Toward a model of successful electronic health record adoption.
Bibliographic record
Abstract
The Canadian healthcare landscape abounds with pressures to address wait times, chronic disease management, aging at home, information and service integration, health human resource shortages, pandemic planning and most importantly health outcomes of individuals receiving care in our system. Investment in clinical information technologies is often touted as significant to the successful resolution of most if not all of these issues. For example, Baker and Norton (2001) uncovered an alarming rate of preventable adverse events occurring within Canadian hospitals. A particularly high error rate associated with the administration of fluids and medications suggests that there is a dire need to introduce processes and tools to reduce human error in healthcare facilities. The implementation of clinical applications such as computerized physician order entry (CPOE) with integrated electronic medication administration records (MAR) has been identified as a key step to safer care (Bates and Gawande 2003; Leape et al. 2002; Leatt et al. 2006). It has been suggested that the full value of electronic health records (EHR) will only be realized with the implementation of CPOE and that its use (by physicians) is a reasonable proxy for adoption (Ash and Bates 2005). Considering recent surveys of Canadian and American hospitals, those that have fully implemented CPOE remain in the minority (Ash et al. 2004; Davis 2007; Gudbranson 2007); most have yet to tackle the challenges of the change imperative and adoption issues associated with the use of a complete EHR
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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 | 0.042 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.022 | 0.020 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".