A FHIR Native Radiology Informatics Platform.

FHIR is a new standard that is rapidly being adopted in healthcare. We describe and implement a Radiology informatics platform (RIS) that is FHIR native and incorporates the ability to execute AI algorithms to aid with the interpretation of scans. Our design utilises the FHIR workflow pattern as an application programming interface with functionality provided by independent micro services thus granting flexibility and expandability.

Clinical Implementation of Routine Whole-genome Sequencing for Hospital Infection Control of Multi-drug Resistant Pathogens.

BACKGROUND: Prospective whole-genome sequencing (WGS)-based surveillance may be the optimal approach to rapidly identify transmission of multi-drug resistant (MDR) bacteria in the healthcare setting. METHODS: We prospectively collected methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant enterococci (VRE), carbapenem-resistant Acinetobacter baumannii (CRAB), extended-spectrum beta-lactamase (ESBL-E), and carbapenemase-producing Enterobacterales (CPE) isolated from blood cultures, sterile sites, or screening specimens across three large tertiary referral hospitals (2 adult, 1 paediatric) in Brisbane, Australia. WGS was used to determine in silico multi-locus sequence typing (MLST) and resistance gene profiling via a bespoke genomic analysis pipeline. Putative transmission events were identified by comparison of core genome single nucleotide polymorphisms (SNPs). Relevant clinical meta-data were combined with genomic analyses via customised automation, collated into hospital-specific reports regularly distributed to infection control teams. RESULTS: Over 4 years (April 2017 to July 2021) 2660 isolates were sequenced. This included MDR gram-negative bacilli (n = 293 CPE, n = 1309 ESBL), MRSA (n = 620), and VRE (n = 433). A total of 379 clinical reports were issued. Core genome SNP data identified that 33% of isolates formed 76 distinct clusters. Of the 76 clusters, 43 were contained to the 3 target hospitals, suggesting ongoing transmission within the clinical environment. The remaining 33 clusters represented possible inter-hospital transmission events or strains circulating in the community. In 1 hospital, proven negligible transmission of non-multi-resistant MRSA enabled changes to infection control policy. CONCLUSIONS: Implementation of routine WGS for MDR pathogens in clinical laboratories is feasible and can enable targeted infection prevention and control interventions.

Investigation of current models of care for genetic heart disease in Australia: A national clinical audit.

BACKGROUND: This sub-study of the Australian Genomics Cardiovascular Genetic Disorders Flagship sought to conduct the first nation-wide audit in Australia to establish the current practices across cardiac genetics clinics. METHOD: An audit of records of patients with a suspected genetic heart disease (cardiomyopathy, primary arrhythmia, autosomal dominant congenital heart disease) who had a cardiac genetics consultation between 1st January 2016 and 31 July 2018 and were offered a diagnostic genetic test. RESULTS: This audit included 536 records at multidisciplinary cardiac genetics clinics from 11 public tertiary hospitals across five Australian states. Most genetic consultations occurred in a clinic setting (90%), followed by inpatient (6%) and Telehealth (4%). Queensland had the highest proportion of Telehealth consultations (9% of state total). Sixty-six percent of patients had a clinical diagnosis of a cardiomyopathy, 28% a primary arrhythmia, and 0.7% congenital heart disease. The reason for diagnosis was most commonly as a result of investigations of symptoms (73%). Most patients were referred by a cardiologist (85%), followed by a general practitioner (9%) and most genetic tests were funded by the state Genetic Health Service (73%). Nationally, 29% of genetic tests identified a pathogenic or likely pathogenic gene variant; 32% of cardiomyopathies, 26% of primary arrhythmia syndromes, and 25% of congenital heart disease. CONCLUSION: We provide important information describing the current models of care for genetic heart diseases throughout Australia. These baseline data will inform the implementation and impact of whole genome sequencing in the Australian healthcare landscape.

Understanding the Barriers to Genomic Healthcare in Queensland Through an Information Management Lens.

Genomic science has the potential to rapidly advance understanding of human biology in the medical context and the subsequent provision of tailored healthcare. Implementing such a disruptive and transformative technology into an existing and stretched health system will require a whole of system approach and a keen understanding of the limitations to be navigated in broadening the system to include genomic healthcare. This paper reports on the barriers to implementation faced by clinical demonstration projects in integrating into the existing infrastructure in Queensland.

Towards achieving semantic interoperability of clinical study data with FHIR.

BACKGROUND: Observational clinical studies play a pivotal role in advancing medical knowledge and patient healthcare. To lessen the prohibitive costs of conducting these studies and support evidence-based medicine, results emanating from these studies need to be shared and compared to one another. Current approaches for clinical study management have limitations that prohibit the effective sharing of clinical research data. METHODS: The objective of this paper is to present a proposal for a clinical study architecture to not only facilitate the communication of clinical study data but also its context so that the data that is being communicated can be unambiguously understood at the receiving end. Our approach is two-fold. First we outline our methodology to map clinical data from Clinical Data Interchange Standards Consortium Operational Data Model (ODM) to the Fast Healthcare Interoperable Resource (FHIR) and outline the strengths and weaknesses of this approach. Next, we propose two FHIR-based models, to capture the metadata and data from the clinical study, that not only facilitate the syntactic but also semantic interoperability of clinical study data. CONCLUSIONS: This work shows that our proposed FHIR resources provide a good fit to semantically enrich the ODM data. By exploiting the rich information model in FHIR, we can organise clinical data in a manner that preserves its organisation but captures its context. Our implementations demonstrate that FHIR can natively manage clinical data. Furthermore, by providing links at several levels, it improves the traversal and querying of the data. The intended benefits of this approach is more efficient and effective data exchange that ultimately will allow clinicians to switch their focus back to decision-making and evidence-based medicines.

Semantic enrichment of longitudinal clinical study data using the CDISC standards and the semantic statistics vocabularies.

BACKGROUND: There is an increasing recognition of the need for the data capture phase of clinical studies to be improved and for more effective sharing of clinical data. The Health Care and Life Sciences community has embraced semantic technologies to facilitate the integration of health data from electronic health records, clinical studies and pharmaceutical research. This paper explores the integration of clinical study data exchange standards and semantic statistic vocabularies to deliver clinical data as linked data in a format that is easier to enrich with links to complementary data sources and consume by a broad user base. METHODS: We propose a Linked Clinical Data Cube (LCDC), which combines the strength of the RDF Data Cube and DDI-RDF vocabulary to enrich clinical data based on the CDISC standards. The CDISC standards provide the mechanisms for the data to be standardised, made more accessible and accountable whereas the RDF Data Cube and DDI-RDF vocabularies provide novel approaches to managing large volumes of heterogeneous linked data resources. RESULTS: We validate our approach using a large-scale longitudinal clinical study into neurodegenerative diseases. This dataset, comprising more than 1600 variables clustered in 25 different sub-domains, has been fully converted into RDF forming one main data cube and one specialised cube for each sub-domain. One sub-domain, the Medications specialised cube, has been linked to relevant external vocabularies, such as the Australian Medicines Terminology and the ATC DDD taxonomy and DrugBank terminology. This provides new dimensions on which to query the data that promote the exploration of drug-drug and drug-disease interactions. CONCLUSIONS: This implementation highlights the effectiveness of the association of the semantic statistics vocabularies for the publication of large heterogeneous data sets as linked data and the integration of the semantic statistics vocabularies with the CDISC standards. In particular, it demonstrates the potential of the two vocabularies in overcoming the monolithic nature of the underlying model and improving the navigation and querying of the data from multiple angles to support richer data analysis of clinical study data. The forecasted benefits are more efficient use of clinicians' time and the potential to facilitate cross-study analysis.

A method for the semantic enrichment of clinical trial data.

Clinical trial data have historically been implemented using relational databases. While this has expedited the dissemination of data among partners, it has hindered on the ability to swiftly query the data by relying on monolithic tables. This paper outlines a project that investigates the semantic enrichment of a large-scale longitudinal clinical trial, the AIBL study, by reusing entities from existing ontologies. The implication of the semantic enrichment of the AIBL study is that it is possible to query the data more effectively and efficiently. We are now able to implement our model and focus on an end-to-end data capture and analysis pipeline to query and visualise clinical trial data. The main contribution of this paper is a discussion of the methodology to semantically enrich clinical trial data using entities from existing ontologies.

Using Australian medicines terminology (AMT) and SNOMED CT-AU to better support clinical research.

A large scale, long term clinical study faced significant quality issues with its medications use data which had been collected from participants using paper forms and manually entered into a data capture system. A method was developed that automatically mapped 72.2% of the unique medication names collected for the study to the AMT and SNOMED CT-AU using Ontoserver, a terminology server for clinical ontologies. These initial results are promising and, with further improvements to the algorithms and evaluation, are expected to greatly improve the analysis of medication data gathered from the study.

On selecting a clinical trial management system for large scale, multi-centre, multi-modal clinical research study.

Clinical research studies offer many challenges for their supporting information systems. AIBL assembled 1112 participants who volunteered crucial information for a comprehensive study on neurodegenerative diseases. This paper discusses the shortcomings of the clinical trial management system chosen to record the results of the study. A set of guidelines was devised and a critique of five systems ensued. OpenClinica was selected as the most appropriate option. The main contribution of this paper is: (i) proposing a set of guidelines to determine the appropriateness of Clinical Trial Management Systems (CTMS) solution; (ii) providing a brief critique of existing commercial and open-sourced CTMS; and (iii) alluding to some data migration issues and providing cues on how to address them. We conclude that open-source CTMS are viable alternatives to the more expensive commercial systems to conduct, record and manage clinical studies.