Bibliographic record
Abstract
What Is the Issue? In 2021, the occupancy rate of acute care hospital beds in Canada was 86.7%. High occupancy rates without turnover to accommodate all hospitalization needs is an indicator of potential bed shortages and health system pressure. Patients have historically remained in hospital beds until their treatment or recovery is complete. Some patients may be well enough to continue their treatment or recovery at home sooner if provided with the right supports. What Are the Technologies? Virtual wards, also known as hospital-at-home programs, support the provision of inpatient-level acute medical care in a patient’s home. There are 2 main models of these programs: admission avoidance and supported early discharge. This report focuses on the latter type. Many of these programs are technology-enabled and incorporate remote monitoring devices to record the patient’s vital signs and tablets or web portals to facilitate data sharing. Video calls with the clinical team are also used in combination with in-person visits by health care providers. What Is the Potential Impact? Hospital beds can be freed up more quickly to provide space for newly admitted patients with more acute care needs. The safety and effectiveness of virtual ward programs have been examined in several systematic reviews in the existing clinical literature. Factors evaluated include mortality, length of stay, hospital readmissions, and costs as outcomes. Both admission avoidance and early supported discharge via hospital-at-home programs had lower or similar mortality and hospital admission outcomes as inpatient care after completion of care. Patients, caregivers, and health care providers appear to be generally satisfied with their participation in virtual ward programs. Comfort and satisfaction can be improved by allowing patients to receive treatment in a familiar and comfortable environment without compromising patient outcomes. However, increased caregiver burden, lack of sufficient training for participants and staff, and difficulties recruiting health care providers were identified as challenges associated with virtual ward programs. What Else Do We Need to Know? The level of technological support required by patients, caregivers, and staff participating in these programs should be considered when developing a program. Adequate training about how to use provided equipment and other tasks needed to manage care in the home (e.g., drug administration, symptom monitoring, communication with health care professionals) is required for patients and caregivers. There should also be provision of all necessary equipment with supports to overcome any barriers (e.g., visual impairment, physical limitations) to ensure comfort and proficiency. Care coordination and communication among the multidisciplinary care team, the patient, and their caregivers is important. Canadian cost data were not identified, but it is generally accepted that virtual ward programs are associated with reduced costs when compared with traditional in-hospital care. The inclusion of digital monitoring and record keeping technologies as part of virtual ward programs may disproportionately exclude people from some groups, including older people, people living in social housing or without housing, people with lower incomes, people who are unemployed, people living with disabilities, and people who live in rural areas without access to such programs. Key recommendations for development of virtual ward programs in Canada include using a single remote patient-monitoring platform that connects with the hospital’s electronic health record system, choosing a technology to connect patients and providers that best fits the needs of the virtual ward program, and ensuring data security, confidentiality, and data management protocols are in place.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".