Innovative technology and change management: E-health applications in Canada.
Bibliographic record
Abstract
Background Focusing on the Canadian healthcare system, this study explores factors influencing the adoption of recent specialized technology in e-health applications due to concerns about the allocation of economic resources and governmental policy formulation. This study focuses on the specific technologies of the Electronic Medical Record (EMR)-based-Personal Health Record (PHR) and their use by physicians and residents of Northern Ontario. Objectives The primary objective of this study is to understand the interdisciplinary factors that predict Northern residents’ attitude toward EMR-based-PHR innovative technology. Conducting this study also serves to increase awareness of patient-driven e-health in Northern Ontario and provides decision makers with useful quantitative data and strategies to support future initiatives. Methods/Materials Using customized data obtained from the National Physician Survey (NPS) in Canada and primary data collected through an adaptation of this survey, a comparative analysis was conducted to understand the electronic patient-physician relationship and explore interdisciplinary factors regarding perception and use of EMR-based-PHR. The data was analyzed using Descriptive Statistics, Z Test for two Population Proportions, ANOVA and Regression Analysis. Results The results indicate significant differences between Northern physicians and patients in usage and preference regarding several technological applications. More Northern patients use websites, social media and mobile applications than Northern physicians. In capturing health information, fewer physicians exclusively prefer to use electronic records than use a combination of paper charts and electronic records, and most Northern patients prefer either a combination of both methods or exclusively paper charts in their healthcare. Interdisciplinary factors related to EMR-based-PHR were significant predictors and explained 69.6% of the variance in the behavioral attitude and 74.5% of the variance in the behavioral intention to adopt this innovative technology. Conclusions. Establishing an electronic patient-physician relationship in the Canadian healthcare system requires coordinated and concerted efforts from all stakeholders involved in this process. Significant cost without benefits is evidence of a misallocation of Canadian resources and requires increased attention. New strategies must address current gaps in educational, technical, managerial, and financial supports. Physician support, however, is ultimately the key to increasing the adoption rate of EMR and fostering positive attitudes toward PHR among the Canadian people.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".