An Intelligent and Visual Clinical Decision Support System for Medication Reconciliation at Admission in a Hospital...

Area of Science:

• Clinical Informatics
• Pharmacy Practice
• Patient Safety

Background:

• Medication reconciliation (MR) is crucial for preventing errors during care transitions, especially at hospital admission.
• Current electronic MR tools often lack essential collaborative and visual functionalities, making the process time-consuming.
• Polypharmacy and complex medication regimens increase the risk of errors during transitions of care.

Purpose of the Study:

• To describe the design of a novel electronic tool for medication reconciliation integrated within the ABiMed clinical decision support system.
• To enhance the efficiency and effectiveness of MR by incorporating collaborative features and visual patient-specific drug data.
• To facilitate the identification, classification, and resolution of medication discrepancies between the best possible medication history and admission orders.

Main Methods:

• Development of an electronic MR tool within the ABiMed system, featuring an ontology-based structure for interoperability.
• Implementation of semi-automatic discrepancy identification and classification between the best possible medication history (BPMH) and admission medication order (AMO).
• Integration of real-time collaboration between pharmacists and prescribers, visual patient drug data, and automatic execution of STOPP/START guidelines.

Main Results:

• The designed tool facilitates comparison of BPMH with AMO, semi-automatically identifying and classifying discrepancies.
• Unresolved discrepancies can be directly communicated to the prescriber for timely resolution.
• The tool incorporates features promoting shared responsibility and clinically relevant pharmacist interventions.

Conclusions:

• The developed electronic tool offers a collaborative and visual approach to medication reconciliation, addressing limitations of existing systems.
• Features like real-time collaboration and automated guideline execution aim to improve pharmacist interventions and patient safety.
• Further clinical evaluation is planned to assess usability, impact on time efficiency, and prescriber acceptance.