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Medication information is one of the most important types of clinical data in electronic medical records. It is critical for healthcare safety and quality, as well as for clinical research that uses electronic medical record data. However, medication data are often recorded in clinical notes as free-text. As such, they are not accessible to other computerized applications that rely on coded data. We describe a new natural language processing system (MedEx), which extracts medication information from clinical notes. MedEx was initially developed using discharge summaries. An evaluation using a data set of 50 discharge summaries showed it performed well on identifying not only drug names (F-measure 93.2%), but also signature information, such as strength, route, and frequency, with F-measures of 94.5%, 93.9%, and 96.0% respectively. We then applied MedEx unchanged to outpatient clinic visit notes. It performed similarly with F-measures over 90% on a set of 25 clinic visit notes.
OBJECTIVE - The goal of this research is to provide a framework to enable the model-based development, simulation, and deployment of clinical information system prototypes with mechanisms that enforce security and privacy policies.
METHODS - We developed the Model-Integrated Clinical Information System (MICIS), a software toolkit that is based on model-based design techniques and high-level modeling abstractions to represent complex clinical workflows in a service-oriented architecture paradigm. MICIS translates models into executable constructs, such as web service descriptions, business process execution language procedures, and deployment instructions. MICIS models are enriched with formal security and privacy specifications, which are enforced within the execution environment.
RESULTS - We successfully validated our design platform by modeling multiple clinical workflows and deploying them onto the execution platform.
CONCLUSIONS - The model-based approach shows great promise for developing, simulating, and evolving clinical information systems with formal properties and policy restrictions.
A profile likelihood algorithm is proposed for quantitative shotgun proteomics to infer the abundance ratios of proteins from the abundance ratios of isotopically labeled peptides derived from proteolysis. Previously, we have shown that the estimation variability and bias of peptide abundance ratios can be predicted from their profile signal-to-noise ratios. Given multiple quantified peptides for a protein, the profile likelihood algorithm probabilistically weighs the peptide abundance ratios by their inferred estimation variability, accounts for their expected estimation bias, and suppresses contribution from outliers. This algorithm yields maximum likelihood point estimation and profile likelihood confidence interval estimation of protein abundance ratios. This point estimator is more accurate than an estimator based on the average of peptide abundance ratios. The confidence interval estimation provides an "error bar" for each protein abundance ratio that reflects its estimation precision and statistical uncertainty. The accuracy of the point estimation and the precision and confidence level of the interval estimation were benchmarked with standard mixtures of isotopically labeled proteomes. The profile likelihood algorithm was integrated into a quantitative proteomics program, called ProRata, freely available at www.MSProRata.org.
Extensive utilization of point-of-care decision support systems will be largely dependent on the development of user interaction capabilities that make them effective clinical tools in patient care settings. This research identified critical design features of point-of-care decision support systems that are preferred by physicians, through a multi-method formative evaluation of an evolving prototype of an Internet-based clinical decision support system. Clinicians used four versions of the system--each highlighting a different functionality. Surveys and qualitative evaluation methodologies assessed clinicians' perceptions regarding system usability and usefulness. Our analyses identified features that improve perceived usability, such as telegraphic representations of guideline-related information, facile navigation, and a forgiving, flexible interface. Users also preferred features that enhance usefulness and motivate use, such as an encounter documentation tool and the availability of physician instruction and patient education materials. In addition to identifying design features that are relevant to efforts to develop clinical systems for point-of-care decision support, this study demonstrates the value of combining quantitative and qualitative methods of formative evaluation with an iterative system development strategy to implement new information technology in complex clinical settings.
Computerization of clinical practice guidelines (CPGs) has been proposed as one solution to enhance the use of guidelines in influencing standard clinical care. However, the conversion of text guidelines to the format required by a computer program is a major barrier. Clinicians who best understand the content of CPGs are typically ill equipped to convert textual guidelines into a computer accessible format. The potential of knowledge acquisition tools to assist in this process has been documented in the literature. In this paper we describe an application prototype, the Guideline Entry Wizard, created to assist in the conversion of text CPGs to a structured format within a relational database. We have tested this application through the input of information from several CPG. The application is a prototype for a more advanced tool. We have used this prototype to enter several CPGs and have demonstrated its effectiveness in inputting guideline content into a knowledge base.
We describe a continuous-speech interface for Quick Medical Reference (QMR), which allows physicians to input spoken descriptions of physical-examination findings, or observations. We analyze the difficulties in designing a continuous-speech interface for systems that use medical terminology. We present a method for matching spoken findings names expressed in natural language to QMR terms. The method is based on a semantic representation of findings that both minimize the effect of misrecognition and derive grammars that are necessary for supporting the recognition process.