Health System Stakeholders’ Perspective on the Role of Mobile Health and Its Adoption in the Swiss Health System: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Digital health solutions have great potential to change the way health care is delivered, including better clinical outcomes and improved processes and access to health services. However, the adoption of mobile health (mHealth) solutions for patient monitoring has been rather slow in Switzerland. The reasons are complex, and a better understanding is needed to leverage the full potential of mHealth. OBJECTIVE: This study aimed to deepen the understanding of the potential relevance and influence of mHealth for the health system and health care provision, and factors influencing its adoption. The findings will be used to provide an outlook on feasible recommendations for action. METHODS: We conducted a qualitative survey using a maximum variation sample of a heterogeneous group of stakeholders (N=50) in the Swiss health care system with a profound knowledge of digital health and medical devices. A semistructured interview guide including open- and closed-ended questions was used to address questions around mHealth relevance and its influence on the health system, the relevance of selected determinants for mHealth adoption, and important influencing factors. A content analysis method was applied. RESULTS: Overall, respondents thought that mHealth would have a beneficial impact on the Swiss health system but that its adoption would evolve slowly. We derived 23 key opportunities regarding patient and patient pathway, treatment of disease, and diseases and health conditions. High consistency in answers among respondents was observed for treatment of disease. Stakeholders' attitudes toward mHealth adoption along the relevance of 23 preselected determinants were relatively consistent. However, we obtained diverging attitudes regarding the influence of trends, enablers, and restraints in Switzerland and translated them into 26 key themes influencing mHealth adoption. Relevant trends comprise changing needs and expectations of patients, a rising need for efficient health care delivery, growing interest in improved outpatient care, and emerging technologies and progressing digitization. Important enablers include growing demand for new financing schemes and incentive concepts, rising demand for comprehensive information on and stronger body of evidence for mHealth use cases, and increasing need for easy to use alternate care approaches. Challenging restraints are rigidness of thinking and siloed actions of health system actors, complexity of changing the existing regulations and structures, little understanding of mHealth use and the role of clinicians, and risk of further polarization of the population. CONCLUSIONS: This study provides a comprehensive look at mHealth in the Swiss health system. It becomes apparent that strong governance is inevitable to foster a sustainable data strategy and to reconcile the different interests of stakeholders. The use of mHealth will add value but will not necessarily reduce the burden on the system caused by emerging societal needs and changing disease prevalence.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".