The Current State of Mobile Apps Owned by Large Pediatric Hospitals in the United States: Systematic Search and Analysis on Google Play and Apple App Stores
Bibliographic record
Abstract
BACKGROUND: Pediatric hospitals in the United States are increasingly leveraging patient-facing mobile apps as their digital front doors for patients, families, and caretakers. These mobile health apps are sanctioned by pediatric hospitals to inform the public or populations about pediatric care to provide individualized information, to enhance communication, and to improve patient experience. Yet the functionalities and user feedback of these hospital mobile apps have not been systematically investigated. OBJECTIVE: Our aim was to understand the current state of hospital-owned mobile apps provided by large pediatric hospitals, comparatively analyze and report the services provided, and identify potential gaps to inform developers and providers. The American Hospital Association defines large hospitals as those having a bed count of more than 400. METHODS: We conducted a systematic search on Google Play and Apple App Store to identify all hospital-owned mobile apps from the large pediatric hospitals included in our review. Our inclusion criteria were (1) apps provided by large pediatric hospitals; (2) hospital-owned apps available in Apple App Store and Google Play; and (3) apps that are provided for general populations. Specialty apps that serve specific user groups or populations focusing on education, telehealth, specific conditions or procedures, or apps intended for research or clinician use were excluded. The features and functionality of the included apps were examined. RESULTS: Of the 16 pediatric hospitals included in our review, 4 (25%) had no general patient-facing apps, 4 (25%) had one app, and 8 (50%) had more than one app available on Google Play or Apple App Store. The 12 hospitals with at least one mobile app had a combined total of 72 apps. Of these 72 apps, 61 (85%) were considered specialty and were excluded from our review, leaving a total of 11 (15%) apps to analyze. Among the 11 apps analyzed, the most common feature was appointment scheduling or reminder (n=9, 82%). Doctor search (n=8, 73%) and patient resources (n=8, 73%) were the second most common, followed by payment, billing, or claims (n=7, 64%), patient portal integration (n=6, 55%), personal health management (n=6, 55%), hospital way finding (n=5, 45%), message a provider (n=4, 36%), urgent care wait times (n=4, 36%), video chat (n=4, 36%), and health information access (n=4, 36%). Parking information (n=3, 27%) was the least common. CONCLUSIONS: Out of the 16 pediatric hospitals identified for our review, 75% (n=12) offer mobile apps. Based on the most common features, these apps were intended to help improve accessibility for patients and families in terms of finding providers, scheduling appointments, and accessing patient resources. We believe the findings will inform pediatric hospital administrators, developers, and other stakeholders to improve app feature offerings and increase their impact on service accessibility and patient experience.
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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 | 0.007 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.031 | 0.027 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".