Patient-Centered Decision Support: Formative Usability Evaluation of Integrated Clinical Decision Support With a Patient Decision Aid for Minor Head Injury in the Emergency Department
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.
Bibliographic record
Abstract
BACKGROUND: The Canadian Computed Tomography (CT) Head Rule, a clinical decision rule designed to safely reduce imaging in minor head injury, has been rigorously validated and implemented, and yet expected decreases in CT were unsuccessful. Recent work has identified empathic care as a key component in decreasing CT overuse. Health information technology can hinder the clinician-patient relationship. Patient-centered decision tools to support the clinician-patient relationship are needed to promote evidence-based decisions. OBJECTIVE: Our objective is to formatively evaluate an electronic tool that not only helps clinicians at the bedside to determine the need for CT use based on the Canadian CT Head Rule but also promotes evidence-based conversations between patients and clinicians regarding patient-specific risk and patients' specific concerns. METHODS: User-centered design with practice-based and participatory decision aid development was used to design, develop, and evaluate patient-centered decision support regarding CT use in minor head injury in the emergency department. User experience and user interface (UX/UI) development involved successive iterations with incremental refinement in 4 phases: (1) initial prototype development, (2) usability assessment, (3) field testing, and (4) beta testing. This qualitative approach involved input from patients, emergency care clinicians, health services researchers, designers, and clinical informaticists at every stage. RESULTS: The Concussion or Brain Bleed app is the product of 16 successive iterative revisions in accordance with UX/UI industry design standards. This useful and usable final product integrates clinical decision support with a patient decision aid. It promotes shared use by emergency clinicians and patients at the point of care within the emergency department context. This tablet computer app facilitates evidence-based conversations regarding CT in minor head injury. It is adaptable to individual clinician practice styles. The resultant tool includes a patient injury evaluator based on the Canadian CT Head Rule and provides patient specific risks using pictographs with natural frequencies and cues for discussion about patient concerns. CONCLUSIONS: This tool was designed to align evidence-based practices about CT in minor head injury patients. It establishes trust, empowers active participation, and addresses patient concerns and uncertainty about their condition. We hypothesize that, when implemented, the Concussion or Brain Bleed app will support-not hinder-the clinician-patient relationship, safely reduce CT use, and improve the patient experience of care.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.034 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it