Co-Design and Creation of a Documentation Support System for Care Workers with Large Language Models

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This co-design study involved care stakeholders to create a documentation support system using large language models, which extracts relevant information from care interactions with 55% average accuracy.

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This study developed and evaluated a documentation support prototype using large language models, created through a multi-phase co-design process with continuous involvement of care stakeholders in Vienna residential care homes. Across four phases with 43 participants (24 care workers, 19 residents), the authors recorded four mock speech interactions for feasibility testing and then 40 real care interactions (average ~11 minutes), analyzed care-worker interview data with thematic analysis, and assessed system performance using ROUGE-1, BERTScore, and care-worker topic annotations. The system generated documentation-relevant information for documentation in 55% of cases on average, but 29.6% of labeled topics were considered not relevant, and 15.4% of summaries contained wrong segments, attributed to transcription errors and missing speaker diarization. Relevance to endometriosis: the paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Abstract Developing meaningful and wanted technologies for the care context presents a significant challenge. This study illustrates the development of a documentation support prototype created with continuous involvement of care stakeholders. The process is examined in terms of its strengths and weaknesses, and key design requirements are identified. We employ a co-design approach involving 43 unique care stakeholders (24 care workers, 19 residents) across four study phases conducted in Vienna residential care homes. Each phase incorporates divergent and convergent methods to identify challenges and define solutions. Four mock speech interactions were recorded for feasibility testing, followed by 40 recordings of real care interactions (avg. ca. 11 min). Qualitative data from interviews with care workers is analyzed using thematic analysis, and system performance was evaluated using ROUGE-1, BERTScore, and care worker annotations. Based on the recordings and interview data, we identify topics in care worker–resident dialogues that are relevant or irrelevant to documentation. We found that the system consistently generates relevant information for documentation (55% on average). Of the labeled topics, 29.6% were identified as not relevant for documentation. We identify transcription errors and lack of speaker diarization for part of the reason for the remaining 15.4% of summaries consisting of wrong segments. We present the design outcomes of a multi-phase co-design study. Our findings demonstrate that the system can extract care-relevant information from care interactions, and we discuss insights related to the co-design process, as well as potential implications for work practices and privacy.
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Co-Design and Creation of a Documentation Support System for Care Workers with Large Language Models | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Co-Design and Creation of a Documentation Support System for Care Workers with Large Language Models Reinhard Kletter, Matthias Hirschmanner, Helena Anna Frijns, Evelyn Mayer-Haas, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8214439/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract Developing meaningful and wanted technologies for the care context presents a significant challenge. This study illustrates the development of a documentation support prototype created with continuous involvement of care stakeholders. The process is examined in terms of its strengths and weaknesses, and key design requirements are identified. We employ a co-design approach involving 43 unique care stakeholders (24 care workers, 19 residents) across four study phases conducted in Vienna residential care homes. Each phase incorporates divergent and convergent methods to identify challenges and define solutions. Four mock speech interactions were recorded for feasibility testing, followed by 40 recordings of real care interactions (avg. ca. 11 min). Qualitative data from interviews with care workers is analyzed using thematic analysis, and system performance was evaluated using ROUGE-1, BERTScore, and care worker annotations. Based on the recordings and interview data, we identify topics in care worker–resident dialogues that are relevant or irrelevant to documentation. We found that the system consistently generates relevant information for documentation (55% on average). Of the labeled topics, 29.6% were identified as not relevant for documentation. We identify transcription errors and lack of speaker diarization for part of the reason for the remaining 15.4% of summaries consisting of wrong segments. We present the design outcomes of a multi-phase co-design study. Our findings demonstrate that the system can extract care-relevant information from care interactions, and we discuss insights related to the co-design process, as well as potential implications for work practices and privacy. Co-design Residential care home Documentation support Large Language Models Prototype Care work Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 15 Mar, 2026 Reviews received at journal 23 Feb, 2026 Reviews received at journal 23 Jan, 2026 Reviewers agreed at journal 17 Jan, 2026 Reviewers agreed at journal 12 Jan, 2026 Reviewers invited by journal 15 Dec, 2025 Editor assigned by journal 10 Dec, 2025 Submission checks completed at journal 27 Nov, 2025 First submitted to journal 26 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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