Patient-clinician digital health interventions for the hip fracture population: a scoping review
Bibliographic record
Abstract
INTRODUCTION: Older adults with hip fracture often require extensive post-surgery care across multiple sectors, making follow-up care even more important to ensure an ideal recovery. With the increased adoption of technology, patient-clinician digital health interventions can potentially improve post-surgery outcomes of hip fracture patients by helping them and their caregivers better understand the various aspects of their care, post-hip fracture surgery. The purpose of this study was to examine the available literature on the impact of hip fracture-specific, patient-clinician digital health interventions on patient outcomes and health care delivery processes. We also aimed to identify the barriers and enablers to the uptake and implementation of these technologies and to provide strategies for improved use of these digital health interventions. METHODS: We conducted a scoping review following the six stages of Arksey and O'Malley's framework and following the PRISMA-ScR reporting format. Searches were conducted in five databases. In addition to hand searching for relevant studies from the references of all included studies, we also conducted a grey literature search to identify relevant primary studies. Screening of titles and abstracts as well as full texts were performed independently by two reviewers. Two reviewers also performed the data extraction of the included studies. RESULTS: After screening 3,638 records, 20 articles met the criteria and 1 article was identified through hand searching. Various patient-clinician digital health interventions were described including telehealth /telerehabilitation programs (n = 6), care transition /follow-up interventions (n = 5), online resources (n = 2), and wearable devices /sensor monitoring (n = 1). Outcomes were varied and included functional status, gait/mobility, quality of life, psychological factors, satisfaction, survival/complications, caregiver outcomes, compliance, technology-user interactions, and feedback on the use of the digital health interventions. For clinicians, a key barrier to the use of the digital health interventions was the acceptability of the technology. However, the usefulness of the digital health intervention by clinicians was seen as both a barrier and an enabler. For patients and caregivers, all the themes were seen as both a barrier and an enabler depending on the study. These themes included: 1) availability and access, 2) usability, 3) knowledge and skills, 4) acceptability, and 5) usefulness of the digital health intervention. CONCLUSION: Many behavioural factors affect the use of patient-clinician digital health interventions. However, a specific attention should be focused on the acceptability of the technology by the clinicians to encourage uptake of the digital health interventions. The results of this scoping review can help to better understand the factors that may be targeted to increase the use of these technologies by clinicians, patients, and caregivers.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".