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Simple Summary: In radiotherapy treatment, organs at risk and target volumes are contoured by the clinicians to prepare a dosimetry plan. In retrospective data, these structures are not often standardised to universal names across the patients plans, which is required to enable data mining and analysis. In this paper, a new method was proposed and evaluated to automatically standardise radiotherapy structures names using machine learning algorithms. The proposed approach was deployed over a dataset with 1613 patients collected from Liverpool & Macarthur Cancer Therapy Centres, New South Wales, Australia. It was concluded that machine learning techniques can standardise the dosimetry plan structures, taking into consideration the integration of multiple modalities representing each structure during the training process. In progressing the use of big data in health systems, standardised nomenclature is required to enable data pooling and analyses. In many radiotherapy planning systems and their data archives, target volumes (TV) and organ-at-risk (OAR) structure nomenclature has not been standardised. Machine learning (ML) has been utilised to standardise volumes nomenclature in retrospective datasets. However, only subsets of the structures have been targeted. Within this paper, we proposed a new approach for standardising all the structures nomenclature by using multi-modal artificial neural networks. A cohort consisting of 1613 breast cancer patients treated with radiotherapy was identified from Liverpool & Macarthur Cancer Therapy Centres, NSW, Australia. Four types of volume characteristics were generated to represent each target and OAR volume: textual features, geometric features, dosimetry features, and imaging data. Five datasets were created from the original cohort, the first four represented different subsets of volumes and the last one represented the whole list of volumes. For each dataset, 15 sets of combinations of features were generated to investigate the effect of using different characteristics on the standardisation performance. The best model reported 99.416% classification accuracy over the hold-out sample when used to standardise all the nomenclatures in a breast cancer radiotherapy plan into 21 classes. Our results showed that ML based automation methods can be used for standardising naming conventions in a radiotherapy plan taking into consideration the inclusion of multiple modalities to better represent each volume. [ABSTRACT FROM AUTHOR]

Chronic disease identification and management is a significant issue in Australia, with general practice being the primary contact point for those at risk of, or living with, chronic disease. However, there is a well-described gap between guideline recommendations for chronic disease management and translation in the general practice setting. In 2018, a group of researchers, clinicians and software developers collaborated to develop a tool to support the identification and management of chronic disease in general practice, with the aim to create a platform that met the needs of general practice. The co-design process drew together core principles and expectations for the establishment of a technological platform, called Future Health Today (FHT), which would sit alongside the electronic medical record (EMR) management system within general practice. FHT used algorithms applied to EMR data to identify patients with, or at risk of, chronic disease and requiring review. Using chronic kidney disease as a clinical focus, the FHT prototype was piloted in a large, metropolitan general practice, and a large regional general practice. Based on user feedback, the prototype was further developed and improved. This paper provides a report on the key features and functionalities that participants identified and implemented in practice. Future Health Today is a new platform, co-designed by general practice for general practice, which aims to streamline the identification and management of chronic disease to improve health outcomes. This paper describes the development of the technology platform and how it was optimised through an implementation study in general practice. [ABSTRACT FROM AUTHOR]

Background: Nurses working in intensive care units make autonomous decisions to manage high‐risk vasoactive medications in critically ill patients. Noradrenaline (norepinephrine) is a vasoactive medication commonly administered to patients in intensive care units. The influence of unit culture and environment on nurse‐decision‐making on noradrenaline (norepinephrine) management is unknown. Aims: The study aimed to investigate nurses' perceptions of the impact of interpersonal interactions, socialisation, and the intensive care environment on decision‐making when managing noradrenaline (norepinephrine). Materials & Methods: An exploratory qualitative study applied thematic analysis to focus group data. A purposive sample of nineteen nurses participated in four focus groups at two intensive care units in Melbourne, Australia, from March to June 2021. The COREQ checklist was used to guide study development and no patients or members of the public were involved in focus groups. Results: Three themes were generated from the researcher's interaction with data, Nursing and Medications; Culture and Decision‐making; and a Safe Practice Environment. Nurses reported decision‐making challenges associated with learning to manage noradrenaline (norepinephrine) early in their intensive care career and discussed feelings of isolation due to staffing resources, and the configuration of the intensive care environment. Nurses developed titration and weaning strategies to support decision‐making in the absence of evidence‐based algorithms. Discussion: Empathetic patient allocation early in nurses' intensive care careers facilitated a safer learning environment, and reduced isolation inherent in single room intensive care units. Nurses developed and used titration and weaning strategies, often learnt from other clinicians to manage practice uncertainty. Conclusions: Management of noradrenaline (norepinephrine) is core business for intensive care nurses worldwide. Development of titration and weaning strategies by nurses indicated unmet need for guidelines to support decision‐making. Identifying contextual elements that impact nurse management of high‐risk medications can guide development of environments, resources and policies that support nurse decision‐making, and reduce nurse anxiety and disempowerment. [ABSTRACT FROM AUTHOR]

Rationale: Traditional whole systems of medicine, such as naturopathy, are founded upon holism; a philosophical paradigm consistent with contemporary complexity science. Naturopathic case management is predicated upon the understanding of an intimately interconnected internal physiological and external context of the human organism—potentially indicating a worldview aligned with a complexity perspective. In this study we investigate naturopathic clinical reasoning using a complexity lens with the aim of ascertaining the extent of correspondence between the two. Method: Mind maps depicting case presentations were sought from Australian degree qualified naturopaths. A network mapping was undertaken, which was then analysed in accordance with a complexity science framework using exploratory data analysis and network analysis processes and tools. Results: Naturopathic case schematics, in the form of mind maps (n = 70), were collected, network mapped, and analysed. A total of 739 unique elements and 2724 links were identified across the network. Integral elements across the network were: stress, fatigue, general anxiety, systemic inflammation, gut dysbiosis, and diet. A modularity algorithm detected 11 communities, the primary ones of these representing the nervous system and mood; the gastrointestinal tract, liver, and nutrition; immune function and the immune system; and diet and nutrients. Conclusions: Naturopathic case management is holistic and based on a perspective of an integrated physiology and external context of the human organism. The traditional concept of holism, when subjected to a complexity lens, leads to the emergence of a contemporary holistic paradigm cognisant of the human organism being a complex system. The application of complexity science to investigate naturopathic case management as employed in this study, demonstrates that it is possible to investigate traditional philosophies and principles in a scientific and critical manner. A complexity science research approach may offer a suitable scientific paradigm to develop our understanding of traditional whole systems of medicine. [ABSTRACT FROM AUTHOR]