Factors Affecting Clinician Readiness to Adopt Smart Home Technology for Remote Health Monitoring: Systematic Review
Bibliographic record
Abstract
BACKGROUND: The population of older adults worldwide continues to increase, placing higher demands on primary health care and long-term care. The costs of housing older people in care facilities have economic and societal impacts that are unsustainable without innovative solutions. Many older people wish to remain independent in their homes and age in place. Assistive technology such as health-assistive smart homes with clinician monitoring could be a widely adopted alternative to aged-care facilities in the future. While studies have found that older persons have demonstrated a readiness to adopt health-assistive smart homes, little is known about clinician readiness to adopt this technology to support older adults to age as independently as possible. OBJECTIVE: The purpose of this systematic review was to identify the factors that affect clinician readiness to adopt smart home technology for remote health monitoring. METHODS: This review was conducted in accordance with the Joanna Briggs Institute methodology for systematic Reviews and followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for reporting. RESULTS: Several factors affected clinicians' perspectives on their readiness to adopt smart home technology for remote health monitoring, including challenges such as patient privacy and dignity, data security, and ethical use of "invasive" technologies. Perceived benefits included enhancing the quality of care and outcomes. CONCLUSIONS: Clinicians, including nurses, reported both challenges and benefits of adopting smart home technology for remote health monitoring. Clear strategies and frameworks to allay fears and overcome professional concerns and misconceptions form key parts of the Readiness for Adoption Pathway proposed. The use of more rigorous scientific methods and reporting is needed to advance the state of the science. TRIAL REGISTRATION: PROSPERO International Prospective Register of Systematic Reviews CRD42020195989; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=195989.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".