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Record W3176868265 · doi:10.1186/s12911-021-01541-7

Decision aids linked to evidence summaries and clinical practice guidelines: results from user-testing in clinical encounters

2021· article· en· W3176868265 on OpenAlex
Anja Fog Heen, Per Olav Vandvik, Linn Brandt, Frankie Achille, Gordon Guyatt, Elie A. Akl, Shaun Treewek, Thomas Agoritsas

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBMC Medical Informatics and Decision Making · 2021
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsMcMaster University
FundersHelse Sør-Øst RHFSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsDecision aidsUsabilityReadabilityHealth informaticsClinical decision support systemDecision support systemMedical educationGuidelineHealth careComputer scienceMedicineKnowledge managementNursingPublic healthAlternative medicineHuman–computer interaction

Abstract

fetched live from OpenAlex

BACKGROUND: Tools for shared decision-making (e.g. decision aids) are intended to support health care professionals and patients engaged in clinical encounters involving shared decision-making. However, decision aids are hard to produce, and onerous to update. Consequently, they often do not reflect best current evidence, and show limited uptake in practice. In response, we initiated the Sharing Evidence to Inform Treatment decisions (SHARE-IT) project. Our goal was to develop and refine a new generation of decision aids that are generically produced along digitally structured guidelines and evidence summaries. METHODS: Applying principles of human-centred design and following the International Patient Decision Aid Standards (IPDAS) and GRADE methods for trustworthy evidence summaries we developed a decision aid prototype in collaboration with the Developing and Evaluating Communication strategies to support Informed Decisions and practice based on Evidence project (DECIDE). We iteratively user-tested the prototype in clinical consultations between clinicians and patients. Semi-structured interviews of participating clinicians and patients were conducted. Qualitative content analysis of both user-testing sessions and interviews was performed and results categorized according to a revised Morville's framework of user-experience. We made it possible to produce, publish and use these decision aids in an electronic guideline authoring and publication platform (MAGICapp). RESULTS: Direct observations and analysis of user-testing of 28 clinical consultations between physicians and patients informed four major iterations that addressed readability, understandability, usability and ways to cope with information overload. Participants reported that the tool supported natural flow of the conversation and induced a positive shift in consultation habits towards shared decision-making. We integrated the functionality of SHARE-IT decision aids in MAGICapp, which has since generated numerous decision aids. CONCLUSION: Our study provides a proof of concept that encounter decision aids can be generically produced from GRADE evidence summaries and clinical guidelines. Online authoring and publication platforms can help scale up production including continuous updating of electronic encounter decision aids, fully integrated with evidence summaries and clinical practice guidelines.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.614
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.792
Threshold uncertainty score0.919

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.614
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.562
GPT teacher head0.594
Teacher spread0.033 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it