mHealth Apps Available in Italy to Support Health Care Professionals in Antimicrobial Stewardship Implementation: Systematic Search in App Stores and Content Analysis
Bibliographic record
Abstract
Background: Antimicrobial resistance (AMR) is a major challenge of the 21st century. Digital technologies are now an increasingly effective means of supporting optimal health care delivery and public health. Objective: The aim of this study was to explore the apps available to support health care professionals in the fight against AMR. Methods: A total of 4 independent researchers conducted a systematic search of the App Store and Google Play Store using the following keywords: "antimicrobial resistance," "antibiotic resistance," "antimicrobial stewardship," "antibiotic stewardship," "antibiotic guide," "antibiotic therapy," and "antimicrobial therapy." The same keywords were also searched in Italian. The apps whose contents were in languages other than Italian or English, or apps which were games, or had multimedia or paid content and advertising, or apps for only specific pathologies were not considered. A set of basic information was collected for all apps found. After downloading the apps, they were evaluated using an 86-item checklist containing expert-validated criteria aggregated in the domains of pathogens and etiological agents, diagnosis and therapy support, AMR, dashboard function, antimicrobial stewardship (AMS), notes and recordings, network, and technical characteristics of the app. Results: First, 115 apps were identified: 31 apps for Android and 84 apps for iOS. By applying the exclusion criteria, 31 apps were excluded (16 for Android and 15 for iOS) for the following reasons: not available in Italian or English (6 apps), not freely available (14 apps), required registration (5 apps), and games (6 apps). The remaining 84 eligible apps (15 for Android and 69 for iOS) were downloaded, installed, and further analyzed using the same criteria, excluding 57 apps (48 for iOS and 9 for Android) for the following reasons: required further registration (16 apps), language other than Italian or English (17 apps), pathology specific (5 apps), paid content (8 apps), specific to veterinarians (4 apps), recreational apps (2 apps), referred to only scientific articles (1 app), no longer available (1 app), and not health care objectives (3 apps). The remaining 27 apps (6 for Android and 21 for iOS) were selected for in-depth analysis. Of the 27 apps that met the inclusion criteria, most apps did not fulfill the desirable aspects and only 2 of them achieved a fulfillment score of 36%. The highest scores were achieved for support for diagnosis and therapy (37%) and technical characteristics of the app (23%). Lower scores were achieved for AMS (8%), pathogens and etiological agents (4%), notes and records (3%), network (2%), AMR (1%), and dashboard function (1%). Conclusions: None of the apps examined successfully provided the desired features and functions. To better engage of prescribers in the fight against AMR, the development of an app that meets the requirements is needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.035 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".