Effects of a refined evidence-based toolkit and mentored implementation on medication reconciliation at 18 hospitals: results of the MARQUIS2 study
Bibliographic record
Abstract
BACKGROUND: The first Multicenter Medication Reconciliation Quality Improvement (QI) Study (MARQUIS1) demonstrated that mentored implementation of a medication reconciliation best practices toolkit decreased total unintentional medication discrepancies in five hospitals, but results varied by site. The objective of this study was to determine the effects of a refined toolkit on a larger group of hospitals. METHODS: We conducted a pragmatic quality improvement study (MARQUIS2) at 18 North American hospitals or hospital systems from 2016 to 2018. Incorporating lessons learnt from MARQUIS1, we implemented a refined toolkit, offering 17 system-level and 6 patient-level interventions. One of eight physician mentors coached each site via monthly calls and performed one to two site visits. The primary outcome was number of unintentional medication discrepancies in admission or discharge orders per patient. Time series analysis used multivariable Poisson regression. RESULTS: A total of 4947 patients were sampled, including 1229 patients preimplementation and 3718 patients postimplementation. Both the number of system-level interventions adopted per site and the proportion of patients receiving patient-level interventions increased over time. During the intervention, patients experienced a steady decline in their medication discrepancy rate from 2.85 discrepancies per patient to 0.98 discrepancies per patient. An interrupted time series analysis of the 17 sites with sufficient data for analysis showed the intervention was associated with a 5% relative decrease in discrepancies per month over baseline temporal trends (adjusted incidence rate ratio: 0.95, 95% CI 0.93 to 0.97, p<0.001). Receipt of patient-level interventions was associated with decreased discrepancy rates, and these associations increased over time as sites adopted more system-level interventions. CONCLUSION: A multicentre medication reconciliation QI initiative using mentored implementation of a refined best practices toolkit, including patient-level and system-level interventions, was associated with a substantial decrease in unintentional medication discrepancies over time. Future efforts should focus on sustainability and spread.
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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 | 0.038 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".