A cluster randomized trial evaluating electronic prescribing in an ambulatory care setting
Bibliographic record
Abstract
BACKGROUND: Medication errors, adverse drug events and potential adverse drug events are common and serious in terms of the harms and costs that they impose on the health system and those who use it. Errors resulting in preventable adverse drug events have been shown to occur most often at the stages of ordering and administration. This paper describes the protocol for a pragmatic trial of electronic prescribing to reduce prescription error. The trial was designed to overcome the limitations associated with traditional study design. DESIGN: This study was designed as a 65-week, cluster randomized, parallel study. METHODS: The trial was conducted within ambulatory outpatient clinics in an academic tertiary care centre in Ontario, Canada. The electronic prescribing software for the study is a Canadian electronic prescribing software package which provides physician prescription entry with decision support at the point of care. Using a handheld computer (PDA) the physician selects medications using an error minimising menu-based pick list from a comprehensive drug database, create specific prescription instructions and then transmit the prescription directly and electronically to a participating pharmacy via facsimile or to the physician's printer using local area wireless technology. The unit of allocation and randomization is by 'week', i.e. the system is "on" or "off" according to the randomization scheme and the unit of analysis is the prescription, with adjustment for clustering of patients within practitioners. DISCUSSION: This paper describes the protocol for a pragmatic cluster randomized trial of point-of-care electronic prescribing, which was specifically designed to overcome the limitations associated with traditional study design. TRIAL REGISTRATION: This trial has been registered with clinicaltrials.gov (ID: NCT00252395).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.184 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".