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The purpose of this study was to evaluate the prescribing behavior of psychotropic drugs in non-psychiatric doctors, in the military hospitals of the south zone. The study was conducted over a six-month period, from May to October 2017, with a sample of 48 physicians from different disciplines and various medico-surgical services from the three hospitals. Physicians responded to an anonymous questionnaire mainly on the main drug classes prescribed psychotropic drugs, the main indications, and their frequency of prescription. The majority of physicians involved in all specialties are prescribers at variable rates. The rate of prescribers among neurologists, cardiologists, dermatologists, anesthesiologists, neurosurgeons, visceral surgeons and urologists was 100% while emergency physicians had a rate of 88.8%. In traumatologists, the rate was 75%. For ophthalmologists the rate was 50%. The rest of the specialties prescribed with a rate of less than 50%. The main psychotropic drugs prescribed were antidepressants and hypnotic / anxiolytic benzodiazepines, while the main indications were depression, anxiety disorders, insomnia and agitation. Virtually 44% of these prescribers were the initiators of treatment, and more than half of the presenters indicated that their knowledge of psychotropic drugs was limited to the therapeutic courses they received during their general medical training at the Faculty of Medicine. It is necessary to grant more hours to therapeutic courses at the faculties of medicine and pharmacy and to establish a compulsory continuing education for every doctor in the field of therapeutics and particularly that of psychotropic drugs, in order to ensure the respect of prescription rules for psychotropic drugs.

Background Surface plasmon resonance is a label-free biophysical technique that is widely used in investigating biomolecular interactions, including protein-protein, protein-DNA, and protein-small molecule binding. Surface plasmon resonance is a very powerful tool in different stages of small molecule drug development and antibody characterization. Both academic institutions and pharmaceutical industry extensively utilize this method for screening and validation studies involving direct molecular interactions. In most applications of the surface plasmon resonance technology, one of the studied molecules is immobilized on a microchip, while the second molecule is delivered through a microfluidic system over the immobilized molecules. Changes in total mass on the chip surface is recorded in real time as an indicator of the molecular interactions. Main body Quality and accuracy of the surface plasmon resonance data depend on experimental variables, including buffer composition, type of sensor chip, coupling chemistry of molecules on the sensor surface, and surface regeneration conditions. These technical details are generally included in materials and methods sections of published manuscripts and are not easily accessible using the common internet browser search engines or PubMed. Herein, we introduce a surface plasmon resonance database, www.sprdatabase.info that contains technical details extracted from 5140 publications with surface plasmon resonance data. We also provide an analysis of experimental conditions preferred by different laboratories. These experimental variables can be searched within the database and help future users of this technology to design better experiments. Conclusion Amine coupling and CM5 chips were the most common methods used for immobilizing proteins in surface plasmon resonance experiments. However, number of different chips, capture methods and buffer conditions were used by multiple investigators. We predict that the database will significantly help the scientific community using this technology and hope that users will provide feedback to improve and expand the database indefinitely. Publicly available information in the database can save a great amount of time and resources by assisting initial optimization and troubleshooting of surface plasmon resonance experiments.

The coronavirus COVID-19 pandemic is the worst health crisis of our time and the greatest challenge we are facing since World War II. It has caused a negative impact on engineering and construction industry, and huge uncertainty about the ability to anticipate a planned recovery for the industry. Waiting for an effective recovery that may take a long time, science has to provide solutions to coexist with this virus and resume the work on construction sites. In fact, it is not the first time that health and safety science deals with such a risk of infection, as many guidelines treat the manner to reduce the risk of infection to at-risk employees in construction, renovation, installation, and maintenance activities. This article aims to perform an infection risk analysis, taking into consideration COVID-19 particularities, and to identify control measures to ensure COVID-19 infection prevention and management on site. The study will be based on a probabilistic approach using the Fault Tree Analysis method. © 2020 Weston Medical Publishing. All rights reserved.

Malignancy of the brain and CNS is unfortunately a common diagnosis. A large subset of these lesions tends to be high grade tumors which portend poor prognoses and low survival rates, and are estimated to be the tenth leading cause of death worldwide. The complex nature of the brain tissue environment in which these lesions arise offers a rich opportunity for translational research. Magnetic Resonance Imaging (MRI) can provide a comprehensive view of the abnormal regions in the brain, therefore, its applications in the translational brain cancer research is considered essential for the diagnosis and monitoring of disease. Recent years has seen rapid growth in the field of radiogenomics, especially in cancer, and scientists have been able to successfully integrate the quantitative data extracted from medical images (also known as radiomics) with genomics to answer new and clinically relevant questions. In this paper, we took raw MRI scans from the REMBRANDT data collection from public domain, and performed volumetric segmentation to identify subregions of the brain. Radiomic features were then extracted to represent the MRIs in a quantitative yet summarized format. This resulting dataset now enables further biomedical and integrative data analysis, and is being made public via the NeuroImaging Tools & Resources Collaboratory (NITRC) repository (https://www.nitrc.org/projects/rembrandt_brain/).

Background: Immune-related adverse events (irAEs) often require treatment with high-dose systemic steroids (SS) and other immunosuppressive agents (ISAs). NCCN Guidelines recommend prophylactic antibiotics for Pneumocystis jirovecii pneumonia (PJP) for patients receiving prolonged SS/ISAs. However, there is a paucity of evidence regarding the incidence of opportunistic infections (OIs) and non-OIs and the role of prophylactic antibiotics in patients on SS/ISAs for irAEs. Methods: A retrospective analysis was conducted of patients treated using immune checkpoint inhibitor (ICI) therapy at 5 MedStar Health hospitals from January 2011 to April 2018. OIs were defined per the Infectious Diseases Society of America guidelines for the prevention and treatment of OIs in patients with HIV. The study cohort included patients who received ≥20 mg daily of a prednisone equivalent for ≥4 weeks to manage irAEs. Results: The study cohort identified 112 (15%) of 758 total patients treated using ICIs. Baseline characteristics included the following: median age was 64 years, 74% (n=82) of patients were White, 89% (n=100) had an ECOG performance status ≤1, 61% (n=68) had melanoma, 19% (n=21) had non–small cell lung cancer, 45% (n=50) were treated using an anti–PD-(L)1 ICI, and 33% (n=37) were treated using an anti–PD-1/anti–CTLA-4 combination. The median starting SS dose was 100 mg of a prednisone equivalent, and 25% of patients required additional ISAs, with infliximab (n=15) and mycophenolate mofetil (n=9) being the most common. We found that 20% (n=22) of patients developed any infection, including 7% (n=8) with OIs (oral candidiasis [n=4], nondisseminated varicella zoster infection [n=2], PJP [n=1], and Listeria monocytogenes endophthalmitis [n=1]) and 13% (n=14) with non-OIs (most common: Clostridium difficile and pneumonia [n=5 each]). PJP prophylaxis with sulfamethoxazole/trimethoprim was given to 13% (n=14) patients, of whom 43% (n=6) developed OIs/non-OIs. Conclusions: Our study highlights the fundamental issues for patients on ICI therapy who require SS/ISAs for irAEs: the degree of immunosuppression and the relative risk of OI. We noted a low incidence of OIs overall and breakthrough infections despite PJP prophylaxis. We question whether PJP prophylaxis is efficacious or necessary. Prospective trials are required to answer these questions.

Background Screening for suicidal ideation in high-risk groups such as U.S. veterans is crucial for early detection and suicide prevention. Currently, screening is based on clinical interviews or self-report measures. Both approaches rely on subjects to disclose their suicidal thoughts. Innovative approaches are necessary to develop objective and clinically applicable assessments. Speech has been investigated as an objective marker to understand various mental states including suicidal ideation. In this work, we developed a machine learning and natural language processing classifier based on speech markers to screen for suicidal ideation in US veterans. Methodology Veterans submitted 588 narrative audio recordings via a mobile app in a real-life setting. In addition, participants completed self-report psychiatric scales and questionnaires. Recordings were analyzed to extract voice characteristics including prosodic, phonation, and glottal. The audios were also transcribed to extract textual features for linguistic analysis. We evaluated the acoustic and linguistic features using both statistical significance and ensemble feature selection. We also examined the performance of different machine learning algorithms on multiple combinations of features to classify suicidal and non-suicidal audios. Results A combined set of 15 acoustic and linguistic features of speech were identified by the ensemble feature selection. Random Forest classifier, using the selected set of features, correctly identified suicidal ideation in veterans with 86% sensitivity, 70% specificity, and an area under the receiver operating characteristic curve (AUC) of 80%. Conclusions Speech analysis of audios collected from veterans in everyday life settings using smartphones offers a promising approach for suicidal ideation detection. A machine learning classifier may eventually help clinicians identify and monitor high-risk veterans.

U.S. veterans are 1.5 times more likely to die by suicide than Americans who never served in the military. Considering such high rates, there is an urgent need to develop innovative approaches for objective and clinically applicable assessments to detect individuals at high risk. We hypothesize that speech in suicidal veterans has a range of distinctive acoustic and linguistic features. The purpose of this work is to build an automated machine learning and natural language processing tool to screen for suicidality. Veterans made 588 narrative audio recordings via a mobile app in a real-life setting. In addition, veterans completed self-report psychiatric scales and questionnaires. Recordings were analyzed to extract voice characteristics including prosodic, phonation, and glottal. The audios were also transcribed to extract textual features for linguistic analysis. We evaluated the acoustic and linguistic features using both statistical significance and ensemble feature selection. We also examined the performance of different machine learning algorithms on multiple combinations of features to classify suicidal and non-suicidal audios. Random Forest classifier correctly identified suicidal ideation in veterans based on the combined set of acoustic and linguistic features of speech with 86% sensitivity, 70% specificity, and an area under the receiver operating characteristic curve (AUC) of 80%. Speech analysis of audios collected from veterans in everyday life settings using smartphones is a promising approach for suicidal ideation detection. A machine learning classifier may eventually help clinicians identify and monitor high-risk veterans.

Background Cardiotoxicity is a serious adverse event associated with some of the most effective breast cancer therapies. Currently, it is difficult to predict which patients will develop cardiotoxicity due to the multiplicity of clinical, behavioral, and biological factors involved. MethodsHere we describe an effort to apply biomedical informatics approaches to patient data from MedStar Health’s EHR systems to discover and characterize factors that contribute to cardiotoxicity in a real world breast cancer population.ResultsData wrangling techniques including merging data from disparate clinical systems, data transformation, and de-identification of personal health information (PHI)were appliedto the raw clinical data to produce a structured integrated dataset for predictive analysis and hypothesis generation. Using this dataset as input, weshowed howpredictive models can be developed to identify patients at high risk for cardiotoxicity. ConclusionsWe demonstrate how suchmodels can be used for hypothesis generation and data exploration with the ultimate goal of developing applications for precision medicine.

[Correction Notice: An Erratum for this article was reported in Vol 9(4) of Psychological Trauma: Theory, Research, Practice, and Policy (see record 2016-54154-001). In the article, the names of authors Adil Alaoui and Anas Belouali were misspelled as Adil Aloui and Anas Beloui respectively. All versions of this article have been corrected.] Objective: Veterans suffering from posttraumatic stress disorder (PTSD) may avoid or fail to follow through with a full course of face-to-face mental health treatment for a variety of reasons. We conducted a pilot effectiveness trial of an online intervention for veterans with current PTSD to determine the feasibility, safety, and preliminary effectiveness of an online writing intervention (i.e., Warriors Internet Recovery & EDucation [WIRED]) as an adjunct to face-to-face psychotherapy. Method: Veterans (N = 34) who had served in Iraq or Afghanistan with current PTSD subsequent to deployment-related trauma were randomized to Veterans Affairs (VA) mental health treatment as usual (TAU) or to treatment as usual plus the online intervention (TAU + WIRED). All research participants were recruited from the Trauma Services Program, VA Medical Center, Washington, DC. They completed baseline assessments as well as assessments 12 weeks and 24 weeks after the baseline assessment. The online intervention consisted of therapist-guided writing, using principles of prolonged exposure and cognitive therapy. The intervention was adapted from an evidence-based treatment used in The Netherlands and Germany for individuals who had been exposed to nonmilitary traumas. Results: In addition to showing that the online intervention was both feasible to develop and implement, as well as being safe, the results showed preliminary evidence of the effectiveness of the TAU + WIRED intervention in this patient population, with particular evidence in reducing PTSD symptoms of hyperarousal. Conclusion: With minor modifications to enhance the therapeutic alliance, this intervention should be tested in a larger clinical trial to determine whether this method of online intervention might provide another alternative to face-to-face treatment for veterans with PTSD. (PsycINFO Database Record

2635 Background: Immune Checkpoint Inhibitors (ICIs) are associated with unique immune-related adverse events (irAEs). IrAEs can occur at any timepoint of ICI treatment. Late irAEs are not well reported in the literature. Herein, we attempt to characterize irAEs that occur 6-month, one year and two years after ICI treatment initiation. Methods: We identified patients treated with ICIs (anti-CTLA-4, anti-PD(L)-1 either alone or in combination or with chemotherapy) across Hackensack Meridian Health hospital and MedStar Georgetown University Health systems from 12011 to 4/2018. Patients' baseline demographics, treatment history, and irAEs were collected from EHR. CTCAE V4.03 was used to grade irAEs. Results: We identified 1332 patients treated with 1443 unique ICIs. The ICI therapies were nivolumab 38% (543), pembrolizumab 23% (332), ipilimumab plus nivolumab 12% (180), ipilimumab 11% (161), Atezolizumab 3% (47) and others 13% (180). Tumor types were lung cancer 34% (496), melanoma 27% (389), GI cancers 6% (92), kidney cancer 6% (87), and other cancers 26% (379). The median age was 66 (21-87), age >75 37% (541), Caucasian 67% (970). We identified a total of 911 any grade irAEs among 37% (552) therapies. Among, 911 irAEs, grade 1-2, grade ≥3 and unknown grade irAEs were 39% (572), 12% (182) and 11% (157), respectively. The most common any grade irAEs were skin rash 22% (202), colitis 13% (120), and hepatitis 12% (108). 84% of all irAEs and 85% of ≥ Grade 3 irAEs occurred within 6 months of treatment initiation. Of the 350, patients on active treatment at six months, 37 % (132) and 7% (26) developed any grade and grade ≥3 irAEs, respectively. irAEs that had > 10% of their occurrences after six months were skin rash and colitis 14% each. Other common irAEs were hypothyroidism, hepatitis, joint pain, pruritis and pneumonitis at 7% each. Among 170 patients on active treatment at one year, 37% (62) and 7% (12) developed any grade and grade ≥3 irAEs respectively. irAEs with >10% incidence after one year of treatment were rash 19% and hepatitis 13%. Conclusions: Our RWE findings suggest although 85% irAEs occurs within the first six months of treatment, late irAEs can occur with ICI treatment. The incidence and pattern of late irAEs appears similar to early irAEs, (e.g., skin rash, colitis, hypothyroidism and hepatitis) with pneumonitis being a notable exception. It is uncertain if these results will be influenced by changing patterns of ICI use (e.g. different diseases and/or regimens) over time.[Table: see text]

2641 Background: Immune Checkpoint Inhibitors (ICI) have revolutionized current cancer treatment. Nevertheless, outcomes data across various patient cohorts are lacking. To address this knowledge gap, we conducted a comprehensive analysis of real-world data (RWD) that included patient cohorts traditionally underrepresented in clinical trials. Methods: We identified patients (pts) treated with ICI (anti-CTLA-4, anti-PD(L)1 or their combination at 6 US academic and community hospitals from 1/2011 – 4/2018. Clinical data obtained from EHR and CTCAE V4.03 was used to define immune-related adverse events (irAEs). Results: A total of 1332 pts treated with 1443 unique ICI treatments were included in the cohort. The median age was 66 (21-87), Male 58% (827), Caucasian 70% (1004), African American (AA) 16% (232), other race 14% (207), ECOG PS 0,1 79% (1130), chronic viral infection 5% [hepatitis B (24), hepatitis C (32) and HIV (17)], with BMI > 30 22% (287) and autoimmune disease (AID) 15% (215). Lung cancer (NSCLC) 34% (423), and melanoma 27% (389) were top 2 tumor types and nivolumab 38% (544), pembrolizumab 23% (332), and ipilimumab plus nivolumab 12% (180) were the most common ICI treatments. Overall survival (OS) was worse for patients with ECOG ≥2 (0.34 - 0.63) vs. ECOG 0,1 (1.27 - 1.73, P 75 27% (120), AA 28% (124), Female 50% (224), ECOG PS ≥2 23% (104), BMI >30 15% (62), chronic viral infections 10% (44), and AID 14% (62). The ICI therapies were nivolumab 55% (245), pembrolizumab 23% (102), and atezolizumab 6% (27) and 16% (others). Data is contained in the table. Conclusions: Overall, in our RWD, OS appeared to be similar across above cohorts except poor OS for pts with ECOG ≥2. irAEs also appeared to be similar across cohorts except less with ECOG ≥2. In NSCLC cohort, we noted similar findings except less irAEs in Male cohort. Prospective studies are needed to confirm the above findings.[Table: see text]

Systems medicine leverages complex computational tools and high dimensional data offering the potential for effective individualized diagnosis, prognosis and treatment options. Our flagship web platform, the Georgetown Database of Cancer (G-DOC), was deployed with the goal of enabling translational research by integrating patient characteristics and clinical outcome data with a variety of high-throughput research data in a unified environment. With the goal of improving health outcomes through genomics research, we present G-DOC Plus, our enhanced web platform offering precision medicine, translational research and population genetics workflows. This enhanced platform takes advantage of cloud computing to handle next generation sequencing (NGS) data so that they can be analyzed in the full context of other omics and clinical information. G-DOC Plus uses cloud computing and other advanced computational tools to enable analysis of NGS and medical images in the full context of other omics and clinical information. It allows translational science researchers to explore data one sample at a time, as a sub-cohort of samples; or as a population as a whole, providing the user with a comprehensive view of the data. G-DOC Plus tools have been leveraged in cancer to support detection of prognostic markers for relapse in colorectal cancer samples, and to detect key metabolites related to disease severity; hypothesis generation; biomarker detection and multi-omic analysis, in-silico and population genetics analysis; and to explore somatic mutation and breast cancer MRI images. The long-term vision of G-DOC Plus is to extend this systems medicine platform to hospital networks to provide clinical decision support using multi-omics and relevant clinical information to support personalized patient care. G-DOC Plus was released in October 2014, and is available at: https://gdoc.georgetown.edu. Citation Format: Krithika Bhuvaneshwar, Anas Belouali, Varun Singh, Robert M. Johnson, Lei Song, Adil Alaoui, Michael Harris, Yuriy Gusev, Robert Clarke, Subha Madhavan. G-DOC Plus: A cloud based next-generation systems medicine platform for precision medicine. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B1-44.

Background G-DOC Plus is a data integration and bioinformatics platform that uses cloud computing and other advanced computational tools to handle a variety of biomedical BIG DATA including gene expression arrays, NGS and medical images so that they can be analyzed in the full context of other omics and clinical information. Results G-DOC Plus currently holds data from over 10,000 patients selected from private and public resources including Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and the recently added datasets from REpository for Molecular BRAin Neoplasia DaTa (REMBRANDT), caArray studies of lung and colon cancer, ImmPort and the 1000 genomes data sets. The system allows researchers to explore clinical-omic data one sample at a time, as a cohort of samples; or at the level of population, providing the user with a comprehensive view of the data. G-DOC Plus tools have been leveraged in cancer and non-cancer studies for hypothesis generation and validation; biomarker discovery and multi-omics analysis, to explore somatic mutations and cancer MRI images; as well as for training and graduate education in bioinformatics, data and computational sciences. Several of these use cases are described in this paper to demonstrate its multifaceted usability. Conclusion G-DOC Plus can be used to support a variety of user groups in multiple domains to enable hypothesis generation for precision medicine research. The long-term vision of G-DOC Plus is to extend this translational bioinformatics platform to stay current with emerging omics technologies and analysis methods to continue supporting novel hypothesis generation, analysis and validation for integrative biomedical research. By integrating several aspects of the disease and exposing various data elements, such as outpatient lab workup, pathology, radiology, current treatments, molecular signatures and expected outcomes over a web interface, G-DOC Plus will continue to strengthen precision medicine research. G-DOC Plus is available at: https://gdoc.georgetown.edu. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1010-0) contains supplementary material, which is available to authorized users.

Web-based diabetes management can be used to provide frequent interactions between patients and providers and thus result in improved glycemic control.In a single-center, prospective feasibility study, 16 poorly controlled patients with either type 1 or 2 diabetes mellitus were enrolled to assess the impact of using MyCareTeam, a web-based diabetes management application, for diabetes management. Patients were asked to transfer their blood glucose data electronically, maintain exercise logs, and communicate with their provider via MyCareTeam. The provider gave clinical interventions to optimize blood glucose control and provided feedback via MyCareTeam. Diabetes, nutrition, and exercise information was also available via MyCareTeam.A significant reduction of over 2.22% points in hemoglobin A1C was seen for the total patient population. Differences between moderate/heavy users (n = 8) versus light/never users (n = 8) of MyCareTeam were evaluated for intergroup differences based upon utilization. Moderate/heavy users had a significant 6-month A1C reduction of 3.15 percentage points compared with a reduction of 1.28 percentage points in light/never users. Other secondary end points were improved as well, including systolic blood pressure, diastolic blood pressure, total cholesterol, high-density lipoprotein, low-density lipoprotein, and triglycerides. However, as expected, body mass index levels increased because of aggressive diabetes management with insulin therapy.These results demonstrate a significant treatment effect from the MyCareTeam application. A larger randomized control trial is under way at the Boston Veterans Administration Healthcare System. If these results are confirmed as expected, then web-based diabetes management may prove to be the link to achieving target American Diabetes Association glycemic goals in patients with poorly controlled diabetes.

This project demonstrates use of OCTAVE, an information security risk assessment method, as an approach to the safe design and planning of a teleradiology system. By adopting this approach to project planning, we intended to provide evidence that including information security as an intrinsic component of project planning improves information assurance and that using information assurance as a planning tool produces and improves the general system management plan. Several considerations justify this approach to planning a safe teleradiology system. First, because OCTAVE was designed as a method for retrospectively assessing and proposing enhancements for the security of existing information management systems, it should function well as a guide to prospectively designing and deploying a secure information system such as teleradiology. Second, because OCTAVE provides assessment and planning tools for use primarily by interdisciplinary teams from user organizations, not consultants, it should enhance the ability of such teams at the local level to plan safe information systems. Third, from the perspective of sociological theory, OCTAVE explicitly attempts to enhance organizational conditions identified as necessary to safely manage complex technologies. Approaching information system design from the perspective of information security risk management proactively integrates health information assurance into a project’s core. This contrasts with typical approaches that perceive “security” as a secondary attribute to be “added” after designing the system and with approaches that identify information assurance only with security devices and user training. The perspective of health information assurance embraces so many dimensions of a computerized health information system’s design that one may successfully deploy a method for retrospectively assessing information security risk as a prospective planning tool. From a sociological perspective, this approach enhances the general conditions as well as establishes specific policies and procedures for reliable performance of health information assurance.

The Department of Radiology, Georgetown University Medical Center executed OCTAVEsm, a self-directed information security risk assessment process, as a method for comprehensively planning a new teleradiology system. Originally designed to evaluate retrospectively the organizational and technical risks to the confidentiality, integrity and availability of existing information management programs, Georgetown surmised that OCTAVEsm should be useful as a prospective planning tool to build information assurance into the teleradiology program from its start. Georgetown also wondered how approaching system planning from the perspective of information assurance would affect the general program planning and development. Implementing the OCTAVEsm process, Georgetown identified the teleradiology program's critical assets, described threats to the assurance of those assets, developed and ran vulnerability scans of a system pilot, evaluated the consequences of security breaches for patients, the teleradiology program and Georgetown and developed a risk management plan to mitigate threats to program assets and implement good information assurance practices in program management. As a result, Georgetown built a teleradiology program that complies with regulatory efforts such as the Health Insurance Portability and Accountability Act of 1996 (HIPAA) and integrates health information assurance into the program's core.

Recent advances in technology and the availability of the Internet, high bandwidth and the acceptance of standards and procedures to protect patients' privacy and confidentiality opened up a new era in Telemedicine and networking of all medical information [A. Alaoui, B.A. Levine, P. Beauchene, E.M. McAlinden, M.J.T. Hy, J. Choi, B.S.J.C. Welch, S.K. Mun, Implementation of a home monitoring network for patients with congestitive heart failure, IEEE EMBS Special topic Conference on Information Technology Applications in Biomedicine, Birmingham, 2003]. In this paper we will detail the implementation of a Teleradiology program in Georgetown University that leverages the availability of the Internet, security measures and procedures to protect patient confidentiality.

Introduction An overarching goal of biomedical research is to improve the use and dissemination of rapidly growing biomedical datasets to support precision medicine. Individualized molecular profiling and the identification of predictive biomarkers can powerfully inform the choice of therapies for cancer patients. However, both require integration of extensive molecular, clinical, and pharmacological data, often from disparate and diverse sources. The Georgetown Database of Cancer (G-DOC) was designed and engineered to be a unique multi-omics data analysis platform to enable translational research and precision medicine. Methods G-DOC is home to 61 datasets that contain data from over 10,000 patients across 14 diseases (10 cancers and 4 non-cancers). 1700+ researchers from over 48 different countries worldwide currently use the platform. The data and tools in the G-DOC system have enabled over 40 research publications. G-DOC has the largest public collection of brain cancer patients from NCI Rembrandt dataset (671 patients).G-DOC integrates clinical, transcriptomic, metabolomic, microRNA, next generation sequencing (NGS) data, and MRI medical images with systems-level analysis tools into a single, user-friendly platform. The “Variant Search” feature in G-DOC currently enables exploratory analysis of mutations based on genes, chromosomes, and functional location. A researcher can use this feature to 1) identify clinically actionable mutations in their dataset 2) identify pathways that may be affected by these mutations, and 3) identify novel mutations in their dataset and explore their potential impact on protein function. Results and Conclusion We are currently working on developing features to support the import, integration, search, and retrieval of CLIA/CAP-certified cancer molecular diagnostic (molDx) data. This will enhance G-DOC’s interoperability with clinical and patient molecular profiling data that may be already stored in other databases. Our vision is to continuously improve and expand G-DOC with the long-term vision of supporting integration of informatics techniques into everyday research and practice. Citation Format: Krithika Bhuvaneshwar, Anas Belouali, Shruti Rao, Adil Alaoui, Yuriy Gusev, Robert Clarke, Louis M. Weiner, Subha Madhavan. The Georgetown Database of Cancer (G-DOC): A web-based data sharing platform for precision medicine [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2604. doi:10.1158/1538-7445.AM2017-2604

Traumatic Brain Injury (TBI) is a problem of major medical and socioeconomic significance, although the pathogenesis of its sequelae is not completely understood. As part of a large, multi-center project to study mild and moderate TBI, a database and informatics system to integrate a wide-range of clinical, biological, and imaging data is being developed. This database constitutes a systems-based approach to TBI with the goals of developing and validating biomarker panels that might be used to diagnose brain injury, predict clinical outcome, and eventually develop improved therapeutics. This paper presents the architecture for an informatics system that stores the disparate data types and permits easy access to the data for analysis.

The creation of an integrated biomedical information database requires diverse and flexible schemas. Although relational database systems seem to be an obvious choice for storage, traditional designs of relational schemas cannot support integrated biomedical information in the most effective ways. Therefore, new models for managing diverse and flexible schemas in relational databases are required for such systems. This paper proposes several schema models for integrated biomedical information using relational tables, and presents an experimental evaluation of their efficiency.

The current trend to wards systems medicine will rely heavily on comput ational and bioinformatics capabilities to collect, integrate, and analyze massive amounts of data from disparate sources. The objective is to use this information to make medical decisions that improve patient care. At Georgetown University Medical Center, we are developing an informatics capability to integrate several research and clinical databases. Our long term goal is to provide researchers at Georgetown’s Lombardi Comprehensive Ca ncer Center better access to aggregated molecular and clinical information facilitating the investigation of new hypotheses that impact patient care. We also recognize the need for data mining tools and intelligent agents to help researchers in these efforts. This paper describes our initial work to create a flexible platform for researchers and physicia ns that provides access to information sources including clinical records, medical im ages, genomic, epigenomic, proteomic and metabolomic data. This paper describes the data sources selected for this pilot project and possible approaches to integrating these databases. We present the different database integration models that we considered. We conclude by outlining the proposed Information Model for the project. Keywords: data integration, biomedical information, interopera bility, information retrieval, master data management

We are witnessing a dramatic increase in the use of complex technological systems for better management and exploitation of abundant data from different sources. The Imaging Science and Information Systems (ISIS) Center, a medical research center at Georgetown University is an agile organization that is subject to fast changing requirements and new application deployments. ISIS requires an agile, secure IT infrastructure based on network management best practices that enables rapid business implementations, accommodates innovative deployments and applications, and supports its business plan. Adapting emerging technologies can facilitate communication, productivity and access but, also entails higher security risks, more assets to manage and increased requirements for compliance with established standards and security rules. In this paper we will describe the approaches we took to build and secure an IT infrastructure at the ISIS Center that enables researchers, collaborators, vendors and contractors to work in an environment that hosts systems for different purposes with no compromise to security and data confidentiality.

The Information Management Toolkit (ImTK) Consortium is an open source initiative to develop robust, freely available tools related to the information management needs of basic, clinical, and translational research. An open source framework and agile programming methodology can enable distributed software development while an open architecture will encourage interoperability across different environments. The ISIS Center has conceptualized a prototype data sharing network that simulates a multi-center environment based on a federated data access model. This model includes the development of software tools to enable efficient exchange, sharing, management, and analysis of multimedia medical information such as clinical information, images, and bioinformatics data from multiple data sources. The envisioned ImTK data environment will include an open architecture and data model implementation that complies with existing standards such as Digital Imaging and Communications (DICOM), Health Level 7 (HL7), and the technical framework and workflow defined by the Integrating the Healthcare Enterprise (IHE) Information Technology Infrastructure initiative, mainly the Cross Enterprise Document Sharing (XDS) specifications.

Home healthcare technologies can help patients maintain their independence, allow them to stay in their own homes, improve health outcomes, and reduce costs. Mindmy-Heart, a CMS-funded project at Georgetown University Medical Center, has successfully implemented multiple technologies, such as home monitoring devices and care management tools, to allow for in-home management of congestive heart failure

Resettled immigrants and refugees in the US do not get appropriate healthcare attention because of organizational barriers, cultural differences, poor explanation of health problems and their geographical distribution in the country. These problems often lead to inappropriate diagnoses and inappropriate treatment. Healthcare providers also face significant challenges partly due to the diversity of languages, cultures, and protocols of immigrants and refugees, and partly due to their inexperience in handling diseases not found among the American population. In response to these issues, we have developed the Refugee Health Information Network (RHINtrade) a Web-based information management system that can improve access to medical and public health information related to refugees and immigrants and to facilitate the exchange of this information between distributed health departments, health professionals and service providers in five states

This paper describes a system that uses electronic forms to collect patient and procedure data for clinical trials. During clinical trials, patients are typically required to provide background information such as demographics and medical history, as well as review and complete any consent forms. Physicians or their assistants then usually have additional forms for recording technical data from the procedure and for gathering follow-up information from patients after completion of the procedure. This approach can lead to substantial amounts of paperwork to collect and manage over the course of a clinical trial with a large patient base. By using e-forms instead, data can be transmitted to a single, centralized database, reducing the problem of managing paper forms. Additionally, the system can provide a means for relaying information from the database to the physician on his/her portable wireless device, such as to alert the physician when a patient has completed the pre-procedure forms and is ready to begin the procedure. This feature could improve the workflow in busy clinical practices. In the future, the system could be expanded so physicians could use their portable wireless device to pull up entire hospital records and view other pre-procedure data and patient images.© (2005) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

In designing complex systems, Engineers, Developers and Systems Architects always have to make quantitative assumptions in order to satisfy anticipated loads and expectations of the final product. Many questions are asked before any complex system design that relate to systems performance, infrastructure and components configuration, behavior prediction and bottlenecks fixes. All these questions can be answered using modeling and simulation tools that allow engineers to predict systems behaviors in different settings and optimize systems in production by identifying bottlenecks and flaws in the infrastructure or workflow.

Internet-enabled systems gained spread use in different areas to accomplish business transactions and leverage the available bandwidth and technologies to facilitate multiple tasks while reducing the cost and increasing speed and efficiency. The healthcare sector is no exception; it is adopting different technology trends and taking advantage of the available infrastructure to enable providers (healthcare professionals) and consumers (patients and their families) access to cutting edge technologies. Some of the trends driving the evolution of healthcare are the maturity of technology, availability of the Internet, development of home health monitoring devices to remotely monitor patients' health and the acceptance of standards and procedures to protect patients' privacy and confidentiality. All these are making the home health monitoring grow to be an integral part of healthcare delivery, promising low cost and patient satisfaction. In this paper we will talk about the technical design and infrastructure that we developed to provide congestive heart monitoring network using the Internet.

This case study details the experience of system engineers of the Imaging Science and Information Systems Center, Georgetown University Medical Center (ISIS) and radiologists from the department of Radiology in the implementation of a new Teleradiology system. The Teleradiology system enables radiologists to view medical images from remote sites under those circumstances where a resident radiologist needs assistance in evaluating the images after hours and during weekends; it also enables clinicians access to patients’ medical images from different workstations within the hospital. The Implementation of the Teleradiology project was preceded by an evaluation phase to perform testing, gather users feedback using a web site and collect information that helped eliminate system bugs, complete recommendations regarding minimum hardware configuration and bandwidth and enhance system’s functions, this phase included a survey-based system assessment of computer configurations, Internet connections, problem identification, and recommendations for improvement, and a testing period with 2 radiologists and ISIS engineers; The second phase was designed to launch the system and make it available to all attending radiologists in the department. To accomplish the first phase of the project a web site was designed and ASP pages were created to enable users to securely logon and enter feedback and recommendations into an SQL database. This efficient, accurate data flow alleviated networking, software and hardware problems. Corrective recommendations were immediately forwarded to the software vendor. The vendor responded with software updates that better met the needs of the radiologists. The ISIS Center completed recommendations for minimum hardware and bandwidth requirements. This experience illustrates that the approach used in collecting the data and facilitating the teamwork between the system engineers and radiologists was instrumental in the project’s success. Major problems with the Teleradiology system were discovered and remedied early by linking the actual practice experience of the physicians to the system improvements.