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Abstract Background Text-based patient medical records are a vital resource in medical research. In order to preserve patient confidentiality, however, the U.S. Health Insurance Portability and Accountability Act (HIPAA) requires that protected health information (PHI) be removed from medical records before they can be disseminated. Manual de-identification of large medical record databases is prohibitively expensive, time-consuming and prone to error, necessitating automatic methods for large-scale, automated de-identification. Methods We describe an automated Perl-based de-identification software package that is generally usable on most free-text medical records, e.g., nursing notes, discharge summaries, X-ray reports, etc. The software uses lexical look-up tables, regular expressions, and simple heuristics to locate both HIPAA PHI, and an extended PHI set that includes doctors' names and years of dates. To develop the de-identification approach, we assembled a gold standard corpus of re-identified nursing notes with real PHI replaced by realistic surrogate information. This corpus consists of 2,434 nursing notes containing 334,000 words and a total of 1,779 instances of PHI taken from 163 randomly selected patient records. This gold standard corpus was used to refine the algorithm and measure its sensitivity. To test the algorithm on data not used in its development, we constructed a second test corpus of 1,836 nursing notes containing 296,400 words. The algorithm's false negative rate was evaluated using this test corpus. Results Performance evaluation of the de-identification software on the development corpus yielded an overall recall of 0.967, precision value of 0.749, and fallout value of approximately 0.002. On the test corpus, a total of 90 instances of false negatives were found, or 27 per 100,000 word count, with an estimated recall of 0.943. Only one full date and one age over 89 were missed. No patient names were missed in either corpus. Conclusion We have developed a pattern-matching de-identification system based on dictionary look-ups, regular expressions, and heuristics. Evaluation based on two different sets of nursing notes collected from a U.S. hospital suggests that, in terms of recall, the software out-performs a single human de-identifier (0.81) and performs at least as well as a consensus of two human de-identifiers (0.94). The system is currently tuned to de-identify PHI in nursing notes and discharge summaries but is sufficiently generalized and can be customized to handle text files of any format. Although the accuracy of the algorithm is high, it is probably insufficient to be used to publicly disseminate medical data. The open-source de-identification software and the gold standard re-identified corpus of medical records have therefore been made available to researchers via the PhysioNet website to encourage improvements in the algorithm.

El objetivo de este artículo es proporcionar una referencia simple y actualizada sobre el manejo que se realiza en la Unidad de Endocrinología Ginecológica de Clínica Alemana de Santiago respecto del enfoque y manejo de la mujer en el período de transición a la menopausia, considerando los aspectos epidemiológicos de las enfermedades que afectan mayormente a las mujeres en este período, la calidad de vida de las mujeres durante esta etapa de la vida y la personalización de la terapia hormonal menopúsica (THM). [ABSTRACT FROM AUTHOR]

Thoracic electrical bioimpedance (TEB) and transthoracic echocardiography (TTE) are non-invasive methods to estimate stroke volume (SV) and cardiac output (CO). Thoracic electrical bioimpedance is not in widespread clinical use with reports of inaccurate cardiac output estimation compared to invasive monitors, particularly in non-healthy populations. We explore its use as a trend monitor by comparing it against thoracic echocardiography in fifteen healthy volunteers undergoing two physical challenges designed to vary cardiac output. Of all paired values, 54.6% showed gross trend agreement and only 1.9% showed direct disagreement between the two monitors. Our results show thoracic bioimpedance may have a role as a non-invasive cardiac output trend monitor in healthy volunteer studies. [ABSTRACT FROM AUTHOR]

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