Medication Reconciliation Improvement Utilizing Process Redesign and Clinical Decision Support

Link to article at PubMed

Jt Comm J Qual Patient Saf. 2020 Jan;46(1):27-36. doi: 10.1016/j.jcjq.2019.09.001. Epub 2019 Oct 22.


BACKGROUND: Despite years of attention, hospitals continue to struggle to implement successful medication reconciliation. This study aimed to increase the percentage of hospital admission medication reconciliation (AdmMedRec) completion to ≥ 95% in 12 months at a large academic children's hospital.

METHODS: A quality improvement (QI) project was initiated in April 2017 by an interdisciplinary team of physicians, nurses, pharmacists, and analysts, co-led by a pediatric hospitalist and chief medical information officer. Interventions were implemented through sequential Plan-Do-Study-Act cycles. Process maps, fishbone diagrams, and failure mode and effects analysis were used to identify AdmMedRec failures. Baseline data from 12,481 admission encounters July 2016-April 2017 were analyzed. Interventions included electronic health record (EHR) workflow redesign, clarification of clinicians' responsibilities, targeted training, Best Practice Advisory alert, and weekly reporting of specialty- and physician-specific performance data. Data from 13,082 postintervention period admission encounters were examined. Reconciliation by therapeutic drug classes was calculated as a proxy for quality.

RESULTS: AdmMedRec completion rate increased from a baseline of 73% to 95% within 7 months from the start of this project and was sustained at 94% during the postintervention period. Psychiatry and hospital medicine demonstrated the largest improvements, with rates increasing from 17% to 88% and 76% to 98%, respectively. Percentages of reconciled medications in all 13 therapeutic classes, including high-risk drugs, improved significantly (p < 0.05).

CONCLUSIONS: Using an interdisciplinary team and interventions focused on process and culture changes, this QI initiative was successful at increasing AdmMedRec rates and reducing omission errors across all therapeutic drug classes.

PMID:31653526 | DOI:10.1016/j.jcjq.2019.09.001

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