Experiences and Outcomes of Using e-Prescribing for Opioids: Rapid Scoping Review
Bibliographic record
Abstract
BACKGROUND: e-Prescribing is designed to assist in facilitating safe and appropriate prescriptions for patients. Currently, it is unknown to what extent e-prescribing for opioids influences experiences and outcomes. To address this gap, a rapid scoping review was conducted. OBJECTIVE: This rapid scoping review aims to (1) explore how e-prescribing has been used clinically; (2) examine the effects of e-prescribing on clinical outcomes, the patient or clinician experience, service delivery, and policy; and (3) identify current gaps in the present literature to inform future studies and recommendations. METHODS: A rapid scoping review was conducted following the guidance of the JBI 2020 scoping review methodology and the World Health Organization guide to rapid reviews. A comprehensive literature search was completed by an expert librarian from inception until November 16, 2022. Three databases were electronically searched: MEDLINE (Ovid), Embase (Ovid), and Scopus (Elsevier). The search criteria were as follows: (1) e-prescribing programs targeted to the use or misuse of opioids, including those that were complemented or accompanied by clinically focused initiatives, and (2) a primary research study of experimental, quasi-experimental, observational, qualitative, or mixed methods design. An additional criterion of an ambulatory component of e-prescribing (eg, e-prescribing occurred upon discharge from acute care) was added at the full-text stage. No language limitations or filters were applied. All articles were double screened by trained reviewers. Gray literature was manually searched by a single reviewer. Data were synthesized using a descriptive approach. RESULTS: Upon completing screening, 34 articles met the inclusion criteria: 32 (94%) peer-reviewed studies and 2 (6%) gray literature documents (1 thesis study and 1 report). All 33 studies had a quantitative component, with most highlighting e-prescribing from acute care settings to community settings (n=12, 36%). Only 1 (3%) of the 34 articles provided evidence on e-prescribing in a primary care setting. Minimal prescriber, pharmacist, and clinical population characteristics were reported. The main outcomes identified were related to opioid prescribing rates, alerts (eg, adverse drug events and drug-drug interactions), the quantity and duration of opioid prescriptions, the adoption of e-prescribing technology, attitudes toward e-prescribing, and potential challenges with the implementation of e-prescribing into clinical practice. e-Prescribing, including key features such as alerts and dose order sets, may reduce prescribing errors. CONCLUSIONS: This rapid scoping review highlights initial promising results with e-prescribing and opioid therapy management. It is important that future work explores the experience of prescribers, pharmacists, and patients using e-prescribing for opioid therapy management with an emphasis on prescribers in the community and primary care. Developing a common set of quality indicators for e-prescribing of opioids will help build a stronger evidence base. Understanding implementation considerations will be of importance as the technology is integrated into clinical practice and health systems.
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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 | 0.032 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.024 | 0.024 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".