Assessing Emerging Health Technologies: An Integrated Perspective
Bibliographic record
Abstract
Healthcare expenditures account for approximately 9% of GDP in OECD countries and are on an upward trajectory (OECD, 2017). This significant financial burden, combined with an aging global population and increasing demand, emphasizes the imperative for sustained research and innovation to enhance health system efficacy. Key to this transformation are technological advancements, including digital health, which presents novel opportunities for improvement. Emerging digital health technologies, such as virtual consultations, complex imaging procedures, and electronic medical records, are fundamental to modern healthcare infrastructures. However, significant gaps remain in the evaluation and understanding of these innovations, especially for nascent technological areas. This thesis addresses this subject, aiming to delineate how individuals and institutions can bolster their evaluative capabilities, strategic decision-making, and planning for the deployment of emerging digital healthcare technologies. The research methodology amalgamates theoretical exploration, literature scrutiny, and empirical examinations, presenting an integrated perspective that intersects the disciplines of Management & Decision Science, Technology & Innovation, and Health Economics & Outcomes Research. From an applied perspective, this research focuses on developing evidence within the context of pediatric healthcare in Canada — a context notably underrepresented in healthcare management research, despite its disproportionate per capita expenditure. Technologically, the predominant focus is on applications in virtual reality (VR) and three-dimensional (3D) printing and virtualization, though with the aspiration of ensuring the generalizability of results. The current thesis is structured to parallel the progression of the research itself, as three distinct phases; this begins with a comprehensive Background that contextualizes the initial intent, exploring the relationship between technology, innovation, and health outcomes. In this phase, each academic discipline is analyzed to identify base theoretical underpinnings for the research. The second phase of this work includes five empirical studies, each preceded by context-specific literature reviews for the respective technological domain, providing insight into the current state of evidence related to value-based assessment and economic evaluation in that area. Empirical studies included a randomized controlled trial and a cost consequences assessment of VR for pre-procedural preparation for medical imaging. Additionally, novel applications of 3D printing and virtualization are critically analyzed, accompanied by a multi-case study on the implementation of 3D printing in pediatrics, a cost-consequences study of applications for thoracoabdominal surgeries, and an outcomes study on the use of 3D virtualization in medical education during the COVID-19 pandemic. The final phase of this research includes the critical analysis and synthesis of findings into a proposal for a novel value-based assessment and decision-making framework specific to emerging health innovations; this framework integrates insights and resources for clinicians, administrators, and policymakers. The outcomes of this research are skewed toward Knowledge Users, but also aims to advance the theoretical basis for health technology assessment (HTA).
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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 | 0.025 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.019 | 0.011 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.035 | 0.031 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".