COVID-19 preVIEW: Semantic Search to Explore COVID-19 Research Preprints

During the current COVID-19 pandemic, the rapid availability of profound information is crucial in order to derive information about diagnosis, disease trajectory, treatment or to adapt the rules of conduct in public. The increased importance of preprints for COVID-19 research initiated the design of the preprint search engine preVIEW. Conceptually, it is a lightweight semantic search engine focusing on easy inclusion of specialized COVID-19 textual collections and provides a user friendly web interface for semantic information retrieval. In order to support semantic search functionality, we integrated a text mining workflow for indexing with relevant terminologies. Currently, diseases, human genes and SARS-CoV-2 proteins are annotated, and more will be added in future. The system integrates collections from several different preprint servers that are used in the biomedical domain to publish non-peer-reviewed work, thereby enabling one central access point for the users. In addition, our service offers facet searching, export functionality and an API access. COVID-19 preVIEW is publicly available at https://preview.zbmed.de.

Importance of Digital Literacy in the Process of Confronting the Stress During COVID-19 Pandemic

COVID-19 pandemic is a global problem that raised the perception of stress to a completely new level, unseen so far in peace conditions. To reduce the level of stress and deal with its accumulated consequences, people enforce different strategies and coping mechanisms. In the vast amount of information available, imposed by the media, and often filled with falsehoods and catastrophizing related topics, it can be very challenging to distinguish the truth from falsehoods. The existence of an adequate level of digital literacy and e-Health literacy can help society to deal with that problem. Digital and e-Health literacy enables society to search for information efficiently, while remaining critical and open-minded, and use the newly adopted knowledge to improve our mental health. The aim of the paper was to present the frequency of news searches and trusting the COVID-19 related information in the group of medical professionals in Croatia. The survey was conducted using an online questionnaire, containing psychological questionnaires and relevant questions about everyday functioning. Based on the results that show a significant correlation between the frequency of information search and the lack of trust in the search results, we discuss the importance of digital and e-Health literacy, primarily within medical professionals.

From OpenEHR to FHIR and OMOP Data Model for Microbiology Findings

HiGHmed is a German Consortium where eight University Hospitals have agreed to the cross-institutional data exchange through novel medical informatics solutions. The HiGHmed Use Case Infection Control group has modelled a set of infection-related data in the openEHR format. In order to establish interoperability with the other German Consortia belonging to the same national initiative, we mapped the openEHR information to the Fast Healthcare Interoperability Resources (FHIR) format recommended within the initiative. FHIR enables fast exchange of data thanks to the discrete and independent data elements into which information is organized. Furthermore, to explore the possibility of maximizing analysis capabilities for our data set, we subsequently mapped the FHIR elements to the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM). The OMOP data model is designed to support the conduct of research to identify and evaluate associations between interventions and outcomes caused by these interventions. Mapping across standard allows to exploit their peculiarities while establishing and/or maintaining interoperability. This article provides an overview of our experience in mapping infection control related data across three different standards openEHR, FHIR and OMOP CDM.

EARS – A WHO Platform for AI-Supported Real-Time Online Social Listening of COVID-19 Conversations

As the COVID-19 pandemic evolves, the accompanying infodemic is being amplified through social media and has challenged effective response. The WHO Early AI-supported Response with Social Listening (EARS) is a platform that summarizes real-time information about how people are talking about COVID-19 in public spaces online in 20 pilot countries and in four languages. The aim of the platform is to better integrate social listening with other data sources and analyses that can inform infodemic response.

A Recommender System to Help Refining Clinical Research Studies

The process of refining the research question in a medical study depends greatly on the current background of the investigated subject. The information found in prior works can directly impact several stages of the study, namely the cohort definition stage. Besides previous published methods, researchers could also leverage on other materials, such as the output of cohort selection tools, to enrich and to accelerate their own work. However, this kind of information is not always captured by search engines. In this paper, we present a methodology, based on a combination of content-based retrieval and text annotation techniques, to identify relevant scientific publications related to a research question and to the selected data sources.

Evaluation of a Clinical Decision Support System in the Domain of Chronic Wound Management

The PosiThera project focuses on the management of chronic wounds, which is multi-professional and multi-disciplinary. For this context, a software prototype was developed in the project, which is intended to support medical and nursing staff with the assistance of artificial intelligence. In accordance with the user-centred design, national workshops were held at the beginning of the project with the involvement of domain experts in wound care in order to identify requirements and use cases of IT systems in wound care, with a focus on AI. In this study, the focus was on involving nursing and nursing science staff in testing the software prototype to gain insights into its functionality and usability. The overarching goal of the iterative testing and adaptation process is to further develop the prototype in a way that is close to care.

An Electronic Health Record Approach to Understanding Drug to Drug Interactions and Associated Knowledge Gaps in Intergrated Care of Multimorbidity

Multimorbid patients are prescribed a number of medications in parallel, which may often interact with each other, resulting in adverse effects. However, clinical guidelines on prescription of medications predominantly focus on individual conditions do not consider the guidance in the context of other guidelines, resulting in conflicts. C3-Cloud is an integrated care architecture managing multimorbidity, which amongst others, provides clinical decision support, based on reconciled guidelines, and active monitoring of drug interactions. To identify the severe interactions that resulted from multimorbidity management, in order to reevaluate guidelines as well as to identify knowledge gaps in prescribing practice. Method: Descriptive statistical analysis of interactions identified by the C3-Cloud clinical decision support, collected from the C3-Cloud FHIR repository. As part of a feasibility study, a number of interactions were identified, along with variable practice in how chemicals are represented in the EHR. 191 known severe interactions were identified. The Atorvastatin/Verapamil interaction was the most frequent. The approach has identified a number of interactions where the severity was not available, highlighting the need for further clinical review.

Pharmacovigilance and Clinical Environment: Utilizing OMOP-CDM and OHDSI Software Stack to Integrate EHR Data

Information Technology (IT) and specialized systems could have a prominent role towards the support of drug safety processes, both in the clinical context but also beyond that. PVClinical project aims to build an IT platform, enabling the investigation of potential Adverse Drug Reactions (ADRs). In this paper, we outline the utilization of Observational Medical Outcomes Partnership – Common Data Model (OMOP-CDM) and the openly available Observational Health Data Sciences and Informatics (OHDSI) software stack as part of PVClinical platform. OMOP-CDM offers the capacity to integrate data from Electronic Health Records (EHRs) (e.g., encounters, patients, providers, diagnoses, drugs, measurements and procedures) via an accepted data model. Furthermore, the OHDSI software stack provides valuable analytics tools which could be used to address important questions regarding drug safety quickly and efficiently, enabling the investigation of potential ADRs in the clinical environment.

IT Evaluation of Foundation Healthcare Group Vanguard Project

The aim of the Foundation Healthcare Group (FHG) Vanguard model was to develop a sustainable local hospital model between two National Health Service (NHS) Trusts (a London Teaching Hospital Trust and a District General Hospital Trust) that makes best use of scarce resources and can be replicated across the NHS, UK. The aim of this study was to evaluate the provision, use and implementation of the IT infrastructure; based on qualitative interviews and focused mainly on the perspectives of the IT staff and the clinicians’ perspectives. In total 24 interview transcripts, along with ‘Acute Care Collaboration’ questionnaire responses, were analysed using a thematic framework for IT infrastructure, sharing themes across the vascular, paediatric and cardiovascular strands of the FHG programme. Findings indicated that Skype for Business had been an innovative and helpful development widely available to be used between the two Trusts. Clinicians initially reported lack of IT support and infrastructure expected at the outset for a national Vanguard project, but later appreciated that remote access to most clinical applications between the two Trusts became operational. The Local Care Record (LCR), an IT project was perceived to have been delivered successfully in South London. Shared technology reduced patient travelling time by providing locally based shared care. Spreading and scaling-up innovations from the Vanguard sites was the aspiration and challenge for system leaders.

High Engagement of Patients Monitored by a Digital Health Ecosystem Indicates Significant Improvements of Key r-hGH Treatment Metrics

The early adoption of digital health solutions in the treatment of growth disorders has enabled the collection and analysis of more than 10 years of real-world data using the easypod™ connect platform. Using this rich dataset, we were able to study the impact of engagement on three key treatment-related outcomes: adherence, persistence of use, and growth. In total, data for 17,906 patients were available. The three features, regularity of injection (≤2h vs >2h), change of comfort setting (yes/no), and opting-in to receive injection reminders (yes/no), were used as a proxy for engagement. Patients were assigned to the low-engagement group (n=1,752) when all of their features had the low-engagement flag (>2h, no, no) and to the high-engagement group (n=1,081) when all of their features had the high-engagement flag (≤2h, yes, yes). The low-engagement group was down-sampled to 1,081 patients (subsample of n=37 for growth) using the iterative proportional fitting algorithm. Statistical tests were used to study the impact of engagement to the outcomes. The results show that all three outcomes were significantly improved by a factor varying from 1.8 up to 2.2 when the engagement level was high. These results should encourage the promotion of engagement and associated behaviors by both patients and healthcare professionals.