A b s t r a c t Objective: The aim of this study was to develop and evaluate a method of extracting noun phrases with full phrase structures from a set of clinical radiology reports using natural language processing (NLP) and to investigate the effects of using the UMLSÒ Specialist Lexicon to improve noun phrase identification within clinical radiology documents.Design: The noun phrase identification (NPI) module is composed of a sentence boundary detector, a statistical natural language parser trained on a nonmedical domain, and a noun phrase (NP) tagger. The NPI module processed a set of 100 XML-represented clinical radiology reports in Health Level 7 (HL7)Ò Clinical Document Architecture (CDA)-compatible format. Computed output was compared with manual markups made by four physicians and one author for maximal (longest) NP and those made by one author for base (simple) NP, respectively. An extended lexicon of biomedical terms was created from the UMLS Specialist Lexicon and used to improve NPI performance.Results: The test set was 50 randomly selected reports. The sentence boundary detector achieved 99.0% precision and 98.6% recall. The overall maximal NPI precision and recall were 78.9% and 81.5% before using the UMLS Specialist Lexicon and 82.1% and 84.6% after. The overall base NPI precision and recall were 88.2% and 86.8% before using the UMLS Specialist Lexicon and 93.1% and 92.6% after, reducing false-positives by 31.1% and false-negatives by 34.3%. Conclusion:The sentence boundary detector performs excellently. After the adaptation using the UMLS Specialist Lexicon, the statistical parser's NPI performance on radiology reports increased to levels comparable to the parser's native performance in its newswire training domain and to that reported by other researchers in the general nonmedical domain.

Hospital-acquired Clostridium difficile infection (CDI) is associated with significant morbidity and mortality. 1 While risk factors like antibiotic exposure modulate susceptibility, infection control efforts aimed at reducing contact with infectious spores are critical to prevent nosocomial transmission. [2][3][4][5] During hospitalization, patients visit many procedural and diagnostic common areas, presenting opportunities for contact with contaminated surfaces. However, these potential exposures are not typically captured in analyses evaluating disease transmission. 6 Electronic health record (EHR) data allow us to track patients in time and space, but these data are not typi-cally leveraged for infection control quality improvement efforts. We evaluated whether using a room within 24 hours of a patient with CDI was associated with increased risk of CDI in specific areas across our hospital.

Hospitalists are increasingly involved in implementing quality improvement initiatives around patient safety, clinical informatics, and transitions of care, but may lack expertise in promoting these important interventions. Developing a sound business case is essential to garnering support and resources for any quality improvement initiative. We present a framework for developing a business case using a structured approach to exploring qualitative and quantitative costs and benefits and describe its application in the experience of developing an electronic discharge summary at the University of California San Francisco (UCSF). At our institution, we found that the primary financial benefits are the cost reductions in eliminating transcription needs and decreasing billing delays, as well as reducing the cost of tracking completion of and dissemination of discharge summaries. Costs incurred from a new information technology (IT) infrastructure, programmer time, maintenance and training must also be accounted for. While benefits may be apparent to front line providers (improved communication, efficiency of data transfer, and increased referring physician satisfaction), implementing and sustaining such an innovation depends on articulating a sound business case with a detailed cost‐benefit analysis to institutional decision making. Journal of Hospital Medicine 2011. © 2010 Society of Hospital Medicine.

BACKGROUND: Medical training programs across the country are bound to a set of work hour regulations, generally monitored via self-report. OBJECTIVE: We developed a computational method to automate measurement of intern and resident work hours, which we validated against self-report. DESIGN, SETTING, AND PARTICIPANTS: We included all electronic health record (EHR) access log data between July 1, 2018, and June 30, 2019, for trainees enrolled in the internal medicine training program. We inferred the duration of continuous in-hospital work hours by linking EHR sessions that occurred within 5 hours as “on-campus” work and further accounted for “out-of-hospital” work which might be taking place at home. MAIN OUTCOMES AND MEASURES: We compared daily work hours estimated through the computational method with self-report and calculated the mean absolute error between the two groups. We used the computational method to estimate average weekly work hours across the rotation and the percentage of rotations where average work hours exceed the 80-hour workweek. RESULTS: The mean absolute error between self-reported and EHR-derived daily work hours for first- (PGY-1), second- (PGY-2), and third- (PGY-3) year trainees were 1.27, 1.51, and 1.51 hours, respectively. Using this computational method, we estimated average (SD) weekly work hours of 57.0 (21.7), 69.9 (12.2), and 64.1 (16.3) for PGY-1, PGY-2, and PGY-3 residents. CONCLUSION: EHR log data can be used to accurately approximate self-report of work hours, accounting for both in-hospital and out-of-hospital work. Automation will reduce trainees’ clerical work, improve consistency and comparability of data, and provide more complete and timely data that training programs need.