Data Integration for Future Medicine (DIFUTURE)

Peer review is a critical process in the publication of scientific findings; trainees and young investigators, however, have few opportunities to learn systematically how to review manuscripts. Journal editors have an opportunity to engage trainees and young investigators in the peer review process early during their career. Methods of Information in Medicine, an official journal of the International Medical Informatics Association, is initiating a Student Editorial Board. The journal invites applications from international graduate and post-doctoral training programs that have a focus on health informatics, biomedical informatics, or a related field. Each year up to six trainees will be invited to join the Student Editorial Board. The trainees will go through a mentored training experience that includes an active involvement in the various aspects of peer review during their one to two-year term of appointment. The journal expects that the Student Editorial Board will benefit trainees and young investigators in becoming skilled reviewers and engaged peers who can offer professional, constructive, and informative feedback and enhance the process of scientific communication.

Modeling in biomedical informatics—An exploratory analysis: Part 2

Background The current gap between the availability of routine imaging data and its provisioning for medical research hinders the utilization of radiological information for secondary purposes. To address this, the German Medical Informatics Initiative (MII) has established frameworks for harmonizing and integrating clinical data across institutions, including the integration of imaging data into research repositories, which can be expanded to routine imaging data.Objectives This project aims to address this gap by developing a large-scale data processing pipeline to extract, convert, and pseudonymize DICOM (Digital Imaging and Communications in Medicine) metadata into “ImagingStudy” Fast Healthcare Interoperability Resources (FHIR) and integrate them into research repositories for secondary use.Methods The data processing pipeline was developed, implemented, and tested at the Data Integration Center of the University Hospital Erlangen. It leverages existing open-source solutions and integrates seamlessly into the hospital's research IT infrastructure. The pipeline automates the extraction, conversion, and pseudonymization processes, ensuring compliance with both local and MII data protection standards. A large-scale evaluation was conducted using the imaging studies acquired by two departments at University Hospital Erlangen within 1 year. Attributes such as modality, examined body region, laterality, and the number of series and instances were analyzed to assess the quality and availability of the metadata.Results Once established, the pipeline processed a substantial dataset comprising over 150,000 DICOM studies within an operational period of 26 days. Data analysis revealed significant heterogeneity and incompleteness in certain attributes, particularly the DICOM tag “Body Part Examined.” Despite these challenges, the pipeline successfully generated valid and standardized FHIR, providing a robust basis for future research.Conclusion We demonstrated the setup and test of a large-scale end-to-end data processing pipeline that transforms DICOM imaging metadata directly from clinical routine into the Health Level 7-FHIR format, pseudonymizes the resources, and stores them in an FHIR server. We showcased that the derived FHIRs offer numerous research opportunities, for example, feasibility assessments within Bavarian and Germany-wide research infrastructures. Insights from this study highlight the need to extend the “ImagingStudy” FHIR with additional attributes and refine their use within the German MII.

This article is part of the Focus Theme of METHODS of Information in Medicine on "Managing Interoperability and Complexity in Health Systems". The increasing availability of electronic clinical data provides great potential for finding eligible patients for clinical research. However, data heterogeneity makes it difficult for clinical researchers to interrogate sources consistently. Existing standard query languages are often not sufficient to query across diverse representations. Thus, a higher-level domain language is needed so that queries become data-representation agnostic. To this end, we define a clinician-readable computational language for querying whether patients meet eligibility criteria (ECs) from clinical trials. This language is capable of implementing the temporal semantics required by many ECs, and can be automatically evaluated on heterogeneous data sources. By reference to standards and examples of existing ECs, a clinician-readable query language was developed. Using a model-based approach, it was implemented to transform captured ECs into queries that interrogate heterogeneous data warehouses. The query language was evaluated on two types of data sources, each different in structure and content. The query language abstracts the level of expressivity so that researchers construct their ECs with no prior knowledge of the data sources. It was evaluated on two types of semantically and structurally diverse data warehouses. This query language is now used to express ECs in the EHR4CR project. A survey shows that it was perceived by the majority of users to be useful, easy to understand and unambiguous. An EC-specific language enables clinical researchers to express their ECs as a query such that the user is isolated from complexities of different heterogeneous clinical data sets. More generally, the approach demonstrates that a domain query language has potential for overcoming the problems of semantic interoperability and is applicable where the nature of the queries is well understood and the data is conceptually similar but in different representations. Our language provides a strong basis for use across different clinical domains for expressing ECs by overcoming the heterogeneous nature of electronic clinical data whilst maintaining semantic consistency. It is readily comprehensible by target users. This demonstrates that a domain query language can be both usable and interoperable.

This article is part of the Focus Theme of Methods of Information in Medicine on "Big Data and Analytics in Healthcare". Hospital readmissions raise healthcare costs and cause significant distress to providers and patients. It is, therefore, of great interest to healthcare organizations to predict what patients are at risk to be readmitted to their hospitals. However, current logistic regression based risk prediction models have limited prediction power when applied to hospital administrative data. Meanwhile, although decision trees and random forests have been applied, they tend to be too complex to understand among the hospital practitioners. Explore the use of conditional logistic regression to increase the prediction accuracy. We analyzed an HCUP statewide inpatient discharge record dataset, which includes patient demographics, clinical and care utilization data from California. We extracted records of heart failure Medicare beneficiaries who had inpatient experience during an 11-month period. We corrected the data imbalance issue with under-sampling. In our study, we first applied standard logistic regression and decision tree to obtain influential variables and derive practically meaning decision rules. We then stratified the original data set accordingly and applied logistic regression on each data stratum. We further explored the effect of interacting variables in the logistic regression modeling. We conducted cross validation to assess the overall prediction performance of conditional logistic regression (CLR) and compared it with standard classification models. The developed CLR models outperformed several standard classification models (e.g., straightforward logistic regression, stepwise logistic regression, random forest, support vector machine). For example, the best CLR model improved the classification accuracy by nearly 20% over the straightforward logistic regression model. Furthermore, the developed CLR models tend to achieve better sensitivity of more than 10% over the standard classification models, which can be translated to correct labeling of additional 400 - 500 readmissions for heart failure patients in the state of California over a year. Lastly, several key predictor identified from the HCUP data include the disposition location from discharge, the number of chronic conditions, and the number of acute procedures. It would be beneficial to apply simple decision rules obtained from the decision tree in an ad-hoc manner to guide the cohort stratification. It could be potentially beneficial to explore the effect of pairwise interactions between influential predictors when building the logistic regression models for different data strata. Judicious use of the ad-hoc CLR models developed offers insights into future development of prediction models for hospital readmissions, which can lead to better intuition in identifying high-risk patients and developing effective post-discharge care strategies. Lastly, this paper is expected to raise the awareness of collecting data on additional markers and developing necessary database infrastructure for larger-scale exploratory studies on readmission risk prediction.

Medical image computing has become a key technology in high-tech applications in medicine and an ubiquitous part of modern imaging systems and the related processes of clinical diagnosis and intervention. Over the past years significant progress has been made in the field, both on methodological and on application level. Despite this progress there are still big challenges to meet in order to establish image processing routinely in health care. In this issue, selected contributions of the German Conference on Medical Image Processing (BVM) are assembled to present latest advances in the field of medical image computing. The winners of scientific awards of the German Conference on Medical Image Processing (BVM) 2008 were invited to submit a manuscript on their latest developments and results for possible publication in Methods of Information in Medicine. Finally, seven excellent papers were selected to describe important aspects of recent advances in the field of medical image processing. The selected papers give an impression of the breadth and heterogeneity of new developments. New methods for improved image segmentation, non-linear image registration and modeling of organs are presented together with applications of image analysis methods in different medical disciplines. Furthermore, state-of-the-art tools and techniques to support the development and evaluation of medical image processing systems in practice are described. The selected articles describe different aspects of the intense development in medical image computing. The image processing methods presented enable new insights into the patient's image data and have the future potential to improve medical diagnostics and patient treatment.

Medicine and biomedical sciences have become data-intensive fields, which, at the same time, enable the application of data-driven approaches and require sophisticated data analysis and data mining methods. Biomedical informatics provides a proper interdisciplinary context to integrate data and knowledge when processing available information, with the aim of giving effective decision-making support in clinics and translational research. To reflect on different perspectives related to the role of data analysis and data mining in biomedical informatics. On the occasion of the 50th year of Methods of Information in Medicine a symposium was organized, which reflected on opportunities, challenges and priorities of organizing, representing and analysing data, information and knowledge in biomedicine and health care. The contributions of experts with a variety of backgrounds in the area of biomedical data analysis have been collected as one outcome of this symposium, in order to provide a broad, though coherent, overview of some of the most interesting aspects of the field. The paper presents sections on data accumulation and data-driven approaches in medical informatics, data and knowledge integration, statistical issues for the evaluation of data mining models, translational bioinformatics and bioinformatics aspects of genetic epidemiology. Biomedical informatics represents a natural framework to properly and effectively apply data analysis and data mining methods in a decision-making context. In the future, it will be necessary to preserve the inclusive nature of the field and to foster an increasing sharing of data and methods between researchers.

Health and medical informatics education has meanwhile gained considerable importance for medicine and for health care. Specialized programs in health/medical informatics have therefore been established within the last decades. This special issue of Methods of Information in Medicine contains papers on health and medical informatics education. It is mainly based on selected papers from the 5th Working Conference on Health/Medical Informatics Education of the International Medical Informatics Association (IMIA), which was held in September 1992 at the University of Heidelberg/Technical School Heilbronn, Germany, as part of the 20 years' celebration of medical informatics education at Heidelberg/Heilbronn. Some papers were presented on the occasion of the 10th anniversary of the health information science program of the School of Health Information Science at the University of Victoria, British Columbia, Canada. Within this issue, programs in health/medical informatics are presented and analyzed: the medical informatics program at the University of Utah, the medical informatics program of the University of Heidelberg/School of Technology Heilbronn, the health information science program at the University of Victoria, the health informatics program at the University of Minnesota, the health informatics management program at the University of Manchester, and the health information management program at the University of Alabama. They all have in common that they are dedicated curricula in health/medical informatics which are university-based, leading to an academic degree in this field. In addition, views and recommendations for health/medical informatics education are presented. Finally, the question is discussed, whether health and medical informatics can be regarded as a separate discipline with the necessity for specialized curricula in this field.(ABSTRACT TRUNCATED AT 250 WORDS)

This article is part of a Focus Theme of METHODS of Information in Medicine on Health Record Banking. In late summer 2010, an organization was formed in greater Phoenix, Arizona (USA), to introduce a health record bank (HRB) in that community. The effort was initiated after market research and was aimed at engaging 200,000 individuals as members in the first year (5% of the population). It was also intended to evaluate a business model that was based on early adoption by consumers and physicians followed by additional revenue streams related to incremental services and secondary uses of clinical data, always with specific permission from individual members, each of whom controlled all access to his or her own data. To report on the details of the HRB experience in Phoenix, to describe the sources of problems that were experienced, and to identify lessons that need to be considered in future HRB ventures. We describe staffing for the HRB effort, the computational platform that was developed, the approach to marketing, the engagement of practicing physicians, and the governance model that was developed to guide the HRB design and implementation. Despite efforts to engage the physician community, limited consumer advertising, and a carefully considered financial strategy, the experiment failed due to insufficient enrollment of individual members. It was discontinued in April 2011. Although the major problem with this HRB project was undercapitalization, we believe this effort demonstrated that basic HRB accounts should be free for members and that physician engagement and participation are key elements in constructing an effective marketing channel. Local community governance is essential for trust, and the included population must be large enough to provide sufficient revenues to sustain the resource in the long term.

This article is part of a For-Discussion-Section of Methods of Information in Medicine about the paper "Computational Electrocardiography: Revisiting Holter ECG Monitoring" written by Thomas M. Deserno and Nikolaus Marx. It is introduced by an editorial. This article contains the combined commentaries invited to independently comment on the paper of Deserno and Marx. In subsequent issues the discussion can continue through letters to the editor.

This article is part of the Focus Theme of Methods of Information in Medicine on "Biosignal Interpretation: Advanced Methods for Neural Signals and Images". Dynamic Causal Modelling (DCM) is a generic formalism to study effective brain connectivity based on neuroimaging data, particularly functional Magnetic Resonance Imaging (fMRI). Recently, there have been attempts at modifying this model to allow for stochastic disturbances in the states of the model. This paper proposes the Multiple-Model Kalman Filtering (MMKF) technique as a stochastic identification model discriminating among different hypothetical connectivity structures in the DCM framework; moreover, the performance compared to a similar deterministic identification model is assessed. The integration of the stochastic DCM equations is first presented, and a MMKF algorithm is then developed to perform model selection based on these equations. Monte Carlo simulations are performed in order to investigate the ability of MMKF to distinguish between different connectivity structures and to estimate hidden states under both deterministic and stochastic DCM. The simulations show that the proposed MMKF algorithm was able to successfully select the correct connectivity model structure from a set of pre-specified plausible alternatives. Moreover, the stochastic approach by MMKF was more effective compared to its deterministic counterpart, both in the selection of the correct connectivity structure and in the estimation of the hidden states. These results demonstrate the applicability of a MMKF approach to the study of effective brain connectivity using DCM, particularly when a stochastic formulation is desirable.

The Medical Product Information found in most medication boxes offer a wealth of information, including terms of active ingredients, excipients, indications, dosage, route of administration, risks, and safety information. Digital health services that help patients, their care givers, and health professionals to manage medication, can be improved with tailored information based on user profile, the patient's Electronic Health Record (EHR) summary, and Medicinal Product Information. The electronic Product information (ePI) comprises the summary of product characteristics, package leaflet, and product label. The European Medicines Agency released in 2021 the first version of the EU proof-of-concept ePI standard based on HL7 FHIR. The Gravitate-Health project uses this common standard as a springboard to implement a federated open-source platform and services that helps advance access, understanding, and adherence by providing trusted medicinal information in an interoperable and scalable way. In this paper, we present the agile technical approach and co-creation process to design, test, and progressively mature interoperability working with the HL7 Vulcan Accelerator and FHIR connectathons.

The evaluation of real-world data (RWD) enables insights to be gained from awide range of patient data collected in routine clinical practice. In addition, multicenter analyses represent abroad and representative patient population and have the potential to capture the actual treatment situation. As abasis for this, the definition of datasets and an infrastructure for data exchange is necessary. Data integration centers (DIC) have already been established at (university) hospitals throughout Germany in order to extract RWD for scientific analyses from the various source systems and integrate them into research-compatible data infrastructures. The project described here aims to demonstrate the added value of this data integration using a case of application in ophthalmology, defining acore dataset as an ophthalmology extension module and establishing across-site data exchange infrastructure. As afirst step, the treatment success of eye diseases treated with intravitreal injection (IVI) should be improved. To achieve this goal adashboard for clinical data is provided that clearly visualizes the merged data. Furthermore, algorithms will be developed to identify new imaging biomarkers that can be used for treatment monitoring and predict treatment outcomes.

As part of the German Medical Informatics Initiative (MII) and Network University Medicine (NUM), a central research terminology service (TS) is provided by the Service Unit Terminology Services (SU-TermServ). This HL7 FHIR-based service depends on the timely and comprehensive availability of FHIR terminology resources to provide the necessary interactions for the distributed MII/NUM infrastructure. While German legislation has recently instituted a national terminology service for medical classifications and terminologies, the scope of the MII and NUM extends beyond routine patient care, encompassing the need for supplementary or specialized services and terminologies that are not commonly utilized elsewhere. The SU-TermServ's processes are based on established FHIR principles and the recently-proposed Canonical Resources Management Infrastructure Implementation Guide, which are outlined in this paper. The strategy and processes implemented within the project can deliver the needed resources both to the central FHIR terminology service, but also to the local data integration centers, in a transparent and consistent fashion. The service currently provides approximately 7000 resources to users via the standardized FHIR API. The professionalized distribution and maintenance of these terminological resources and the provision of a powerful TS implementation aids both the development of the Core Data Set and the data integration centers, and ultimately biomedical researchers requesting access to this rich data.

ZusammenfassungDie Arbeitsgruppe Interoperabilität der Medizininformatik-Initiative (MII) ist die Plattform für die Abstimmung übergreifender Vorgehensweisen, Datenstrukturen und Schnittstellen zwischen den Datenintegrationszentren (DIZ) der Universitätskliniken und nationalen bzw. internationalen Interoperabilitätsgremien. Ziel ist die gemeinsame inhaltliche und technische Ausgestaltung einer über das Forschungsdatenportal für Gesundheit nutzbaren verteilten Infrastruktur zur Sekundärnutzung klinischer Versorgungsdaten. Wichtige Rahmenbedingungen sind dabei Datenschutz und IT-Sicherheit für die Nutzung von Gesundheitsdaten in der biomedizinischen Forschung. Hierfür werden in dezidierten Taskforces geeignete Methoden eingesetzt, um prozessuale, syntaktische und semantische Interoperabilität für Datennutzungsprojekte zu ermöglichen. So wurde der MII-Kerndatensatz, bestehend aus mehreren Modulen mit zugehörigen Informationsmodellen, entwickelt und mittels des Standards HL7® FHIR® implementiert, um fachliche und technische Vorgaben für die interoperable Datenbereitstellung von Versorgungsdaten durch die DIZ zu ermöglichen. Zur näheren Beschreibung dieser Datensätze dienen internationale Terminologien und konsentierte Metadaten. Die Gesamtarchitektur, einschließlich übergreifender Schnittstellen, setzt die methodischen und rechtlichen Anforderungen an eine verteilte Datennutzungsinfrastruktur z. B. durch Bereitstellung pseudonymisierter Daten oder föderierte Analysen um. Mit diesen Ergebnissen der Arbeitsgruppe Interoperabilität stellt die MII eine zukunftsweisende Lösung für den Austausch und die Nutzung von Routinedaten vor, deren Anwendbarkeit über den Zweck der Forschung hinausgeht und eine wesentliche Rolle in der digitalen Transformation des Gesundheitswesens spielen kann.