A blockchain-enabled sharing platform for personal health records.

Background: Longitudinal personal health record (PHR) provides a foundation for managing patients' health care, but we do not have such a system in the U.S. except for the patients in the Department of Veterans Affairs. Such a gap exists mainly in the rest of the U.S. by the fact that patients' electronic health records are scattered across multiple health care facilities and often not shared due to privacy, security, and business interests concerns from both patients and health care organizations. In addition, patients have ethical concerns related to consent. To patients, data security, privacy, and consent are based on trustfulness, rather than patients' engagement in ensuring only authorized people can view their PHRs with patient-managed granularity. Resolving these challenges is an important step in making longitudinal PHR useful for patient care. Objective: This research aims to design and implement a blockchain-enabled sharing platform prototype for PHR with desired patient-controlled data security, privacy, and consent granularity. Methods: Built upon our prior work of a blockchain-enabled access control (BAC) model, we design a blockchain-enabled sharing platform for PHR with patient-controlled security, privacy, and consent granularity. We further implement the construct by building a prototypical platform among a patient and two typical health care organizations. Health organizations that hold the patient's electronic health records can join the platform with trust based on the validation from the patient. The mutual trust can be established through a rigorous validation process by both the patient and the built-in Hyperledger Fabric blockchain consensus mechanism. Results: We proposed a system trusted by patients and health care providers and constructed a Web-based PHR sharing platform with patient-controlled security, privacy, and consent granularity. We analyzed the system scalability in three aspects and showed millisecond range of performance when simultaneously changing access permissions on hundreds of PHRs. Consent, security and privacy of the model are ensured by the merits of the BAC model. We discovered the current blockchain model limits the system scalability due to using a non-graphical database. A new graphical database is suggested for future improvements. Conclusions: In this research, we report a solution to electronically sharing and managing patients' electronic health records originating from multiple organizations, focusing on privacy, security, and granularity control of consent in the U.S. Specifically, the system protects data security and privacy, and provides auditability, scalability, distributedness, patient consent autonomy, and zero-trust capabilities. The prototypical instantiation of the designed model suggested the feasibility of combining emerging blockchain technology with next generation access control model to tackle a longstanding longitudinal PHR problem.

Automated Error Labeling in Radiation Oncology via Statistical Natural Language Processing.

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.

Special Issue: "Machine Learning for Computer-Aided Diagnosis in Biomedical Imaging".

The radiology imaging community has been developing computer-aided diagnosis (CAD) tools since the early 1990s before the imagination of artificial intelligence (AI) fueled many unbound healthcare expectations and other industries [...].

A DICOM dataset for evaluation of medical image de-identification.

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.

Recent Advances in Systems and Network Medicine: Meeting Report from the First International Conference in Systems and Network Medicine.

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.

Prototype of a Military Medic Smartphone Medical Graphical User Interface for Use by Medics in Deployed Environments.

INTRODUCTION: Prompt 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 METHODS: The 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. RESULTS: The prototype MMS was field tested during live exercises to generate qualitative feedback from potential users, which provided significant guidance for future enhancements. CONCLUSIONS: The widespread deployment of this type of device will enable more effective health care, limit the impact of battlefield injuries, and save lives.

Artificial Intelligence for the Future Radiology Diagnostic Service.

Radiology historically has been a leader of digital transformation in healthcare. The introduction of digital imaging systems, picture archiving and communication systems (PACS), and teleradiology transformed radiology services over the past 30 years. Radiology is again at the crossroad for the next generation of transformation, possibly evolving as a one-stop integrated diagnostic service. Artificial intelligence and machine learning promise to offer radiology new powerful new digital tools to facilitate the next transformation. The radiology community has been developing computer-aided diagnosis (CAD) tools based on machine learning (ML) over the past 20 years. Among various AI techniques, deep-learning convolutional neural networks (CNN) and its variants have been widely used in medical image pattern recognition. Since the 1990s, many CAD tools and products have been developed. However, clinical adoption has been slow due to a lack of substantial clinical advantages, difficulties integrating into existing workflow, and uncertain business models. This paper proposes three pathways for AI's role in radiology beyond current CNN based capabilities 1) improve the performance of CAD, 2) improve the productivity of radiology service by AI-assisted workflow, and 3) develop radiomics that integrate the data from radiology, pathology, and genomics to facilitate the emergence of a new integrated diagnostic service.

JOURNAL CLUB: Computer-Aided Detection of Lung Nodules on CT With a Computerized Pulmonary Vessel Suppressed Function.

OBJECTIVE: 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. MATERIALS AND METHODS: 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. RESULTS: 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 (p < 0.01). 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 (p < 0.01). Radiologists detected 80.0% of cancers with VIS/CADe versus 64.45% of cancers unaided (p < 0.01); specificity decreased from 89.9% to 84.4% (p < 0.01). Radiologist interpretation time significantly decreased by 26%. CONCLUSION: 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.

Interactive Feature Space Explorer© for multi-modal magnetic resonance imaging.

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.

Exploring the relationship among user satisfaction, compliance, and clinical outcomes of telemedicine services for glucose control.

BACKGROUND: 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. MATERIALS AND METHODS: 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. RESULTS: 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 (p<0.05). Overall satisfaction and compliance were positively correlated with HbA1c improvement. In addition, income level was also an important variable for overall satisfaction. CONCLUSIONS: 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.

Perspectives on clinical informatics: integrating large-scale clinical, genomic, and health information for clinical care.

The advances in electronic medical records (EMRs) and bioinformatics (BI) represent two significant trends in healthcare. The widespread adoption of EMR systems and the completion of the Human Genome Project developed the technologies for data acquisition, analysis, and visualization in two different domains. The massive amount of data from both clinical and biology domains is expected to provide personalized, preventive, and predictive healthcare services in the near future. The integrated use of EMR and BI data needs to consider four key informatics areas: data modeling, analytics, standardization, and privacy. Bioclinical data warehouses integrating heterogeneous patient-related clinical or omics data should be considered. The representative standardization effort by the Clinical Bioinformatics Ontology (CBO) aims to provide uniquely identified concepts to include molecular pathology terminologies. Since individual genome data are easily used to predict current and future health status, different safeguards to ensure confidentiality should be considered. In this paper, we focused on the informatics aspects of integrating the EMR community and BI community by identifying opportunities, challenges, and approaches to provide the best possible care service for our patients and the population.

Lessons learned from implementing the patient-centered medical home.

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.

Background and Mathematical Analysis of Diffusion MRI Methods.

The addition of a pair of magnetic field gradient pulses had initially provided the measurement of spin motion with nuclear magnetic resonance (NMR) techniques. In the adaptation of 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 diffusion weighted (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.

Patient-centered medical home: an emerging primary care model and the military health system.

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.

Technologies in the patient-centered medical home: examining the model from an enterprise perspective.

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.

Imaging components for a robotic casualty evaluation system.

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.

Conversion to use of digital chest images for surveillance of coal workers' pneumoconiosis (black lung).

To protect the health of active U.S. underground coal miners, the National Institute for Occupational Safety and Health (NIOSH) has a mandate to carry out surveillance for coal workers' pneumoconiosis, commonly known as Black Lung (PHS 2001). This is accomplished by reviewing chest x-ray films obtained from miners at approximately 5-year intervals in approved x-ray acquisition facilities around the country. Currently, digital chest images are not accepted. Because most chest x-rays are now obtained in digital format, NIOSH is redesigning the surveillance program to accept and manage digital x-rays. This paper highlights the functional and security requirements for a digital image management system for a surveillance program. It also identifies the operational differences between a digital imaging surveillance network and a clinical Picture Archiving Communication Systems (PACS) or teleradiology system.

The executive summary of the National Forum on the Future of Defense Health Information Systems.

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.

Software architecture and engineering for patient records: current and future.

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.

Blast-related brain injury: imaging for clinical and research applications: report of the 2008 st. Louis workshop.

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.