HIMSS Analytics 2009 ICT Study: The State of E-Health in British Columbia
Bibliographic record
Abstract
introduction In recent years, rhetorical and real support for e-health innovation has grown in Canada and elsewhere. Increasingly, healthcare stakeholders are impressed by positive healthcare outcomes from advanced clinical applications and new technologies, including hand-held devices. A growing body of evidence is confirming that information technology (IT) implementations in clinical care produce substantial returns on hefty financial and manpower investments, sooner rather than later. Not only does the continuity and efficiency of healthcare improve, but – more importantly – patient outcomes and patient safety increase. However, harder realities underlying the current healthcare environment clash with the optimism of e-health rhetoric. Whether it will be feasible to sustain the momentum favouring ongoing IT improvements and investments in Canada and elsewhere depends on the consequences of powerful political, financial and cultural factors that are slowing down and even threatening to curtail more than a decade of favourable results for e-health. The current climate for healthcare in British Columbia (BC) is a case study in the challenges facing healthcare stakeholders who are dedicated to employing information management (IM)/IT to improve patient healthcare. BC, Canada’s third largest and second fastest-growing province in population, delivers a full continuum of healthcare through five regional health authorities (HAs) and specialized cancer, women’s and children’s care through a single consolidated Provincial Health Services Authority (PHSA). There are some long-standing factors that bedevil BC’s efforts to streamline province-wide healthcare delivery by implementing better technology:
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.018 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".