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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]

Clinical free-text data represent a vast, untapped source of rich information. If more accessible for research it would supplement information captured in structured fields. Data need to be de-identified prior to being reused for research. However, a lack of transparency with existing de-identification software tools makes it difficult for data custodians to assess potential risks associated with the release of de-identified clinical free-text data. This case study describes the development of a framework for releasing de-identified clinical free-text data in two local health districts in NSW, Australia. A sample of clinical documents (n = 14 768 965), including progress notes, nursing and medical assessments and discharge summaries, were used for development. An algorithm was designed to identify and mask patient names without damaging data utility. For each note, the algorithm output the (i) note length before and after de-identification, (ii) the number of patient names and (iii) the number of common words. These outputs were used to iteratively refine the algorithm performance. This was followed by manual review of a random subset of records by a health information manager. Notes that were not correctly de-identified were fixed, and performance was reassessed until resolution. All notes in this sample were suitably de-identified using this method. Developing a transparent method for de-identifying clinical free-text data enables informed-decision making by data custodians and the safe re-use of clinical free-text data for research and public benefit. [ABSTRACT FROM AUTHOR]