Proof of Concept for a Digital Framework to Support a Shared Agenda at Surgical Ward Rounds: Participatory Design Study
Bibliographic record
Abstract
BACKGROUND: Surgical ward rounds (SWRs) are often unstructured and deprioritized compared to traditional surgical tasks, leading to limited interdisciplinary collaboration, unprepared patients, and low family attendance. OBJECTIVE: This study aimed to co-design and develop a digital framework to facilitate a shared agenda for SWRs, ensuring all core participants can attend and participate effectively. METHODS: Participatory Design (PD) methodologies were employed, utilizing user-engaging activities within an iterative process. A multidisciplinary team, including patients, relatives, healthcare providers, technology designers, and researchers, collaborated in workshops and testing to translate user needs into prototypes of technologies consisting of the digital framework. RESULTS: A logistics system was developed for nurses to pre-book the SWRs in designated time slots, enabling them to prepare relevant data and partake in the dialogue with patients. Additionally, a mobile health application (mHealth app) displayed the schedule for patients and relatives, helping them to participate and prepare questions in advance. Multiple iterations ensured that the digital framework met user needs and was feasible for clinical practice. CONCLUSIONS: Our findings underscore the importance of collaboration between users and technology designers in developing digital health technologies. Engaging the users helped identify technical and organizational constraints that needed to be addressed to integrate the digital framework into clinical settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.081 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".