Evidence map›Paper›PMID 39671160›Full record

ArticleJournal of the American Geriatrics Society2025

The Age-Friendly Learning Healthcare System: Replicating electronic health record based documentation metrics for 4Ms care.

Jorie M Butler, Timothy W Farrell, Megan Puckett, Claude Nanjo, Phillip Warner, David Shields, Mark A Supiano, Kensaku Kawamoto

Abstract read
In one paragraph

Article in Journal of the American Geriatrics Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

8 authors.

Jorie M ButlerDepartment of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, Utah, USA.ORCID 0000-0003-4519-7997
Timothy W FarrellDepartment of Internal Medicine, Division of Geriatrics, University of Utah School of Medicine, Salt Lake City, Utah, USA.ORCID 0000-0003-0070-8757
Megan PuckettDepartment of Internal Medicine, Division of Geriatrics, University of Utah School of Medicine, Salt Lake City, Utah, USA.
Claude NanjoDepartment of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, Utah, USA.
Phillip WarnerDepartment of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, Utah, USA.
David ShieldsDepartment of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, Utah, USA.
Mark A SupianoDepartment of Internal Medicine, Division of Geriatrics, University of Utah School of Medicine, Salt Lake City, Utah, USA.ORCID 0000-0002-5438-5087
Kensaku KawamotoDepartment of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, Utah, USA.

Funding

HRSA HHS U1QHP28741John A. Hartford Foundation 2020-056
6 · The paper itself

Abstract

backgroundUniversity of Utah Health (UUH) is an academic medical center that achieved "committed to care excellence" in age-friendly care in 2021 and has a long-standing culture of quality improvement central to a learning health system. University of California San Francisco (UCSF) developed electronic health record (EHR) documentation metrics for inpatient assessment of the 4Ms (What Matters, Medication, Mentation, and Mobility) based on the Institute for Healthcare Improvement's recommended care practice for an Age-Friendly Healthcare System. In partnership with UCSF, we replicated the assessment and action EHR metrics with local adaptations for each of the 4Ms at UUH.

methodsThe UCSF team shared 4Ms documentation metrics and Structured Query Language code used to assess 4Ms care at UCSF. At UUH, this code was adapted for a different relational database management system and local clinical context. We assessed 4Ms care, individual M, and composite measures of all 4Ms, for all patients aged 65 and older admitted to UU Hospital between January 1, 2019 and December 31, 2021. We conducted a clinical validation of individual patient cases to confirm accuracy of 4Ms queries.

resultsIn the 3-year study period, 16,489 qualifying patients, mean age 74.2, were admitted to UU Hospital in a total of 25,070 admissions with mean length of stay of 6.08 days. We were able to replicate 14 of the 16 EHR metrics of individual 4Ms developed at UCSF and five composite measures. For the composite measure addressing completeness of 4Ms care, 50% of patient encounters had all 4Ms administered during their encounter.

conclusionIndicators of the completeness of 4Ms care can be measured using EHR data to validate implementation of the 4Ms at multiple academic medical centers. Key lessons to support future scaled-up assessments include the importance of adapting EHR measures to local activities and involving expert data analysts.

Indexed as

DocumentationElectronic Health RecordsLearning Health SystemQuality ImprovementAcademic Medical CentersAgedAged, 80 and overFemaleHumansMaleUtah4Msage‐friendly healthcare systemelectronic health recordlearning

Identifiers

PMID39671160
PMCPMC11907747

What Socratic holds

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LicenceCC BY-NC-ND
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Registered trials

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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 Socratic graph.