Impacts of Telehomecare on Patients, Providers, and Organizations
Bibliographic record
Abstract
Over the last decades, development of home care services is an important component of ongoing health care systems reforms. However, their full integration into hospital or primary care services is still progressing slowly. It appears that telehomecare (THC) could help create networks of services between hospital and primary care providers. Even though their potential to increase access to services and improve quality of care and health outcomes is recognized, their widespread adoption has not yet been achieved. Various barriers need to be overcome. In this paper, we present our comparative exploratory process analysis of the use of THC to follow the treatment of elderly people suffering from severe chronic conditions (chronic obstructive pulmonary disease [COPD], hypertension, cardiac insufficiency). The technology was first introduced as a pilot project in three sites (one site in Quebec and two sites in Manitoba, Canada). Our study is based on qualitative methods. It includes a longitudinal analysis of implementation processes and monitoring of results. Our analysis allows us to identify some of the major impacts on patients and providers, and explain how they may be achieved. Also, because of the major changes in work processes, THC introduces new models of home care delivery. Two models are identified: a specialized model and a planned polyvalent model. Such profound changes raise two major challenges for managers and providers. First, the organisation of work, traditionally based upon preestablished intervention plans, must adapt to respond to ad hoc patients' needs and alerts. Second, constant linkages between the traditional and new models of services delivery become mandatory.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 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".