Cardiovascular and interventional radiology
August 24, 2026 · via Europe PMC
Quality Improvement Through Morbidity and Mortality Meetings in Interventional Radiology: A Retrospective Review.
Soh NYT, Tashi S, Tan BS, +10 more, Chandramohan S.
AI summary — for doctors, generated from the abstract only
In a multi-site IR service, morbidity and mortality meetings generated 70 recommendations from 139 cases, of which 64% were implemented, especially process-related changes.
What was studied
Retrospective review of IR M&M meetings (2020-2023) in a multi-site service with over 20 consultants, assessing recommendations and their implementation after at least 12 months.
Key findings
- 139 cases discussed yielded 70 recommendations; 45 (64.3%) implemented
- Process-related factors more likely to generate recommendations (85.7%; adjusted OR 20.6, 95% CI 3.3-127.6)
- Department process recommendations were most frequent (45.7%) and most often implemented (87.5%); specific statistic not fully captured in the abstract
Clinical relevance
This supports structured M&M meetings as a practical quality improvement tool in IR, particularly when focused on departmental processes, though data come from a single service.
Who should care
Limitations
Single-service retrospective review, implementation measured by documentation, and impact on patient outcomes not assessed.
IR M&M meetings drive real practice changes, especially when they target departmental processes.
This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.
Abstract
Purpose
To evaluate the role of morbidity and mortality (M&M) meetings in supporting quality improvement within interventional radiology (IR), by examining recommendations arising from meetings and their subsequent implementation.
Materials and methods
A retrospective review of M&M meetings held between 2020 and 2023 within a multi-site IR service employing more than 20 consultant IRs. Cases were presented using a standardized format and open discussion encouraged. Meeting records were reviewed for procedure details, complications, morbidity or mortality and resulting recommendations. For analysis, cases were classified by their contributing factors and recommendations by their theme. Institutional guidelines and records were reviewed after at least a 12-month interval to determine whether recommendations had been successfully implemented, defined as documented adoption into institutional practice, such as protocol, policy or workflow changes.
Results
A total of 139 cases were discussed, yielding 70 recommendations, of which 45 (64.3%) were successfully implemented. Cases with a process-related contributing factor were more likely to result in a recommendation (85.7%, adjusted OR 20.6, 95%CI 3.3-127.6, p=0.001), compared to those with procedure, device, patient, anesthesia or other department-related factors. Recommendations related to department processes were the most frequent (45.7%), and the most likely to be implemented (87.5%, p<0.001) when compared to those related to procedural technique, clinical management, scholarly activity or inter-departmental initiatives.
Conclusion
M&M meetings support identification and implementation of quality improvement measures in IR. A substantial proportion of cases discussed generate recommendations, of which a majority are subsequently implemented. Discussions related to process-related factors and recommendations for change in departmental processes are particularly productive.
DOI
10.1007/s00270-026-04584-3
PMID
42637829
Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.
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