Modeling the Application of IHE QRPH Profiles in Data Integration Centers: A Practical Approach with 3LGM2.

The Data Integration Centers (DICs), all part of the German Medical Informatics Initiative (MII), prepare routine care data captured in university hospitals to enable its reuse in clinical research. Tackling this challenging task requires them to maintain multiple data stores, implement the necessary transformation processes, and provide the required terminology services, all while also addressing the use case specific needs researchers might have. An MII wide application of the standardized profiles defined in the IHE QRPH domain might therefore be able to drastically reduce the overhead at any one DIC. The MII DIC reference model built in 3LGM2, a method to describe complex information system architectures, serves as a starting point to evaluate whether such an application is possible. We first extend the IHE modeling capabilities of 3LGM2 to also support the five profiles from the QRPH domain that our experts evaluated as relevant in the MII DIC context. We then expand the DIC reference model by some IHE QRPH actors and transactions, showing that their application could be beneficial in the MII DIC context, provided they surpass their trial status.

Multi-Stakeholder Design for Complex Digital Health Systems: Development of a Modular Open Research Platform (MORE).

BACKGROUND: The Modular Open Research Platform (MORE) is being developed as an open-source platform for long-term situated digital health (DH) research and observations with the potential to facilitate studies, evaluations of DH interventions, and remote telehealth monitoring. OBJECTIVES: To implement an iterative development approach that integrates multi-stakeholder perspectives to support a single platform development process. METHOD: Capture, fulfil and balance the requirements of a multi-disciplinary group of stakeholders interacting with the system through a Delphi-inspired, iterative and participatory design process encompassing a series of workshops and online surveys. RESULTS: Through interaction with a multi-disciplinary group of key platform stakeholders, diverse feedback and requirements for the design and development process were elicited and integrated. CONCLUSION: Findings from the initial rounds of stakeholder involvement lay the stepping stone towards further iterations in the process. Experts who participated in the process reported being generally supportive of and feeling involved in the development process.

Combined strategy of knowledge-based rule selection and historical data percentile-based range determination to improve an autoverification system for clinical chemistry test results.

BACKGROUND: Current autoverification, which is only knowledge-based, has low efficiency. Regular historical data analysis may improve autoverification range determination. We attempted to enhance autoverification by selecting autoverification rules by knowledge and ranges from historical data. This new system was compared with the original knowledge-based system. METHODS: New types of rules, extreme values, and consistency checks were added and the autoverification workflow was rearranged to construct a framework. Criteria for creating rules for extreme value ranges, limit checks, consistency checks, and delta checks were determined by analyzing historical Zhongshan laboratory data. The new system's effectiveness was evaluated using pooled data from 20 centers. Efficiency improvement was assessed by a multicenter process. RESULTS: Effectiveness was evaluated by the true positive rate, true negative rate, and overall consistency rate, as compared to manual verification, which were 77.55%, 78.53%, and 78.3%, respectively for the new system. The original overall consistency rate was 56.2%. The new pass rates, indicating efficiency, were increased by 19%-51% among hospitals. Further customization using individualized data increased this rate. CONCLUSIONS: The improved system showed a comparable effectiveness and markedly increased efficiency. This transferable system could be further improved and popularized by utilizing historical data from each hospital.

Utilization of LED Grow Lights for Optical Wireless Communication-Based RF-Free Smart-Farming System.

Indoor smart-farming based on artificial grow lights has gained attention in the past few years. In modern agricultural technology, the growth status is generally monitored and controlled by radio-frequency communication networks. However, it is reported that the radio frequency (RF) could negatively impact the growth rate and the health condition of the vegetables. This work proposes an energy-efficient solution replacing or augmenting the current RF system by utilizing light-emitting diodes (LEDs) as the grow lights and adopting visible light communications and optical camera communication for the smart-farming systems. In particular, in the proposed system, communication data is modulated via a 24% additional green grow LED light that is also known to be beneficial for the growth of the vegetables. Optical cameras capture the modulated green light reflected from the vegetables for the uplink connection. A combination of white ceiling LEDs and photodetectors provides the downlink, enabling an RF-free communication network as a whole. In the proposed architecture, the smart-farming units are modularized, leading to flexible mobility. Following theoretical analysis and simulations, a proof-of-concept demonstration presents the feasibility of the proposed architecture by successfully demonstrating the maximum data rates of 840 b/s (uplink) and 20 Mb/s (downlink).

Mapping the Role of Digital Health Technologies in Prevention and Control of COVID-19 Pandemic: Review of the Literature.

BACKGROUND: Coronavirus Disease (COVID-19) is currently spreading exponentially around the globe. Various digital health technologies are currently being used as weapons in the fight against the pandemic in different ways by countries. The main objective of this review is to explore the role of digital health technologies in the fight against the COVID-19 pandemic and address the gaps in the use of these technologies for tackling the pandemic. METHODS: We conducted a scoping review guided by the Joanna Briggs Institute guidelines. The articles were searched using electronic databases including MEDLINE (PubMed), Cochrane Library, and Hinari. In addition, Google and Google scholar were searched. Studies that focused on the application of digital health technologies on COVID-19 prevention and control were included in the review. We characterized the distribution of technological applications based on geographical locations, approaches to apply digital health technologies and main findings. The study findings from the existing literature were presented using thematic content analysis. RESULTS: A total of 2,601 potentially relevant studies were generated from the initial search and 22 studies were included in the final review. The review found that telemedicine was used most frequently, followed by electronic health records and other digital technologies such as artificial intelligence, big data, and the internet of things (IoT). Digital health technologies were used in multiple ways in response to the COVID-19 pandemic, including screening and management of patients, methods to minimize exposure, modelling of disease spread, and supporting overworked providers. CONCLUSION: Digital health technologies like telehealth, mHealth, electronic medical records, artificial intelligence, the internet of things, and big data/internet were used in different ways for the prevention and control of the COVID-19 pandemic in different settings using multiple approaches. For more effective deployment of digital health tools in times of pandemics, development of a guiding policy and standard on the development, deployment, and use of digital health tools in response to a pandemic is recommended.

Consumer Informatics and COVID-19 Pandemics: Challenges and Opportunities for Research.

OBJECTIVE: To summarise the state of the art during the year 2020 in consumer health informatics and education, with a special emphasis on "Managing Pandemics with Health Informatics - Successes and Challenges". METHODS: We conducted a systematic search of articles published in PubMed using a predefined set of queries, which identified 147 potential articles for review. These articles were screened according to topic relevance and 15 were selected for consideration of best paper candidates, which were then presented to a panel of international experts for full paper review and scoring. The top five papers were discussed in a consensus meeting. Three papers received the highest score from the expert panel, and these papers were selected to be representative papers on consumer informatics for managing pandemics in the year 2020. RESULTS: Bibliometrics analysis conducted on words found in abstracts of the candidate papers revealed 4 clusters of articles, where the clustering outcomes explained 77.04% of the dispersion. The first cluster composed of articles related to the use of mobile apps for video consultation and telehealth during the pandemic. The second revealed studies reporting the lived experience of healthcare workers and patients during COVID-19. The third focused on ways people used the internet to seek for health information during the pandemic and the dissemination of fake news. The last cluster composed of articles reporting the use of social listening methods (e.g., via tweet hashtags) to explore the spread of the virus around the world. CONCLUSIONS: The pandemic outbreak of the novel coronavirus disease (COVID-19) constitutes a grave risk to the global community and sparks a significant increase in public interest and media coverage, especially on social media. Consumers are facing a new set of challenges that were not considered before COVID-19, often finding themselves in a world that is constantly changing-blended with facts and fake information-and unable to decide what to do next. Despite most people understanding the good will behind public health policies, one must not forget it is individuals we are supporting and that their personal circumstances may affect how they perceive and comply with these policies. Consumers more than ever need help to make sense of the uncertainty and their situation and we need to help them navigate the best option in a world that is constantly evolving.

Survey on Optical Wireless Communications-Based Services Applied to the Tourism Industry: Potentials and Challenges.

In this paper, we explore the potential applications of Optical Wireless Communications in the tourism industry, considering both indoor and outdoor scenarios and different transmission speeds. They range from high-speed atmospheric outdoor links (Free-Space Optics (FSO)) to indoor systems based on high-speed lighting networks (known under the trade name LiFi©) or low-speed services support the Internet of Things networks, using visible light (VLC) or IR emitters, with receivers based on either on classical photodiodes or in image sensors, known as Optical Camera Communications. The avant-garde applications of this technology have been studied focusing on three possible use scenarios: the traveler himself, in what we have called TAN (Tourist Area Network); the tourist facility, which includes not only the hotel but also leisure areas (theme parks, museums, natural protected areas) or services (restaurants, shopping areas, etc.); and the entire destination, which can be both the city or the territory where the tourist is received, within the paradigm of the Smart Tourist Destination (STD). In addition to the classic services based on radio frequency and wired broadband networks, these technologies will make it possible to meet the tourist's challenging needs, the establishment, and the destination. Besides, they cover the services imposed by the new marketing services related to location or context and feed the big data systems used to study tourist behavior.

Internet of Things and Robotics in Transforming Current-Day Healthcare Services.

Technology has become an integral part of everyday lives. Recent years have witnessed advancement in technology with a wide range of applications in healthcare. However, the use of the Internet of Things (IoT) and robotics are yet to see substantial growth in terms of its acceptability in healthcare applications. The current study has discussed the role of the aforesaid technology in transforming healthcare services. The study also presented various functionalities of the ideal IoT-aided robotic systems and their importance in healthcare applications. Furthermore, the study focused on the application of the IoT and robotics in providing healthcare services such as rehabilitation, assistive surgery, elderly care, and prosthetics. Recent developments, current status, limitations, and challenges in the aforesaid area have been presented in detail. The study also discusses the role and applications of the aforementioned technology in managing the current pandemic of COVID-19. A comprehensive knowledge has been provided on the prospect of the functionality, application, challenges, and future scope of the IoT-aided robotic system in healthcare services. This will help the future researcher to make an inclusive idea on the use of the said technology in improving the healthcare services in the future.

Modeling of network based digital contact tracing and testing strategies, including the pre-exposure notification system, for the COVID-19 pandemic.

With more than 1.7 million COVID-19 deaths, identifying effective measures to prevent COVID-19 is a top priority. We developed a mathematical model to simulate the COVID-19 pandemic with digital contact tracing and testing strategies. The model uses a real-world social network generated from a high-resolution contact data set of 180 students. This model incorporates infectivity variations, test sensitivities, incubation period, and asymptomatic cases. We present a method to extend the weighted temporal social network and present simulations on a network of 5000 students. The purpose of this work is to investigate optimal quarantine rules and testing strategies with digital contact tracing. The results show that the traditional strategy of quarantining direct contacts reduces infections by less than 20% without sufficient testing. Periodic testing every 2 weeks without contact tracing reduces infections by less than 3%. A variety of strategies are discussed including testing second and third degree contacts and the pre-exposure notification system, which acts as a social radar warning users how far they are from COVID-19. The most effective strategy discussed in this work was combining the pre-exposure notification system with testing second and third degree contacts. This strategy reduces infections by 18.3% when 30% of the population uses the app, 45.2% when 50% of the population uses the app, 72.1% when 70% of the population uses the app, and 86.8% when 95% of the population uses the app. When simulating the model on an extended network of 5000 students, the results are similar with the contact tracing app reducing infections by up to 79%.

How to Improve Information Technology to Support Healthcare to Address the COVID-19 Pandemic: an International Survey with Health Informatics Experts.

OBJECTIVES: To identify the ways in which healthcare information and communication technologies can be improved to address the challenges raised by the COVID-19 pandemic. METHODS: The study population included health informatics experts who had been involved with the planning, development and deployment of healthcare information and communication technologies in healthcare settings in response to the challenges presented by the COVID-19 pandemic. Data were collected via an online survey. A non-probability convenience sampling strategy was employed. Data were analyzed with content analysis. RESULTS: A total of 65 participants from 16 countries responded to the conducted survey. The four major themes regarding recommended improvements identified from the content analysis included: improved technology availability, improved interoperability, intuitive user interfaces and adoption of standards of care. Respondents also identified several key healthcare information and communication technologies that can help to provide better healthcare to patients during the COVID-19 pandemic, including telehealth, advanced software, electronic health records, remote work technologies (e.g., remote desktop computer access), and clinical decision support tools. CONCLUSIONS: Our results help to identify several important healthcare information and communication technologies, recommended by health informatics experts, which can help to provide better care to patients during the COVID-19 pandemic. The results also highlight the need for improved interoperability, intuitive user interfaces and advocating the adoption of standards of care.

Development of safety and usability guideline for clinical information system.

ABSTRACT: Clinical information systems (CISs) that do not consider usability and safety could lead to harmful events. Therefore, we aimed to develop a safety and usability guideline of CISs that is comprehensive for both users and developers. And the guideline was categorized to apply actual clinical workflow and work environment.The guideline components were extracted through a systematic review of the articles published between 2000 and 2015, and existing CIS safety and/or usability design guidelines. The guideline components were categorized according to clinical workflow and types of user interface (UI). The contents of the guideline were evaluated and validated by experts with 3 specialties: medical informatics, patient safety, and human engineering.Total 1276 guideline components were extracted through article and guideline review. Of these, 464 guideline components were categorized according to 5 divisions of the clinical workflow: "Data identification and selection," "Document entry," "Order entry," "Clinical decision support and alert," and "Management". While 521 guideline components were categorized according to 4 divisions of UI: UIs related to information process steps, "Perception," "Recognition," "Control," and "Feedback". We developed a guideline draft with 219 detailed guidance for clinical task and 70 for UI. Overall appropriateness and comprehensiveness were proven to achieve more than 90% in experts' survey. However, there were significant differences among the groups of specialties in the judgment of appropriateness (P < .001) and comprehensiveness (P = .038).We developed and verified a safety and usability guideline for CIS that qualifies the requirements of both clinical workflows and usability issues. The developed guideline can be a practical tool to enhance the usability and safety of CISs. Further validation is required by applying the guideline for designing the actual CIS.

A Proteome-Wide Immunoinformatics Tool to Accelerate T-Cell Epitope Discovery and Vaccine Design in the Context of Emerging Infectious Diseases: An Ethnicity-Oriented Approach.

Emerging infectious diseases (EIDs) caused by viruses are increasing in frequency, causing a high disease burden and mortality world-wide. The COVID-19 pandemic caused by the novel SARS-like coronavirus (SARS-CoV-2) underscores the need to innovate and accelerate the development of effective vaccination strategies against EIDs. Human leukocyte antigen (HLA) molecules play a central role in the immune system by determining the peptide repertoire displayed to the T-cell compartment. Genetic polymorphisms of the HLA system thus confer a strong variability in vaccine-induced immune responses and may complicate the selection of vaccine candidates, because the distribution and frequencies of HLA alleles are highly variable among different ethnic groups. Herein, we build on the emerging paradigm of rational epitope-based vaccine design, by describing an immunoinformatics tool (Predivac-3.0) for proteome-wide T-cell epitope discovery that accounts for ethnic-level variations in immune responsiveness. Predivac-3.0 implements both CD8+ and CD4+ T-cell epitope predictions based on HLA allele frequencies retrieved from the Allele Frequency Net Database. The tool was thoroughly assessed, proving comparable performances (AUC ~0.9) against four state-of-the-art pan-specific immunoinformatics methods capable of population-level analysis (NetMHCPan-4.0, Pickpocket, PSSMHCPan and SMM), as well as a strong accuracy on proteome-wide T-cell epitope predictions for HIV-specific immune responses in the Japanese population. The utility of the method was investigated for the COVID-19 pandemic, by performing in silico T-cell epitope mapping of the SARS-CoV-2 spike glycoprotein according to the ethnic context of the countries where the ChAdOx1 vaccine is currently initiating phase III clinical trials. Potentially immunodominant CD8+ and CD4+ T-cell epitopes and population coverages were predicted for each population (the Epitope Discovery mode), along with optimized sets of broadly recognized (promiscuous) T-cell epitopes maximizing coverage in the target populations (the Epitope Optimization mode). Population-specific epitope-rich regions (T-cell epitope clusters) were further predicted in protein antigens based on combined criteria of epitope density and population coverage. Overall, we conclude that Predivac-3.0 holds potential to contribute in the understanding of ethnic-level variations of vaccine-induced immune responsiveness and to guide the development of epitope-based next-generation vaccines against emerging pathogens, whose geographic distributions and populations in need of vaccinations are often well-defined for regional epidemics.

DimLift: Interactive Hierarchical Data Exploration Through Dimensional Bundling.

The identification of interesting patterns and relationships is essential to exploratory data analysis. This becomes increasingly difficult in high dimensional datasets. While dimensionality reduction techniques can be utilized to reduce the analysis space, these may unintentionally bury key dimensions within a larger grouping and obfuscate meaningful patterns. With this work we introduce DimLift, a novel visual analysis method for creating and interacting with dimensional bundles. Generated through an iterative dimensionality reduction or user-driven approach, dimensional bundles are expressive groups of dimensions that contribute similarly to the variance of a dataset. Interactive exploration and reconstruction methods via a layered parallel coordinates plot allow users to lift interesting and subtle relationships to the surface, even in complex scenarios of missing and mixed data types. We exemplify the power of this technique in an expert case study on clinical cohort data alongside two additional case examples from nutrition and ecology.

Evaluation of audio-voice guided application for neonatal resuscitation: a prospective, randomized, pilot study.

OBJECTIVES: To examine whether audio-voice guidance application improves adherence to resuscitation sequence and recommended time frames during neonatal resuscitation. METHODS: A prospective, randomized, pilot study examining the use of an audio-voice application for guiding resuscitation on newborn mannequins, based on the Neonatal Resuscitation Program (NRP) algorithm. Two different scenarios, with and without voice guidance, were presented to 20 medical personnel (2 midwives, 8 nurses, and 10 physicians) in random order, and their performance videotaped. RESULTS: Audio-voice guided resuscitation compared with non-guided resuscitation, resulted in significantly better compliance with NRP order sequence (p<0.01), correct use of oxygen supplementation (p<0.01) and performance of MR SOPA (Mask, reposition, suction, open mouth, pressure, airway) (p<0.01), and shortened the time to "positive pressure ventilation" (p<0.01). CONCLUSIONS: In this pilot study, audio-voice guidance application for newborn resuscitation simulation on mannequins, based on the NRP algorithm, improved adherence and performance of NRP guidelines.

Unveiling the silent threat among us: leveraging health information technology in the search for asymptomatic COVID 19 healthcare workers.

Assuring the safety of both patients and healthcare workers (HCWs) in hospitals has been the primary focus of every healthcare organization during the COVID 19 pandemic. This article discusses the NIH Clinical Center's interdisciplinary approach to deploying an organizational Asymptomatic Staff Testing System.

The emerging role of clinical informatics fellows in service learning during the COVID-19 pandemic.

OBJECTIVE: The study sought to describe the contributions of clinical informatics (CI) fellows to their institutions' coronavirus disease 2019 (COVID-19) response. MATERIALS AND METHODS: We designed a survey to capture key domains of health informatics and perceptions regarding fellows' application of their CI skills. We also conducted detailed interviews with select fellows and described their specific projects in a brief case series. RESULTS: Forty-one of the 99 CI fellows responded to our survey. Seventy-five percent agreed that they were "able to apply clinical informatics training and interest to the COVID-19 response." The most common project types were telemedicine (63%), reporting and analytics (49%), and electronic health record builds and governance (32%). Telehealth projects included training providers on existing telehealth tools, building entirely new virtual clinics for video triage of COVID-19 patients, and pioneering workflows and implementation of brand-new emergency department and inpatient video visit types. Analytics projects included reports and dashboards for institutional leadership, as well as developing digital contact tracing tools. For electronic health record builds, fellows directly contributed to note templates with embedded screening and testing guidance, adding COVID-19 tests to order sets, and validating clinical triage workflows. DISCUSSION: Fellows were engaged in projects that span the breadth of the CI specialty and were able to make system-wide contributions in line with their educational milestones. CONCLUSIONS: CI fellows contributed meaningfully and rapidly to their institutions' response to the COVID-19 pandemic.

A digital health profile & maturity assessment toolkit: cocreation and testing in the Pacific Islands.

INTRODUCTION: Countries need to determine their level of digital health capability maturity to assess and mobilize their knowledge, skills, and resources to systematically develop, implement, evaluate, scale up and maintain large-scale implementations of standards-based interoperable digital health tools. OBJECTIVE: Develop a Digital Health Profile and Maturity Assessment Toolkit (DHPMAT) to assist Pacific Island Countries (PICs) to harness digital tools to support national health priorities. MATERIALS AND METHODS: A literature review guided the development of the conceptual framework to underpin the DHPMAT. Key informants collaborated to collect key digital health features and indicators to inform their country's digital health maturity assessment. The DHPMAT was tested with country stakeholders at a Pacific Health Information Network workshop in 2019. RESULTS: A comprehensive list of indicators to describe country digital health profiles (DHP). A digital health maturity assessment tool that uses criteria codeveloped with country stakeholders to assess essential digital health foundations and quality improvement. DHPs created and maturity assessed and packaged into individualized DHPMATs for 13 PICs. PIC users perceived the DHPMAT as useful, especially the congruence with the 2017 WHO WPRO Regional Strategy but noted a "cognitive overload" from a plethora of complex digital health toolkits. CONCLUSIONS: The cocreation approach optimized currency, accuracy, and appropriateness of information in the DHP, understanding, and use of the DHPMAT to facilitate informed iterative discussion by PICs on their digital health maturity to harness digital tools to strengthen country health systems. The DHPMAT can rationalize the choice and use of existing tools and reduce cognitive overload.