identifying patients with relapsing remitting multiple sclerosis using algorithms applied to us integrated delivery network healthcare data

Abstract Background Relapsing-remitting multiple sclerosis (RRMS) has a major impact on affected patients; therefore, improved understanding of RRMS is important, particularly in the context of real-world evidence. Objectives To develop and validate algorithms for identifying patients with RRMS in both unstructured clinical notes found in electronic health records (EHRs) and structured/coded health care claims data. Methods US Integrated Delivery Network data (2010–2014) were queried for study inclusion criteria (possible multiple sclerosis [MS] base cohort): one or more MS diagnosis code, patients aged 18 years or older, 1 year or more baseline history, and no other demyelinating diseases. Sets of algorithms were developed to search narrative text of unstructured clinical notes (EHR clinical notes–based algorithms) and structured/coded data (claims-based algorithms) to identify adult patients with RRMS, excluding patients with evidence of progressive MS. Medical records were reviewed manually for algorithm validation. Positive predictive value was calculated for both EHR clinical notes–based and claims-based algorithms. Results From a sample of 5308 patients with possible MS, 837 patients with RRMS were identified using only the EHR clinical notes–based algorithms and 2271 patients were identified using only the claims-based algorithms; 779 patients were identified using both algorithms. The positive predictive value was 99.1% (95% confidence interval [CI], 94.2%–100%) for the EHR clinical notes–based algorithms and 94.6% (95% CI, 89.1%–97.8%) to 94.9% (95% CI, 89.8%–97.9%) for the claims-based algorithms. Conclusions The algorithms evaluated in this study identified a real-world cohort of patients with RRMS without evidence of progressive MS that can be studied in clinical research with confidence

MS

thesisAlthough digital teaching files are important to radiology education, there is no current satisfactory solutions for export of Digital Imaging and Communications in Medicine (DICOM) images from Picture Archiving and Communication Systems (PACS) in desktop publishing format. The Vendor-Neutral Digital Teaching File (VNDTF) offers radiology institutions an efficient tool for harvesting interesting cases from PACS without requiring modifications of the PACS configurations. Radiologists push studies from PACS to the VNDTF via the standard DICOM send and the VNDTF server automatically converts the DICOM images into the Joint Photographic Experts Group (JPEG) image format, a desktop publishing format. They can then select the key images and create the interesting case series. The VNDTF tested successfully against multiple unmodified commercial PACS. Using the VNDTF, radiologist with minimal disruption in clinical workflow. Window and level settings continue to present a major obstacle for the conversion of images. In addition, evaluation of the window and level values of the DICOM header demonstrates that they cannot be relied upon for generation of useful JPEG images for key image selection. Therefore, an algorithm was developed to automatically calculate appropriate window and level settings for Magnetic Resonance Imaging (MRI) images. As images are received by the VNDTF server, their pixel data is read and a histogram of the grayscale vales is generated. A portion of the grayscale values is then used to calculate the most appropriate window and level settings for image conversion, allowing for the automated presentation of exported series in JPEG format. The algorithm was evaluated and found effective for appropriate window and level calculation for key image selection. In the creation if interesting radiological cases in a digital teaching file, it is necessary to adjust the window and level settings of an image to effectively display the educational focus. A Web-based applet presents and effective solution for real-time window and level adjustments without leaving the PACS workstation. Optimized images are created as user-defined parameters are based between the applet and sevelet on the VNDTF server

Overthrow: Don't distort the facts

news article addressing the facts of the overthro

Automatic, near real-time reporting of communicable diseases via web syndication

posterReal-time, automatic surveillance of reportable diseases remains an obstacle in Public Health. Reports are generated manually by healthcare provided and sent to local or state public health offices. Web syndication is the way to publish and transmit information to subscribed users. This is typically done via web feeds such as Really Simple Syndication (RSS), via XML specifications. We propose the use of web syndication for automatic reporting of communicable diseases: Public Health Surveillance Syndication (PHSS)