Mobile Health Apps Providing Information on Drugs for Adult Emergency Care: Systematic Search on App Stores and Content Analysis
Bibliographic record
Abstract
BACKGROUND: Drug-referencing apps are among the most frequently used by emergency health professionals. To date, no study has analyzed the quantity and quality of apps that provide information on emergency drugs. OBJECTIVE: This study aimed to identify apps designed to assist emergency professionals in managing drugs and to describe and analyze their characteristics. METHODS: We performed an observational, cross-sectional, descriptive study of apps that provide information on drugs for adult emergency care. The iOS and Android platforms were searched in February 2021. The apps were independently evaluated by 2 hospital clinical pharmacists. We analyzed developer affiliation, cost, updates, user ratings, and number of downloads. We also evaluated the main topic (emergency drugs or emergency medicine), the number of drugs described, the inclusion of bibliographic references, and the presence of the following drug information: commercial presentations, usual dosage, dose adjustment for renal failure, mechanism of action, therapeutic indications, contraindications, interactions with other medicinal products, use in pregnancy and breastfeeding, adverse reactions, method of preparation and administration, stability data, incompatibilities, identification of high-alert medications, positioning in treatment algorithms, information about medication reconciliation, and cost. RESULTS: Overall, 49 apps were identified. Of these 49 apps, 32 (65%) were found on both digital platforms; 11 (22%) were available only for Android, and 6 (12%) were available only for iOS. In total, 41% (20/49) of the apps required payment (ranging from €0.59 [US $0.64] to €179.99 [US $196.10]) and 22% (11/49) of the apps were developed by non-health care professionals. The mean weighted user rating was 4.023 of 5 (SD 0.71). Overall, 45% (22/49) of the apps focused on emergency drugs, and 55% (27/49) focused on emergency medicine. More than half (29/47, 62%) did not include bibliographic references or had not been updated for more than a year (29/49, 59%). The median number of drugs was 66 (range 4 to >5000). Contraindications (26/47, 55%) and adverse reactions (24/47, 51%) were found in only half of the apps. Less than half of the apps addressed dose adjustment for renal failure (15/47, 32%), interactions (10/47, 21%), and use during pregnancy and breastfeeding (15/47, 32%). Only 6% (3/47) identified high-alert medications, and 2% (1/47) included information about medication reconciliation. Health-related developer, main topic, and greater amount of drug information were not statistically associated with higher user ratings (P=.99, P=.09, and P=.31, respectively). CONCLUSIONS: We provide a comprehensive review of apps with information on emergency drugs for adults. Information on authorship, drug characteristics, and bibliographic references is frequently scarce; therefore, we propose recommendations to consider when developing an app of these characteristics. Future efforts should be made to increase the regulation of drug-referencing apps and to conduct a more frequent and documented review of their clinical content.
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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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.038 | 0.025 |
| 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".