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Inefficiencies in clinical trial data collection cause delays, increase costs, and may reduce clinician participation in medical research. In this proof-of-concept study, we examine the feasibility of using point-of-care data capture for both the medical record and clinical research in the setting of a working clinical trial. We hypothesized that by doing so, we could increase reuse of patient data, eliminate redundant data entry, and minimize disruption to clinic workflow.

Computerized clinical reminders (CRs) were designed to reduce clinicians' reliance on their memory and to present evidence-based guidelines at point of care. However, the literature indicates that CR adoption and effectiveness has been variable. We examined the impact of four design modifications to CR software on learnability, efficiency, usability, and workload for intake nursing personnel in an outpatient clinic setting. These modifications were included in a redesign primarily to address [...]

Electronic Knowledge Resources (EKRs) are increasingly used by physicians, but their situational relevance has not been systematically examined.

A major problem faced in biomedical informatics involves how best to present information retrieval results. When a single query retrieves many results, simply showing them as a long list often provides poor overview. With a goal of presenting users with reduced sets of relevant citations, this study developed an approach that retrieved and organized MEDLINE citations into different topical groups and prioritized important citations in each group.

To specify and identify disease and patient care processes represented by temporal patterns in clinical events and observations, and retrieve patient populations containing those patterns from clinical data repositories, in support of clinical research, outcomes studies, and quality assurance.

Electronic health records (EHRs) have great potential to improve safety, quality, and efficiency in medicine. However, adoption has been slow, and a key concern has been that clinicians will require more time to complete their work using EHRs. Most previous studies addressing this issue have been done in primary care.

In recent years, influenza surveillance data has expanded to include alternative sources such as emergency department data, absenteeism reports, pharmaceutical sales, website access and health advice calls. This study presents a review of alternative data sources for influenza surveillance, summarizes the time advantage or timeliness of each source relative to traditional reporting and discusses the strengths and weaknesses of competing approaches.

Part-of-speech tagging represents an important first step for most medical natural language processing (NLP) systems. The majority of current statistically-based POS taggers are trained using a general English corpus. Consequently, these systems perform poorly on medical text. Annotated medical corpora are difficult to develop because of the time and labor required. We investigated a heuristic-based sample selection method to minimize annotated corpus size for retraining a Maximum Entropy (ME) POS [...]

This paper describes a successful approach to de-identification that was developed to participate in a recent AMIA-sponsored challenge evaluation.

To facilitate and survey studies in automatic de-identification, as a part of the i2b2 (Informatics for Integrating Biology to the Bedside) project, authors organized a Natural Language Processing (NLP) challenge on automatically removing private health information (PHI) from medical discharge records. This manuscript provides an overview of this de-identification challenge, describes the data and the annotation process, explains the evaluation metrics, discusses the nature of the systems that addressed the [...]