The VA Point-of-Care Precision Oncology Program: Balancing Access with Rapid Learning in Molecular Cancer Medicine.

The Department of Veterans Affairs (VA) recognized the need to balance patient-centered care with responsible creation of generalizable knowledge on the effectiveness of molecular medicine tools. Embracing the principles of the rapid learning health-care system, a new clinical program called the Precision Oncology Program (POP) was created in New England. The POP integrates generalized knowledge about molecular medicine in cancer with a database of observations from previously treated veterans. The program assures access to modern genomic oncology practice in the veterans affairs (VA), removes disparities of access across the VA network of clinical centers, disseminates the products of learning that are generalizable to non-VA settings, and systematically presents opportunities for patients to participate in clinical trials of targeted therapeutics.

Implementation of a Precision Oncology Program as an Exemplar of a Learning Health Care System in the VA.

The program determines and disseminates precision oncology best practices; enhances patient and provider engagement; and fosters collaboration among the VA, National Cancer Institute, academia, other health care systems, and industry to provide cancer patients with access to clinical trial participation.

Natural Language Processing for Real-Time Catheter-Associated Urinary Tract Infection Surveillance: Results of a Pilot Implementation Trial.

BACKGROUND: Incidence of catheter-associated urinary tract infection (CAUTI) is a quality benchmark. To streamline conventional detection methods, an electronic surveillance system augmented with natural language processing (NLP), which gathers data recorded in clinical notes without manual review, was implemented for real-time surveillance. OBJECTIVE: To assess the utility of this algorithm for identifying indwelling urinary catheter days and CAUTI. SETTING: Large, urban tertiary care Veterans Affairs hospital. METHODS: All patients admitted to the acute care units and the intensive care unit from March 1, 2013, through November 30, 2013, were included. Standard surveillance, which includes electronic and manual data extraction, was compared with the NLP-augmented algorithm. RESULTS: The NLP-augmented algorithm identified 27% more indwelling urinary catheter days in the acute care units and 28% fewer indwelling urinary catheter days in the intensive care unit. The algorithm flagged 24 CAUTI versus 20 CAUTI by standard surveillance methods; the CAUTI identified were overlapping but not the same. The overall positive predictive value was 54.2%, and overall sensitivity was 65% (90.9% in the acute care units but 33% in the intensive care unit). Dissimilarities in the operating characteristics of the algorithm between types of unit were due to differences in documentation practice. Development and implementation of the algorithm required substantial upfront effort of clinicians and programmers to determine current language patterns. CONCLUSIONS: The NLP algorithm was most useful for identifying simple clinical variables. Algorithm operating characteristics were specific to local documentation practices. The algorithm did not perform as well as standard surveillance methods.

Educating translational researchers in research informatics principles and methods: an evaluation of a model online course and plans for its dissemination.

Translational research generates and/or uses very large amounts of diverse data. Informatics principles and methods address datasets that are large and complex, whereas few translational researchers know these principles and methods and many cannot design, carry out, or analyze the results of these studies optimally. With few exceptions, informatics education has not been directed to researchers, especially established researchers. To fill this gap, we carried out a formal needs assessment of research informatics education of translational researchers, focusing on established researchers. Using the results, we developed a model curriculum for educating researchers in research informatics and a first generation model online course in research informatics for researchers. We are completing a formal evaluation of this online course with a diverse group of translational researchers. From the results of this evaluation, we will create a second version of the online course, a dissemination plan to make it available to researchers nationally, and a plan to enhance the course over time. We will discuss the implications for the future of translational research and research informatics.

Development of DASH Mobile: a mHealth lifestyle change intervention for the management of hypertension.

Several landmark studies based on the DASH diet have established the effectiveness of a lifestyle approach to blood pressure control that emphasizes a diet rich in fruits and vegetables with moderate portions of low-fat dairy and lean protein along with increased physical activity and reduced sodium intake. However, this evidence base remains underused due feasibility limitations of implementing these intense in-person interventions and poor engagement with desktop computer based versions. Mobile technologies such as smartphones and wireless sensors have the ability to deliver behavioral interventions in-the-moment and with reduced user burden. DASH Mobile is a new mHealth system being developed to deliver this evidence-based lifestyle intervention to hypertensive patients. The system consists of an Android based "app" that facilitates easy tracking of DASH food portions, integrated Bluetooth blood pressure, weight and pedometer monitoring, goal setting, simple data visualizations and multimedia video clips to train patients in the basic concepts of the lifestyle change plan. At present, the system is undergoing usability testing with a pilot clinical trial planned for Spring 2013.