Ebook: MEDINFO 2023 — The Future Is Accessible

After many years of constrained travel, social movement, and large gatherings of people, the Editorial Committee for MedInfo 2023 welcome you to the resumption of our in-person biennial conference in the beautiful harbour city of Sydney, Australia. MedInfo conferences require considerable effort by numerous people, many of whom are volunteers, and our gratitude is extended to all those involved in the organisation of this 19th World Congress on Medical and Health Informatics. Our community will come together again to showcase our latest research and perspectives, reacquaint with trusted colleagues, and form new professional friendships. MedInfo conferences not only energise those attending but provide a common platform to commence or continue discussions focusing on applied approaches to data, information, knowledge, and wisdom in health and wellness.

The last few years have indeed demonstrated the theme of the conference – The future is accessible. The pandemic highlighted the value of global partnerships, the proficiency of converting laborious manual/analogue processes to an online/digital format for various health transactions, and the flurry of inventive approaches to improving the patient-centric journey and healthcare provider efficiency. It also emphasised the difference in the healthcare system when all its component parts collaborate and work together to deliver patient- and citizen-centric quality care. Finally, it accentuated the growing empowerment of citizens when they feel included in their health and wellness ventures and have access to their data.

During this time, enormous pressure was placed on the discipline of biomedical and health informatics to innovate quickly whilst the healthcare system adapted to the new landscape. Telehealth and remote monitoring were elevated from optional and nice-to-have to essential tools for many countries attempting to support the health outcomes of their citizens residing in urban, rural, and remote locations. Those with chronic disease enjoyed the saved opportunity costs derived from healthcare consultations via teleconferencing methods. Many healthcare providers and citizens like this new connected health landscape and fear the system will revert to its former offerings rather than embrace the future.

Unfortunately, not all countries had the existing infrastructure and could not pivot so quickly, which displayed the negative consequences of the digital divide. Preparing for a digital healthcare ecosystem required multifactorial opportunities – educating and upskilling of current staff, attracting, recruiting and retaining talent in digital health, and creating digital health career pathways. Emergent concerns around data sovereignty required many countries to review their laws and government policies to ensure compliance with privacy and data security obligations. Poorly designed IoT devices available in the healthcare environment reiterated the need for co-design to ensure the user experience was positive and healthcare providers were willing to prescribe them to other citizens. Lastly, digital literacy has become more prominent for both citizens and healthcare providers, and continued effort is required in this area.

The significant leap forward in artificial intelligence, machine learning, augmented reality, virtual reality, and genomics holds great hope for future healthcare planning, delivery, management, education, evaluation, and research. These themes feature in the 8–12 July MedInfo 2023 sessions, displaying how these digital approaches will revolutionise the healthcare environment through improved quality health outcomes and reduced potential for error. It is anticipated that attendees at MedInfo 2023 will not only exploit the benefits of these technologies but also collectively identify ways to overcome their associated challenges.

Dr Jen Bichel-Findlay FAIDH CHIA

MedInfo 2023 Editorial Chair

Modern clinical studies collect longitudinal and multimodal data about participants, treatments and responses, biospecimens, and molecular and multiomics data. Such rich and complex data requires new common data models (CDM) to support data dissemination and research collaboration. We have developed the ARDaC CDM for the Alcoholic Hepatitis Network (AlcHepNet) Research Data Commons (ARDaC) to support clinical studies and translational research in the national AlcHepNet consortium. The ARDaC CDM bridges the gap between the data models used by the AlcHepNet electronic data capture platform (REDCap) and the Genomic Data Commons (GDC) data model used by the Gen3 data commons framework. It extends the GDC data model for clinical studies; facilitates the harmonization of research data across consortia and programs; and supports the development of the ARDaC. ARDaC CDM is designed as a general and extensible CDM for addressing the needs of modern clinical studies. The ARDaC CDM is available at https://dev.ardac.org/DD.

Procurement of health information systems (HIS) is a complex and critical task that requires early identification of interoperability requirements. However, specifying adequate requirements is often associated with several challenges. We examined relevant peer-reviewed literature and public documents (policy documents, annual reports, and newspapers) to summarize existing challenges in specifying interoperability requirement during procurement of HISs. In this study, 32 public documents and 2343 peer-reviewed articles were found using Google search engine, Springer, PubMed and ScienceDirect. Collected data were analyzed using a thematic coding schema. Our result shows that challenges related to describing the needs properly, conflicting needs and knowledge gaps are shared between most articles. Further research in the direction of developing a model that can bridge knowledge gaps, facilitate interdisciplinary collaboration, and help to avoid fuzzy requirements is needed.

This paper describes the development of Health Level Seven Fast Healthcare Interoperability Resource (FHIR) profiles for pathology reports integrated with whole slide images and clinical data to create a pathology research database. A report template was designed to collect structured reports, enabling pathologists to select structured terms based on a checklist, allowing for the standardization of terms used to describe tumor features. We gathered and analyzed 190 non–small-cell lung cancer pathology reports in free text format, which were then structured by mapping the itemized vocabulary to FHIR observation resources, using international standard terminologies, such as the International Classification of Diseases, LOINC, and SNOMED CT. The resulting FHIR profiles were published as an implementation guide, which includes 25 profiles for essential data elements, value sets, and structured definitions for integrating clinical data and pathology images associated with the pathology report. These profiles enable the exchange of structured data between systems and facilitate the integration of pathology data into electronic health records, which can improve the quality of care for patients with cancer.

Adhering to FAIR principles (findability, accessibility, interoperability, reusability) ensures sustainability and reliable exchange of data and metadata. Research communities need common infrastructures and information models to collect, store, manage and work with data and metadata. The German initiative NFDI4Health created a metadata schema and an infrastructure integrating existing platforms based on different information models and standards. To ensure system compatibility and enhance data integration possibilities, we mapped the Investigation-Study-Assay (ISA) model to Fast Healthcare Interoperability Resources (FHIR). We present the mapping in FHIR logical models, a resulting FHIR resources’ network and challenges that we encountered. Challenges mainly related to ISA’s genericness, and to different structures and datatypes used in ISA and FHIR. Mapping ISA to FHIR is feasible but requires further analyses of example data and adaptations to better specify target FHIR elements, and enable possible automatized conversions from ISA to FHIR.

The critical need for system interoperability and robust information infrastructure in public health was highlighted during the COVID-19 pandemic. An assessment of the evolving interoperability between immunization information system (IIS) in a state-based public health agency and electronic health records (EHRs) including pandemic-driven evolution/use was conducted. The Minnesota Immunization Information Connection (MIIC), the IIS for Minnesota (US) supports interoperability with EHRs using HL7v2.5.1 standards-based queries. Structured interviews were conducted with 28 experts across 12 healthcare systems and public health clinics (n=286 sites) between April - July 2022. Though all reported use of MIIC, most (83%) had MIIC integration within their EHRs, and high EHR queries to MIIC (∼6 million/month), numerous organizational/technical barriers were identified including standard vaccine-naming need in EHRs, app access issues, limited resources and informatics-staff shortage in public health. Results underscore vital role of IIS, on-going interoperability evaluation to address issues and promote standards-based bi-directional EHR-IIS data exchanges.

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.

In digital healthcare, data heterogeneity is a reoccurring issue caused by proprietary source systems. It is often overcome by utilizing ETL processes resulting in data warehouses, which ensure common data models for interoperability. Unfortunately, the achieved interoperability is usually limited to an institutional level. The broad solution space to achieve interoperability with different health data standards is part of the problem, resulting in different standards used at various institutions. For cross-institutional use cases like federated feasibility queries, the issue of heterogeneity is reintroduced. This work showcases how the existing German infrastructure for federated feasibility queries based on Hl7 FHIR can be extended to support openEHR without further data transformation. By utilizing an intermediate query format that can be transferred to FHIR Search, CQL, and AQL.

To achieve interoperability of health data, stakeholders must overcome various socio-technical challenges. The “Mind the GAPS, Fill the GAPS” framework was created by the Asia eHealth Information Network (AeHIN) in 2017 to help countries with their challenges with interoperability. A year later, AeHIN formed the Community of Interoperability Labs (COIL), a group of labs from six countries to share knowledge and resources. Since interoperability requires data exchange between disparate entities, it is imperative to establish a trustworthy space where stakeholders can come together and solve their common problems. The networked learning approach of the COIL makes possible the potential for interoperability within and between countries contributing to national and international understanding.

Although health information exchange (HIE) networks exist in multiple nations, providers still require access multiple sources to obtain medical records. We sought to measure and compare differences in data presence and concordance across regional HIE and EHR vendor-based networks. Using 1,054 randomly selected patients from a large health system in the US, we generated consolidated clinical document architecture (C-CDA) documents from each network. 778 (74%) patients had at least one C-CDA document present from either source. Among these patients, two-thirds had information in only one source. All documents contained demographics, but less than half of patients had data in clinical data domains. Moreover, data across HIE networks were not concordant. Results suggest that HIE networks have different, likely complementary, data available for the same patient, suggesting the need for better integration and deduplication for national HIE efforts.

Observational Medical Outcome Partners - Common Data Model (OMOP-CDM) is an international standard model for standardizing electronic medical record data. However, unstructured data such as medical image data which is beyond the scope of standardization by the current OMOP-CDM is difficult to be used in multi-institutional collaborative research. Therefore, we developed the Radiology-CDM (R-CDM) which standardizes medical imaging data. As a proof of concept, 737,500 Optical Coherence Tomography (OCT) data from two tertiary hospitals in South Korea is standardized in the form of R-CDM. The relationship between chronic disease and retinal thickness was analyzed by using the R-CDM. Central macular thickness and retinal nerve fiber layer (RNFL) thickness were significantly thinner in the patients with hypertension compared to the control cohort. It is meaningful in that multi-institutional collaborative research using medical image data and clinical data simultaneously can be conducted very efficiently.

Observational research utilizes patient information from many disparate databases worldwide. To be able to systematically analyze data and compare the results of such research studies, information about exposure to drugs or classes of drugs needs to be harmonized across these data. The NLM’s RxNorm drug terminology and WHO’s ATC classification serve these needs but are currently not satisfactorily combined into a common system. Creating such system is hampered by a number of challenges, resulting from different approaches to representing attributes of drugs and ontological rules. Here, we present a combined ATC-RxNorm drug hierarchy, allowing to use ATC classes for retrieval of drug information in large scale observational data. We present the heuristic for maintaining this resource and evaluate it in a real world database containing drug and drug classification information.

The 11th revision of the International Classification of Diseases (ICD) is now available for use. A literature search was conducted to review and summarize the research conducted to date. In addition to the ease of integration into electronic health records using standard digital tools such as uniform resource identifiers and application programming interfaces, ICD-11 and the World Health Organization provided linearization for mortality and morbidity, ICD-11-MMS, promise improved backward compatibility to ICD-10; increased availability in multiple languages; greater detail for clinical use, including traditional Chinese medicine; and enhanced maintenance for continued relevance. The studies reviewed here support the superior content and utility of ICD-11-MMS. Meaningful planning for implementation has begun, including the provision of a framework. It is time for the world to adopt a digitally prepared ICD.

SNOMED CT is a comprehensive medical ontology used in health care sectors across the world covering a wide range of concepts that support diversity at the point of healthcare. However, not all these concepts are needed for every use case; it is better to concentrate on those parts that apply to the particular application while preserving the meaning of relevant concepts. This paper considers the application of a novel subontology extraction method to create a new resource, called the IPS terminology, which functions as a standalone ontology with the same features as SNOMED CT, but is designed for cross-border patient care. The IPS terminology has been released for free use under an open license, with the intention of promoting interoperability of health information worldwide.

Electronic health records (EHRs) and other real-world data (RWD) are critical to accelerating and scaling care improvement and transformation. To efficiently leverage it for secondary uses, EHR/RWD should be optimally managed and mapped to industry standard concepts (ISCs). Inherent challenges in concept encoding usually result in inefficient and costly workflows and resultant metadata representation structures outside the EHR. Using three related projects to map data to ISCs, we describe the development of standard, repeatable processes for precisely and unambiguously representing EHR data using appropriate ISCs within the EHR platform lifecycle and mappings specific to SNOMED-CT for Demographics, Specialty and Services. Mappings in these 3 areas resulted in ISC mappings of 779 data elements requiring 90 new concept requests to SNOMED-CT and 738 new ISCs mapped into the workflow within an accessible, enterprise-wide EHR resource with supporting processes.

A continuing global desire to be using clinical systems within a digital health ecosystem, able to facilitate data flows and information exchange as required to support person-centred, predictive, preventative, participatory and precision (5p) health and medical care can best be supported through the use of the standard categorial structure able to represent not only the clinical nursing practice domain but also other clinical disciplines by the generic labelling of some high-level categories. It is hypothesised that adoption of this generic clinical categorial structure within any electronic health/medical record within a well connected digital health ecosystem, supported by a cloud based openEHR platform, will enable the 5p support to be realized. This presentation provides the results of the latest update of this technical standard based on the 20+ year nursing practice categorial structure development process adopted to achieve this aim and a summary about linking this categorial structure to standard terminologies and to standard EHR/EMR system architectures.

Data maps to translate information recorded in one code system to another code system are common in digital health. In the past these were used for data aggregation and national reporting where minor errors caused little impact. Today these maps are used invisibly behind the scenes when sharing clinical data. This is a data quality and safety bomb ready to blow. The International Standards Organization (ISO) have prepared to review their standard on map quality, a standard which when used can identify safety and quality issues in mapped data and assist in development of a pathway to improvement. The key determinants of map quality are discussed here and their impact on patient safety considered based upon real world experiences. Suggestions are included on the potential minimal requirements for any map used in a clinical environment, whether for use for interoperability or for other purposes. Alternatives to encourage improvement in map quality are also suggested.

In a proof of concept study, we assessed the feasibility of designing a first-order logic (FOL) framework capable of translating SNOMED CT’s terminological view on patient data as referencing concepts, into the realism-based view of the Basic Formal Ontology and the Ontology for General Medical Science according to which patient data represent instances of types. Because within the subject domain of this study, SNOMED CT’s terminological coverage was excellent, and its EL++ axioms can be automatically translated into FOL as well as the antecedent part of bridging axioms between SNOMED CT and realism-based ontologies, we conclude that this is an area of R&D that deserves further attention and that may lead to new ways of federating terminologies with ontologies.

Medical ontologies are mostly available in English. This presents a language barrier that is a limitation in research and automated processing of patient data. The manual translation of ontologies is complex and time-consuming. However, there are commercial translation tools that have shown promising results in the field of medical terminology translation. The aim of this study is to translate selected terms of the Human Phenotype Ontology (HPO) from English into German using commercial translators. Six medical experts evaluated the translation candidates in an iterative process. The results show commercial translators, with DeepL in the lead, provide translations that are positively evaluated by experts. With a broader study scope and additional optimization techniques, commercial translators could support and facilitate the process of translating medical ontologies.

Drug development in rare diseases is challenging due to the limited availability of subjects with the diseases and recruiting from a small patient population. The high cost and low success rate of clinical trials motivate deliberate analysis of existing clinical trials to understand status of clinical development of orphan drugs and discover new insight for new trial. In this project, we aim to develop a user centered Rare disease based Clinical Trial Knowledge Graph (RCTKG) to integrate publicly available clinical trial data with rare diseases from the Genetic and Rare Disease (GARD) program in a semantic and standardized form for public use. To better serve and represent the interests of rare disease users, user stories were defined for three types of users, patients, healthcare providers and informaticians, to guide the RCTKG design in supporting the GARD program at NCATS/NIH and the broad clinical/research community in rare diseases.

Metadata are often the first access to data repositories for researchers within secondary use. Through automatic metadata generation and metadata harvesting the amount of data about data has been growing ever since. In order to make data not only FAIR but also reliable, the aspect of metadata quality has to be considered. But as earlier assessments of metadata of different repositories showed, metadata quality still lacks behind its capability. Providing an extensive literature review the authors conclude nine measures to assess metadata in relation to clinical care repositories, such as Medical Data Integration Centers (MeDICs). Proceeding from these measures the authors propose an addition of the FAIR Guiding Principles by adding a fifth block for Reliability including three principles, that resulted from the measures presented. The results form the basis for the future work of an assessment of metadata, that is stored in a MeDIC.

In Norway, the process of developing a national shared medication list has been underway for several years. The shared medication list provides an overview of all the medications used by a patient. However, its proper use requires that it be maintained regularly through so-called medication reconciliation processes in which health personnel clarify – and ask patients – what and how much medication they use. We explore the work embedded in the bedside medication reconciliation process at a hospital, the health personnel conducting this work and the implications for the shared medication list. We argue that reconciliation processes can be conceptualized as collective repair work that needs to be continued after the shared medication list is implemented.

Natural Language Processing (NLP) is a powerful technique for extracting valuable information from unstructured electronic health records (EHRs). However, a prerequisite for NLP is the availability of high-quality annotated datasets. To date, there is a lack of effective methods to guide the research effort of manually annotating unstructured datasets, which can hinder NLP performance. Therefore, this study develops a five-step workflow for manually annotating unstructured datasets, including (1) annotator training and familiarising with the text corpus, (2) vocabulary identification, (3) annotation schema development, (4) annotation execution, and (5) result validation. This framework was then applied to annotate agitation symptoms from the unstructured EHRs of 40 Australian residential aged care facilities. The annotated corpus achieved an accuracy rate of 96%. This suggests that our proposed annotation workflow can be used in manual data processing to develop annotated training corpus for developing NLP algorithms.

While research on the effects of patient access to health records is increasing, a basic understanding of the spread of patient-accessible electronic health records worldwide is lacking. In this survey of healthcare experts with professional and personal experience from 29 countries, we explored the state of patient online record access (ORA). We asked participants whether ORA exists in their country and which information is available through it. Experts in all polled countries reported having some national access to health records, with 6 (21%) countries providing exclusively paper-based records and 23 (79%) countries having ORA. Overview of test/lab results and prescription/medication lists were the most commonly available information. Free-text clinical notes were accessible in less than half of the surveyed countries (12, 41%). We will continue to map the state of patient ORA, focusing on traditionally underrepresented countries.