Effect of a Mobile App on Preoperative Patient Preparation for Major Ambulatory Surgery: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Inadequate preoperative patient preparation causes organizational, economic, and emotional problems to patients and professionals. In Spain, no current evidence is available on either the rate of compliance or the impact of good compliance with preoperative recommendations by patients in the ambulatory setting. However, it is known that around 25% of surgical cancellations in the major ambulatory surgery (MAS) are due to poor compliance with these recommendations and, therefore, avoidable. Introducing innovative tools based on mobile health (mHealth) apps may help patients meet the preoperative recommendations and, consequently, reduce the rate of cancellations in the ambulatory setting. OBJECTIVE: The objective of this study was to evaluate the effectiveness of the Listeo+ mHealth app as a tool for improving compliance with preoperative recommendations in MAS versus standard of care (SOC). METHODS: A multicenter, randomized, open-label clinical trial that compares SOC with the additional use of Listeo+, a specific mHealth app for MAS preoperative patient monitoring, is being conducted. The study will include patients aged ≥18 years with surgical indication for MAS who meet the necessary technological and connectivity requirements. Patients in the control group will receive written preoperative recommendations, while those in the intervention group will additionally use the Listeo+ mHealth app. There will be a competitive recruitment of 790 patients during 6 months in 4 hospitals in Andalusia (Spain) that belong to the National Health System. The primary efficacy outcome is the level of compliance with preoperative recommendations. Secondary outcomes include the rate of cancellations, associated resource consumption, and perceived usability and utility with Listeo+ by participants of the intervention group. Simple randomization 1:1 procedure will be used to allocate patients to each study group. RESULTS: The technological development of Listeo+ and the integration and interoperability of information systems was completed in September 2017. Subsequently, simulation tests were performed with Listeo+, and a pilot study was initiated with real patients that concluded successfully in October 2017. Patient recruitment began in December 2017 in the 4 participating centers. After an intermediate analysis performed 10 months after the start of the recruitment phase, the data collection and cleaning phases are estimated to be completed in April 2019, and the analysis with the final results will be conducted in July 2019. CONCLUSIONS: Progress in the integration and interoperability of information systems represents a major step forward in the field of mHealth. The app will allow health professionals to monitor in real-time patients' preparation and critical preoperative recommendations fulfillment. We expect a reduction in avoidable preoperative cancellations due to a lack of or a poor patient preparation. Self-assessed Web-based questionnaires and focus group will provide important information about the perceived usability and utility of Listeo+ app among patients and health care professionals. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/10938.
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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 | 0.013 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".