Evidence map›Paper›PMID 42154208›Full record

ArticleInternational journal of computer assisted radiology and surgery2026

Unwritten rules of healthcare assistance for robotic systems: multi-method workflow analysis of postoperative wound care treatments for the development of robotic assistive systems.

Sonja Stabenow, Franziska Jurosch, Alissa Jell, Cristina Piazza, Klaus Bengler, Dirk Wilhelm

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Article in International journal of computer assisted radiology and surgery, 2026. 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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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

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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

6 authors.

Sonja Stabenow *Research Group MITI, School of Medicine and Health, TUM University Hospital, Technical University of Munich, Munich, Germany. sonja.stabenow@tum.de.ORCID http://orcid.org/0009-0007-7023-5162
Franziska Jurosch *Research Group MITI, School of Medicine and Health, TUM University Hospital, Technical University of Munich, Munich, Germany.ORCID http://orcid.org/0000-0002-9974-6886
Alissa JellResearch Group MITI, School of Medicine and Health, TUM University Hospital, Technical University of Munich, Munich, Germany.ORCID http://orcid.org/0000-0002-7153-3803
Cristina PiazzaDepartment of Computer Engineering, School of Computation, Information and Technology, Technical University of Munich, Munich, Germany.ORCID http://orcid.org/0000-0002-0358-8677
Klaus BenglerChair of Ergonomics, School of Engineering and Design, Technical University of Munich, Munich, Germany.ORCID http://orcid.org/0000-0003-3906-6093
Dirk WilhelmResearch Group MITI, School of Medicine and Health, TUM University Hospital, Technical University of Munich, Munich, Germany.ORCID http://orcid.org/0000-0002-2972-9802

Funding

Bayerisches Staatsministerium für Wissenschaft und Kunst 1200/2022Bundesministerium für Bildung und Forschung 16KIS2416
6 · The paper itself

Abstract

purposeUnderstanding and modeling the context of patient care interventions, such as wound dressing changes, is essential for developing intelligent robotic assistive systems. While context awareness is widely acknowledged as a requirement in robotics research, the specific information needed to create adapted behavior remains insufficiently defined for many clinical tasks. By analyzing the wound dressing procedure, which we take as a model for care provision on a surgical ward, we aim to identify and model the requirements for such systems in terms of time and used materials, as well as other contextual factors that influence the execution of the task.

methodsWe collected observational and quantitative data during postoperative wound dressing procedures to capture contextual triggers and task dependencies. Material-use tracking and communication annotations provided the data foundation. Using FRAM, we developed a sociotechnical process model that represents the workflow and its requirements. Expert interviews further contextualize the boundaries for robotic assistance.

resultsHuman assistance is supportive in the background, and its positive impact is beyond a mere requested helper, taking for instance the mental load of preparation. Good assistance is proactive, consultative, and adapts to the care provider. For robotic systems to become embedded in the environment, they need procedural and patient-adapted behavior to interact during wound dressing.

conclusionsIntelligent robotic systems capable of supporting healthcare professionals during patient interventions have to adapt to the rules of hospital environments. Workflow models, as presented in our work, bring human expertise to robotic autonomy.

Indexed as

Assistance systemsFRAMMaterial assessmentPostoperativeRobotWound care

Identifiers

PMID42154208

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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.