Online at Will: A Novel Protocol for Mutual Authentication in Peer-to-Peer Networks for Patient-Centered Health Care Information Systems

Patient-centered health care information systems (PHSs) on peer-to-peer (P2P) networks promise decentralization benefits. P2P PHSs, such as decentralized personal health records or interoperable Covid-19 proximity trackers, can enhance data sovereignty and resilience to single points of failure, but the openness of P2P networks introduces new security issues. We propose a novel, simple, and secure mutual authentication protocol that supports offline access, leverages independent and stateless encryption services, and enables patients and medical professionals to establish secure connections when using P2P PHSs. Our protocol includes a virtual smart card (software-based) feature to ease integration of authentication features of emerging national health-IT infrastructures. The security evaluation shows that our protocol resists most online and offline threats while exhibiting performance comparable to traditional, albeit less secure, password-based authentication methods. Our protocol serves as foundation for the design and implementation of P2P PHSs that will make use of P2P PHSs more secure and trustworthy

Security Engineering of Patient-Centered Health Care Information Systems in Peer-to-Peer Environments: Systematic Review

Background: Patient-centered health care information systems (PHSs) enable patients to take control and become knowledgeable about their own health, preferably in a secure environment. Current and emerging PHSs use either a centralized database, peer-to-peer (P2P) technology, or distributed ledger technology for PHS deployment. The evolving COVID-19 decentralized Bluetooth-based tracing systems are examples of disease-centric P2P PHSs. Although using P2P technology for the provision of PHSs can be flexible, scalable, resilient to a single point of failure, and inexpensive for patients, the use of health information on P2P networks poses major security issues as users must manage information security largely by themselves. Objective: This study aims to identify the inherent security issues for PHS deployment in P2P networks and how they can be overcome. In addition, this study reviews different P2P architectures and proposes a suitable architecture for P2P PHS deployment. Methods: A systematic literature review was conducted following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) reporting guidelines. Thematic analysis was used for data analysis. We searched the following databases: IEEE Digital Library, PubMed, Science Direct, ACM Digital Library, Scopus, and Semantic Scholar. The search was conducted on articles published between 2008 and 2020. The Common Vulnerability Scoring System was used as a guide for rating security issues. Results: Our findings are consolidated into 8 key security issues associated with PHS implementation and deployment on P2P networks and 7 factors promoting them. Moreover, we propose a suitable architecture for P2P PHSs and guidelines for the provision of PHSs while maintaining information security. Conclusions: Despite the clear advantages of P2P PHSs, the absence of centralized controls and inconsistent views of the network on some P2P systems have profound adverse impacts in terms of security. The security issues identified in this study need to be addressed to increase patients\u27 intention to use PHSs on P2P networks by making them safe to use

"What else to say?" – Primary health care in times of COVID-19 from the perspective of German general practitioners: an exploratory analysis of the open text field in the PRICOV-19 study

Background The international collaboration study PRICOV-19 –Primary Health Care in times of COVID-19 aims to assess the impact of the COVID-19 pandemic on the organisation of primary health care. The German part focuses on the subjective perceptions of general practitioners on primary health care and the impact of political measures during the second wave of the COVID-19 pandemic. Within this survey, the “open text field” of the questionnaire was utilised remarkably frequently and extensively by the respondents. It became clear that the content that was named needed to be analysed in an exploratory manner. Accordingly, this paper addresses the following question: What preoccupies general practitioners in Germany during COVID-19 that we have not yet asked them enough? Methods The data collection took place throughout Germany from 01.02.2021 to 28.02.2021with a quantitative online questionnaire consisting of 53 items arranged across six topics as well as an “open text field” for further comments. The questionnaire’s open text field was analysed following the premises of the qualitative content analysis. Results The topics discussed by the respondents were: insufficient support from health policies, not being prioritised and involved in the vaccination strategy, feeling insufficient prepared, that infrastructural changes and financial concerns threatened the practice, and perceiving the own role as important, as well as that health policies affected the wellbeing of the respondents. One of the main points was the way general practitioners were not sufficiently acknowledged for their contribution to ensuring high-quality care during the pandemic. Discussion German general practitioners perceived their work and role as highly relevant during the COVID-19 pandemic. In controversy with their perception, they described political conditions in which they were the ones who contributed significantly to the fight against the pandemic but were not given enough recognition

Mobility recorded by wearable devices and gold standards: the Mobilise-D procedure for data standardization

Wearable devices are used in movement analysis and physical activity research to extract clinically relevant information about an individual's mobility. Still, heterogeneity in protocols, sensor characteristics, data formats, and gold standards represent a barrier for data sharing, reproducibility, and external validation. In this study, we aim at providing an example of how movement data (from the real-world and the laboratory) recorded from different wearables and gold standard technologies can be organized, integrated, and stored. We leveraged on our experience from a large multi-centric study (Mobilise-D) to provide guidelines that can prove useful to access, understand, and re-use the data that will be made available from the study. These guidelines highlight the encountered challenges and the adopted solutions with the final aim of supporting standardization and integration of data in other studies and, in turn, to increase and facilitate comparison of data recorded in the scientific community. We also provide samples of standardized data, so that both the structure of the data and the procedure can be easily understood and reproduced

Quality assurance process within the RAdiosurgery for VENtricular TAchycardia (RAVENTA) trial for the fusion of electroanatomical mapping and radiotherapy planning imaging data in cardiac radioablation

A novel quality assurance process for electroanatomical mapping (EAM)-to-radiotherapy planning imaging (RTPI) target transport was assessed within the multi-center multi-platform framework of the RAdiosurgery for VENtricular TAchycardia (RAVENTA) trial. A stand-alone software (CARDIO-RT) was developed to enable platform independent registration of EAM and RTPI of the left ventricle (LV), based on pre-generated radiotherapy contours (RTC). LV-RTC were automatically segmented into the American-Heart-Association 17-segment-model and a manual 3D-3D method based on EAM 3D-geometry data and a semi-automated 2D-3D method based on EAM screenshot projections were developed. The quality of substrate transfer was evaluated in five clinical cases and the structural analyses showed substantial differences between manual target transfer and target transport using CARDIO-RT

Leveraging the Potential of Digital Technology for Better Individualized Treatment of Parkinson's Disease.

Recent years have witnessed a strongly increasing interest in digital technology within medicine (sensor devices, specific smartphone apps) and specifically also neurology. Quantitative measures derived from digital technology could provide Digital Biomarkers (DMs) enabling a quantitative and continuous monitoring of disease symptoms, also outside clinics. This includes the possibility to continuously and sensitively monitor the response to treatment, hence opening the opportunity to adapt medication pathways quickly. In addition, DMs may in the future allow early diagnosis, stratification of patient subgroups and prediction of clinical outcomes. Thus, DMs could complement or in certain cases even replace classical examiner-based outcome measures and molecular biomarkers measured in cerebral spinal fluid, blood, urine, saliva, or other body liquids. Altogether, DMs could play a prominent role in the emerging field of precision medicine. However, realizing this vision requires dedicated research. First, advanced data analytical methods need to be developed and applied, which extract candidate DMs from raw signals. Second, these candidate DMs need to be validated by (a) showing their correlation to established clinical outcome measures, and (b) demonstrating their diagnostic and/or prognostic value compared to established biomarkers. These points again require the use of advanced data analytical methods, including machine learning. In addition, the arising ethical, legal and social questions associated with the collection and processing of sensitive patient data and the use of machine learning methods to analyze these data for better individualized treatment of the disease, must be considered thoroughly. Using Parkinson's Disease (PD) as a prime example of a complex multifactorial disorder, the purpose of this article is to critically review the current state of research regarding the use of DMs, discuss open challenges and highlight emerging new directions

Clinical manifestations and immunomodulatory treatment experiences in psychiatric patients with suspected autoimmune encephalitis: a case series of 91 patients from Germany

Autoimmune encephalitis (AE) can rarely manifest as a predominantly psychiatric syndrome without overt neurological symptoms. This study’s aim was to characterize psychiatric patients with AE; therefore, anonymized data on patients with suspected AE with predominantly or isolated psychiatric syndromes were retrospectively collected. Patients with readily detectable neurological symptoms suggestive of AE (e.g., epileptic seizures) were excluded. Patients were classified as “probable psychiatric AE (pAE),” if well-characterized neuronal IgG autoantibodies were detected or “possible pAE” (e.g., with detection of nonclassical neuronal autoantibodies or compatible cerebrospinal fluid (CSF) changes). Of the 91 patients included, 21 (23%) fulfilled our criteria for probable (autoantibody-defined) pAE and 70 (77%) those for possible pAE. Among patients with probable pAE, 90% had anti-NMDA receptor (NMDA-R) autoantibodies. Overall, most patients suffered from paranoid-hallucinatory syndromes (53%). Patients with probable pAE suffered more often from disorientation (p < 0.001) and impaired memory (p = 0.001) than patients with possible pAE. Immunotherapies were performed in 69% of all cases, mostly with high-dose corticosteroids. Altogether, 93% of the patients with probable pAE and 80% of patients with possible pAE reportedly benefited from immunotherapies (p = 0.251). In summary, this explorative, cross-sectional evaluation confirms that autoantibody-associated AE syndromes can predominantly manifest as psychiatric syndromes, especially in anti-NMDA-R encephalitis. However, in three out of four patients, diagnosis of possible pAE was based on nonspecific findings (e.g., slight CSF pleocytosis), and well-characterized neuronal autoantibodies were absent. As such, the spectrum of psychiatric syndromes potentially responding to immunotherapies seems not to be limited to currently known autoantibody-associated AE. Further trials are needed

Recommendations on the structure, personal, and organization of intensive care units

BackgroundIntensive care units (ICU) are central facilities of medical care in hospitals world-wide and pose a significant financial burden on the health care system.ObjectivesTo provide guidance and recommendations for the requirements of (infra)structure, personal, and organization of intensive care units.Design and settingDevelopment of recommendations based on a systematic literature search and a formal consensus process from a group of multidisciplinary and multiprofessional specialists from the German Interdisciplinary Association of Intensive Care and Emergency Medicine (DIVI). The grading of the recommendation follows the report from an American College of Chest Physicians Task Force.ResultsThe recommendations cover the fields of a 3-staged level of intensive care units, a 3-staged level of care with respect to severity of illness, qualitative and quantitative requirements of physicians and nurses as well as staffing with physiotherapists, pharmacists, psychologists, palliative medicine and other specialists, all adapted to the 3 levels of ICUs. Furthermore, proposals concerning the equipment and the construction of ICUs are supplied.ConclusionThis document provides a detailed framework for organizing and planning the operation and construction/renovation of ICUs

Machine learning-based improvement of an online rheumatology referral and triage system

IntroductionRheport is an online rheumatology referral system allowing automatic appointment triaging of new rheumatology patient referrals according to the respective probability of an inflammatory rheumatic disease (IRD). Previous research reported that Rheport was well accepted among IRD patients. Its accuracy was, however, limited, currently being based on an expert-based weighted sum score. This study aimed to evaluate whether machine learning (ML) models could improve this limited accuracy.Materials and methodsData from a national rheumatology registry (RHADAR) was used to train and test nine different ML models to correctly classify IRD patients. Diagnostic performance was compared of ML models and the current algorithm was compared using the area under the receiver operating curve (AUROC). Feature importance was investigated using shapley additive explanation (SHAP).ResultsA complete data set of 2265 patients was used to train and test ML models. 30.5% of patients were diagnosed with an IRD, 69.3% were female. The diagnostic accuracy of the current Rheport algorithm (AUROC of 0.534) could be improved with all ML models, (AUROC ranging between 0.630 and 0.737). Targeting a sensitivity of 90%, the logistic regression model could double current specificity (17% vs. 33%). Finger joint pain, inflammatory marker levels, psoriasis, symptom duration and female sex were the five most important features of the best performing logistic regression model for IRD classification.ConclusionIn summary, ML could improve the accuracy of a currently used rheumatology online referral system. Including further laboratory parameters and enabling individual feature importance adaption could increase accuracy and lead to broader usage