Key Factors Shaping Successful Implementation of the Internet of Things (IoT) in Health Care: Qualitative Study
Bibliographic record
Abstract
Background: The utilization of the Internet of Things (IoT) can significantly enhance health care. However, successful implementation of IoT requires a holistic approach including factors beyond technology alone. Objective: This paper seeks to advance understanding of the factors influencing the successful implementation of IoT solutions in the health care sector, expanding beyond a purely technological focus. Methods: Using data from 22 semistructured interviews with a diverse group of stakeholders-including health care professionals, researchers, municipal and regional officials, and private companies-this study examines 5 leading IoT projects in Sweden. Results: Grounded in sociotechnical systems theory, the research identifies five critical subsystems impacting IoT implementation: (1) laws and regulations, which present challenges due to their complexity and misalignment with rapid technological advances; (2) organizational support, highlighting the essential commitment and resources from management to drive innovation; (3) user focus, emphasizing the importance of engaging end-users-such as patients and health care providers-in the design and implementation of IoT solutions; (4) resources, encompassing both financial investments and human capital needed for effective deployment; and (5) infrastructure, which addresses the technological foundations required to support IoT systems reliably. Conclusions: By shifting attention from adoption to the complexities of implementation, this study fills a critical gap in the literature, which has largely emphasized adoption and technical aspects over practical implementation challenges. The findings provide a nuanced understanding of the primary factors influencing IoT implementation in health care, illuminating both the challenges and potential avenues for successful integration. Ultimately, this research advances the sociotechnical systems theory and also offers valuable insights for managers and policymakers tasked with driving digital transformation in health care systems.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".