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This article describes a system developed for the 2009 i2b2 Medication Extraction Challenge. The purpose of this challenge is to extract medication information from hospital discharge summaries.

In the i2b2 Medication Extraction Challenge, medication names together with details of their administration were to be extracted from medical discharge summaries.

The authors used the i2b2 Medication Extraction Challenge to evaluate their entity extraction methods, contribute to the generation of a publicly available collection of annotated clinical notes, and start developing methods for ontology-based reasoning using structured information generated from the unstructured clinical narrative.

This study presents a system developed for the 2009 i2b2 Challenge in Natural Language Processing for Clinical Data, whose aim was to automatically extract certain information about medications used by a patient from his/her medical report. The aim was to extract the following information for each medication: name, dosage, mode/route, frequency, duration and reason.

To develop an automated system to extract medications and related information from discharge summaries as part of the 2009 i2b2 natural language processing (NLP) challenge. This task required accurate recognition of medication name, dosage, mode, frequency, duration, and reason for drug administration.

Medication information comprises a most valuable source of data in clinical records. This paper describes use of a cascade of machine learners that automatically extract medication information from clinical records.

The objective of this review is to describe the implementation of human factors principles for the design of alerts in clinical information systems. First, we conduct a review of alarm systems to identify human factors principles that are employed in the design and implementation of alerts. Second, we review the medical informatics literature to provide examples of the implementation of human factors principles in current clinical information systems using alerts [...]

The American College of Medical Informatics is an honorary society established to recognize those who have made sustained contributions to the field. Its highest award, for lifetime achievement and contributions to the discipline of medical informatics, is the Morris F Collen Award. Dr Collen's own efforts as a pioneer in the field stand out as the embodiment of creativity, intellectual rigor, perseverance, and personal integrity. The Collen Award, given once [...]

Many e-prescribing systems allow for both structured and free-text fields in prescriptions, making possible internal discrepancies. This study reviewed 2914 electronic prescriptions that contained free-text fields. Internal discrepancies were found in 16.1% of the prescriptions. Most (83.8%) of the discrepancies could potentially lead to adverse events and many (16.8%) to severe adverse events, involving a hospital admission or death. Discrepancies in doses, routes or complex regimens were most likely to [...]

Health IT implementations often introduce radical changes to clinical work processes and workflow. Prior research investigating this effect has shown conflicting results. Recent time and motion studies have consistently found that this impact is negligible; whereas qualitative studies have repeatedly revealed negative end-user perceptions suggesting decreased efficiency and disrupted workflow. We speculate that this discrepancy may be due in part to the design of the time and motion studies, which [...]