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Objective To develop a generalizable method for identifying patient cohorts from electronic health record (EHR) data--in this case, patients having dialysis--that uses simple information retrieval (IR) tools. Methods We used the coded data and clinical notes from the 24 506 adult patients in the Multiparameter Intelligent Monitoring in Intensive Care database to identify patients who had dialysis. We used SQL queries to search the procedure, diagnosis, and coded nursing observations tables based on ICD-9 and local codes. We used a domain-specific search engine to find clinical notes containing terms related to dialysis. We manually validated the available records for a 10% random sample of patients who potentially had dialysis and a random sample of 200 patients who were not identified as having dialysis based on any of the sources. Results We identified 1844 patients that potentially had dialysis: 1481 from the three coded sources and 1624 from the clinical notes. Precision for identifying dialysis patients based on available data was estimated to be 78.4% (95% CI 71.9% to 84.2%) and recall was 100% (95% CI 86% to 100%). Conclusions Combining structured EHR data with information from clinical notes using simple queries increases the utility of both types of data for cohort identification. Patients identified by more than one source are more likely to meet the inclusion criteria; however, including patients found in any of the sources increases recall. This method is attractive because it is available to researchers with access to EHR data and off-the-shelf IR tools. [ABSTRACT FROM AUTHOR]

Objective To understand the factors that influence success in scrubbing personal names from narrative text. Materials and methods We developed a scrubber, the NLM Name Scrubber (NLM-NS), to redact personal names from narrative clinical reports, hand tagged words in a set of gold standard narrative reports as personal names or not, and measured the scrubbing success of NLM-NS and that of four other scrubbing/name recognition tools (MIST, MITdeid, LingPipe, and ANNIE/GATE) against the gold standard reports. We ran three comparisons which used increasingly larger name lists. Results The test reports contained more than 1 million words, of which 2388 were patient and 20 160 were provider name tokens. NLM-NS failed to scrub only 2 of the 2388 instances of patient name tokens. Its sensitivity was 0.999 on both patient and provider name tokens and missed fewer instances of patient name tokens in all comparisons with other scrubbers. MIST produced the best all token specificity and F-measure for name instances in our most relevant study (study 2), with values of 0.997 and 0.938, respectively. In that same comparison, NLM-NS was second best, with values of 0.986 and 0.748, respectively, and MITdeid was a close third, with values of 0.985 and 0.796 respectively. With the addition of the Clinical Center name list to their native name lists, Ling Pipe, MITdeid, MIST, and ANNIE/ GATE all improved substantially. MITdeid and Ling Pipe gained the most--reaching patient name sensitivity of 0.995 (F-measure=0.705) and 0.989 (F-measure=0.386), respectively. Discussion The privacy risk due to two name tokens missed by NLM-NS was statistically negligible, since neither individual could be distinguished among more than 150 000 people listed in the US Social Security Registry. Conclusions The nature and size of name lists have substantial influences on scrubbing success. The use of very large name lists with frequency statistics accounts for much of NLM-NS scrubbing success. [ABSTRACT FROM AUTHOR]

Background Many clinicians depend solely on journal abstracts to guide clinical decisions. Objectives This study aims to determine if there are differences in the accuracy of responses to simulated cases between resident physicians provided with an abstract only and those with full-text articles. It also attempts to describe their information-seeking behaviour. Methods Seventy-seven resident physicians from four specialty departments of a tertiary care hospital completed a paper-based questionnaire with clinical simulation cases, then randomly assigned to two intervention groups-access to abstracts-only and access to both abstracts and full-text. While having access to medical literature, they completed an online version of the same questionnaire. Findings The average improvement across departments was not significantly different between the abstracts only group and the full-text group ( p=0.44), but when accounting for an interaction between intervention and department, the effect was significant (p=0.049) with improvement greater with full-text in the surgery department. Overall, the accuracy of responses was greater after the provision of either abstracts-only or full-text (p<0.0001). Although some residents indicated that 'accumulated knowledge' was sufficient to respond to the patient management questions, in most instances (83% of cases) they still sought medical literature. Conclusions Our findings support studies that doctors will use evidence when convenient and current evidence improved clinical decisions. The accuracy of decisions improved after the provision of evidence. Clinical decisions guided by full-text articles were more accurate than those guided by abstracts alone, but the results seem to be driven by a significant difference in one department. [ABSTRACT FROM AUTHOR]

Introduction: Two thirds of United States adults are overweight or obese, which puts them at higher risk of developing chronic diseases and of death compared with normal-weight individuals. However, recent studies have found that overweight and obesity by themselves may be protective in some contexts, such as hospitalization in an intensive care unit (ICU). Our objective was to determine the relation between body mass index (BMI) and mortality at 30 days and 1 year after ICU admission. Methods: We performed a cohort analysis of 16,812 adult patients from MIMIC-II, a large database of ICU patients at a tertiary care hospital in Boston, Massachusetts. The data were originally collected during the course of clinical care, and we subsequently extracted our dataset independent of the study outcome. Results: Compared with normal-weight patients, obese patients had 26% and 43% lower mortality risk at 30 days and 1 year after ICU admission, respectively (odds ratio (OR), 0.74; 95% confidence interval (CI), 0.64 to 0.86) and 0.57 (95% CI, 0.49 to 0.67)); overweight patients had nearly 20% and 30% lower mortality risk (OR, 0.81; 95% CI, 0.70 to 0.93) and OR, 0.68 (95% CI, 0.59 to 0.79)). Severely obese patients (BMI ⩾ 40 kg/m2) did not have a significant survival advantage at 30 days (OR, 0.94; 95% CI, 0.74 to 1.20), but did have 30% lower mortality risk at 1 year (OR, 0.70 (95% CI, 0.54 to 0.90)). No significant difference in admission acuity or ICU and hospital length of stay was found across BMI categories. Conclusion: Our study supports the hypothesis that patients who are overweight or obese have improved survival both 30 days and 1 year after ICU admission. [ABSTRACT FROM AUTHOR]