Ebook: ICT for Health Science Research

Peter Leo Reichertz (1930–1987) was a pioneer in the application of information and communication technology (ICT), not only to health care but also to health science research.

Together with François Grémy, he introduced the phrase Medical Informatics. His key paradigm was: providing the right information in the right place at the right time. He started his academic work with computers in medicine as a cardiologist at the Medical Faculty of Bonn before he moved to the US, where he worked with Donald Lindberg and other early pioneers in Missouri. In 1969, he received a full professorship at the newly founded Hannover Medical School (MHH), Germany. The chair was denominated Medical Documentation and Data Processing and later renamed Medical Informatics. As was usual in those days, the department included the Medical Computer Center. Reichertz was an initiator and the second president of the European Federation for Medical Informatics (EFMI). In 1983, Reichertz co-founded the German Professional Association of Medical Informatics (Berufsverband Medizinischer Informatiker, BVMI) and was elected as its first president.

In the seventies, Reichertz established the first Medical Informatics courses at the TU Braunschweig, Germany, with Jochen Möhr and Otto Rienhoff. Later, Peter Pretschner, who had collaborated with the Reichertz team at the MHH became the first chair of Medical Informatics at TU Braunschweig. In 2007, the departments in Hanover and Braunschweig joined forces to form the Peter L. Reichertz Institute for Medical Informatics (PLRI). The PLRI is proud to host EFMI STC 2019 on behalf of the German EFMI member Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie (GMDS) e.V. in collaboration with several IMIA, EFMI and GMDS working groups.

Against this background, the EFMI Special Topic Conference (STC) 2019 is dedicated to Peter L. Reichertz, celebrating the 50th anniversary of the start of his work in Hanover.

The current volume presents accepted papers from the EFMI STC 2019, held on April 7th to April 10th, 2019, in Hanover, Germany. By the assigned deadline, we had received more than 90 submissions, from which, after review, the Scientific Program Committee (SPC) accepted 50 as full papers to be included in this volume of proceedings. In addition, 16 poster presentations were accepted. The SPC would like to present these scientific outcomes from the EFMI STC 2019 to the academic community. EFMI STC 2019 is the latest annual conference in the series of special topic conferences organized by EFMI, which each year focus on a specific topic or topics of interest to the biomedical and health informatics community. With respect to Prof. Reichertz, ICT for Health Science Research was selected as the focus topic. The conference is dedicated to recent research & development supporting information systems in biomedical, translational and clinical research, as well as semantic interoperability across such systems for the purpose of data sharing and the analytics of cross-system integrated data. ICT systems for health science research are used in application domains such as clinical trials and the development of drug and medical devices, as well as translational medicine, aiming eventually at better prevention, diagnosis, and interventions in health and care.

A major challenge in this field is the communication with healthcare ICT systems, since much of the data for health science research comes from healthcare. Research often requires data of higher resolution, precision and quality than is typically available in healthcare ICT systems, thus, healthcare data sets are extracted, transformed and loaded into research data warehouses, which leads to a duplication of data and can challenge the integrity of data sets relating to a specific individual across research and healthcare systems, possibly hindering personalized medicine and translational research.

This volume incorporates not only the full papers accepted for oral presentation at the conference, but also the accepted poster presentations (as 2-pages abstracts and not indexed in Medline). As EFMI STC 2019 will have also a special issue in the Journal of Medical Systems (JOMS), some accepted oral papers have also been converted into a non-indexed abstract to avoid self-plagiarism and double-publications. The oral presentations are organized into 12 sessions:

• Digitization of systems medicine

• Data quality, privacy, and security

• Health interoperability through standards

• Medical registries and clinical trials

• Interfaces with diagnostic or therapeutic systems

• Novel approaches in ICT for health research

• Big data analytics

• Towards interoperable health information systems

• Improving the understanding of health data

• Data-driven decision support in systems medicine

• Mobile data capture and electronic patient reported outcomes (ePRO)

• ICT for clinical trials.

The proceedings are published by IOS Press in the internationally indexed series of Studies in Health Technology and Informatics.

The editors would like to thank the local organizing committee – and all reviewers in particular – who performed in an outstandingly professional and objective way throughout the process of refereeing the submitted scientific work, producing a high-quality publication for a successful scientific event.

Furthermore, we would like to thank the keynote speakers. Prof. Dipak Kalra, President of the European Institute for Innovation through Health Data will deliver a talk on Raising the impact of real world evidence. Dr. Pierre Meulien, Executive Director of The Innovative Medicines Initiative (IMI) is going to present on How to harness big data for the benefit of patients.

Unique for this STC, the meeting has been extended to a third day in honor of Peter L. Reichertz. The Reichertz Symposium will be open to the public (no conference fee) and will present invited talks and interviews. It will address the history of medical informatics, lessons learned, and future perspectives in research, education and practice. At this symposium, an extraordinary EFMI Peter L. Reichertz Young Scientist Prize will be presented for the best paper and presentation at EFMI STC 2019. The Young Scientist Prize comes with a check for Euro 1,000 and is usually reserved for the EFMI main conference, the Medical Informatics Europe (MIE).

With all this in mind, we wish all of you a successful and exciting conference that establishes personal connections in all topics for ICT in Health Science Research.

Hanover, April 2019

Amnon Shabo (Shvo)

Inge Madsen

Hans-Ulrich Prokosch

Kristiina Häyrinen

Klaus-Hendrik Wolf

Fernando Martin-Sanchez

Matthias Löbe

Thomas M. Deserno

In an environment in which most regulatory and authoritative health strategy decisions are made on the basis of randomised control trials, real-world evidence (RWE), primarily derived from electronic health records, remains a second-class citizen. Real world evidence is widely taken to include Pragmatic clinical trials and insights derived from the distributed analysis of large volumes of routinely collected health data and registry data (so called big data). This presentation will look at the growing scale and reputation of big health data, the ways in which good governance principles and better quality data are creating reusable data at scale, how platforms and tools are enabling better quality evidence generation, and the perspectives of different stakeholders towards the positioning of RWE in decision making: by regulators, health technology assessment agencies, outcomes benchmarking and value based care. This talk will review how we are presently able to generate trustworthy real world evidence, what we mean by that, and the barriers that remain to trusting it. These remaining barriers will need to be tackled by future health informatics research.

IMI is a large public private partnership which has committed Euro 5 Billion from the European Commission, the European Pharmaceutical sector and other partners, to enable and accelerate bringing medical innovation to patients. IMI is now 10 years old and already has changed the ecosystem and way of working in developing innovative medicines across the public/private divide. Big data represents a significant piece of this investment and projects in this area are attempting to provide solutions to major challenges including:

1. Data integration from many sources across many jurisdictions and institutions and which need to be validated with regard to their quality and robustness

2. Data interfaces, for example, between research and clinical data and how to overcome the challenges around data protection and access

3. Data standards and who establishes these and how they are deployed

4. How do we scale and sustain some of the successful pilots to ensure the value of the investment?

5. How do we take advantage of the digital revolution to enable more relevant and accurate data capture in the real world?

Much has been done already through IMI projects and beyond and now we need to consolidate through intelligent implementation across the European landscape. With a mixture of generic and disease specific investments, and although many challenges remain, significant progress has been made and examples of success will be described.

As hospital information systems are complex and the requirements for interoperability grow with the increasing networking in healthcare, careful planning becomes more and more necessary. The use of standards as described in IHE profiles, for example, are an important prerequisite for enabling interoperability. Enterprise Architecture Planning (EAP) methods should support this, but none of the currently available EAP methods offers the option of using IHE profiles. The 3LGM2IHE project wants to close this gap and implement the support of IHE profiles in the 3LGM² tool. This paper describes how requirements for this tool were determined and presents the results.

Informed consent of patients to research studies is a cornerstone to modern healthcare, which has lead to considerable administrative effort. The purpose of this paper is to show how forms and questionnaires and their respective answers can be captured in a standardized, structured way, in order to enable automated verification. The use of the HL7 FHIR resources Questionnaire and QuestionnaireResponse is discussed with respect to the different implementation options of Extensions, POST Interceptors, FHIR Operations, and CDS Hooks. These four approaches are described and it is determined whether they produce standard-compliant results and how they can be integrated with other solutions. Since all approaches yield advantages and disadvantages, the choice amongst any option must be based on the actual use case.

Secondary use of electronic health records using data warehouses (DW) has become an attractive approach to support clinical research. In order to increase the volume of underlying patient data DWs at different institutions can be connected to research networks. Two obstacles to connect a DW to such a network are the syntactical differences between the involved DW technologies and differences in the data models of the connected DWs. The current work presents an approach to tackle both problems by translating queries from the DW system openEHR into queries from the DW system i2b2 and vice versa. For the subset of queries expressible in the query languages of both systems, the presented approach is well feasible.

i2b2 and REDCap are two widely adopted solutions respectively to facilitate data re-use for research purpose and to manage non-for-profit research studies. REDCap provides the design specifications to build a web service used to import data from an external source with a procedure called DDP. In this work we have developed a web service that implements these specifications in order to import data from i2b2. Our approach has been tested with a real REDCap study.

Background: University Hospital Erlangen provides clinical decision support (CDS) functions in the intensive care setting, that are based on the Arden Syntax standard. These CDS functions generate extensive output, including patient data charts. In the course of the migration of our CDS platform we revised the charting tool because although the tool was generally perceived as useful, the clinical users reported several shortcomings.

Objective: During the migration of our CDS platform, we aimed at resolving the reported shortcomings and at developing a reusable and parameterizable charting tool, driven by best practices and requirements of local clinicians.

Methods: We conducted a requirements analysis with local clinicians and searched the literature for well-established guidelines for clinical charts. Using a charting library, we then implemented the tool based on the found criteria and provided it with a REST interface.

Results: The criteria catalog included 18 requirements, all of which were successfully implemented. The new charting tool fully replaced the previous implementation in clinical routine. It also provides a web interface that enables clinicians to configure charts without programming skills.

Conclusion: The new charting tool combines local preferences with best practices for visualization of clinical time series data. With its REST interface and reusable design it can be easily integrated in existing CDS platforms.

Adverse Childhood Experiences (ACEs) have been proven to be linked to increased risks of a multitude of negative health outcomes and conditions when children reach adulthood and beyond. To better understand the relationship between ACEs and the associated health outcomes and eventually to pan and implement preventive interventions, access to an integrated coherent actionable data set is crucial. In this paper, we introduce a formal reusable ontological framework to capture the knowledge in the domain of Adverse Childhood Experiences to improve ACEs surveillance and response.

The Arden Syntax is a standard for clinical decision support functions in the form of Medical Logic Modules (MLMs). While the data type system of the early versions was limited to flat lists, later versions introduced an object type, supporting complex data structures, even up to entire electronic medical records (EMRs). Such objects are static insofar as their structure cannot be modified at MLM runtime. University Hospital Erlangen uses an experimental Arden Syntax version termed PLAIN, which provides an integrated mapper for arbitrary data structures, including entire EMRs. To facilitate knowledge encoding and reduce MLM complexity, we searched for a way to complement patient records with precalculated data items. We modified the object data type in two ways. The first was to include a statement for the explicit creation of new attributes; the second was to implicitly create an attribute whenever a value is assigned to a previously non-existing attribute. As a proof of concept, we complemented the ventilation section of every accessed EMR with a patient-individual recommendation for the expiratory tidal volume. A means to extend the structure of an object at runtime provides several advantages. The precalculated data items need no longer be calculated by the MLMs themselves, which reduces complexity and facilitates code maintenance. This might be beneficial not only for clinical decision support, but also with respect to the use of Arden Syntax language constructs for phenotyping queries, as well as with respect to the frequently required preprocessing of EMR data.

To manage medical information semantic interoperability is essential. Mapping of concepts and mapping of terminologies are two objectives to reach semantic interoperability. Russian proprietary health information system and FHIR overlaps (60%) were calculated to estimate possibility of standardization. Russian terminology directories and FHIR overlaps were calculated to estimate possibility of use Russian terminologies and codifications in FHIR-based information system. The result is promising, however, requires more wide investigation using automatic tools.

Background: The cBioPortal is a prevalent open-source translational research platform, allowing private instances and extensions.

Objective: Our aim was to build up an own instance of cBioPortal, identify missing functionality by interviewing researchers, and implementing these extensions.

Methods: We examined the code base of the cBioPortal and conducted a requirements analysis with researchers. Then an own extension was implemented and a usability evaluation was performed.

Results: We developed a new tab in the results view of cBioPortal adding the option to analyze the correlation of gene expression and mutation patterns.

Conclusion: While extending the cBioPortal is possible, there are still some challenges to overcome. A plug-in concept and a more detailed documentation would greatly facilitate the development of own extensions.

Belgium is in a transition from paper-based prescriptions to electronic prescriptions (ePrescriptions). Since patients still receive a paper proof of the ePrescription, this proof is sometimes used as a paper-based prescription. In this study, the frequency of incorrect use of the paper proof was evaluated and possible reasons for incorrect use were hypothesized. In 10,000 prescriptions, 226 ePrescriptions (2.26 %) were handled incorrectly. Possible reasons for this handling are (1) non-compliance of the community pharmacist; (2) errors in software or handling of the community pharmacist; (3) errors at the prescriber side or patient tries to fraud; (4) incorrectly revoking the ePrescription; and (5) errors in prescriber's software. The presence of incentives and penalties might help in preventing this erroneous type of handling.

Aims: To compare the characteristics of scientific publications performed in hospitals that used with those that didn't use an obstetric electronic health record (EHR).

Methods: This study included two reviews (A and B). Review A was an exploratory analysis of all 100 abstracts presented at the Scientific Meeting of the Portuguese Society of Obstetrics and Maternal-Fetal Medicine, in November 2017. Review B was a systematic review of studies in obstetrics, performed in Portugal and published between 2016–18 and indexed in PubMed. In both reviews, the included papers/abstracts were divided into two groups: from hospitals that used ObsCare^® (ObsCare group) and from hospitals without a specific obstetric EHR or that didn't use ObsCare (sObsCare group).

Results: In both reviews, the sample size was significantly higher in hospitals from the ObsCare group. In review B, the length of the study period was also significantly longer in ObsCare group; no significant difference was found in review A.

Conclusion: Publications from hospitals that used an obstetric specific EHR (ObsCare), included a higher number of patients and longer study periods.

Numerous studies have reported that inconsistencies exist between clinical practice guidelines (CPGs) elaborated on the same topic and at the same date. These results are usually established from the analyses handled on narrative CPGs or on their semi-structured version. In the context of the European-funded DESIREE project, we have developed a guideline-based decision support system embedding various contemporary CPGs on breast cancer management. We have run the GL-DSS on a sample of 571 retrospective clinical cases and specifically assessed the level of inconsistencies between the recommendations issued by the US National Comprehensive Cancer Network (NCCN) Guidelines for Breast Cancer and those issued by Assistance Publique – Hôpitaux de Paris (AP-HP, France) CPGs. We proposed a typology with six different situations from total incompatibility to complete identity as a result of the comparison of NCCN and AP-HP CPGs for each clinical case. It was interesting to observe that we got 38% of inconsistencies with 3% of total incompatibility, and 62% of similarity with 0% of complete identity. The silence of one CPG was resolved by the other CPG providing recommendations in 21% of the cases.

Health care organizations are worried about information security because they generate important and valuable information in the field of health informatics. Therefore, the present study was conducted to investigate the health information management staff's viewpoint on non-technical security management factors. A descriptive cross-sectional study was conducted between Feb to Apr 2018 in 12 academic hospitals in Mashhad, north-eastern Iran. Data were collected through a paper-based questionnaire that was designed based on previous studies and published literature. From the views of staff, the information security management had the highest average (Mean = 3.63) while organizational culture had the lowest average (Mean = 3.32). The results of this study showed that security controls are essential for protecting critical information. Organizations must also consider appropriate security actions for protecting critical organizational information.

Background: To make patient care data more accessible for research, German university hospitals join forces in the course of the Medical Informatics Initiative. In a first step, the administrative data of university hospitals is made available for federated utilization. Project-specific de-identification of this data is necessary to satisfy privacy laws.

Objective: We want to make a statement about the population uniqueness of the data. By generalizing the data, we try to reduce uniqueness and improve k-anonymity.

Methods: We analyze quasi-identifying attributes of the Erlangen University Hospital's billing data regarding population uniqueness and re-identification risk. We count individuals per equality class (k) to measure uniqueness.

Results: Because of the diagnoses and procedures being particularly unique in combination with sex and age of the patients, the data set is not anonymized in matters of k-anonymity with k > 1 . We are able to reduce population uniqueness with generalization and suppression of unique domains.

Conclusion: To create k-anonymity with k > 1 while still maintaining a particular utility of the data, we need to apply further established strategies of de-identification.

Provision of security and privacy to genomic data is a key issue in current genomic information representation. Existing formats do not give a solution to these issues (or they provide a partial one), so new solutions are demanded. MPEG-G (ISO/IEC 23092, Genomic Information Representation) is an International Standard for the representation of genomic information being defined by the MPEG Committee (Moving Pictures Expert Group, ISO/IEC JTC1 SC29/WG11). We provide flexible protection to the information stored inside the MPEG-G format with a combination of security techniques and privacy rules.

Introduction: We describe principles of leveraging clinical information models (CIMs) for data quality (DQ) checks and present the exemplary application of these principles.

Methods: openEHR compliant CIMs are used to express DQ-checks as constraints. Test setting is the process of extracting, transforming and loading (ETL) assisted ventilation data from two patient data management systems (PDMS) of a pediatric intensive care unit into a local openEHR-based data repository.

Results: A generic component logs aggregated DQ-check results for ~28 million entries. DQ-issue types in the presented results are range-, format- and value set violations.

Discussion: CIMs are suitable means to define DQ-checks for range-, format-, value set and cardinality constraints. However, they cannot constitute a complete solution for standardized DQ-assessment.

Many healthcare IT systems in Germany are unable to interoperate with other systems through standardised data formats. Therefore it is difficult to store and retrieve data and to establish a systematic collection of data with provenance across systems and even healthcare institutions. We outline the concept for a Transformation Pipeline that can act as a processor for proprietary medical data formats from multiple sources. Through a modular construction, the pipeline relies on different data extraction and data enrichment modules as well as on interfaces to external definitions for interoperability standards. The developed solution is extendable and reusable, enabling data transformation independent from current format definitions and entailing the opportunity of collaboration with other research groups.

In patient care and medical research patient data often has to be transferred between different electronic systems. These systems can be very heterogeneous, sometimes even legacy systems, and thus, often do not support standardized interfaces for data transfer. Since nowadays barcode scanners are commonly used in clinical routine and smartphones are accessible to most patients, we implemented different interfaces based on Data Matrix codes to transfer patient data between several medical applications. Objective of this work is to show different use cases in which Data Matrix codes have been successfully applied and discuss the lessons we have learned during the process of implementation and practical usage.

Sickle cell disease is a major public health problem in Senegal. It is an inherited disease that affects about 300,000 births worldwide each year. There are 70 million people affected worldwide, 80% of whom live in sub-Saharan Africa. In Senegal, 1 in 10 people carries the sickle cell disease gene. This disease requires follow-up from birth and for life. The patient care requires the integration and the analysis of biological, clinical, social, economic data., etc. In this paper, we propose a health information system for data management of the blood sampling from the newborn at the maternity wards and the disease screening at the Center for Research and Ambulatory Care of the Sickle Cell Disease (CERPAD).

PACS as a medical imaging technology is an information system that provides quick and convenient access to the medical images, as well as the quick and easy exchange of images and electronic reports among specialists in different departments. The objective of the study was to evaluate the PACS from the point of view of users in hospitals affiliated to Mashhad University of Medical Sciences. The study population consisted of the PACS' users (radiologists and the radiology technologists) in the radiology departments (103 staff). Data were collected through a questionnaire that was designed based on previous studies and published literature. From the views of users, quality of information of PACS had the highest average (Mean = 3.57±1.02), while quality of services had the lowest average (Mean = 2.99±0.19). About the quality of information, the highest and lowest averages were dedicated to information security (Mean = 4±0.69) and quality of pictures (Mean = 3.28±0.87), respectively. Generally, the findings of study indicate that there are problems in different parts of PACS quality in Iran and according to the high cost of purchasing, implementation, maintenance, and updating the PACS in hospitals, proper selection and use of software and hardware as well as proper maintenance of the system may lead to not only return on investment but also provision of telemedicine.