Antiretroviral and Medication Errors in Hospitalized HIV-Positive Patients
Bibliographic record
Abstract
OBJECTIVE: To summarize the literature regarding antiretroviral and other medication errors in hospitalized HIV-positive patients and to discuss potential interventions and solutions that have been studied to minimize drug error. DATA SOURCES: A systematic search of MEDLINE, PubMed, and EMBASE (2000-April 2014) was conducted. Search terms included HIV/AIDS, HAART, hospitalization, patient admission, inpatient, patient transfer, medication error, inappropriate prescribing, drug interaction, drug omission, drug toxicity, and contraindication. STUDY SELECTION AND DATA EXTRACTION: English-language research articles, case reports, conference abstracts, and letters to the editor were reviewed. DATA SYNTHESIS: A high overall medication error rate was reported in HIV-positive inpatients. Errors occurred mainly at the time of prescribing on admission but were also detected throughout hospitalization and at discharge. Errors in the antiretroviral regimen, dosing, scheduling, and drug-drug and drug-food interactions were the most common. The most successful interventions involved a clinical pharmacist, who specializes in infectious diseases and/or HIV, completing medication reconciliation on admission, reviewing orders daily, and screening for errors at discharge. CONCLUSIONS: Although studies varied greatly in methodology, overall, a large number of medication errors occurred in this patient population. This underscores the important role the pharmacist has in optimizing care to hospitalized HIV-positive patients and provides further insights into the types of medication errors that occur and proposed solutions to reduce these errors. Because medication errors are multifactorial, ongoing initiatives to improve the quality of medication reconciliation processes, educate the health care team on antiretroviral medications, and improve the drug distribution system are required.
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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 | 0.004 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".