An Approach for Generating Realistic Australian Synthetic Healthcare Data.

Healthcare data is a scarce resource and access is often cumbersome. While medical software development would benefit from real datasets, the privacy of the patients is held at a higher priority. Realistic synthetic healthcare data can fill this gap by providing a dataset for quality control while at the same time preserving the patient's anonymity and privacy. Existing methods focus on American or European patient healthcare data but none is exclusively focused on the Australian population. Australia is a highly diverse country that has a unique healthcare system. To overcome this problem, we used a popular publicly available tool, Synthea, to generate disease progressions based on the Australian population. With this approach, we were able to generate 100,000 patients following Queensland (Australia) demographics.

Indigenous data governance approaches applied in research using routinely collected health data: a scoping review.

Globally, there is a growing acknowledgment of Indigenous Peoples' rights to control data related to their communities. This is seen in the development of Indigenous Data Governance standards. As health data collection increases, it's crucial to apply these standards in research involving Indigenous communities. Our study, therefore, aims to systematically review research using routinely collected health data of Indigenous Peoples, understanding the Indigenous Data Governance approaches and the associated advantages and challenges. We searched electronic databases for studies from 2013 to 2022, resulting in 85 selected articles. Of these, 65 (77%) involved Indigenous Peoples in the research, and 60 (71%) were authored by Indigenous individuals or organisations. While most studies (93%) provided ethical approval details, only 18 (21%) described Indigenous guiding principles, 35 (41%) reported on data sovereignty, and 28 (33%) addressed consent. This highlights the increasing focus on Indigenous Data Governance in utilising health data. Leveraging existing data sources in line with Indigenous data governance principles is vital for better understanding Indigenous health outcomes.

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.

Classification of pathology reports for cancer registry notifications.

OBJECTIVE: To develop a system for the automatic classification of pathology reports for Cancer Registry notifications. METHOD: A two pass approach is proposed to classify whether pathology reports are cancer notifiable or not. The first pass queries pathology HL7 messages for known report types that are received by the Queensland Cancer Registry (QCR), while the second pass aims to analyse the free text reports and identify those that are cancer notifiable. Cancer Registry business rules, natural language processing and symbolic reasoning using the SNOMED CT ontology were adopted in the system. RESULTS: The system was developed on a corpus of 500 histology and cytology reports (with 47% notifiable reports) and evaluated on an independent set of 479 reports (with 52% notifiable reports). RESULTS show that the system can reliably classify cancer notifiable reports with a sensitivity, specificity, and positive predicted value (PPV) of 0.99, 0.95, and 0.95, respectively for the development set, and 0.98, 0.96, and 0.96 for the evaluation set. High sensitivity can be achieved at a slight expense in specificity and PPV. CONCLUSION: The system demonstrates how medical free-text processing enables the classification of cancer notifiable pathology reports with high reliability for potential use by Cancer Registries and pathology laboratories.

Pathling: analytics on FHIR.

BACKGROUND: Health data analytics is an area that is facing rapid change due to the acceleration of digitization of the health sector, and the changing landscape of health data and clinical terminology standards. Our research has identified a need for improved tooling to support analytics users in the task of analyzing Fast Healthcare Interoperability Resources (FHIR®) data and associated clinical terminology. RESULTS: A server implementation was developed, featuring a FHIR API with new operations designed to support exploratory data analysis (EDA), advanced patient cohort selection and data preparation tasks. Integration with a FHIR Terminology Service is also supported, allowing users to incorporate knowledge from rich terminologies such as SNOMED CT within their queries. A prototype user interface for EDA was developed, along with visualizations in support of a health data analysis project. CONCLUSIONS: Experience with applying this technology within research projects and towards the development of analytics-enabled applications provides a preliminary indication that the FHIR Analytics API pattern implemented by Pathling is a valuable abstraction for data scientists and software developers within the health care domain. Pathling contributes towards the value proposition for the use of FHIR within health data analytics, and assists with the use of complex clinical terminologies in that context.

Developing a national emergency department data reference set based on SNOMED CT.

Emergency departments around Australia use a range of software to capture data on patients' reason for encounter, presenting problem and diagnosis. The data collected are mainly based on descriptions and codes of the International Classification of Diseases, 10th revision, Australian modification (ICD-10-AM), with each emergency department having a tailored list of terms. The National E-Health Transition Authority is introducing a standard clinical terminology, the Systematized Nomenclature of Medicine--Clinical Terms (SNOMED CT), as one of the building blocks of an e-health infrastructure in Australia. The Australian e-Health Research Centre has developed a software platform, Snapper, which facilitates mapping of existing clinical terms to the SNOMED CT terminology. Using the Snapper software, reference sets of terms for emergency departments are being developed, based on the Australian version of SNOMED CT (SNOMED CT-AU). Existing software systems need to be able to implement these reference sets to support standardised recording of data at the point of care. As the terms collected will be part of a larger terminology, they will be useful for patients' admission and discharge summaries and for computerised clinical decision making. Mapping existing sets of clinical terms to a national emergency department SNOMED CT reference set will facilitate consistency between emergency department data collections and improve the usefulness of the data for clinical and analytical purposes.

Mapping the Queensland Health iPharmacy Medication File to the Australian Medicines Terminology Using Snapper.

An exploratory exercise in mapping approximately 8000 medication terms from the Queensland Health iPharmacy Medication File to the Australian Medicines Terminology (AMT) was carried out to determine coverage, build specialist knowledge, and inform future clinical terminology strategies. Snapper was the mapping tool selected for this exercise. The Automap function of the tool mapped 39.2% of the items that were successfully mapped, and the remainder were manually mapped. A total of 51.8% of the sample items were mapped to a semantically equivalent AMT concept with 50.0% of terms being mapped to a satisfactory fully specified term, and 1.8% of terms being mapped to a fully specified term that was considered unsuitable for QH clinical purposes. Rules and guidelines on how to deal with the emerging differences between the two terminologies were developed during the course of the project. Snapper was found to be an appropriate tool for this exercise; its functionality is being constantly refined to assist users. As a result, this exercise will provide NEHTA with input for the national scope and content for AMT, and QH will endeavour to prepare the iPharmacy medication file for future interfaces with other terminologies.

Automatic extraction of cancer characteristics from free-text pathology reports for cancer notifications.

OBJECTIVE: To develop a system for the automatic classification of Cancer Registry notifications data from free-text pathology reports. METHOD: The underlying technology used for the extraction of cancer notification items is based on the symbolic rule-based classification methodology, whereby formal semantics are used to reason with the systematised nomenclature of medicine - clinical terms (SNOMED CT) concepts identified in the free text. Business rules for cancer notifications used by Cancer Registry coding staff were also incorporated with the aim to mimic Cancer Registry processes. RESULTS: The system was developed on a corpus of 239 histology and cytology reports (with 60% notifiable reports), and then evaluated on an independent set of 300 reports (with 20% notifiable reports). Results show that the system can reliably classify notifiable reports with 96% and 100% specificity, and achieve an overall accuracy of 82% and 74% for classifying notification items from notifiable reports at a unit record level from the development and evaluation set, respectively. CONCLUSION: Cancer Registries collect a multitude of data that requires manual review, slowing down the flow of information. Extracting and providing an automatically coded cancer pathology notification for review can lessen the reliance on expert clinical staff, improving the efficiency and availability of cancer information.

FHIR OWL: Transforming OWL ontologies into FHIR terminology resources.

The FHIR specification provides a mechanism to access clinical terminologies using a standard API, and many existing terminologies, such as SNOMED CT, are well supported. However, in areas such as genomics, terminologies from other domains are starting to be used in clinical settings. Many of these are authored or distributed in Web Ontology Language (OWL) format. In this paper we describe a transformation between OWL ontologies and FHIR terminology resources. The results show that there are several challenges in implementing the transformation, with the major one being the lack of a modularisation mechanism in the FHIR code system resource that resembles the import mecha nism available in OWL. A workaround with minimal drawbacks was successfully implemented in this solution. The availability of this transformation is significant because it enables a broad range of terminologies that are currently available in OWL to be available using the FHIR API.