Evidence map›Paper›PMID 39534236›Full record

ArticleGlobal journal on quality and safety in healthcare2024

Using the Failure Mode and Effect Analysis Tool to Improve the Automatic Stop Order Process.

Ghada Hussain Al Mardawi, Rajkumar Rajendram, Arwa Balharith, Abdulaziz Alomaim

Abstract read
In one paragraph

Article in Global journal on quality and safety in healthcare, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Ghada Hussain Al MardawiDepartment of Quality Improvement, King Abdulaziz Medical City, King Abdullah International Medical Research Center, Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia.ORCID https://orcid.org/0000-0002-3820-0863
Rajkumar RajendramDepartment of Medicine, King Abdulaziz Medical City, King Abdullah International Medical Research Center, Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia.
Arwa BalharithDepartment of Quality Improvement, King Abdulaziz Medical City, King Abdullah International Medical Research Center, Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia.
Abdulaziz AlomaimDepartment of Quality Improvement, King Abdulaziz Medical City, King Abdullah International Medical Research Center, Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Automatic stop orders (ASOs) in computerized prescription order entry (CPOE) systems predefine the length of treatment. This can improve resource use for select therapies (e.g., empirical antibiotics). However, root cause analysis of dose omission errors identified inappropriate ASO-directed termination of medications without prescriber notification. This quality improvement initiative aimed to identify potential failures of the medication ASO processes to develop a new workflow and anticipate issues that may arise after implementation. Methods: A failure mode and effect analysis (FMEA) was conducted following Institute of Healthcare Improvement guidance. A multidisciplinary ASO-FMEA team reviewed the existing workflow. Failure modes, risk priority numbers (RPNs), and interventions were identified and assessed. The RPNs calculated for the proposed new workflow (assuming all recommendations were implemented) were compared with those of the existing workflow. Results: Eight failure modes, 17 effects, and 31 causes were identified in the five workflow steps (mean RPN 365.4; median 280). Specific, measurable, achievable, realistic, and time-bound interventions were proposed. Assuming successful implementation of all recommendations, the RPNs of the proposed workflow (mean 117.6; median 112) were significantly lower ( Conclusion: When modifying existing CPOE systems, FMEA may identify possible failures that can be addressed before the implementation of a new process. This may prevent errors, improving medication safety. Regardless, continuous audit and monitoring are required to ensure the effectiveness of implemented changes.

Indexed as

failure mode and effect analysis (FEMA)medication safetyroot cause analysis (RCA)

Identifiers

PMID39534236
PMCPMC11554394

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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.