Cost analysis and Potential Reduction of Medication Errors Due to Implementation of CPOE & BCMA in the Fraser Health Authority
Bibliographic record
Abstract
Medication errors are blunders in the patient treatment process that have the potential to cause harm. Currently, there are an estimated 70,000 preventable adverse events per year in Canada, and one-quarter of the events are related to medication errors; resulting in 700 deaths a year. The cost of these medication errors is estimated to be $2.6 billion per year in Canada, each preventable adverse drug event in a hospitalized patient is estimated to cost $4,685 CAD ($6,750 inflation adjusted to 2017) and increases the length of stay by 4.6 days. The most frequently cited method for preventing medication errors is incorporating a closed-loop medication management system (CLMM). The ideal CLMM system seamlessly integrates information technology from automated dispensing devices (ADD), computerised provider order entry (CPOE), and bedside bar-coded medication administration (BCMA). The integration will enable each stage of the medication management process such as prescribing, transcription, dispensing, and administration to be consolidated into an efficient and save structure to optimize patient health. Given the significant capital investment required to implement CLMM, the question Fraser Health Authority executives may have is how many medication errors can realistically be prevented and is it worth the cost? Fraser Health is British Columbia’s largest Health Authority, it is comprised of 12 acute care hospitals, 7,760 residential care beds, 25,000 staff, and had an operating budget of over $3 billion. Presently, Abbotsford Regional Hospital, Surrey Memorial, Royal Columbian, and Chilliwack Hospital have deployed ADDs, and the rest of the Fraser Health hospitals will eventually receive them; however, implementation of CPOE and BCMA is only in the planning stages. When modelling the cost-benefit of a CLMM system, challenges include estimating the true number of medication errors (as these are often self-reported), estimating the cost of change management, and estimating the cost of inevitable implementation delays. In the published literature, economic analyses of the individual components of CLMM systems can be found. For example, the average cost of implementing CPOE in a single hospital is estimated to be $5.3 million ranging from $2.3 million for a hospital with less than 200 beds to $20.3 million for a hospital with more than 500 beds (the numbers are inflation adjusted to 2017 and currency converted to CAD). Nonetheless, investment in CPOE appears to be cost-effective over time, one NHS study showed that predicted effects of CPOE implementation on a 400 bed hospital had a net health valuations of 62 million CAD (currency converted and inflation adjusted to 2017) over a 5-year period . Another study at Brigham and Women’s Hospital (793 beds), where CPOE was pioneered, estimated a net benefit of $4.3 million CAD (inflation adjusted 2017) per year, just in time-savings with staff (unit clerks, nurses and pharmacists). With regards to improving safety, it is estimated that CPOE can reduce up to 50% to 88% of medical errors within US hospitals. The other CLMM component that Fraser Health has yet to incorporate is BCMA, a technology that checks the 5 rights of medication administration (right drug, right dose, right route, right patient, and right time) at the bedside. BCMA has been shown to reduce medication errors by up to 49% to 51% and generate an annual savings of $2.2 million from time saving. Thus, we hypothesize that If a CLMM system was previously deployed in Fraser Health, a significant number of medication errors would have been prevented, partially or completely offsetting its cost. This analysis examines the potential benefits of implementing BCMA and CPOE in Fraser Health, a system that will already have ADDs.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".