Good Intentions and Bad Investments: EHealth and the Reality of Market Forces
Bibliographic record
Abstract
Introduction: Eysenbach’s well-cited Law of Attrition was the first peer-reviewed journal article that effectively captured the phenomenon of users dropping out of controlled trials. Moreover, attrition permeates all areas of Web 2.0 technology and is not exclusive to controlled trials. Other ongoing challenges include sustaining search engine rankings, competitive IP and software platforms, program conversion rates, and investor confidence. For eHealth programs to be successful, overcoming diminishing returns requires a consistent refueling of reinvention, reinvestment and capitalization. Objective: This presentation is designed to assist researchers, inventors, entrepreneurs and investors by illustrating real-world examples of potential roadblocks and strategies for overcoming them when working in the field of eHealth. Background: For over nine years Evolution Health (formerly V-CC Systems Inc.) has built, maintained, and promoted Internet-based brief interventions, CBT programs and social networks for governments, pharmaceutical companies, non-profit organizations, universities, insurance companies and health plans. While the majority of experience has been in Canada, the United States and the United Kingdom, the company has recently expanded its reach to Japan, Brazil and Argentina. Through real-world examples, this presentation will underscore the need for projects and enterprises to embrace technical design principles, expandable software platforms and effective promotion. Discussion: The vast majority of failed projects are the result of misallocation of budgets and lack of realistic planning, or what the company refers to as Internet Math. Conversely, successful long-term projects begin with a realistic understanding of ongoing investment. eHealth programs, while never perfect, are only effective if they are current, maintained, upgraded and promoted. Programs that are statistically effective at time of publication are often rendered ineffective when converted to a non-academic environment due to their inability to compete in the marketplace. Conclusions: While there are financial barriers to entering the field of eHealth, there is still much promise for patient researchers, inventors, entrepreneurs and investors. However, these stakeholders should be prepared to navigate time-consuming costs surrounding development, launch, ongoing upgrades, maintenance and legal fees. Sales cycles for business-to-business models are long and the vast majority of business-to-consumer models have failed. If eHealth programs are to reach their potential and augment the traditional delivery of health care services, an increase in government funding and ongoing partnerships between vendors and research institutions is required. []
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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 | 0.019 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.025 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 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".