Project Details - Ongoing
- Grant Number:R01 HS025136
- Funding Mechanism:
- AHRQ Funded Amount:$1,978,886
- Principal Investigator:
- Project Dates:7/1/2018 to 4/30/2023
- Care Setting:
- Type of Care:
- Health Care Theme:
The acute care medication prescribing process involves careful coordination between physicians, nurses, and pharmacists: health information technology (IT) applications can help facilitate this process. Health IT applications that are designed and used appropriately support the medication process, resulting in fewer medication errors, but poorly designed and implemented IT can create errors. Physicians order medications using computerized provider order entry (CPOE), while nurses and pharmacists manage medication administration using electronic medication administration records (eMARs) and barcode medication administration (BCMA). In particular, some eMARs do not support the cognitive needs of physicians, nurses, and pharmacists, resulting in poor communication, suboptimal information flow, and a lack of situational awareness for patients’ medications.
This study will examine eMAR usability and safety hazards with a focus on communication and information flow among eMARs and other health IT applications. The objective of the project is to reduce the patient safety hazards associated with eMARs by, (1) understanding usability and safety gaps, and (2) creating design and development documents, wireframes, and prototypes to serve as the foundation for future eMARs to eliminate these gaps. Researchers will take a systems approach by examining interdisciplinary users to ensure needs are being met across the medication process.
The specific aims of the project are as follows:
- Analyze a diverse set of medication-related patient safety event reports to identify specific problematic aspects of CPOE-eMAR-BCMA activities that contribute to medication errors.
- Conduct multi-method usability evaluations of current eMAR-related processes.
- Iteratively design, develop, test, and disseminate eMAR wireframes and prototypes.
Researchers will analyze a large dataset of 1.7 million patient safety event reports using natural language processing models. They will also conduct interviews and observations of physicians, nurses, and pharmacists. Researchers will shadow participants during their eMAR use and take field notes, capturing specific information for each task, such as who collects information, how is it collected, and when is it collected. The findings will be used to create a medication error and usability taxonomy of safety events spanning the medication ordering process to identify risks and hazards, and develop strategies to better support the medication process with eMARs and health IT.