Sentence examples for medication discrepancy detection from inspiring English sources

The recall propagation showed that both the entity detection and medication matching processes contributed to the loss of detection on discrepant medications; therefore, further refinements in these components are required to improve medication discrepancy detection.

The proposed medication discrepancy detection algorithm is diagramed in Figure  2.

The prototype needs to be transferred to a production environment to adequately estimate the practicality of automated medication discrepancy detection.

However, even at this early stage of development, automated medication discrepancy detection shows a promising outcome in assisting medication reconciliation.

The error analysis on algorithm outputs identified challenges to be addressed in order to improve medication discrepancy detection.

We evaluated the three processes of automated medication discrepancy detection individually and in combination using double-annotated, gold-standard medication reconciliation data.

Finally, the patient population investigated in the study usually has long medication lists documented in clinical notes, providing a potentially more suitable foundation for medication entity and discrepancy detection algorithms.

Our specific aims are: (1) to develop a hybrid automated algorithm for discrepancy detection between patients' discharge prescriptions (structured data) and medications documented in free-text clinical notes (unstructured data); and (2) to assess the performance of the algorithm on gold-standard-based real-world medication reconciliation data.

There was very good agreement among evaluators in judging the potential clinical effect of medication discrepancy.

Greater than 50% chance that the medication discrepancy identified had potential to harm patient.

Employing a standardized Medication Discrepancy Tool [ 35] will also ensure consistency among the data.