ePrescribing: An Important Part of Medication
Bibliographic record
Abstract
ePrescribing is the next service tool that will enable pharmacists and providers the ability to manage a patient’s current and historical medications. Allowing the provider to easily send an electronic prescription directly to the pharmacist will help eliminate errors in prescribing, reduce fraud and narcotic abuse, decrease the waiting time at the clinic and pharmacy, and focus on patient safety and continuity of care. Going to the doctor can sometimes be tough when your community does not have a permanent provider in the community. Time is taken to get an appointment, explain your medical history to a travelling locum, and then get a prescription. The patient then needs to go to the local or closest pharmacy and wait again for the prescription to be filled. A patient can wait for up to two hours for the whole process to be completed. There has to be a more efficient way to get the proper information, access and care the patient needs. Between 2009 and 2014, there were approximately 655 deaths reported from fentanyl overdoses in Canada. With ePrescribing, the patient will never touch the prescription, hence minimising the risk of multiple prescriptions being filled. Instant messaging between the provider and pharmacist will save on fax and phone communication, and allow the right information to be communicated promptly to the right person. The instant communication between a provider and pharmacist can ensure that the drug being prescribed is not going to interact with other medications and ensure that the patient has not already filled the prescription. The pharmacist can also give details on the current course of medication, and inform the provider if the patient is covered by their current insurance. While a patient’s healthcare record is changing from paper to an electronic format, we need to ensure we are providing the tools necessary to help healthcare providers attain the best healthcare for patients. Hence optimal use of ePrescribing will ultimately reduce medication error rates and other safety risks.
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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 | 0.009 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.090 | 0.140 |
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".