Bibliographic record
Abstract
The disruption to healthcare caused due to the bottom-up adoption of mobile technologies has brought forth signals of health seekers managing their own health. Propelling this movement forward is the consumer technology sector’s growing interest in health care. By realizing an unmet need for health management tools, direct-to-consumer technology products and services are targeted at those who prefer to supervise their own health. However, their efforts have had limited success in integrating with the actual needs of the health care providers or physicians that rely on clinical data for diagnosis and disease management. Adding to this, there is an increasing demand for systems that can connect clinical data with extra clinical data platforms to bridge the continuity gaps in patient care. \n \nThis research uses Brenda Dervin’s sense-making methodology as a framework for introspection into the current system and extrospection of the emergent environment for the drivers of this change in the health data landscape. Through a systemic analysis, current challenges and barriers that limit the integration of extra clinical platforms with clinical platforms have been brought forth to highlight points of intervention. Foresight tools and methodologies have been employed to explore future strategies, within the areas of opportunities that can be utilized to bridge the currently disconnected worlds of health data. Through expert interviews and literature reviews, the aim was to identify key stakeholders and analyze their needs to build criteria and determine the success of these future strategies. The two-pronged approach is a means to base the design interventions and maximize the impact of strategies built around the principle of holistic experience of care for health seekers and patients. \n \nBy identifying the scope and the scale of the current health data landscape, criteria for the design of the connected health system was based on the existing principles of the Canada Health Act; allowing them to be grounded on the Canadian values of equality and solidarity, simultaneously calling for a reflection needed to expand and revolutionize the present scope. The strategic recommendations aim at instilling tolerance and a culture of innovation; devising comprehensive incentive models for physician-provider compensation that allow for the successful implementation of out-patient strategies; and formation of a digital health governance model through the creation of a unique digital health identity. The design principles and strategic recommendations are proposed to bridge the disconnected health data worlds and provide a seamless care experience to health seekers and patients.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.012 | 0.042 |
| Scholarly communication | 0.030 | 0.025 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.031 | 0.007 |
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".