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Dr Randolph A Miller begins a self-imposed JAMIA retirement on January 1, 2011 after serving as Editor-in-Chief for eight and a half years. He lauds the selection of Lucia Ohno-Machado as an energetic, innovative, and highly qualified successor.

Advances in clinical and translational science, along with related national-scale policy and funding mechanisms, have provided significant opportunities for the advancement of applied clinical research informatics (CRI) and translational bioinformatics (TBI). Such efforts are primarily oriented to application and infrastructure development and are critical to the conduct of clinical and translational research. However, they often come at the expense of the foundational CRI and TBI research needed to grow these [...]

To identify challenges in mapping internal International Classification of Disease, 9th edition, Clinical Modification (ICD-9-CM) encoded legacy data to Systematic Nomenclature of Medicine (SNOMED), using SNOMED-prescribed compositional approaches where appropriate, and to explore the mapping coverage provided by the US National Library of Medicine (NLM)'s SNOMED clinical core subset.

To ascertain if outpatients with moderate chronic kidney disease (CKD) had their condition documented in their notes in the electronic health record (EHR).

To examine the effect of interruptions and task complexity on error rates when prescribing with computerized provider order entry (CPOE) systems, and to categorize the types of prescribing errors.

This paper presents Lancet, a supervised machine-learning system that automatically extracts medication events consisting of medication names and information pertaining to their prescribed use (dosage, mode, frequency, duration and reason) from lists or narrative text in medical discharge summaries.

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.

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.