Implementation, evaluation, and recommendations for extension of AHRQ Common Formats to capture patient- and carepartner-generated safety data

Abstract Objectives The Common Formats, published by the Agency for Healthcare Research and Quality, represent a standard for safety event reporting used by Patient Safety Organizations (PSOs). We evaluated its ability to capture patient-reported safety events. Materials and methods We formally evaluated gaps between the Common Formats and a safety concern reporting system for use by patients and their carepartners (ie friends/families) at Brigham and Women’s Hospital. Results Overall, we found large gaps between Common Formats (versions 1.2, 2.0) and our patient/carepartner reporting system, with only 22–30% of the data elements matching. Discussion We recommend extensions to the Common Formats, including concepts that capture greater detail about the submitter and safety categories relevant to unsafe conditions and near misses that patients and carepartners routinely observe. Conclusion Extensions to the Common Formats could enable more complete safety data sets and greater understanding of safety from key stakeholder perspectives, especially patients, and carepartners. </jats:sec

Fortalecimiento de los conocimientos metodológicos y de elaboración del Trabajo Fin de Grado y Master (II)

Memoría de la segunda parte del proyecto de innovación docente 224 del curso 19/20. Se actualizan objetivos y se anexa material creado: 1. Actualización de guía para el desarrollo de trabajos final de grado o master 2. Vídeos elaborados por el equipo de investigadores para cumplimentar dudas frecuentes de los alumnos en la elaboración de trabajos finales de grado o máster 3. Autoevaluaciones para que los alumnos puedan detectar los conocimientos adquirido

The Tell-Tale Heart: Population-Based Surveillance Reveals an Association of Rofecoxib and Celecoxib with Myocardial Infarction

Background. COX-2 selective inhibitors are associated with myocardial infarction (MI). We sought to determine whether population health monitoring would have revealed the effect of COX-2 inhibitors on population-level patterns of MI. Methodology/Principal Findings. We conducted a retrospective study of inpatients at two Boston hospitals, from January 1997 to March 2006. There was a population-level rise in the rate of MI that reached 52.0 MI-related hospitalizations per 100,000 (a two standard deviation exceedence) in January of 2000, eight months after the introduction of rofecoxib and one year after celecoxib. The exceedence vanished within one month of the withdrawal of rofecoxib. Trends in inpatient stay due to MI were tightly coupled to the rise and fall of prescriptions of COX-2 inhibitors, with an 18.5 % increase in inpatient stays for MI when both rofecoxib and celecoxib were on the market (P,0.001). For every million prescriptions of rofecoxib and celecoxib, there was a 0.5 % increase in MI (95%CI 0.1 to 0.9) explaining 50.3 % of the deviance in yearly variation of MI-related hospitalizations. There was a negative association between mean age at MI and volume of prescriptions for celecoxib and rofecoxib (Spearman correlation, 20.67, P,0.05). Conclusions/Significance. The strong relationship between prescribing and outcome time series supports a population-level impact of COX-2 inhibitors on MI incidence. Further, mean age at MI appears to have been lowered by use of these medications. Use of a population monitoring approach as an adjunct t

All-sky Medium Energy Gamma-ray Observatory: Exploring the Extreme Multimessenger Universe

The All-sky Medium Energy Gamma-ray Observatory (AMEGO) is a probe class mission concept that will provide essential contributions to multimessenger astrophysics in the late 2020s and beyond. AMEGO combines high sensitivity in the 200 keV to 10 GeV energy range with a wide field of view, good spectral resolution, and polarization sensitivity. Therefore, AMEGO is key in the study of multimessenger astrophysical objects that have unique signatures in the gamma-ray regime, such as neutron star mergers, supernovae, and flaring active galactic nuclei. The order-of-magnitude improvement compared to previous MeV missions also enables discoveries of a wide range of phenomena whose energy output peaks in the relatively unexplored medium-energy gamma-ray band

Evolución de la demencia en la enfermedad de Parkinson

Tesis doctoral original leída en la Universidad Autónoma de Madrid, Facultad de Medicina. Fecha de lectura: 25 de Septiembre de 1990

KBANNS and the Classification of 31 P MRS of Malignant Mammary Tissues

Knowledge-based artificial neural networks (KBANNs) is a hybrid methodology that combines knowledge of a domain in the form of simple rules with connectionist learning. This combination allows the use of small sets of data (typical of medical diagnosis tasks) to train the network. The initial structure is set from the dependencies of a set of rules and it is only necessary to refine these rules by training. In this paper we present such KBANNs with a topology derived from knowledge elicited from the domain of metabolic features of malignant mammary tissues. KBANN performance is assessed over the classification of 26 in vivo P-31 spectra of normal and cancerous breast tissues. Results presented in this paper confirm the suitability of KBANNs a computational aid capable of classifying complex and limited data in a medical domain. The present study is part of an ongoing investigation into normal and abnormal breast physiology which may allow non-invasive early detection of breast cancer [27,28]

KBANNs and the Classification of &amp;sup3;&amp;sup1;P MRS of Malignant Mammary Tissues

Knowledge-based artificial neural networks (KBANNs) is a hybrid methodology that combines knowledge of a domain in the form of simple rules with connectionist learning. This combination allows the use of small sets of data (typical of medical diagnosis tasks) to train the network. The initial structure is set from the dependencies of a set of rules and it is only necessary to refine these rules by training. In this paper we present such KBANNs with a topology derived from knowledge elicited from the domain of metabolic features of malignant mammary tissues. KBANNs&apos; performance is assessed over the classification of 26 in vivo &amp;sup3;&amp;sup1;P spectra of normal and cancerous breast tissues. Results presented in this paper confirm the suitability of KBANNs a computational aid capable of classifying complex and limited data in a medical domain. The present study is part of an ongoing investigation into normal and abnormal breast physiology which may allow non-invasive early detection of breast cancer [29, 30]