What works in medication reconciliation: an on-treatment and site analysis of the MARQUIS2 study

Abstract

Background: The second Multicenter Medication Reconciliation Quality Improvement Study demonstrated a marked reduction in medication discrepancies per patient. The aim of the current analysis was to determine the association of patient exposure to each system-level intervention and receipt of each patient-level intervention on these results.

Methods: This study was conducted at 17 North American Hospitals, the study period was 18 months per site, and sites typically adopted interventions after 2-5 months of preintervention data collection. We conducted an on-treatment analysis (ie, an evaluation of outcomes based on patient exposure) of system-level interventions, both at the category level and at the individual component level, based on monthly surveys of implementation site leads at each site (response rate 65%). We then conducted a similar analysis of patient-level interventions, as determined by study pharmacist review of documented activities in the medical record. We analysed the association of each intervention on the adjusted number of medication discrepancies per patient in admission and discharge orders, based on a random sample of up to 22 patients per month per site, using mixed-effects Poisson regression with hospital site as a random effect. We then used a generalised linear mixed-effects model (GLMM) decision tree to determine which patient-level interventions explained the most variance in discrepancy rates.

Results: Among 4947 patients, patient exposure to seven of the eight system-level component categories was associated with modest but significant reductions in discrepancy rates (adjusted rate ratios (ARR) 0.75-0.97), as were 15 of the 17 individual system-level intervention components, including hiring, reallocating and training personnel to take a best possible medication history (BPMH) and training personnel to perform discharge medication reconciliation and patient counselling. Receipt of five of seven patient-level interventions was independently associated with large reductions in discrepancy rates, including receipt of a BPMH in the emergency department (ED) by a trained clinician (ARR 0.40, 95% CI 0.37 to 0.43), admission medication reconciliation by a trained clinician (ARR 0.57, 95% CI 0.50 to 0.64) and discharge medication reconciliation by a trained clinician (ARR 0.64, 95% CI 0.57 to 0.73). In GLMM decision tree analyses, patients who received both a BPMH in the ED and discharge medication reconciliation by a trained clinician experienced the lowest discrepancy rates (0.08 per medication per patient).

Conclusion and relevance: Patient-level interventions most associated with reductions in discrepancies were receipt of a BPMH of admitted patients in the ED and admission and discharge medication reconciliation by a trained clinician. System-level interventions were associated with modest reduction in discrepancies for the average patient but are likely important to support patient-level interventions and may reach more patients. These findings can be used to help hospitals and health systems prioritise interventions to improve medication safety during care transitions.

Keywords: medication reconciliation; medication safety; pharmacists; quality improvement.

Figure 1.. Generalized Linear Mixed-Effects Model (GLMM) Decision Tree Analysis of Patient-Level Interventions.

Observed medication discrepancy rates per medication are shown for each combination of interventions. Downstream nodes are conditional on the previous node. Where receipt vs. not-receipt of an intervention resulted in a relative difference of at least 25% in discrepancy rates, the option with lower rates is shown in green and the one with higher rates is shown in red. At the “terminal node” of each branch, observed discrepancy rates ≤0.10 per medication are shown in green, rates >0.10 and ≤0.20 are shown in yellow, and those >0.20 are shown in red. In every node of the plotted tree, we report the splitting variable and corresponding Bonferroni corrected p-value from the parameter stability test. At every branch, we note the number of patients that received that combination of interventions. BPMH: best possible medication history; ED: emergency department; Med Rec: medication reconciliation.