Digital Health Value Realization Through Active Change Efforts
Bibliographic record
Abstract
Digital health has massive potential in health care but has been slow to evolve in comparison to other information-intensive industries, which have more readily taken advantage of new technology. One of the key barriers has been the complex relationship between the perceived return on investment for the investor and the resulting value to patients and caregivers. Those actors who pay for technologies do not always see an appreciable return for themselves, while those actors who must apply the technology to generate value are not always incentivized to do so. This misalignment across health system payers and administrators, clinicians and patients must be better understood and addressed to help accelerate digital health. This paper will examine this challenge through the clinician experience, using empirical case examples from Canada to illustrate opportunities for change. While many factors may influence digital health adoption, this paper specifically aims to explore the shifts in the balance of the perceived value of implementing digital health tools, vs. the efforts required to adopt them. It will explore two contrasting case examples: clinical adoption of EMRs in Canada from 2009 to 2015, and clinical adoption of virtual care technologies during the COVID-19 pandemic from 2020 to 2021. In 2006, Canada lagged peer countries significantly in the adoption of electronic medical records (EMR) in community-based care. Financial support and cooperation of multiple levels of government and clinical stakeholders were required to address the misaligned incentives, which led to significant uptake by care providers. The rapid adoption of virtual care in Canada in response to the pandemic provides another relevant example of the importance of alignment among the factors of clinical workflows, clinical appropriateness, technology integration and payment models. Experts have highlighted the need for standardization, regulation, and clear policy to ensure sustainable, high quality virtual care that complements in-person care. In both cases, the costs and effort of adopting new technologies outweighed direct clinician value, requiring change initiatives to catalyze progress. This imbalance could be unique to these examples in Canada, and may not be globally generalizable to the adoption of all digital health tools. However, how change efforts can be tailored to adjust to a rapidly evolving health care workforce, spanning diverse jurisdictions and stakeholder groups will be critical to the sustainability of virtual care adoption. Furthermore, what key elements must be considered to guide change initiatives for successful implementation, designed to influence change while adding value for patients, clinicians and Canada's health care systems? Using insights from successful change initiatives past and present, this paper aims to answer these questions to enable a smoother transition to digital health innovations of the future.
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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 | 0.016 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".