How and When eHealth is a Good Investment for Patients Managing Chronic Disease
Bibliographic record
Abstract
In this article, we elaborate on the cost-effectiveness of eHealth solutions and the need to evaluate the return on investment as is done routinely with all other major expenditures. To this end, we discuss the theory that exists today to explain some of the usage principles affiliated with information technology implementation in healthcare; namely, we reflect on the Technology Adoption Criteria in Health (TEACH) model and Wagner's Chronic Disease Management model. The basic premise of the TEACH model is that adoption requires work; this work must be recognized at the outset, and the progress to overcome the workload increase must be measured for the adoption to continue. Furthermore, both of these models have emphasized that the trade-off between cost and work and the benefits realized (as seen through measurement) must first be applied to patients that use the system frequently and on an ongoing basis (ie, the chronically ill). We refer to these ongoing users as consumers of healthcare resources-Consumers with Chronic Conditions (the 3C patients). In this article, we show that the benefits outweigh the costs only when we do, in fact, apply the analysis to 3C patients. Once an effective eHealth system has been developed for the 3C patients, then it can be straightforwardly extended to include all patients and other stakeholders.
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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 | 0.017 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".