Evidence map›Paper›PMID 38017443›Full record

ArticleBMC health services research2023

A comparison between a maximum care university hospital and an outpatient clinic - potential for optimization in arthroscopic workflows?

Martin Schenk, Juliane Neumann, Nadine Adler, Tilo Trommer, Jan Theopold, Thomas Neumuth, Pierre Hepp

Abstract read
In one paragraph

Article in BMC health services research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

1 citing paper in PubMed.

  1. Article
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

7 authors.

Martin SchenkInnovation Center Computer Assisted Surgery (ICCAS), Leipzig University, Semmelweisstr. 14, 04103, Leipzig, Germany. Martin.Schenk@medizin.uni-leipzig.de.
Juliane NeumannInnovation Center Computer Assisted Surgery (ICCAS), Leipzig University, Semmelweisstr. 14, 04103, Leipzig, Germany.ORCID http://orcid.org/0000-0002-5696-9473
Nadine AdlerInnovation Center Computer Assisted Surgery (ICCAS), Leipzig University, Semmelweisstr. 14, 04103, Leipzig, Germany.
Tilo TrommerSportklinik.ERFURT, Erfurt, Germany.
Jan TheopoldDepartment of Orthopaedic, Trauma and Plastic Surgery, Division of Arthroscopic Surgery and Sports Medicine, University of Leipzig Medical Center, Leipzig, Germany.ORCID http://orcid.org/0000-0003-2884-8030
Thomas NeumuthInnovation Center Computer Assisted Surgery (ICCAS), Leipzig University, Semmelweisstr. 14, 04103, Leipzig, Germany.ORCID http://orcid.org/0000-0001-6999-5024
Pierre HeppDepartment of Orthopaedic, Trauma and Plastic Surgery, Division of Arthroscopic Surgery and Sports Medicine, University of Leipzig Medical Center, Leipzig, Germany.ORCID http://orcid.org/0000-0002-6510-2943

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDue to the growing economic pressure, there is an increasing interest in the optimization of operational processes within surgical operating rooms (ORs). Surgical departments are frequently dealing with limited resources, complex processes with unexpected events as well as constantly changing conditions. In order to use available resources efficiently, existing workflows and processes have to be analyzed and optimized continuously. Structural and procedural changes without prior data-driven analyses may impair the performance of the OR team and the overall efficiency of the department. The aim of this study is to develop an adaptable software toolset for surgical workflow analysis and perioperative process optimization in arthroscopic surgery.

methodsIn this study, the perioperative processes of arthroscopic interventions have been recorded and analyzed subsequently. A total of 53 arthroscopic operations were recorded at a maximum care university hospital (UH) and 66 arthroscopic operations were acquired at a special outpatient clinic (OC). The recording includes regular perioperative processes (i.a. patient positioning, skin incision, application of wound dressing) and disruptive influences on these processes (e.g. telephone calls, missing or defective instruments, etc.). For this purpose, a software tool was developed ('s.w.an Suite Arthroscopic toolset'). Based on the data obtained, the processes of the maximum care provider and the special outpatient clinic have been analyzed in terms of performance measures (e.g. Closure-To-Incision-Time), efficiency (e.g. activity duration, OR resource utilization) as well as intra-process disturbances and then compared to one another.

resultsDespite many similar processes, the results revealed considerable differences in performance indices. The OC required significantly less time than UH for surgical preoperative (UH: 30:47 min, OC: 26:01 min) and postoperative phase (UH: 15:04 min, OC: 9:56 min) as well as changeover time (UH: 32:33 min, OC: 6:02 min). In addition, these phases result in the Closure-to-Incision-Time, which lasted longer at the UH (UH: 80:01 min, OC: 41:12 min).

conclusionThe perioperative process organization, team collaboration, and the avoidance of disruptive factors had a considerable influence on the progress of the surgeries. Furthermore, differences in terms of staffing and spatial capacities could be identified. Based on the acquired process data (such as the duration for different surgical steps or the number of interfering events) and the comparison of different arthroscopic departments, approaches for perioperative process optimization to decrease the time of work steps and reduce disruptive influences were identified.

Indexed as

ArthroscopyOperating RoomsHospitals, UniversityHumansWorkflowArthroscopyOperating room managementSurgical process optimizationSurgical workflow analysis

Identifiers

PMID38017443
PMCPMC10685488

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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.