Digital Health Technology Post-Discharge for Follow-up and Management of Geriatric Patients: A Systematic Review
Bibliographic record
Abstract
Background: The COVID-19 pandemic has greatly increased the burden on the healthcare system, especially tertiary healthcare settings, such as hospitals. Hospitalization require a great amount of resources and cost, and often require patient follow-up and continued support to the patient once they are discharged. Older adults, a rapidly growing population that makes up a large proportion of Canadians has more hospitalizations, longer length of hospital stay and higher cost associated with hospitalizations. Older adults who comprised 14% of the Canadian population in 2011 occupied 40% of acute hospital care beds (1). Moreover, there is an essential need to support older adults post-discharge with different levels of support as they are more likely to be readmitted to the hospital and have decline in their activities of daily living (2). In Canada 12% of older adults discharged from the hospital are readmitted within 30 days (3). The use of digital interventions may be an innovative approach to tackle the limited healthcare resources including staffing shortages. This systematic review will identify points where digital health technologies can create innovative solutions to support integrative care at the community level in order to fill an ongoing gap in post-discharge follow-up and continuity of care Methods: Pubmed, Scopus and Web of Science were used for this systematic review. The following keywords were used (Digital OR Virtual OR Tele*) AND (management) AND (discharge) AND (Geriatric*). A total of 435 articles were identified in this search. Results: This is an ongoing study, results will be available in January 2024. Discussion: This systematic review will provide an overview of the different types of digital health technologies being used to follow up with older adults post-hospital discharge, the components that have been successfully implemented and barriers. Successful digital health technologies may reduce the resources and personnel required, reduce rehospitalizations and improve the continuity of care for older adults. References: https://www.cihi.ca/en/dementia-in-canada/dementia-care-across-the-health-system/dementia-in-hospitals Boyd CM, Landefeld CS, Counsell SR, Palmer RM, Fortinsky RH, Kresevic D, Burant C, Covinsky KE. Recovery of activities of daily living in older adults after hospitalization for acute medical illness. Journal of the American Geriatrics Society. 2008 Dec;56(12):2171-9. Gruneir A, Fung K, Fischer HD, Bronskill SE, Panjwani D, Bell CM, Dhalla I, Rochon PA, Anderson G. Care setting and 30-day hospital readmissions among older adults: a population-based cohort study. Cmaj. 2018 Sep 24;190(38):E1124-33.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".