Mobile Health Apps That Act as Surgical Preparatory Guides: App Store Search and Quality Evaluation
Bibliographic record
Abstract
BACKGROUND: Mobile health (mHealth) apps are becoming increasingly common in surgical practices for training, education, and communication. Factors leading to increased delays, morbidity, and mortality in surgery include inadequate preoperative patient preparation due to a failure to identify patients and procedure details, and missing instruments and equipment required for the procedure. Many apps are available for supporting preoperative, intraoperative, and postoperative care. However, there is a lack of studies that assess the quality of apps that act as surgical preparatory guides. OBJECTIVE: The aim of this study is to evaluate the quality of apps that act as surgical preparatory guides for operating room personnel through an in-house quality assessment tool. METHODS: The quality assessment tool comprises 35 questions categorized into 5 sections: (1) engagement (customization, interactivity, target audience; 19 points), (2) functionality (performance, ease of use, navigation; 12 points), (3) aesthetics (layout, visual appeal; 6 points), (4) information (quality and quantity of information, visual information, credibility; 29 points), and (5) privacy and security (4 points). An app search was conducted in the Australian Apple and Google Play stores using the following keywords: "surgical apps", "surgical preferences", "surgeon preferences", "operating room", and "perioperative procedures". The overall total scores and scores for each section were reported as medians and IQRs, expressed as raw scores and percentages. RESULTS: A total of 5 unique apps were evaluated on both iOS and Android platforms. The median overall score across all apps was 35/70 (50%; IQR 38.6%-64.3%). ScrubUp (48/70, 69%) and MySurgeon (42/70, 60%) had the highest overall scores, followed by PrefCard (35/70, 50%) and Scrubnote (28/70, 40%). The lowest scoring app was BrainPadd (26/70, 37%). The sections with the highest median scores, in decreasing order, were privacy and security (4/4, 100%; IQR 75%-100%), aesthetics (5/6, 83%; IQR 75%-91.7%), engagement (15/19, 79%; IQR 57.9%-86.8%), functionality (7/12, 58%; IQR 29.2%-75%), and information (5/29, 17%; IQR 15.5%-34.5%). Most apps scored well (4/4, 100%) on privacy and security, except for Scrubnote (2/4, 50%). ScrubUp received a perfect score for aesthetics (6/6, 100%). MySurgeon (17/19, 90%) had the highest engagement score, while ScrubUp and MySurgeon had the highest functionality scores (9/12, 75% each). All apps scored below 50% for the information section, with ScrubUp having the highest score of 13/29 (45%). CONCLUSIONS: ScrubUp and MySurgeon had the highest quality scores and can be used as adjuncts to hospital protocols by operating room personnel for their surgical preparation. Developers are encouraged to develop appropriate apps for surgical preparation based on relevant guidelines and standards, as well as the quality evaluation criteria in our tool. Operating room personnel can also use this tool as a guide to select and assess their preferred apps in their practices.
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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 | 0.058 | 0.157 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".