Digital health as an enabler for hospital@home: A rising trend or just a vision?
Bibliographic record
Abstract
Background: Hospital@home is a model of healthcare, where healthcare professionals actively treat patients in their homes for conditions that may otherwise require hospitalization. Similar models of care have been implemented in jurisdictions around the world over the past few years. However, there are new developments in health informatics including digital health and participatory health informatics that may have an impact on hospital@home approaches. Objectives: This study aims to identify the current state of implementation of emerging concepts into the hospital@home research and models of care; to identify strengths and weaknesses, opportunities, and threats associated with the models of care; and to suggest a research agenda. Methods: ." Relevant information was extracted from the included articles. Results: Title and abstract review were conducted on 1,371 articles. The full-text review was conducted on 82 articles. Data were extracted from 42 articles that met our review criteria. Most of the studies originated from the United States and Spain. Several medical conditions were considered. The use of digital tools and technologies was rarely reported. In particular, innovative approaches such as wearables or sensor technologies were rarely used. The current landscape of hospital@home models of care simply delivers hospital care in the patient's home. Tools or approaches from taking a participatory health informatics design approach involving a range of stakeholders (such as patients and their caregivers) were not reported in the literature reviewed. In addition, emerging technologies supporting mobile health applications, wearable technologies, and remote monitoring were rarely discussed. Conclusion: There are multiple benefits and opportunities associated with hospital@home implementations. There are also threats and weaknesses associated with the use of this model of care. Some weaknesses could be addressed by using digital health and wearable technologies to support patient monitoring and treatment at home. Employing a participatory health informatics approach to design and implementation could help to ensure the acceptance of such care models.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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; both teacher heads agree on what is shown here.
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".