Comparison of Information Available in the Medication Profile of an Electronic Health Record and the Inpatient Best Possible Medication History in a Mother and Child Teaching Hospital Center.
Bibliographic record
Abstract
BackgroundMedication reconciliation (MedRec) can improve patient safety. In Canada, most provinces are implementing electronic health records (EHR). The Quebec Health Record (QHR) can theoretically be used for medication reconciliation. However, the quantity and the quality of information available in this EHR have not been studied. ObjectivesThe main objective was to compare the quantity and quality of the information collected between the inpatient best possible medication history (BPMH) and the QHR. MethodsThis is a descriptive prospective study conducted at CHU Sainte-Justine, a 500-bed tertiary mother-and-child university hospital center. All inpatients from May 19-26, 2015 were considered for inclusion. Every prescription line in the BPMH and QHR were compared. ResultsThe study included 344 patients and 1,039 prescription lines were analyzed. The medications' name and dosing were more often available in the QHR (95%) than in the BPMH (61%). Concordance between the medication names between QHR and BPMH was found in 48% of the prescription lines; this rate fell to 29% when also factoring daily dosage. ConclusionsThis study suggests that the QHR can provide high-quality information to support the MedRec hospital process. However, it should be used as a second source to optimize the BPMH obtained from a thorough interview with the patient and/or his or her family. More studies are required to confirm the most optimal way to integrate the QHR to the MedRec process in hospitals.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".