Selling the Return on Investment for Digital Health
Bibliographic record
Abstract
Background: Advancing digital health requires a realistic conversation that moves past innovation and evaluates digital tools the same as any other device being introduced into the health system. There needs to be a focus on return on investment. Methods: As part of a symposium, we presented hypothetical pitches to an expert panel. The experts include representatives from health systems, payers, and investors. The pitches were related to remote patient monitoring, tele-triage in the emergency department, and comprehensive in-patient telemedicine program including virtual sitting and e-nursing. Results: Although each pitch led to a different discussion, there was uniform agreement that health systems should focus on whether the proposal helps solve an institutional problem; the payment model in which the product can be used (value-based, fee-for-service, or both) needs to be identified; fitting the new product into preexisting workflow (included electronic health system integration) is critical; there needs to be an understanding of whether patients and providers engage with it; and there needs to be a clear return on investment. Discussion: Navigating complex decision-making in health care requires a blend of strategic foresight, practical considerations, and a deep understanding of organizational dynamics. Rather than a specific strategic plan focused on digital or virtual care, there should be a focus on the enterprise strategic plan and how can digital enable that.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".