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Record W3165165794 · doi:10.3233/shti210323

Designing Shift Handoff Software: Clinical Learners and Design Students Collaborate Using the “Design Thinking” Process

2021· book-chapter· en· W3165165794 on OpenAlexaff
Blake Lesselroth, Hannah Park, Helen Monkman, Ashten R. Duncan, Gabriel Thompson, Ryan Yarnall

Bibliographic record

VenueStudies in health technology and informatics · 2021
Typebook-chapter
Languageen
FieldMedicine
TopicHospital Admissions and Outcomes
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsWorkflowUsabilityComputer scienceInformaticsProcess (computing)Health careHealth informaticsEngineering managementMedical educationKnowledge managementProcess managementSoftware engineeringMedicineEngineeringNursingHuman–computer interaction

Abstract

fetched live from OpenAlex

Handoffs in patient care responsibilities between practitioners are common in the hospital setting. Because inadequate communication can lead to patient harm, professional organizations have published recommendations and practical guides to support standardized workflow. However, currently available electronic medical record (EMR) tools rarely provide the requisite functionality to support work and often suffer from major usability flaws. Our internal medicine residency program sponsored a quality improvement initiative to improve the design of handoff tools. To support this initiative, our medical informatics program collaborated with a school of architecture and design to identify requirements and ideate interface prototypes. In this article, we describe how we used Design Thinking principles and methods to inform our product design lifecycle, create novel designs, and teach inter-professional students health systems science concepts.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.393
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.131
GPT teacher head0.436
Teacher spread0.305 · 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.

Study designQualitative
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

Citations2
Published2021
Admission routes1
Has abstractyes

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