[Interoperability Working Group: core dataset and information systems for data integration and data exchange in the Medical Informatics Initiative]. / Arbeitsgruppe Interoperabilität: Kerndatensatz und Informationssysteme für Integration und Austausch von Daten in der Medizininformatik-Initiative.

The interoperability Working Group of the Medical Informatics Initiative (MII) is the platform for the coordination of overarching procedures, data structures, and interfaces between the data integration centers (DIC) of the university hospitals and national and international interoperability committees. The goal is the joint content-related and technical design of a distributed infrastructure for the secondary use of healthcare data that can be used via the Research Data Portal for Health. Important general conditions are data privacy and IT security for the use of health data in biomedical research. To this end, suitable methods are used in dedicated task forces to enable procedural, syntactic, and semantic interoperability for data use projects. The MII core dataset was developed as several modules with corresponding information models and implemented using the HL7® FHIR® standard to enable content-related and technical specifications for the interoperable provision of healthcare data through the DIC. International terminologies and consented metadata are used to describe these data in more detail. The overall architecture, including overarching interfaces, implements the methodological and legal requirements for a distributed data use infrastructure, for example, by providing pseudonymized data or by federated analyses. With these results of the Interoperability Working Group, the MII is presenting a future-oriented solution for the exchange and use of healthcare data, the applicability of which goes beyond the purpose of research and can play an essential role in the digital transformation of the healthcare system.

Explorative cost-effectiveness analysis of colorectal cancer recurrence detection with next-generation sequencing liquid biopsy in Spain, France, and Germany.

Background: Next-generation sequencing liquid biopsy (NGS-LB) for colorectal cancer (CRC) detection and surveillance remains an expensive technology as economies of scale have not yet been realized. Nevertheless, the cost of sequencing has decreased while sensitivity has increased, raising the question of whether cost-effectiveness (CE) has already been achieved from the perspective of European healthcare systems. Objectives: This health economic (HE) modeling study explores the CE of NGS-LB for CRC based on direct treatment costs compared to standard care without liquid biopsy in Spain, France, and Germany. Methods: A structured literature search was used to collect evidence from 2009 to 2020 on the stage-dependent quality of life (quality-adjusted life-years, QALY), efficacy, and total direct treatment costs (TDC) of NGS-LB. A decision-analytic Markov model was developed. Over the remaining lifetime, cumulative life expectancy (LE), TDC, and QALYs were calculated for 60-year-old men and women in CRC stage III with different assumed effects of NGS-LB of 1% or 3% on improved survival and reduced stage progression, respectively. Results: The use of NGS-LB increases LE by 0.19 years in Spanish men (France: 0.19 years, Germany: 0.13 years) and by 0.21 years in Spanish women (France: 0.21 years, Germany: 0.14 years), respectively. The 3% discounted cost per QALY gained was 35,571.95 € for Spanish men (France: 31,705.15 €, Germany: 37,537.68 €) and 35,435.71 € for Spanish women (France: 31,295.57 €, Germany: 38,137.08 €) in the scenario with 3% improved survival and reduced disease progression. Compared to the other two countries, Germany has by far the highest TDC, which can amount to >80k euros in the last treatment year. Conclusion: In this explorative HE modeling study, NGS-LB achieves generally accepted CE levels in CRC treatment from the health system perspective in three major European economies under assumptions of small improvements in cancer recurrence and survival. Confirmation of these findings through clinical trials is encouraged.

Is it worthwhile to use next generation liquid biopsy for cancer recurrence detection on patients with colorectal cancer? Colon cancer is common. Worldwide, almost one million people die from it every year. Next Generation Sequencing Liquid Biopsy is a very sensitive technology for detecting cancer cells and their genetic information in the blood. Therefore, it is a good way to detect cancer and to detect early recurrence of a previously treated tumor. This test procedure is not yet used very often. Therefore, it is still expensive. Furthermore, there are still no studies that have demonstrated that and how liquid biopsy can aid doctors and patients after initial treatment. The research team of this study has developed an analytical model to investigate what performance liquid biopsy should have to demonstrate an affordable patient benefit in terms of quality of life, survival and cost per additional quality-adjusted life year gained. To do this, they studied the existing medical literature and many cost studies on colorectal cancer for the countries of Spain, France and Germany to feed their model. Then, they made different assumptions about the performance of liquid biopsy and did calculations. In the process, they also particularly examined the significance of specific influencing factors such as costs or disease progression in so-called sensitivity analyses. As a result, the authors found that there are large differences in treatment costs for colorectal cancer between the three countries Spain, France and Germany. Furthermore, even small improvements in the progression of cancer and the survival of cancer patients lead to the economic efficiency of liquid biopsy for the health care system. However, these are still thought experiments, so the research team of this study says that there should be further clinical trials to assess the impact of liquid biopsy on cancer progression and patient survival by using this technology. By this, one could confirm or contradict the authors' educated assumptions and possibly pave a new way towards medical progress for people with colorectal cancer.

Interoperability Is a Process - The Data Sharing Framework.

Common syntax and data semantics are core components of healthcare interoperability standards. However, interoperable data exchange processes are also needed to enable the integration of existing systems between organizations. While solutions for healthcare delivery processes are available and have been widely adopted, support for processes targeting bio-medical research is limited. Our Data Sharing Framework creates a platform to implement research processes like cohort size estimation, reviews and approvals of research proposals, consent checks, record linkage, pseudonymization and data sharing across organizations. The described framework implements a distributed business process engine for executing BPMN 2.0 processes with synchronization and data exchange using FHIR R4 resources. Our reference implementation has been rolled out to 38 organizations across three research consortia in Germany and is available as open source under the Apache 2.0 license.

Monitoring Distributed Business Processes in Biomedical Research.

In biomedical research, business processes, such as data-sharing or feasibility queries, span across several healthcare organizations. Due to the growing number of data-sharing projects and connected organizations, the management of distributed processes gets more complex over time. This leads to an increased need for administrating, orchestrating, and monitoring all distributed processes of a single organization. A proof of concept for a decentralized and use case agnostic monitoring dashboard was developed for the Data Sharing Framework, which most German university hospitals have deployed. The implemented dashboard can handle current, changing, and upcoming processes using only information for cross-organizational communication. This differentiates our approach from other existing use case specific content visualizations. The presented dashboard is a promising solution to provide administrators with an overview of the status of their distributed process instances. Therefore, this concept will be further developed in upcoming releases.

No Transfer Without Validation: A Data Sharing Framework Use Case.

Availability and accessibility are important preconditions for using real-world patient data across organizations. To facilitate and enable the analysis of data collected at a large number of independent healthcare providers, syntactic- and semantic uniformity need to be achieved and verified. With this paper, we present a data transfer process implemented using the Data Sharing Framework to ensure only valid and pseudonymized data is transferred to a central research repository and feedback on success or failure is provided. Our implementation is used within the CODEX project of the German Network University Medicine to validate COVID-19 datasets at patient enrolling organizations and securely transfer them as FHIR resources to a central repository.

Assessment of SARS-CoV-2 Infection among Healthcare Workers of a German COVID-19 Treatment Center.

To date, more than 160 million people have been infected with COVID-19 worldwide. In the present study, we investigated the history of SARS-CoV-2 infection among 3067 healthcare workers (HCW) in a German COVID-19 treatment center during the early phase of the pandemic (July 2020) based on the seroprevalence of SARS-CoV-2 antibodies and self-reported previous PCR results. The results demonstrate a low prevalence of SARS-CoV-2 infection (n = 107 [3.5%]) with no increased risk for employees with a high level of patient exposure in general or working in COVID-19-confined areas in particular. This suggests that the local hygiene standards implemented in our hospital during the first wave of COVID-19 pandemic were effective in preventing patient-to-HCW transmission. No evidence for highly mobile staff serving as a vector for SARS-CoV-2 transmission could be found. In addition, impairment of smell and/or taste was strongly associated with SARS-CoV-2 history.

PrositNG - A Machine Learning Supported Disease Model Generation Software.

Decision models (DM), especially Markov Models, play an essential role in the economic evaluation of new medical interventions. The process of DM generation requires expert knowledge of the medical domain and is a time-consuming task. Therefore, the authors propose a new model generation software PrositNG that is connectable to database systems of real-world routine care data. The structure of the model is derived from the entries in a database system by the help of Machine Learning algorithms. The software was implemented with the programming language Java. Two data sources were successfully utilized to demonstrate the value of PrositNG. However, a good understanding of the local documentation routine and software is paramount to use real-world data for model generation.