Rico.

participants3 877 563 dual-eligible beneficiaries with ≥8 months enrolment in Medicare Parts A, B and D during 2023. Mean age 64.9 years; 56% female; 65% White, 15% Black, 7% Hispanic and 6% Asian. OUTCOME MEASURES: Primary: prospective Medicare spending (per-member-per-month) and acute care utilisation (≥1 hospitalisation/emergency department visit) July-December 2023. Secondary: concurrent spending January-June 2023.

resultsAdding healthcare system context to patient data improved model performance from R²=0.454 (0.450-0.458) to 0.615 (95% CI 0.611 to 0.619) for prospective spending prediction (a 35% improvement, p<0.001). The sensitivity of predicting acute care visits improved from 25.0% (24.2-25.8%) to 33.8% (32.9-34.7%, p<0.001), while maintaining specificity (>97%). System context reduced predicted risk for 86% of beneficiaries (mean -8.8 percentage points), while increasing risk for 14%. Black beneficiaries experienced higher rates of contextual risk amplification (15.8% vs 13.6% White) and showed distinct vulnerability to large practice coordination and workforce composition factors, while Hispanic populations' risk was primarily associated with geographic access barriers.

conclusionsHealthcare delivery context substantially improves prediction of future spending among dual-eligible beneficiaries. These findings suggest risk adjustment incorporating context should account for differential racial impacts.

Indexed as

Fee-for-Service PlansHealthcare DisparitiesHealth ExpendituresMedicareAgedFemaleHumansMaleMiddle AgedPuerto RicoRetrospective StudiesRisk AssessmentUnited StatesHealth Care CostsHEALTH ECONOMICSHealth EquityHealth ServicesMachine Learning

Identifiers

PMID41857842
PMCPMC13007092

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.