Evidence map›Paper›PMID 41770590›Full record

ArticleJMIR cancer2026

Revealing the Physician's Task Conceptualization in Melanoma Treatment: Qualitative User Experience Study.

Eva Maria Hartmann, Alisa Küper, Jessica Swoboda, Catharina Lena Beckmann, Georg Christian Lodde, Elisabeth Livingstone, Dirk Schadendorf, Sabine Sachweh

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Article in JMIR cancer, 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

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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Eva Maria HartmannData Integration Center, Essen University Hospital, Essen, Germany.ORCID 0009-0000-2600-7217
Alisa KüperDepartment of Social Psychology: Media and Communication, University of Duisburg-Essen, Duisburg, Germany.ORCID 0000-0002-1018-2210
Jessica SwobodaInstitute for Artificial Intelligence in Medicine, Essen University Hospital, Essen, Germany.ORCID 0000-0003-1683-7959
Catharina Lena BeckmannDepartment of Computer Science, Dortmund University of Applied Sciences and Arts, Dortmund, Germany.ORCID 0000-0001-7991-2587
Georg Christian LoddeClinic for Dermatology, Essen University Hospital, Essen, Germany.ORCID 0000-0002-6930-2694
Elisabeth LivingstoneClinic for Dermatology, Essen University Hospital, Essen, Germany.ORCID 0000-0001-8279-9239
Dirk SchadendorfClinic for Dermatology, Essen University Hospital, Essen, Germany.ORCID 0000-0003-3524-7858
Sabine SachwehDepartment of Computer Science, Dortmund University of Applied Sciences and Arts, Dortmund, Germany.ORCID 0000-0003-1343-3553

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTo support physicians in focusing on relevant information during melanoma treatment, it is essential to design information systems (ISs) that integrate into their workflow.

objectiveThis study aimed to identify the work steps, knowledge sources, and key information during tumor board registration, revealing the physician's cognitive workflow and task conceptualization.

methodsWe applied the concurrent think-aloud method with 10 dermatologists from the University Hospital Essen's skin cancer unit, providing direct insights into their work. Participants had varying levels of experience, from a few weeks to over a decade in the department, and differing levels of previous patient knowledge. Voice and screen recordings were transcribed and inductively annotated across 3 categories: "work step," "information in focus," and "program used." Furthermore, 4 annotators from medical informatics and social psychology conducted the annotation process. These annotations were used to map the workflow model via a sequence diagram, alongside a quantitative analysis of code occurrences and overlaps.

resultsPatient-related data in clinical IS are distributed across nested submodules, requiring physicians to extract key information through extensive system interactions. The workflow consists of 4 distinct phases with 10 work steps, taking on average 13 minutes to complete. Physicians switched between 3 programs approximately 25 times, requiring a minimum of 16 interactions to access essential information from the hospital IS. The analysis identified 7 fundamental pain points: time-intensive login and patient selection (consuming 7% of workflow time), need for multiple system instances, data fragmentation across systems, hierarchical information modularization, interface structures hindering simultaneous viewing, repetitive data insertion (27% of time), and inappropriate input fields forcing external text editor use (28% of time). Collectively, approximately 55% of workflow time was dedicated to system interactions and manual data compilation rather than clinical decision-making. Physicians frequently copied information from various reports to compile a free-text case summary, representing an extensive workaround addressing data fragmentation. External editing of the case summary was highly iterative, with an average of 3.5 repetitions. The workflow phase "updating the knowledge" showed the greatest variety in step sequences, with physicians accessing radiology reports, laboratory values, tumor board protocols, and progress documentation in varying orders.

conclusionsThese insights offer a deeper understanding of physicians' intentions when handling medical information and their challenges with current clinical IS. The workflow can be understood as a comprehensive workaround due to data distribution, with physicians compensating through experience-based adaptations. This understanding of physicians' task conceptualization forms the basis for designing software solutions that better support workflows at the point of care by organizing and presenting relevant data more effectively.

Indexed as

MelanomaPhysiciansSkin NeoplasmsHumansQualitative ResearchWorkflowclinical information systemsdecision makingelectronic health recordshuman-computer interaction & usabilityknowledge managementmelanomauser research

Identifiers

PMID41770590
PMCPMC12993274

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LicenceCC BY
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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.