Analysis of Apps With a Medication List Functionality for Older Adults With Heart Failure Using the Mobile App Rating Scale and the IMS Institute for Healthcare Informatics Functionality Score: Evaluation Study
Bibliographic record
Abstract
BACKGROUND: Managing the care of older adults with heart failure (HF) largely centers on medication management. Because of frequent medication or dosing changes, an app that supports these older adults in keeping an up-to-date list of medications could be advantageous. During the COVID-19 pandemic, HF outpatient consultations are taking place virtually or by telephone. An app with the capability to share a patient's medication list with health care professionals before consultation could support clinical efficiency, for example, by reducing consultation time. However, the influence of apps on maintaining an up-to-date medication history for older adults with HF in Ireland remains largely unexplored. OBJECTIVE: The aims of this review are twofold: to review apps with a medication list functionality and to assess the quality of the apps included in the review using the Mobile App Rating Scale (MARS) and the IMS Institute for Healthcare Informatics functionality scale. METHODS: A systematic search of apps was conducted in June 2019 using the Google Play Store and iTunes App Store. The MARS was used independently by 4 researchers to assess the quality of the apps using an Android phone and an iPad. Apps were also evaluated using the IMS Institute for Healthcare Informatics functionality score. RESULTS: Google Play and iTunes App store searches identified 483 potential apps (292 from Google Play and 191 from iTunes App stores). A total of 6 apps (3 across both stores) met the inclusion criteria. Of the 6 apps, 4 achieved an acceptable MARS score (3/5). The Medisafe app had the highest overall MARS score (4/5), and the Medication List & Medical Records app had the lowest overall score (2.5/5). On average, the apps had 8 functions based on the IMS functionality criteria (range 5-11). A total of 2 apps achieved the maximum score for number of features (11 features) according to the IMS Institute for Healthcare Informatics functionality score, and 2 scored the lowest (5 features). Peer-reviewed publications were identified for 3 of the apps. CONCLUSIONS: The quality of current apps with medication list functionality varies according to their technical aspects. Most of the apps reviewed have an acceptable MARS objective quality (ie, the overall quality of an app). However, subjective quality (ie, satisfaction with the apps) was poor. Only 3 apps are based on scientific evidence and have been tested previously. A total of 2 apps featured all the IMS Institute for Healthcare Informatics functionalities, and half did not provide clear instructions on how to enter medication data, did not display vital parameter data in an easy-to-understand format, and did not guide users on how or when to take their medication.
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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 | 0.017 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.010 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".