MétaCan
Menu
Back to cohort
Record W3135117411 · doi:10.1177/0272989x20984134

User Involvement in the Design and Development of Patient Decision Aids and Other Personal Health Tools: A Systematic Review

2021· review· en· W3135117411 on OpenAlexafffund
Gratianne Vaisson, Thierry Provencher, Michèle Dugas, Marie-Ève Trottier, Selma Chipenda Dansokho, Heather Colquhoun, Angela Fagerlin, Anik Giguère, Hina Hakim, Lynne Haslett, Aubri Hoffman, Noah Ivers, Anne‐Sophie Julien, France Légaré, Jean‐Sébastien Renaud, Dawn Stacey, Robert J. Volk, Holly O. Witteman

Bibliographic record

VenueMedical Decision Making · 2021
Typereview
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsUniversity of OttawaRegent Park Community Health CentreWomen's College HospitalMcMaster UniversityUniversity of TorontoUniversité Laval
FundersCanadian Institutes of Health ResearchPatient-Centered Outcomes Research Institute
KeywordsUsabilityDecision aidsContext (archaeology)User-centered designComputer scienceKnowledge managementProcess managementHuman–computer interactionMedicineEngineering

Abstract

fetched live from OpenAlex

BACKGROUND: When designing and developing patient decision aids, guidelines recommend involving patients and stakeholders. There are myriad ways to do this. We aimed to describe how such involvement occurs by synthesizing reports of patient decision aid design and development within a user-centered design framework and to provide context by synthesizing reports of user-centered design applied to other personal health tools. METHODS: We included articles describing at least one development step of 1) a patient decision aid, 2) user- or human-centered design of another personal health tool, or 3) evaluation of these. We organized data within a user-centered design framework comprising 3 elements in iterative cycles: understanding users, developing/refining prototype, and observing users. RESULTS: We included 607 articles describing 325 patient decision aid projects and 65 other personal health tool projects. Fifty percent of patient decision aid projects reported involving users in at least 1 step for understanding users, 35% in at least 1 step for developing/refining the prototype, and 84% in at least 1 step for observing users' interaction with the prototype. In comparison, other personal health tool projects reported 91%, 49%, and 92%, respectively. A total of 74% of patient decision aid projects and 92% of other personal health tool projects reported iterative processes, both with a median of 3 iterative cycles. Preliminary evaluations such as usability or feasibility testing were reported in 66% of patient decision aid projects and 89% of other personal health tool projects. CONCLUSIONS: By synthesizing design and development practices, we offer evidence-based portraits of user involvement. Those wishing to further align patient decision aid design and development with user-centered design methods could involve users earlier, design and develop iteratively, and report processes in greater detail.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.048
metaresearch head score (Gemma)0.154
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.048
Threshold uncertainty score0.252

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0480.154
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.006
Bibliometrics0.0130.014
Science and technology studies0.0010.002
Scholarly communication0.0040.005
Open science0.0030.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.422
GPT teacher head0.513
Teacher spread0.091 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations141
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueMedical Decision MakingSame topicMental Health and Patient InvolvementFrench-language works237,207