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.

Radiology has a long history of adopting state-of-the-art digital technology to provide better diagnostic services and facilitate advances in image-based therapeutics throughout the healthcare system. The radiology community has been developing diagnostic artificial intelligence (AI) tools over the past 20 years, long before AI became fashionable in the public press. Currently, there are approximately four hundred Food and Drug Administration approved imaging AI products. However, the clinical adoption of these products in radiology has been relatively dismal, indicating that the current technology-push model needs to evolve into the demand-pull model. We will review the current state of AI use in radiology from the perspective of clinical adoption and explore the ways in which AI products will become an ensemble of critically important tools to help radiology transition from volume-based service to value-based healthcare. This transition will create new demands for AI technologies. We contrast the current “technology-push” model with a “demand-pull” model that will aligns technology with user priorities. We summarize the lessons learned from AI experience over the past twenty years, mainly working with computer-aided detection for breast cancers and lung cancers. The radiology community calls for AI tools that can do more than detection with increasing attention toward higher workflow efficiency and higher productivity of radiologists. Major radiological societies of North America and Europe promulgated the emerging concept of value-based radiology service, an integral part of overall value-based healthcare. The transition to value-based radiology will happen and that higher value will come from the effective use of AI throughout the radiology workflow. The value-based radiology will need to work with a full range of machine learning tools, including supervised, unsupervised, and reinforcement learning, as well as natural language processing and large language models (e.g., chatbots). The engineering community is rapidly developing many concepts and sophisticated software tools for data orchestration, AI orchestration, and automation orchestration. Current radiology operation has been supported by PACS, a monolithic IT infrastructure of past generations. This system will need to migrate to an intelligence management system to support the new workflow needed for high value radiology.

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.

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 [...]

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.

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 (LROC-AUC) 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.

Transformationally invariant processors constructed by transformed input vectors or operators have been suggested and applied to many applications. In this study, transformationally identical processing based on combining results of all sub-processes with corresponding transformations at one of the processing steps or at the beginning step were found to be equivalent for a given condition. This property can be applied to most convolutional neural network (CNN) systems. Specifically, a transformationally identical CNN can be constructed by arranging internally symmetric operations in parallel with the same transformation family that includes a flatten layer with weights sharing among their corresponding transformation elements. Other transformationally identical CNNs can be constructed by averaging transformed input vectors of the family at the input layer followed by an ordinary CNN process or by a set of symmetric operations. Interestingly, we found that both types of transformationally identical CNN systems are mathematically equivalent by either applying an averaging operation to corresponding elements of all sub-channels before the activation function or without using a non-linear activation function.

Mathematically speaking, a transformationally invariant operator, such as a transformationally identical (TI) matrix kernel (i.e., K= T{K}), commutes with the transformation (T{.}) itself when they operate on the first operand matrix. We found that by consistently applying the same type of TI kernels in a convolutional neural networks (CNN) system, the commutative property holds throughout all layers of convolution processes with and without involving an activation function and/or a 1D convolution across channels within a layer. We further found that any CNN possessing the same TI kernel property for all convolution layers followed by a flatten layer with weight sharing among their transformation corresponding elements would output the same result for all transformation versions of the original input vector. In short, CNN[ Vi ] = CNN[ T{Vi} ] providing every K = T{K} in CNN, where Vi denotes input vector and CNN[.] represents the whole CNN process as a function of input vector that produces an output vector. With such a transformationally identical CNN (TI-CNN) system, each transformation, that is not associated with a predefined TI used in data augmentation, would inherently include all of its corresponding transformation versions of the input vector for the training. Hence the use of same TI property for every kernel in the CNN would serve as an orientation or a translation independent training guide in conjunction with the error-backpropagation during the training. This TI kernel property is desirable for applications requiring a highly consistent output result from corresponding transformation versions of an input. Several C programming routines are provided to facilitate interested parties of using the TI-CNN technique which is expected to produce a better generalization performance than its ordinary CNN counterpart.

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.

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.

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.

The U.S. Department of Veterans Affairs (VA) operates more than 140 hospitals and 1,000 clinics for the care of America's Military Veterans. VA has made a strategic decision to incorporate an open source software strategy into the modernization plan for its healthcare information system, VistA, as a part of the Open Government Policy. VA established the Open Source Electronic Health Record Alliance (OSEHRA) in 2011 as an independent nonprofit organization outside of government to be a hub for collaboration and rapid innovation. OSEHRA was chartered to build a community that included government as well as members of a global private sector that has developed commercial systems and services incorporating VistA software that has been in the public domain. VA's strategy is a bold attempt to harness the power of community collaboration in support of critical government infrastructure. This paper describes how OSEHRA facilitates transparent interactions between the global private sector and a major federal government agency, while dealing with traditional rules and regulations that often impede open collaboration. It describes several key milestones in the process, including the establishment of an independent certification process for open source code, the community-based collaborative development of a groundbreaking visualization tool to promote community understanding of the system, adoption by VA of major open source code improved and certified by the community, and the first community-wide consensus on code convergence. VA's participation in the code convergence has created the opportunity for a jointly-maintained code base that will benefit not only the U.S. Government, but also healthcare providers around the world. While much work remains in realizing the benefits of this strategy, determined efforts within and outside Government have created a significant opportunity and an informative case study in e-Government.

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 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.

Objective: To demonstrate that concepts of patient-centeredness and technology-centeredness must work together within the context of the transformation to the patient-centered medical home (PCMH), a primary care model that emphasizes coordinated, comprehensive, accessible, and cost-effective care. Materials and Methods: Information in this article was gathered fromaworkshop on the Medical Home in Alexandria, VA in June 2010 that brought together civilian and military medical providers, researchers, and other stakeholders in PCMH to discuss their experiences in transitioning from traditional primary care to PCMH in addition to a literature review of articles from medical journals. Results: Patient-centeredness is often only vaguely defined as being in opposition to provider-centered or technology-centered. Our analysis shows that focusing on either technological improvements or enhancing patient-centered care will not improve the fragmented healthcare system in the United States. We argue that these two concepts are not incompatible as sometimes believed, but rather it is critical that we recognize they must work together in routine practices in order to truly improve the state of healthcare. Conclusion: Health information technology (HIT) supports many of the core principles of PCMH, but there are still several challenges as not all technologies have functionalities yet that facilitate the model. We suggest patient-centeredness be one of the main concepts that drives the redesign and implementation of new health technologies in primary care. It is no longer about just implementing new technologies; these technologies must enhance patient-provider relationships, communication, access, and patients' engagement in their own care.

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.

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.

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.