A FORMAL MEDICATION RECONCILIATION PROGRAMME IN A HAEMODIALYSIS UNIT CAN IDENTIFY MEDICATION DISCREPANCIES AND POTENTIALLY PREVENT ADVERSE DRUG EVENTS
Bibliographic record
Abstract
BACKGROUND: Patients on haemodialysis have been identified as high-risk for medication discrepancies and adverse drug events. Medication reconciliation is an important patient safety initiative to prevent adverse drug events. The primary objective of our study was to determine the number and types of medication discrepancies and drug therapy problems (DTPs) identified in patients on haemodialysis. Our second objective was to assess the potential clinical impact and severity of all unintentional medication discrepancies identified. METHODS: Patients in an academic haemodialysis unit were interviewed to obtain a best possible medication history (BPMH) between May and August 2010. The BPMH was documented and discrepancies were identified, classified and resolved with the interprofessional team. An interprofessional panel conducted a discrepancy clinical impact assessment for potential adverse drug events. RESULTS: Two hundred and twenty-eight patients on haemodialysis were interviewed and 512 discrepancies were identified for 151 patients (3.4 discrepancies per patient). Of these, 174 (34%) were undocumented intentional discrepancies and 338 (66%) were unintentional discrepancies. The unintentional discrepancies were classified as 21% omissions, 36% commissions and 43% incorrect dose/frequency. Most drug therapy problems were related to patient taking a medication that was not indicated (25%), medication required but patient not taking (25%), patient not willing to take the medication as prescribed (28%) or incorrect dosing of a drug (20%). Overall, 6% of discrepancies were classified as clinically significant potential adverse drug events. CONCLUSION: Medication discrepancies appear to be common in patients on haemodialysis. Formal interprofessional medication reconciliation practice models are essential to identify discrepancies and prevent patients from experiencing adverse drug events.
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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 | 0.007 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".