Evidence map›Paper›PMID 36209889›Full record

ArticleJournal of the American Medical Directors Association2022

The Long-Term Care Data Cooperative: The Next Generation of Data Integration.

David D Dore, Lonnita Myles, Amy Recker, Dustin Burns, Catherine Rogers Murray, David Gifford, Vincent Mor

Abstract read
In one paragraph

Article in Journal of the American Medical Directors Association, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

  1. Article
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  5. Article
  6. Article
  7. Review
  8. Article
  9. Review
  10. Article
  11. Article
  12. Article
  13. Article
  14. Nursing home infection control strategies during the COVID-19 pandemic.Journal of the American Geriatrics Society · 2023
    Article
  15. The Burden and Treatment of Chronic Constipation Among US Nursing Home Residents.Journal of the American Medical Directors Association · 2023
    Article
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

7 authors.

David D DoreExponent Inc, Menlo Park, CA, USA; Department of Health Services, Policy, and Practice, School of Public Health, Brown University, Providence, RI, USA.
Lonnita MylesResearch Department, American Health Care Association and the National Center for Assisted Living, Washington, DC, USA.
Amy ReckerDepartment of Health Services, Policy, and Practice, School of Public Health, Brown University, Providence, RI, USA.
Dustin BurnsExponent Inc, Menlo Park, CA, USA.
Catherine Rogers MurrayExponent Inc, Menlo Park, CA, USA. Electronic address: crogersmurray@exponent.com.
David GiffordResearch Department, American Health Care Association and the National Center for Assisted Living, Washington, DC, USA; Center of Health Policy and Evaluation in Long-Term Care, American Health Care Association, Washington, DC, USA.
Vincent MorDepartment of Health Services, Policy, and Practice, School of Public Health, Brown University, Providence, RI, USA; Veterans Administration Medical Center, Providence, RI, USA.

Funding

Training Core (I)U54AG063546 · NIA · BROWN UNIVERSITY · PI Vincent Mor · 2019 to 2026
$125.9M
NIA NIH HHS U54 AG063546
6 · The paper itself

Abstract

Despite important advances in the linkage of residents' Medicare claims and Minimum Data Set (MDS) information, the data infrastructure for long-term care remains inadequate for public health surveillance and clinical research. It is widely known that the evidence base supporting treatment decisions for older nursing home residents is scant as residents are systematically excluded from clinical trials. Electronic health records (EHRs) hold the promise to improve this population's representation in clinical research, especially with the more timely and detailed clinical information available in EHRs that are lacking in claims and MDS. The COVID-19 pandemic shined a spotlight on the data gap in nursing homes. To address this need, the National Institute on Aging funded the Long-Term Care (LTC) Data Cooperative, a collaboration among providers and stakeholders in academia, government, and the private sector. The LTC Data Cooperative assembles residents' EHRs from major specialty vendors and facilitates linkage of these data with Medicare claims to create a comprehensive, longitudinal patient record. These data serve 4 key purposes: (1) health care operations and population health analytics; (2) public health surveillance; (3) observational, comparative effectiveness research; and (4) clinical research studies, including provider and patient recruitment into Phase 3 and Phase 4 randomized trials. Federally funded researchers wanting to conduct pragmatic trials can now enroll their partnering sites in this Cooperative to more easily access the clinical data needed to close the evidence gaps in LTC. Linkage to Medicare data facilitates tracking patients' long-term outcomes after being discharged back to the community. As of August 2022, nearly 1000 nursing homes have joined, feedback reports to facilities are being piloted, algorithms for identifying infections are being tested, and proposals for use of the data have been reviewed and approved. This emerging EHR system is a substantial innovation in the richness and timeliness of the data infrastructure of the nursing home population.

Indexed as

COVID-19Long-Term CareAgedComparative Effectiveness ResearchHumansMedicarePandemicsUnited Statesdementiaelectronic health recordsMedicare claimsNursing homespragmatic trialspublic health surveillance

Identifiers

PMID36209889
PMCPMC9742312

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.