Evidence map›Paper›PMID 37904072›Full record

ArticleJournal of general internal medicine2024

Equity M&M - Adaptation of the Morbidity and Mortality Conference to Analyze and Confront Structural Inequity in Internal Medicine.

Anisha P Ganguly, Hannah Oren, Helen E Jack, Ryan Abe

Abstract read
In one paragraph

Article in Journal of general internal medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

4 authors.

Anisha P GangulyCenter of Innovation and Value at Parkland, Parkland Health, Dallas, TX, USA. Anisha.Ganguly@phhs.org.ORCID 0000-0002-5304-3739
Hannah OrenDivision of General Internal Medicine, Department of Medicine, University of Washington, Seattle, WA, USA.
Helen E JackDivision of General Internal Medicine, Department of Medicine, University of Washington, Seattle, WA, USA.
Ryan AbeDivision of General Internal Medicine, Department of Medicine, University of Washington, Seattle, WA, USA.

Funding

Practice facilitation and supervision to strengthen depression treatment in primary care in ZimbabweK23MH129420 · NIMH · UNIVERSITY OF WASHINGTON · PI Helen Elizabeth Jack · 2022 to 2026
$922k
NIMH NIH HHS K23 MH129420NIMH NIH HHS K23MH129420
6 · The paper itself

Abstract

backgroundAt morbidity and mortality (M&M) conferences, medical teams review cases for medical education and system improvement. Adverse outcomes are often driven by social inequity, but processes to analyze such outcomes are lacking.

aimAdapt quality improvement and patient safety (QIPS) tools in the M&M format to systematically analyze adverse patient outcomes rooted in social and structural determinants of health (SSDH).

settingOne-hour conferences conducted in health systems in Seattle, WA, and Dallas, TX.

participantsEquity M&M conferences were held 11 times, each with approximately 45 participants comprised of internal medicine trainees, faculty, and non-medical staff. PROGRAM DESCRIPTION: Conferences included a case narrative and counternarrative highlighting SSDH, an equity-framed root cause analysis, and potential interventions. PROGRAM EVALUATION: Conferences were received well across both institutions. Following conferences, most respondents reported increased identification of opportunities for action towards equity (88.5%) and confidence in discussing equity issues with colleagues (92.3%). DISCUSSION: Equity M&M conferences are a structured tool for deconstructing and confronting structural inequity that leads to adverse patient outcomes. Evaluations demonstrate educational impact on participants. Anecdotal examples suggest institutional impact. Other health systems could adopt this model for similar advocacy and system improvement.

Indexed as

Internal MedicineCongresses as TopicHealthcare DisparitiesHealth EquityHumansMorbidityPatient SafetyQuality ImprovementSocial Determinants of Healthhealth equitymorbidity and mortality conferencequality improvement and patient safetysocial determinants of healthstructural inequity

Identifiers

PMID37904072
PMCPMC11043282

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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 OpenQuestion graph.