Real-World Mobile Health Implementation and Patient Safety: Multicenter Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Mobile health (mHealth) is increasingly being used in contemporary health care provision owing to its portability, accessibility, ability to facilitate communication, improved interprofessional collaboration, and benefits for health outcomes. However, there is limited discourse on patient safety in real-world mHealth implementation, especially as care settings extend beyond traditional center-based technology usage to home-based care. OBJECTIVE: This study aimed to explore health care professionals' perspectives on the safety aspects of mHealth integration in real-world service provision, focusing on Hong Kong Special Administrative Region (SAR) and Wuhan city in mainland China. In Hong Kong SAR, real-world mHealth care provision is largely managed by the Hospital Authority, which has released various mobile apps for home-based care, such as Stoma Care, Hip Fracture, and HA Go. In contrast, mHealth care provision in Wuhan is institutionally directed, with individual hospitals or departments using consultation apps, WeChat mini-programs, and the WeChat Official Accounts Platform (a subapp within the WeChat ecosystem). METHODS: A multicenter qualitative study design was used. A total of 27 participants, including 22 nurses and 5 physicians, from 2 different health care systems were interviewed individually. Thematic analysis was used to analyze the data. RESULTS: The mean age of the participants was 32.19 (SD 3.74) years, and the mean working experience was 8.04 (SD 4.05) years. Most participants were female (20/27, 74%). Nearly half of the participants had a bachelor's degree (13/27, 48%), some had a master's degree (9/27, 33%), and few had a diploma degree (3/27, 11%) or a doctoral degree (2/27, 7%). Four themes emerged from the data analysis. Considering the current uncertainties surrounding mHealth implementation, participants emphasized "liability" concerns when discussing patient safety. They emphasized the need for "change management," which includes appropriate referral processes, adequate resources and funding, informed mHealth usage, and efficient working processes. They cautioned about the risks in providing mHealth information without ensuring understanding, appreciated the current regulations available, and identified additional regulations that should be considered to ensure information security. CONCLUSIONS: As health care systems increasingly adopt mHealth solutions globally to enhance both patient care and operational efficiency, it becomes crucial to understand the implications for patient safety in these new care models. Health care professionals recognized the importance of patient safety in making mHealth usage reliable and sustainable. The promotion of mHealth should be accompanied by the standardization of mHealth services with institutional, health care system, and policy-level support. This includes fostering mHealth acceptance among health care professionals to encourage appropriate referrals, accommodate changes, ensure patient comprehension, and proactively identify and address threats to information security.
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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 | 0.023 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".