A Smartphone App Designed to Empower Patients to Contribute Toward Safer Surgical Care: Community-Based Evaluation Using a Participatory Approach
Bibliographic record
Abstract
BACKGROUND: MySurgery is a smartphone app designed to increase patient and carer involvement in behaviors that contribute toward safety in surgical care. OBJECTIVE: This study presents a pilot evaluation of MySurgery in which we evaluated surgical patients' perceptions of the app in terms of its content, usability, and potential impacts on communication and safety. METHODS: A participatory action research (PAR) approach was used to formulate a research steering group consisting of 5 public representatives and 4 researchers with equal decision-making input. Surgical patients were recruited from the community using multiple approaches, including Web based (eg, social media, recruitment websites, and charitable or voluntary organizations) and face to face (via community centers). Participants referred to MySurgery before, during, and after their surgery and provided feedback via an embedded questionnaire and using reflective notes. RESULTS: A diverse mix of 42 patients took part with good representation from 2 "seldom heard" groups: those with a disability and those from a black, Asian, or minority ethnic group. Most were very supportive of MySurgery, particularly those with previous experience of surgery and those who felt comfortable to be involved in conversations and decisions around their care. The app showed particular potential to empower patients to become involved in their care conversations and safety-related behaviors. Perceptions did not differ according to age, ethnicity, or length of hospital stay. Suggestions for improving the app included how to make it more accessible to certain groups, for example, those with a disability. CONCLUSIONS: MySurgery is a novel technology-driven approach for empowering patients to play a role in improving surgical safety that seems feasible for use within the United Kingdom's National Health Service. Adopting a PAR approach and the use of a diversity strategy considerably enhanced the research process in terms of gaining diverse participant recruitment and patient and public involvement. Further testing with stakeholder groups will follow.
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How this classification was reachedexpand
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".