Evidence map›Paper›PMID 32583912›Full record

ArticleJournal of applied clinical medical physics2020

Using failure mode and effects analysis (FMEA) to generate an initial plan check checklist for improved safety in radiation treatment.

Prema Rassiah, Fan-Chi Frances Su, Y Jessica Huang, Dan Spitznagel, Vikren Sarkar, Martin W Szegedi, Hui Zhao, Adam B Paxton, Geoff Nelson, Bill J Salter

Abstract read
In one paragraph

Article in Journal of applied clinical medical physics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 2 pooled it
–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

17 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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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

10 authors.

Prema RassiahDepartment of Radiation Oncology, University of Utah, Salt Lake City, UT, USA.
Fan-Chi Frances SuDepartment of Radiation Oncology, University of Utah, Salt Lake City, UT, USA.
Y Jessica HuangDepartment of Radiation Oncology, University of Utah, Salt Lake City, UT, USA.
Dan SpitznagelAvera Cancer Institute, Sioux Falls, SD, USA.
Vikren SarkarDepartment of Radiation Oncology, University of Utah, Salt Lake City, UT, USA.
Martin W SzegediDepartment of Radiation Oncology, University of Utah, Salt Lake City, UT, USA.
Hui ZhaoDepartment of Radiation Oncology, University of Utah, Salt Lake City, UT, USA.
Adam B PaxtonDepartment of Radiation Oncology, University of Utah, Salt Lake City, UT, USA.
Geoff NelsonDepartment of Radiation Oncology, University of Utah, Salt Lake City, UT, USA.
Bill J SalterDepartment of Radiation Oncology, University of Utah, Salt Lake City, UT, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo apply failure mode and effect analysis (FMEA) to generate an effective and efficient initial physics plan checklist.

methodsA team of physicists, dosimetrists, and therapists was setup to reconstruct the workflow processes involved in the generation of a treatment plan beginning from simulation. The team then identified possible failure modes in each of the processes. For each failure mode, the severity (S), frequency of occurrence (O), and the probability of detection (D) was assigned a value and the risk priority number (RPN) was calculated. The values assigned were based on TG 100. Prior to assigning a value, the team discussed the values in the scoring system to minimize randomness in scoring. A local database of errors was used to help guide the scoring of frequency.

resultsTwenty-seven process steps and 50 possible failure modes were identified starting from simulation to the final approved plan ready for treatment at the machine. Any failure mode that scored an average RPN value of 20 or greater was deemed "eligible" to be placed on the second checklist. In addition, any failure mode with a severity score value of 4 or greater was also considered for inclusion in the checklist. As a by-product of this procedure, safety improvement methods such as automation and standardization of certain processes (e.g., dose constraint checking, check tools), removal of manual transcription of treatment-related information as well as staff education were implemented, although this was not the team's original objective. Prior to the implementation of the new FMEA-based checklist, an in-service for all the second checkers was organized to ensure further standardization of the process.

conclusionThe FMEA proved to be a valuable tool for identifying vulnerabilities in our workflow and processes in generating a treatment plan and subsequently a new, more effective initial plan checklist was created.

Indexed as

Healthcare Failure Mode and Effect AnalysisAutomationChecklistHumansRadiotherapy Planning, Computer-AssistedRisk AssessmentWorkflowFMEAinitial plan checkquality assurance

Identifiers

PMID32583912
PMCPMC7484852

What OpenQuestion holds

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LicenceCC BY
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.