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Record W2042725563 · doi:10.2147/ppa.s51285

Use of an interdisciplinary, participatory design approach to develop a usable patient self-assessment tool in atrial fibrillation

2013· article· en· W2042725563 on OpenAlexaffabout
Lori MacCallum, Heather McGaw, Nazanin Meshkat, Alissia Valentinis, Leslie Beard Ashley, R. Sacha Bhatia, Kaye Benson, Noah Ivers, Kori Leblanc, Dante Morra

Bibliographic record

VenuePatient Preference and Adherence · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsUniversity Health NetworkWomen's College HospitalTrillium Health CentreUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsUsabilityMedicineParticipatory designUSableHealth careDemographicsAtrial fibrillationPopulationMedical emergencyComputer scienceOperations managementHuman–computer interactionEngineering

Abstract

fetched live from OpenAlex

After identifying that significant care gaps exist within the management of atrial fibrillation (AF), a patient-focused tool was developed to help patients better assess and manage their AF. This tool aims to provide education and awareness regarding the management of symptoms and stroke risk associated with AF, while engaging patients to identify if their condition is optimally managed and to become involved in their own care. An interdisciplinary group of health care providers and designers worked together in a participatory design approach to develop the tool with input from patients. Usability testing was completed with 22 patients of varying demographics to represent the characteristics of the patient population. The findings from usability testing interviews were used to further improve and develop the tool to improve ease of use. A physician-facing tool was also developed to help to explain the tool and provide a brief summary of the 2012 Canadian Cardiovascular Society atrial fibrillation guidelines. By incorporating patient input and human-centered design with the knowledge, experience, and medical expertise of health care providers, we have used an approach in developing the tool that tries to more effectively meet patients' needs.

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 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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.309
Threshold uncertainty score0.900

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.512
GPT teacher head0.424
Teacher spread0.088 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations5
Published2013
Admission routes2
Has abstractyes

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