eHealth Advances in Support of People with Complex Care Needs: Case Examples from Canada, Scotland and the US
Bibliographic record
Abstract
Information technology (IT) in healthcare, also referred to as eHealth technologies, may offer a promising solution to the provision of better care and support for people who have multiple conditions and complex care needs, and their caregivers. eHealth technologies can include electronic medical records, telemonitoring systems and web-based portals, and mobile health (mHealth) technologies that enable information sharing between providers, patients, clients and their families. IT often acts as an enabler of improved care delivery, rather than being an intervention per se. But how are different countries seeking to leverage adoption of these technologies to support people who have chronic conditions and complex care needs? This article presents three case examples from Ontario (Canada), Scotland and Kaiser Permanente Colorado (United States) to identify how these jurisdictions are currently using technology to address multimorbidity. A SWOT (strengths, weaknesses, opportunities, threats) analysis is presented for each case and a final discussion addresses the future of eHealth for complex care needs. The case reports presented in this manuscript mark the foundational work of the Multi-National eHealth Research Partnership Supporting Complex Chronic Disease and Disability (the eCCDD Network); a CIHR-funded project intended to support the international development and uptake of eHealth tools for people with complex care needs.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.017 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".