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A report published in 2000 from the Institute of Medicine revealed that medical errors were a leading cause of patient deaths, and urged the development of error detection and reporting systems. The field of radiation oncology is particularly vulnerable to these errors due to its highly complex process workflow, the large number of interactions among various systems, devices, and medical personnel, as well as the extensive preparation and treatment delivery steps. Natural language processing (NLP)-aided statistical algorithms have the potential to significantly improve the discovery and reporting of these medical errors by relieving human reporters of the burden of event type categorization and creating an automated, streamlined system for error incidents. In this paper, we demonstrate text-classification models developed with clinical data from a full service radiation oncology center (test center) that can predict the broad level and first level category of an error given a free-text description of the error. All but one of the resulting models had an excellent performance as quantified by several metrics. The results also suggest that more development and more extensive training data would further improve future results. Published version

In the United States, longitudinal personal health record (LPHR) adoption rate has been low in the past two decades. Patients’ privacy and security concern is a major roadblock. Patients like to control the privacy and security of their own LPHR distributed across multiple information systems at various facilities. However, little is known how a scalable and interoperable LPHR can be constructed with patient-controlled security and privacy that both patients and providers trust. As an effort to increase LPHR adoption rate and improve the efficiency and quality of care, we propose a blockchain-enabled trusted LPHR (BET-LPHR) design in which security and privacy are protected while patients have full control of the access permissions. Two limitations associated with the proposed design are discussed. Options and practical resolutions are presented to stimulate future research.

BACKGROUND The longitudinal personal health record (LPHR) is a foundation for managing patients’ health, but we do not have such a system in the US except for the patients in the Veterans Affairs (VA) Health Care service. The fact that individual health records are scattered in multiple health care facilities without any standards make it very difficult to build such system. In addition, patients have been raising privacy and ethical concerns related to consent and granular control of LPHR. Consent is desired to be specific. However, the current consent in the industry is not that granular. At most, there is an opt-in or opt-out choice. “A scalable and interoperable LPHR is desired with patient-controlled privacy and confidentiality that preserves patients’ health information integrity and availability” [1]. To patients, consent, data security and privacy are translated to trustfulness. Patients want to be engaged and ensure only authorized people can view their personal health records with patient-managed granularity. Solving this challenge of patient-controlled consent granularity on LPHR is an important step in making LPHR useful for patient care. OBJECTIVE This research aims to design a secure LPHR with patient-controlled consent granularity, data security and privacy that both patients and providers can trust in the United States. METHODS Built upon our prior work of the blockchain-enabled next generation access control (BeNGAC) model, we designed a blockchain-enabled personalized health record (BEPHR) sharing platform with patient-controlled consent granularity capability. We implemented the construct for a patient’s LPHR with a Web-based application prototype consisting of two health care organizations with their EHRs. RESULTS In this work, we proposed a BEPHR model trusted by patients and health care providers and implemented a Web-based BEPHR sharing platform with patient-controlled consent granularity. Consent, security and privacy of BEPHR are ensured by the merits of the BeNGAC model. The instantiation of the designed model suggested the feasibility of combining emerging blockchain technology with next generation access control model to tackle a longstanding health care LPHR problem CONCLUSIONS Our BEPHR solution provides patients with granularity, security and privacy they can trust and strengthens informed consent process. Jointly, the blockchain technology and NGAC offer security, privacy and confidentiality, data integrity, auditability, scalability, distributedness, patient consent autonomy, and zero-trust capabilities. The always-validate next generation access control model prevents the insider threats. A Fast Healthcare Interoperability Resources (FHIR) interface is incorporated to show readiness of LPHR interoperability and integration.

The First International Conference in Systems and Network Medicine gathered together 200 global thought leaders, scientists, clinicians, academicians, industry and government experts, medical and graduate students, postdoctoral scholars and policymakers. Held at Georgetown University Conference Center in Washington D.C. on September 11-13, 2019, the event featured a day of pre-conference lectures and hands-on bioinformatic computational workshops followed by two days of deep and diverse scientific talks, panel discussions with eminent thought leaders, and scientific poster presentations. Topics ranged from: Systems and Network Medicine in Clinical Practice; the role of -omics technologies in Health Care; the role of Education and Ethics in Clinical Practice, Systems Thinking, and Rare Diseases; and the role of Artificial Intelligence in Medicine. The conference served as a unique nexus for interdisciplinary discovery and dialogue and fostered formation of new insights and possibilities for health care systems advances.

IntroductionPrompt and effective combat casualty care is essential for decreasing morbidity and mortality during military operations. Similarly, accurate documentation of injuries and treatments enables quality care, both in the immediate postinjury phase and the longer-term recovery. This article describes efforts to prototype a Military Medic Smartphone (MMS) for use by combat medics and other health care providers who work in austere environments.Materials and MethodsThe MMS design builds on previous electronic health record systems and is based on observations of medic workflows. It provides several functions including a compact yet efficient physiologic monitor, a communications device for telemedicine, a portable reference library, and a recorder of casualty care data from the point of injury rearward to advanced echelons of care. Apps and devices communicate using an open architecture to support different sensors and future expansions.ResultsThe prototype MMS was field tested during live exercises to generate qualitative feedback from potential users, which provided significant guidance for future enhancements.ConclusionsThe widespread deployment of this type of device will enable more effective health care, limit the impact of battlefield injuries, and save lives.

In the past two decades, longitudinal personal health record (LPHR) adoption rate has been low in the United States. Patients’ privacy and security concerns was the primary negative factor impacting LPHR adoption. Patients desire to control the privacy of their own LPHR in multiple information systems at various facilities. However, little is known how to model and construct a scalable and interoperable LPHR with patient-controlled privacy and confidentiality that preserves patients’ health information integrity and availability. Understanding this problem and proposing a practical solution are considered important to increase LPHR adoption rate and improve the efficiency as well as the quality of care. Even though having the state-of-the-art encryption methodologies being applied to patients’ data, without a set of secure access control policies being implemented, LPHR patient data privacy is not guaranteed due to insider threats. We proposed a definition of “secure LPHR” and argued LPHR is secure when the security and privacy requirements are fulfilled through adopting an access control security model. In searching for an access control model, we enhanced the National Institute of Standards and Technology (NIST) next generation access control (NGAC) model by replacing the centralized access control policy database with a permissioned blockchain peer-to-peer database, which not only eases the race condition in NGAC, but also provides patient-managed access control policy update capability. We proposed a novel blockchain-enabled next generation access control (BeNGAC) model to protect security and privacy of LPHR. We sketched BeNGAC and LPHR architectures and identified limitations of the design. Published version

We developed a DICOM dataset that can be used to evaluate the performance of de-identification algorithms. DICOM objects (a total of 1,693 CT, MRI, PET, and digital X-ray images) were selected from datasets published in the Cancer Imaging Archive (TCIA). Synthetic Protected Health Information (PHI) was generated and inserted into selected DICOM Attributes to mimic typical clinical imaging exams. The DICOM Standard and TCIA curation audit logs guided the insertion of synthetic PHI into standard and non-standard DICOM data elements. A TCIA curation team tested the utility of the evaluation dataset. With this publication, the evaluation dataset (containing synthetic PHI) and de-identified evaluation dataset (the result of TCIA curation) are released on TCIA in advance of a competition, sponsored by the National Cancer Institute (NCI), for algorithmic de-identification of medical image datasets. The competition will use a much larger evaluation dataset constructed in the same manner. This paper describes the creation of the evaluation datasets and guidelines for their use., Measurement(s) Deidentification • Clinical Data Technology Type(s) data synthesis • digital curation Factor Type(s) imaging type Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.14802774

(1) Background: Prostate cancer risk classifiers have been used for predicting surgical and radiation therapy outcomes, however, a classifier for predicting biochemical recurrence (BCR) in patients undergoing stereotactic body radiation therapy (SBRT) is not available. We attempted to develop a model that creates a risk classifier to predict BCR in patients considering SBRT. (2) Methods: We studied the outcomes of 809 patients treated with SBRT between August 2007 and November 2016. We used Cox regression analysis with time to BCR as the outcome to develop a model that calculates a prostate clinical outlook (PCO) score based on age at diagnosis, clinical-radiological staging, and a modified risk level. We then created the PCO classifier application, which uses the model we created to categorize patients into risk groups based on multiple factors. We assessed the concordance index (c-index) to determine the accuracy of the PCO classifier application and compared the results to the D&rsquo, Amico and Kattan nomogram classifications. (3) Results: The calculated PCO scores ranged from 0 to 156 points. The PCO classifier application categorized patients into three risk-groups, with 5-year BCR-free survival rates of 98.3% for low risk (n = 137), 95.4% for intermediate risk (n = 570), and 86.4% for high risk (n = 102). We demonstrated the improved prognostic power of the PCO classifier application, with a c-index of 0.75 (training set) and 0.67 (validation set), the c-index of the Kattan nomogram was 0.62 and 0.63, respectively, and that of the D&rsquo, Amico classifier was 0.64 and 0.64, respectively. (4) Conclusions: The PCO classifier application is a predictive tool for employing readily available clinical parameters to stratify prostate cancer patients and to predict the probability of BCR after SBRT.

The purpose of this study is to evaluate radiologists' performance in detecting actionable nodules on chest CT when aided by a pulmonary vessel image-suppressed function and a computer-aided detection (CADe) system.A novel computerized pulmonary vessel image-suppressed function with a built-in CADe (VIS/CADe) system was developed to assist radiologists in interpreting thoracic CT images. Twelve radiologists participated in a comparative study without and with the VIS/CADe using 324 cases (involving 95 cancers and 83 benign nodules). The ratio of nodule-free cases to cases with nodules was 2:1 in the study. Localization ROC (LROC) methods were used for analysis.In a stand-alone test, the VIS/CADe system detected 89.5% and 82.0% of malignant nodules and all nodules no smaller than 5 mm, respectively. The false-positive rate per CT study was 0.58. For the reader study, the mean area under the LROC curve (LROCAUC) for the detection of lung cancer significantly increased from 0.633 when unaided by VIS/CADe to 0.773 when aided by VIS/CADe (p0.01). For the detection of all clinically actionable nodules, the mean LROC-AUC significantly increased from 0.584 when unaided by VIS/CADe to 0.692 when detection was aided by VIS/CADe (p0.01). Radiologists detected 80.0% of cancers with VIS/CADe versus 64.45% of cancers unaided (p0.01); specificity decreased from 89.9% to 84.4% (p0.01). Radiologist interpretation time significantly decreased by 26%.The VIS/CADe system significantly increased radiologists' detection of cancers and actionable nodules with somewhat lower specificity. With use of the VIS/CADe system, radiologists increased their interpretation speed by a factor of approximately one-fourth. Our study suggests that the technique has the potential to assist radiologists in the detection of additional actionable nodules on thoracic CT.

The purpose of this investigation is to determine the relative contribution of five types of social support to improved patient health. This analysis suggests that emotional and esteem social support messages are associated with improved patient health as measured by a decrease in average blood glucose levels among diabetic patients. In addition, when two system feature variables, two system use variables, two measures of learning, one measure of self-efficacy, and one measure of affect toward their HCP were added to the baseline model, a third significant factor emerged. Perceptions about learning about diabetes from reading the digital messages sent by their HCP also predicted improved patient health. Cognitive-Emotional Theory of Esteem Support Messages suggests a combination of esteem social support and emotional social support messages enhanced our ability to predict improved patient health by change in patient hemoglobin A1c (HbA1c) scores. While a nonrandomized prospective study, this investigation provides support for the notion that provider-patient interaction is related to improved patient health and that both emotional and esteem social support messages play a role in that process. Finally, the study suggests some types of social support are and other types are not associated with improved patient health; this is consistent with the optimal matching hypothesis.

This investigation examined the impact of social support messages on patient health outcomes. Forty-one American Indian, Alaska Native, and Native Hawaiian patients received a total of 618 e-mail messages from their healthcare provider (HCP). The e-mail messages were divided into 3,565 message units and coded for instances of emotional social support. Patient glycosulated hemoglobin scores (HbA1c) showed significantly improved glycemic control and emotional social support messages were associated with significant decreases in HbA1c values. Patient involvement with the system, measured by system login frequency and the frequency of uploaded blood glucose scores to the HCP, did not predict change in HbA1c.

The addition of a pair of magnetic field gradient pulses had initially enabled the measurement of spin motion to nuclear magnetic resonance (NMR) experiments. In the adaptation of diffusion weighted (DW)-NMR techniques to magnetic resonance imaging (MRI), the taxonomy of mathematical models is divided in two categories: model matching and spectral methods. In this review, the methods are summarized starting from early DW NMR models followed up with their adaptation to DW MRI. Finally, a newly introduced Fourier analysis based unifying theory, so-called Complete Fourier Direct MRI, is included to explain the mechanisms of existing methods. © 2012 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 22, 44–52, 2012 © 2012 Wiley Periodicals, Inc.

The Patient-Centered Medical Home (PCMH) is a primary care model that provides coordinated and comprehensive care to patients to improve health outcomes. This paper addresses practical issues that arise when transitioning a traditional primary care practice into a PCMH recognized by the National Committee for Quality Assurance (NCQA). Individual organizations' experiences with this transition were gathered at a PCMH workshop in Alexandria, Virginia in June 2010. An analysis of their experiences has been used along with a literature review to reveal common challenges that must be addressed in ways that are responsive to the practice and patients’ needs. These are: NCQA guidance, promoting provider buy-in, leveraging electronic medical records, changing office culture, and realigning workspace in the practice to accommodate services needed to carry out the intent of PCMH. The NCQA provides a set of standards for implementing the PCMH model, but these standards lack many specifics that will be relied on in location situations. While many researchers and providers have made critiques, we see this vagueness as allowing for greater flexibility in how a practice implements PCMH.

The patient-centered medical home (PCMH) is a primary care model that aims to provide quality care that is coordinated, comprehensive, and cost-effective. PCMH is hinged upon building a strong patient-provider relationship and using a team-based approach to care to increase continuity and access. It is anticipated that PCMH can curb the growth of health care costs through better preventative medicine and lower utilization of services. The Navy, Air Force, and Army are implementing versions of PCMH, which includes the use of technologies for improved documentation, better disease management, improved communication between the care teams and patients, and increased access to care. This article examines PCMH in the Military Health System by providing examples of the transition from each of the branches. The authors argue that the military must overcome unique challenges to implement and sustain PCMH that civilian providers may not face because of the deployment of patients and staff, the military's mission of readiness, and the use of both on-base and off-base care by beneficiaries. Our objective is to lay out these considerations and to provide ways that they have been or can be addressed within the transition from traditional primary care to PCMH.

Background: When a patient presents with localized prostate cancer, referral for radiation oncology consultation includes a discussion of likely outcomes of therapy. Among current radiation treatments for prostate cancers, hypo-fractionated stereotactic body radiation therapy (SBRT) has gained clinical acceptance based on efficacy, short duration of treatment, and the potential radiobiological advantages. The Prostate Clinical Outlook Visualization System (PCOVS) was developed to provide the patient and the clinician with a tool to visualize probable treatment outcomes using institutional, patient specific data for comparing results of treatment. Methods: We calculated the prostate cancer outcomes—for each prospective patient using the EPIC-26 quality of life parameters based on clinical outcomes data of 580 prostate cancer patients who were treated with SBRT. We applied Kaplan-Meier analysis using the ASTRO definition for biochemical recurrence (BCR) free survival and likely outcome and the PCOVS nomogram to calculate parameters for quality of life. Open-source R, RShiny, and MySQL were used to develop a modularized architecture system. Results: The PCOVS presents patient specific risk scores in a gauge chart style and risk free probability bar plots to compare the treatment data of patients treated with SBRT. The PCOVS generates reports, in PDF, which consists of a comparison charts of risk free probabilities late effects and gauge charts of risk scores. This system is now being expanded as a web-based service to patients. Conclusions: The PCOVS visualized patient specific likely outcomes were compared to treatment data from a single department, helping the patient and the clinician to visualize likely outcomes. The PCOVS approach can be expanded to other specialties of oncology with the flexible, modularized architecture, which can be customized by changing independent modules.

Wider information content of multi-modal biomedical imaging is advantageous for detection, diagnosis and prognosis of various pathologies. However, the necessity to evaluate a large number images might hinder these advantages and reduce the efficiency. Herein, a new computer aided approach based on the utilization of feature space (FS) with reduced reliance on multiple image evaluations is proposed for research and routine clinical use. The method introduces the physician experience into the discovery process of FS biomarkers for addressing biological complexity, e.g., disease heterogeneity. This, in turn, elucidates relevant biophysical information which would not be available when automated algorithms are utilized. Accordingly, the prototype platform was designed and built for interactively investigating the features and their corresponding anatomic loci in order to identify pathologic FS regions. While the platform might be potentially beneficial in decision support generally and specifically for evaluating outlier cases, it is also potentially suitable for accurate ground truth determination in FS for algorithm development. Initial assessments conducted on two different pathologies from two different institutions provided valuable biophysical perspective. Investigations of the prostate magnetic resonance imaging data resulted in locating a potential aggressiveness biomarker in prostate cancer. Preliminary findings on renal cell carcinoma imaging data demonstrated potential for characterization of disease subtypes in the FS.

The exchange of information between a Radiology Information System (RIS) and a PACS is essential to optimizing the utility of a PACS. Some of the benefits awarded by implementing an interface include a reduction or elimination of repetitious data entry, the availability of more accurate information on the PACS, a reduction in workload for the technologists, registration clerks, transcriptionists, etc, and the availability of more accurate data for automating the PACS. This paper discusses the Georgetown experience of interfacing an HIS/RIS and PACS, by describing the development of the interface and its impact on clinical operations.

e16549 Background: When a patient presents with localized prostate cancer, referral for radiation oncology consultation includes a discussion of likely outcomes of therapy. Among current radiation treatments for prostate cancers, hypo-fractionated stereotactic body radiation therapy (SBRT) has gained clinical acceptance based on efficacy, short duration of treatment and potential radiobiological advantages. The prostate Clinical Outlook (PCO) visualization system was developed to provide the patient and the clinician with a tool to visualize probable treatment outcomes using institutional, patient specific data for comparing results of treatment. Methods: We calculated the prostate cancer outlooks for each prospective patient using the EPIC-26 quality of life parameters based on clinical outcomes data of 580 cyberknife SBRT treated prostate cancer patients. We applied Kaplan-Meier analysis using the ASTRO method for disease free likely outcome and the PCO nomogram to calculate parameters for quality of life. Open source R, R/Shiny, and MySQL were used to develop a modularized architecture system. Results: PCO presents patient specific risk scores in a gauge chart style and risk free probability bar plots to compare treatment data of cyberknife patients. PCO generates reports, in PDF and HTML, which consist of a comparison chart of risk free probabilities and gauge charts of risk scores. This system is now being expanded as a web-based service to patients. Conclusions: PCO visualized patient specific likely outcomes were compared to treatment data from a single department, helping the patient and the clinician to visualize likely outcomes. The PCO approach can be expanded to other specialties of oncology with the flexible, modularized architecture which can be customized by changing parameter variables for the modules.

e16552 Background: Although prostate cancer risk classifiers have been developed for predicting surgical and radiation therapy outcomes, a classifier suitable for predicting biochemical recurrence (BCR) in patients undergoing stereotactic body radiation therapy (SBRT) remains to be defined. SBRT is delivered in large fractions of highly conformal radiation therapy and such treatments are believed to be radiobiologically more effective in treating prostate cancers. The aim of this study is to develop a new classifier specifically for informing patients electing to undergo prostate cancer treatment with SBRT. Methods: We have studied outcomes of 809 patients treated with SBRT between August 2007 and November 2016 at MedStar-Georgetown University Hospital. A Cox regression to BCR was performed and the Prostate Clinical Outlook (PCO) score was calculated at diagnosis based on age at diagnosis, clinical-radiological staging, pre-treatment PSA and Gleason score. Accuracy of the PCO classifier was assessed with concordance (c)-indexes. The results were also compared to classifications by D’Amico and National Comprehensive Cancer Network (NCCN) recurrence risk groups. Results: PCO total scores range from zero to 156 points. The PCO classifier splits patients into 3 risk-groups with the following 5-year BCR-free survival: for low-risk 98%; for intermediate-risk 95%; for high-risk 86%. Our classifier outperforms D’Amico and NCCN for all of the evaluated end-points, with concordance indices of 74 % versus 64 % and 66%, respectively. Conclusions: The PCO classifier is a potential tool for employing readily available parameters to stratify prostate cancer patients and to predict probabilities of BCR after SBRT.

Although research has shown that telemedicine services for blood glucose control can be useful in managing diabetes, the relationships among user satisfaction, compliance, and clinical outcomes have not been well studied. A positive assessment of telemedicine services can improve user satisfaction, which can increase compliance and improve clinical outcomes. These relationships are validated with actual telemedicine projects for diabetes management.The assessment of user satisfaction for telemedicine services is composed of the following six variables: usefulness, ease of use, compatibility, facilitating conditions, accessibility, and intimacy. The relationship between user satisfaction and compliance or hemoglobin A1c (HbA1c) improvement was analyzed. Data were collected from 81 type 2 diabetic patients who used telemedicine services. We used multiple regression analysis, logistic regression analysis, simple linear regression, and the Wilcoxon signed-rank test to analyze the data.The user assessments of the telemedicine services were very high, ranging from 5.8 to 6 points. Of the six variables, compatibility, ease of use, intimacy, and usefulness had a positive effect on overall satisfaction (p0.05). Overall satisfaction and compliance were positively correlated with HbA1c improvement. In addition, income level was also an important variable for overall satisfaction.Our results indicate that patient assessments of telemedicine services are important factors for clinical outcome improvement. In addition, higher satisfaction and more frequent self-assessments can improve clinical outcomes.

The Department of Defense (DoD) has been engaged in the development and deployment of the longitudinal health record (LHR). It has achieved remarkable technological success by handling vast amounts of patient data coming from clinical sites around the globe. Interoperability between DoD and VA has improved and this information sharing capability is expected to continue to expand as the defense health information system becomes an integral part of the national network. On the other hand, significant challenges remain in terms of user acceptance, ability to incorporate innovations, software acquisition methodology, and overall systems architecture.

Fee-for-service reimbursement has fragmented the healthcare system. Providers are paid based on the number of services rendered instead of quality, leading to the cost of care rising at a faster rate than its value. One approach to counter this is the Patient-Centered Medical Home (PCMH), a primary care model that emphasizes team-based medicine, a partnership between patients and providers, and expanded access and communication. The transition to PCMH is facilitated by innovative technologies, such as telemedicine for additional services, electronic medical records to document patients' health needs, and online portals for electronic visits and communication between patients and providers. Implementing these technologies involves tremendous investment of funds and time from practices and healthcare organizations. Although PCMH does not require such technologies, they facilitate its success, as care coordination and population management necessitated by the model are difficult to do without. This article argues that there is a paradox in PCMH and technology is at its center. Although PCMH intends to be cost effective by reducing hospital admissions and ER visits through providing better preventative services, it is actually a financial risk due to the very real upfront costs of implementing and sustaining technologies needed to carry out the intent of the PCMH model, which may not be made up immediately, if ever. This article delves into the rationale behind why payers, providers, and patients have adopted PCMH regardless of this risk and in doing so, maps out the roles that innovative technologies play in the conversion to PCMH.

Blast-related traumatic brain injury (bTBI) and post-traumatic stress disorder (PTSD) have been of particular relevance to the military and civilian health care sectors since the onset of the Global War on Terror, and TBI has been called the "signature injury" of this war. Currently there are many questions about the fundamental nature, diagnosis, and long-term consequences of bTBI and its relationship to PTSD. This workshop was organized to consider these questions and focus on how brain imaging techniques may be used to enhance current diagnosis, research, and treatment of bTBI. The general conclusion was that although the study of blast physics in non-biological systems is mature, few data are presently available on key topics such as blast exposure in combat scenarios, the pathological characteristics of human bTBI, and imaging signatures of bTBI. Addressing these gaps is critical to the success of bTBI research. Foremost among our recommendations is that human autopsy and pathoanatomical data from bTBI patients need to be obtained and disseminated to the military and civilian research communities, and advanced neuroimaging used in studies of acute, subacute, and chronic cases, to determine whether there is a distinct pathoanatomical signature that correlates with long-term functional impairment, including PTSD. These data are also critical for the development of animal models to illuminate fundamental mechanisms of bTBI and provide leads for new treatment approaches. Brain imaging will need to play an increasingly important role as gaps in the scientific knowledge of bTBI and PTSD are addressed through increased coordination, cooperation, and data sharing among the academic and military biomedical research communities.

Combat medics have a vital role in the protection of wounded soldiers in the battlespace. However, their duties expose them to great risks. Furthermore, these medics are a limited resource and must be carefully tasked in order to provide maximum benefit to their units. For these reasons, we are applying the American GNC Corporation's (AGNC) Coremicro(R) Robotic System for autonomous evaluation of battlefield casualties. These robots are intended to navigate to a casualty, determine his/her overall health status, and perform limited diagnostic imaging in order to assess the presence of injuries that would prevent or complicate extraction. In this paper, we describe development work on some of the key components of the proposed robotic system, namely the overall concept of operations (ConOps) and initial testing of infrared and ultrasound imaging cameras. When fully deployed, this system will act as a medical force multiplier, enabling improved care of wounded soldiers and protecting the health and safety of military medical personnel.

During the "The National Forum on the Future of the Defense Health Information System," a track focusing on "Systems Architecture and Software Engineering" included eight presenters. These presenters identified three key areas of interest in this field, which include the need for open enterprise architecture and a federated database design, net centrality based on service-oriented architecture, and the need for focus on software usability and reusability. The eight panelists provided recommendations related to the suitability of service-oriented architecture and the enabling technologies of grid computing and Web 2.0 for building health services research centers and federated data warehouses to facilitate large-scale collaborative health care and research. Finally, they discussed the need to leverage industry best practices for software engineering to facilitate rapid software development, testing, and deployment.

The National Forum on the Future of Defense Health Information Systems (National Forum) held in Washington, DC in March 2008 provided a unique opportunity to conduct a case study of developing a large complex health information system. The concept of the health information system has evolved from the days to electronic hospital information system (HIS) to longitudinal health record (LHR) which refers to a complete information system that adequately captures what occurs to service persons from “cradle to grave”, including but not limited to the clinical record. As originally imagined after the first Gulf War, the LHR is supposed to serve many functions in occupational health, medical logistics, health planning, and medical surveillance as well as clinical care. The LHR has become central to both DoD and VA health systems. As LHR emerges, the interoperable national health information system is becoming critical for the future. DoD and VA collaboration has accelerated in the last two years as exemplified by current efforts to exchange information between AHLTA (DoD Health IT system) and VistA (VA health IT system). With almost 70% of defense and veteran healthcare happening outside of the DoD/VA system, the LHR must accept information from the private sector not only Government sources. Given their size and position, DoD and VA have the potential to lead the nation in supporting standards for national health information network (NHIN). With that in mind, the VA and DoD are coordinating their efforts with the Office of the National Coordinator Health Information Technology (ONCHIT) that is leading the nation's interoperability and information exchange effort. Knowledge discovery and management is vital to bringing benefits of data repository to the clinicians and researchers. The growth of clinical data, image files, physiologic data, and omic data among others has produced an information explosion in healthcare and forced successful organizations to become knowledge-driven enterprises. Moreover, the recent IOM Roundtable on “Data for the Public Good” discussed the current challenges of aggregating large sets of data while taking the position that exploiting large databases would significantly accelerate science. Although DoD and VA have numerous databases and two of the largest growing clinical data tissue repositories in the world, they lack a common healthcare data model to link the databases together and sponsor no significant effort to create an infrastructure to make this data available for research. The MHS currently also lacks a strategy for managing its growing amount of data. Recently, MHS has worked with TATRC and IBM to develop a roadmap for developing a strategy but much more work remains. There are increasing needs for efficient software engineering and ability to integrate innovations in IT development process. Many participants expressed dissatisfaction with the current performance of AHLTA. Slow and awkward to use, AHLTA delays clinical work at a time when maximizing physician throughput really matters. The IT development occurred in a closed environment, insulated from evolving new ideas and best practices outside the DoD. As a result, while the previous efforts developed and implemented software for health care applications, global advances in information technology render it obsolete by the time it becomes available for use. With these problems and consequences in mind, participants in the National Forum consistently called for more open process of software engineering, program management and knowledge sharing to allow lessons learned to flow freely among government, academic and industrial partners in the software development process. As the health IT and personal health records (PHR) have become important national agenda, the experiences gained from the development and deployment of health IT systems in DoD and VA should provide important lessons for the national community.

Georgetown University Hospital has been operating an image management and communications system (IMACS or PACS) for three-and-a-half years. This work was initially funded under the Army Medical Research and Development Command Digital Imaging Network Systems (DINS) project. The system was taken from a research system supporting only radiology tasks to one extended to clinical use, and has been used in clinical work for two-and-a-half years. This paper will summarize our PACS clinical experience and will describe the operational features implemented and those still necessary.

A 10-bit or 12-bit gray scale is provided in commercial laser film digitizers. The true contrast resolution on the digitized image within a laser spot area of 200 microns in diameter is limited by both the quantum mottle and instrumentation noise. In this report, we investigated the mean value, standard deviation, and adjacent pixel correlation coefficient on a calibrated step wedge film with two laser digitizers. The results were disappointing, because we found that the evaluated contrast information is inferior to the manufacturers' specifications. On the output side, the brightnesses of different gray levels from a clinical monitor were measured with a narrow angle luminance probe and evaluated by a brief human perception study. In addition, the implications for teleradiology applications are discussed.

The objective of this research is to exploit the findings of Project Vanguard Phase I and II to produce more robust scientific tools for graded alerting of transnational biological threats using Venezuelan Equine Encephalitis (VEE), a mosquito borne viral disease, as a case study. These new tools will play an essential role in future research and contribute to advancing TATRC s mission in the use of Indications and Warnings (I&Ws) biosurveillance for biodefense. Indications and Warnings (I&Ws) potentially alert U.S. responders of an imminent foreign bioevent weeks to months in advance. I&Ws are markers occurring globally, outside of U.S. borders, before an outbreak can affect U.S. interests, forces or domestic territory, thus allowing the U.S. time to respond. In effect, I&Ws can prime the national response infrastructure by alerting agencies of an evolving threat that could ultimately be highly disruptive or catastrophic. Venezuelan equine encephalitis (VEE) virus is a zoonotic, mosquito-borne, viral disease affecting humans and equines where equines serve as amplifying hosts. It is an RNA alphavirus of the Togaviridae genus that is serologically classified into six antigenic subtypes: I-VI and six varieties: A, AB, C, D, E, F (1). Epizootic/epidemic type IAB and IC are the only subtypes associated with significant human and equine outbreaks (1,2). VEE has caused periodic outbreaks in humans and equines in Latin America since the early 1920s. Considering that epizootic VEE has not been diagnosed or isolated in the United States since 1971, there are concerns that VEE would make an effective bioterrorist agent (1,3,4). VEE is considered an incapacitating agent rather than a lethal agent such as anthrax or plague. Past outbreaks have suggested that a low infective dose is necessary for transmission (4,5).

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.