A National, Semantic-Driven, Three-Pillar Strategy to Enable Health Data Secondary Usage Interoperability for Research Within the Swiss Personalized Health Network: Methodological Study

The Role of Informatics in Advancing Emergency Medicine: A Comprehensive Review

Emergency Medicine Informatics (EMI) is a rapidly advancing field that utilizes information technology to enhance the delivery of emergency medical services. This comprehensive literature review explores the key components, benefits, challenges, and future directions of EMI. By integrating Electronic Health Records, Clinical Decision Support Systems, telemedicine, data analytics, interoperability, and patient monitoring systems, EMI has the potential to significantly improve patient outcomes and operational efficiency in emergency departments. However, the implementation of these technologies faces several obstacles, including interoperability issues, data security concerns, usability challenges, and high costs. This review highlights how these technologies are transforming emergency care, discusses the barriers to their implementation, and provides perspectives on potential solutions and future progress in the field.

Electronic Health Record and Semantic Issues Using Fast Healthcare Interoperability Resources: Systematic Mapping Review

BACKGROUND

The increasing use of electronic health records and the Internet of Things has led to interoperability issues at different levels (structural and semantic). Standards are important not only for successfully exchanging data but also for appropriately interpreting them (semantic interoperability). Thus, to facilitate the semantic interoperability of data exchanged in health care, considerable resources have been deployed to improve the quality of shared clinical data by structuring and mapping them to the Fast Healthcare Interoperability Resources (FHIR) standard.

OBJECTIVE

The aims of this study are 2-fold: to inventory the studies on FHIR semantic interoperability resources and terminologies and to identify and classify the approaches and contributions proposed in these studies.

METHODS

A systematic mapping review (SMR) was conducted using 10 electronic databases as sources of information for inventory and review studies published during 2012 to 2022 on the development and improvement of semantic interoperability using the FHIR standard.

RESULTS

A total of 70 FHIR studies were selected and analyzed to identify FHIR resource types and terminologies from a semantic perspective. The proposed semantic approaches were classified into 6 categories, namely mapping (31/126, 24.6%), terminology services (18/126, 14.3%), resource description framework or web ontology language-based proposals (24/126, 19%), annotation proposals (18/126, 14.3%), machine learning (ML) and natural language processing (NLP) proposals (20/126, 15.9%), and ontology-based proposals (15/126, 11.9%). From 2012 to 2022, there has been continued research in 6 categories of approaches as well as in new and emerging annotations and ML and NLP proposals. This SMR also classifies the contributions of the selected studies into 5 categories: framework or architecture proposals, model proposals, technique proposals, comparison services, and tool proposals. The most frequent type of contribution is the proposal of a framework or architecture to enable semantic interoperability.

CONCLUSIONS

This SMR provides a classification of the different solutions proposed to address semantic interoperability using FHIR at different levels: collecting, extracting and annotating data, modeling electronic health record data from legacy systems, and applying transformation and mapping to FHIR models and terminologies. The use of ML and NLP for unstructured data is promising and has been applied to specific use case scenarios. In addition, terminology services are needed to accelerate their use and adoption; furthermore, techniques and tools to automate annotation and ontology comparison should help reduce human interaction.

What are the bottlenecks to health data sharing in Switzerland? An interview study

BACKGROUND

While health data sharing for research purposes is strongly supported in principle, it can be challenging to implement in practice. Little is known about the actual bottlenecks to health data sharing in Switzerland.

AIMS OF THE STUDY

This study aimed to assess the obstacles to Swiss health data sharing, including legal, ethical and logistical bottlenecks.

METHODS

We identified 37 key stakeholders in data sharing via the Swiss Personalised Health Network ecosystem, defined as being an expert on sharing sensitive health data for research purposes at a Swiss university hospital (or a Swiss disease cohort) or being a stakeholder in data sharing at a public or private institution that uses such data. We conducted semi-structured interviews, which were transcribed, translated when necessary, and de-identified. The entire research team discussed the transcripts and notes taken during each interview before an inductive coding process occurred.

RESULTS

Eleven semi-structured interviews were conducted (primarily in English) with 17 individuals representing lawyers, data protection officers, ethics committee members, scientists, project managers, bioinformaticians, clinical trials unit members, and biobank stakeholders. Most respondents felt that it was not the actual data transfer that was the bottleneck but rather the processes and systems around it, which were considered time-intensive and confusing. The templates developed by the Swiss Personalised Health Network and the Swiss General Consent process were generally felt to have streamlined processes significantly. However, these logistics and data quality issues remain practical bottlenecks in Swiss health data sharing. Areas of legal uncertainty include privacy laws when sharing data internationally, questions of "who owns the data", inconsistencies created because the Swiss general consent is perceived as being implemented differently across different institutions, and definitions and operationalisation of anonymisation and pseudo-anonymisation. Many participants desired to create a "culture of data sharing" and to recognise that data sharing is a process with many steps, not an event, that requires sustainability efforts and personnel. Some participants also stressed a desire to move away from data sharing and the current privacy focus towards processes that facilitate data access.

CONCLUSIONS

Facilitating a data access culture in Switzerland may require legal clarifications, further education about the process and resources to support data sharing, and further investment in sustainable infrastructureby funders and institutions.

The SIB Swiss Institute of Bioinformatics Semantic Web of data

The SIB Swiss Institute of Bioinformatics (https://www.sib.swiss/) is a federation of bioinformatics research and service groups. The international life science community in academia and industry has been accessing the freely available databases provided by SIB since its inception in 1998. In this paper we present the 11 databases which currently offer semantically enriched data in accordance with the FAIR principles (Findable, Accessible, Interoperable, Reusable), as well as the Swiss Personalized Health Network initiative (SPHN) which also employs this enrichment. The semantic enrichment facilitates the manipulation of large data sets from public databases and private data sets. Examples are provided to illustrate that the data from the SIB databases can not only be queried using precise criteria individually, but also across multiple databases, including a variety of non-SIB databases. Data manipulation, be it exploration, extraction, annotation, combination, and publication, is possible using the SPARQL query language. Providing documentation, tutorials and sample queries makes it easier to navigate this web of semantic data. Through this paper, the reader will discover how the existing SIB knowledge graphs can be leveraged to tackle the complex biological or clinical questions that are being addressed today.

A knowledge graph-based data harmonization framework for secondary data reuse

BACKGROUND AND OBJECTIVE

The adoption of new technologies in clinical care systems has propitiated the availability of a great amount of valuable data. However, this data is usually heterogeneous, requiring its harmonization to be integrated and analysed. We propose a semantic-driven harmonization framework that (1) enables the meaningful sharing and integration of healthcare data across institutions and (2) facilitates the analysis and exploitation of the shared data.

METHODS

The framework includes an ontology-based common data model (i.e. SCDM), a data transformation pipeline and a semantic query system. Heterogeneous datasets, mapped to different terminologies, are integrated by using an ontology-based infrastructure rooted in a top-level ontology. A graph database is generated by using these mappings, and web-based semantic query system facilitates data exploration.

RESULTS

Several datasets from different European institutions have been integrated by using the framework in the context of the European H2020 Precise4Q project. Through the query system, data scientists were able to explore data and use it for building machine learning models.

CONCLUSIONS

The flexible data representation using RDF, together with the formal semantic underpinning provided by the SCDM, have enabled the semantic integration, query and advanced exploitation of heterogeneous data in the context of the Precise4Q project.

Visualizing Data Interoperability for Food Systems Sustainability Research-From Spider Webs to Neural Networks

Food systems represent all elements and activities needed to feed the growing global population. Research on sustainable food systems is transdisciplinary, relying on the interconnected domains of health, nutrition, economics, society, and environment. The current lack of interoperability across databases poses a challenge to advancing research on food systems transformation. Crosswalks among largely siloed data on climate change, soils, agricultural practices, nutrient composition of foods, food processing, prices, dietary intakes, and population health are not fully developed. Starting with US Department of Agriculture FoodData Central, we assessed the interoperability of databases from multiple disciplines by identifying existing crosswalks and corresponding visualizations. Our visual demonstration serves as proof of concept, identifying databases in need of expansion, integration, and harmonization for use by researchers, policymakers, and the private sector. Interoperability is the key: ontologies and well-defined crosswalks are necessary to connect siloed data, transcend organizational barriers, and draw pathways from agriculture to nutrition and health.

Advancing Precision Medicine in South Tyrol, Italy: A Public Health Development Proposal for a Bilingual, Autonomous Province

This paper presents a comprehensive development plan for advancing precision medicine in the autonomous province of South Tyrol, Italy, a region characterized by its bilingual population and unique healthcare challenges. This study highlights the need to address the shortage of healthcare professionals proficient in language for person-centered medicine, the lag in healthcare sector digitalization, and the absence of a local medical university, all within the context of an initiated pharmacogenomics program and a population-based precision medicine study known as the "Cooperative Health Research in South Tyrol" (CHRIS) study. The key strategies for addressing these challenges and integrating CHRIS study findings into a broader precision medicine development plan are discussed, including workforce development and training, investment in digital infrastructure, enhanced data management and analytic capabilities, collaboration with external academic and research institutions, education and capacity building, securing funding and resources, and promoting a patient-centered approach. This study emphasizes the potential benefits of implementing such a comprehensive development plan, including improved early detection, personal ized treatment, and prevention of chronic diseases, ultimately leading to better healthcare outcomes and overall well-being in the South Tyrolean population.

The Swiss Pathogen Surveillance Platform - towards a nation-wide One Health data exchange platform for bacterial, viral and fungal genomics and associated metadata

The Swiss Pathogen Surveillance Platform (SPSP) is a shared secure surveillance platform between human and veterinary medicine, to also include environmental and foodborne isolates. It enables rapid and detailed transmission monitoring and outbreak surveillance of pathogens using whole genome sequencing data and associated metadata. It features controlled data access, complex dynamic queries, dedicated dashboards and automated data sharing with international repositories, providing actionable results for public health and the vision to improve societal well-being and health.

Linguistic and ontological challenges of multiple domains contributing to transformed health ecosystems

This paper provides an overview of current linguistic and ontological challenges which have to be met in order to provide full support to the transformation of health ecosystems in order to meet precision medicine (5 PM) standards. It highlights both standardization and interoperability aspects regarding formal, controlled representations of clinical and research data, requirements for smart support to produce and encode content in a way that humans and machines can understand and process it. Starting from the current text-centered communication practices in healthcare and biomedical research, it addresses the state of the art in information extraction using natural language processing (NLP). An important aspect of the language-centered perspective of managing health data is the integration of heterogeneous data sources, employing different natural languages and different terminologies. This is where biomedical ontologies, in the sense of formal, interchangeable representations of types of domain entities come into play. The paper discusses the state of the art of biomedical ontologies, addresses their importance for standardization and interoperability and sheds light to current misconceptions and shortcomings. Finally, the paper points out next steps and possible synergies of both the field of NLP and the area of Applied Ontology and Semantic Web to foster data interoperability for 5 PM.

FAIRification of health-related data using semantic web technologies in the Swiss Personalized Health Network

The Swiss Personalized Health Network (SPHN) is a government-funded initiative developing federated infrastructures for a responsible and efficient secondary use of health data for research purposes in compliance with the FAIR principles (Findable, Accessible, Interoperable and Reusable). We built a common standard infrastructure with a fit-for-purpose strategy to bring together health-related data and ease the work of both data providers to supply data in a standard manner and researchers by enhancing the quality of the collected data. As a result, the SPHN Resource Description Framework (RDF) schema was implemented together with a data ecosystem that encompasses data integration, validation tools, analysis helpers, training and documentation for representing health metadata and data in a consistent manner and reaching nationwide data interoperability goals. Data providers can now efficiently deliver several types of health data in a standardised and interoperable way while a high degree of flexibility is granted for the various demands of individual research projects. Researchers in Switzerland have access to FAIR health data for further use in RDF triplestores.

Interoperability of heterogeneous health information systems: a systematic literature review

BACKGROUND

The lack of interoperability between health information systems reduces the quality of care provided to patients and wastes resources. Accordingly, there is an urgent need to develop integration mechanisms among the various health information systems. The aim of this review was to investigate the interoperability requirements for heterogeneous health information systems and to summarize and present them.

METHODS

In accordance with the PRISMA guideline, a broad electronic search of all literature was conducted on the topic through six databases, including PubMed, Web of science, Scopus, MEDLINE, Cochrane Library and Embase to 25 July 2022. The inclusion criteria were to select English-written articles available in full text with the closest objectives. 36 articles were selected for further analysis.

RESULTS

Interoperability has been raised in the field of health information systems from 2003 and now it is one of the topics of interest to researchers. The projects done in this field are mostly in the national scope and to achieve the electronic health record. HL7 FHIR, CDA, HIPAA and SNOMED-CT, SOA, RIM, XML, API, JAVA and SQL are among the most important requirements for implementing interoperability. In order to guarantee the concept of data exchange, semantic interaction is the best choice because the systems can recognize and process semantically similar information homogeneously.

CONCLUSIONS

The health industry has become more complex and has new needs. Interoperability meets this needs by communicating between the output and input of processor systems and making easier to access the data in the required formats.

Interactive process mining of cancer treatment sequences with melanoma real-world data

The growing availability of clinical real-world data (RWD) represents a formidable opportunity to complement evidence from randomized clinical trials and observe how oncological treatments perform in real-life conditions. In particular, RWD can provide insights on questions for which no clinical trials exist, such as comparing outcomes from different sequences of treatments. To this end, process mining is a particularly suitable methodology for analyzing different treatment paths and their associated outcomes. Here, we describe an implementation of process mining algorithms directly within our hospital information system with an interactive application that allows oncologists to compare sequences of treatments in terms of overall survival, progression-free survival and best overall response. As an application example, we first performed a RWD descriptive analysis of 303 patients with advanced melanoma and reproduced findings observed in two notorious clinical trials: CheckMate-067 and DREAMseq. Then, we explored the outcomes of an immune-checkpoint inhibitor rechallenge after a first progression on immunotherapy versus switching to a BRAF targeted treatment. By using interactive process-oriented RWD analysis, we observed that patients still derive long-term survival benefits from immune-checkpoint inhibitors rechallenge, which could have direct implications on treatment guidelines for patients able to carry on immune-checkpoint therapy, if confirmed by external RWD and randomized clinical trials. Overall, our results highlight how an interactive implementation of process mining can lead to clinically relevant insights from RWD with a framework that can be ported to other centers or networks of centers.

Achieving Inclusivity by Design: Social and Contextual Information in Medical Knowledge

OBJECTIVES

To select, present, and summarize the most relevant papers published in 2020 and 2021 in the field of Knowledge Representation and Knowledge Management, Medical Vocabularies and Ontologies, with a particular focus on health inclusivity and bias.

METHODS

A broad search of the medical literature indexed in PubMed was conducted. The search terms 'ontology'/'ontologies' or 'medical knowledge management' for the dates 2020-2021 (search conducted November 26, 2021) returned 9,608 records. These were pre-screened based on a review of the titles for relevance to health inclusivity, bias, social and contextual factors, and health behaviours. Among these, 109 papers were selected for in-depth reviewing based on full text, from which 22 were selected for inclusion in this survey.

RESULTS

Selected papers were grouped into three themes, each addressing one aspect of the overall challenge for medical knowledge management. The first theme addressed the development of ontologies for social and contextual factors broadening the scope of health information. The second theme addressed the need for synthesis and translation of knowledge across historical disciplinary boundaries to address inequities and bias. The third theme encompassed a growing interest in the semantics of datasets used to train medical artificial intelligence systems and on how to ensure they are free of bias.

CONCLUSIONS

Medical knowledge management and semantic resources have much to offer efforts to tackle bias and enhance health inclusivity. Tackling inequities and biases requires relevant, semantically rich data, which needs to be captured and exchanged.

A scoping review of semantic integration of health data and information

OBJECTIVE

We summarized a decade of new research focusing on semantic data integration (SDI) since 2009, and we aim to: (1) summarize the state-of-art approaches on integrating health data and information; and (2) identify the main gaps and challenges of integrating health data and information from multiple levels and domains.

MATERIALS AND METHODS

We used PubMed as our focus is applications of SDI in biomedical domains and followed the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) to search and report for relevant studies published between January 1, 2009 and December 31, 2021. We used Covidence-a systematic review management system-to carry out this scoping review.

RESULTS

The initial search from PubMed resulted in 5,326 articles using the two sets of keywords. We then removed 44 duplicates and 5,282 articles were retained for abstract screening. After abstract screening, we included 246 articles for full-text screening, among which 87 articles were deemed eligible for full-text extraction. We summarized the 87 articles from four aspects: (1) methods for the global schema; (2) data integration strategies (i.e., federated system vs. data warehousing); (3) the sources of the data; and (4) downstream applications.

CONCLUSION

SDI approach can effectively resolve the semantic heterogeneities across different data sources. We identified two key gaps and challenges in existing SDI studies that (1) many of the existing SDI studies used data from only single-level data sources (e.g., integrating individual-level patient records from different hospital systems), and (2) documentation of the data integration processes is sparse, threatening the reproducibility of SDI studies.

Swiss Frailty Network and Repository: protocol of a Swiss Personalized Health Network's driver project observational study

INTRODUCTION

Early identification of frailty by clinical instruments or accumulation of deficit indexes can contribute to improve healthcare for older adults, including the prevention of negative outcomes in acute care. However, conflicting evidence exists on how to best capture frailty in this setting. Simultaneously, the increasing utilisation of electronic health records (EHRs) opens up new possibilities for research and patient care, including frailty.

METHODS AND ANALYSIS

The Swiss Frailty Network and Repository (SFNR) primarily aims to develop an electronic Frailty Index (eFI) from routinely available EHR data in order to investigate its predictive value against length of stay and in-hospital mortality as two important clinical outcomes in a study sample of 1000-1500 hospital patients aged 65 years and older. In addition, we will examine the correlation between the eFI and a test-based clinical Frailty Instrument to compare both concepts in Swiss older adults in acute care settings. As a Swiss Personalized Health Network (SPHN) driver project, our study will report on the characteristics and usability of the first nationwide eFI in Switzerland connecting all five Swiss University Hospitals' Geriatric Departments with a representative sample of patients aged 65 years and older admitted to acute care.

ETHICS AND DISSEMINATION

The study protocol was approved by the competent ethics committee of the Canton of Zurich (BASEC-ID 2019-00445). All acquired data will be handled according to SPHN's ethical framework for responsible data processing in personalised health research. Analyses will be performed within the secure BioMedIT environment, a national infrastructure to enable secure biomedical data processing, an integral part of SPHN.

TRIAL REGISTRATION NUMBER

NCT04516642.