Evidence map›Paper›PMID 33077512›Full record

ArticleBMJ quality & safety2021

Work systems analysis of sterile processing: assembly.

Myrtede Alfred, Ken Catchpole, Emily Huffer, Larry Fredendall, Kevin M Taaffe

Open access · bronzeAbstract read
In one paragraph

Article in BMJ quality & safety, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.8field-weighted citation impact, top 11% of its field
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

7 citing papers in PubMed, 17 citations in OpenAlex.

  1. Article
  2. Towards smart sterile supply cycles.International journal of computer assisted radiology and surgery · 2026
    Article
  3. A review of the sterile processing department's reprocessing of surgical instruments.International journal for quality in health care : journal of the International Society for Quality in Health Care · 2026
    Review
  4. Article
  5. Article
  6. Article
  7. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors at 2 institutions in 1 country.

Myrtede AlfredDepartment of Anesthesia and Perioperative Medicine, Medical University of South Carolina, Charleston, South Carolina, USA alfredm@musc.edu.ORCID 0000-0003-0045-0426
Ken CatchpoleDepartment of Anesthesia and Perioperative Medicine, Medical University of South Carolina, Charleston, South Carolina, USA.ORCID 0000-0003-4073-3025
Emily HufferDepartment of Industrial Engineering, Clemson University, Clemson, South Carolina, USA.
Larry FredendallDepartment of Management, Clemson University, Clemson, South Carolina, USA.
Kevin M TaaffeDepartment of Industrial Engineering, Clemson University, Clemson, South Carolina, USA.
Clemson University · USMedical University of South Carolina · US

Funding

A Work Systems Analysis of Sterile ProcessingR03HS025538 · AHRQ · MEDICAL UNIVERSITY OF SOUTH CAROLINA · PI CATCHPOLE, KEN · 2017 to 2017
$100k
AHRQ HHS R03 HS025538
6 · The paper itself

Abstract

backgroundSterile processing departments (SPDs) play a crucial role in surgical safety and efficiency. SPDs clean instruments to remove contaminants (decontamination), inspect and reorganise instruments into their correct trays (assembly), then sterilise and store instruments for future use (sterilisation and storage). However, broken, missing or inappropriately cleaned instruments are a frequent problem for surgical teams. These issues should be identified and corrected during the assembly phase.

objectiveA work systems analysis, framed within the Systems Engineering Initiative for Patient Safety (SEIPS) model, was used to develop a comprehensive understanding of the assembly stage of reprocessing, identify the range of work challenges and uncover the inter-relationship among system components influencing reliable instrument reprocessing.

methodsThe study was conducted at a 700-bed academic hospital in the Southeastern United States with two reprocessing facilities from October 2017 to October 2018. Fifty-six hours of direct observations, 36 interviews were used to iteratively develop the work systems analysis. This included the process map and task analysis developed to describe the assembly system, the abstraction hierarchy developed to identify the possible performance shaping factors (based on SEIPS) and a variance matrix developed to illustrate the relationship among the tasks, performance shaping factors, failures and outcomes. Operating room (OR) reported tray defect data from July 2016 to December 2017 were analysed to identify the percentage and types of defects across reprocessing phases the most common assembly defects.

resultsThe majority of the 3900 tray defects occurred during the assembly phase; impacting 5% of surgical cases (n=41 799). Missing instruments, which could result in OR delays and increased surgical duration, were the most commonly reported assembly defect (17.6%, n=700). High variability was observed in the reassembling of trays with failures including adding incorrect instruments, omitting instruments and failing to remove damaged instrument. These failures were precipitated by technological shortcomings, production pressures, tray composition, unstandardised instrument nomenclature and inadequate SPD staff training.

conclusionsSupporting patient safety, minimising tray defects and OR delays and improving overall reliability of instrument reprocessing require a well-designed instrument tracking system, standardised nomenclature, effective coordination of reprocessing tasks between SPD and the OR and well-trained sterile processing technicians.

Indexed as

SterilizationSurgical InstrumentsHumansOperating RoomsReproducibility of ResultsSystems Analysishealth services researchhuman factorspatient safetyqualitative research

Identifiers

PMID33077512
PMCPMC7979531
OpenAlexW3092816778

What OpenQuestion holds

Textmetadata
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.