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While essential for patient care, information related to medication is often written as free text in clinical records and, therefore, difficult to use in computerized systems. This paper describes an approach to automatically extract medication information from clinical records, which was developed to participate in the i2b2 2009 challenge, as well as different strategies to improve the extraction.

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

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.

Within the context of the Third i2b2 Workshop on Natural Language Processing Challenges for Clinical Records, the authors (also referred to as 'the i2b2 medication challenge team' or 'the i2b2 team' for short) organized a community annotation experiment.

Electronic health records (EHRs) and EHR-connected patient portals offer patient-provider collaboration tools for visit-based care. During a randomized controlled trial, primary care patients completed pre-visit electronic journals (eJournals) containing EHR-based medication, allergies, and diabetes (study arm 1) or health maintenance, personal history, and family history (study arm 2) topics to share with their provider. Assessment with surveys and usage data showed that among 2027 patients invited to complete an eJournal [...]

Current research suggests that the rate of adoption of health information technology (HIT) is low, and that HIT may not have the touted beneficial effects on quality of care or costs. The twin issues of the failure of HIT adoption and of HIT efficacy stem primarily from a series of fallacies about HIT. We discuss 12 HIT fallacies and their implications for design and implementation. These fallacies must be understood [...]

Pharmacotherapy is an integral part of any medical care process and plays an important role in the medical history of most patients. Information on medication is crucial for several tasks such as pharmacovigilance, medical decision or biomedical research.

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.

We aim to build and evaluate an open-source natural language processing system for information extraction from electronic medical record clinical free-text. We describe and evaluate our system, the clinical Text Analysis and Knowledge Extraction System (cTAKES), released open-source at http://www.ohnlp.org. The cTAKES builds on existing open-source technologies-the Unstructured Information Management Architecture framework and OpenNLP natural language processing toolkit. Its components, specifically trained for the clinical domain, create rich linguistic and [...]

In 2005, the American Medical Informatics Association undertook a set of activities relating to clinical decision support (CDS), with support from the office of the national coordinator and the Agency for Healthcare Research and Quality. They culminated in the release of the roadmap for national action on CDS in 2006. This article assesses progress toward the short-term goals within the roadmap, and recommends activities to continue to improve CDS adoption [...]