Patients' and clinicians' views on shared decision making in cancer care: a qualitative study of Aotearoa New Zealand patients' and clinicians' perspectives.

Aims: Oncology stakeholders' view on shared decision making (SDM) in Aotearoa New Zealand is not well described in the literature. This study aimed to explore the perspectives of patients, clinicians and other cancer care stakeholders on shared decision making, and how and why shared decision making in cancer care can be viable and appropriate for patients and healthcare providers.

Methods: Non-random, purposive sampling, combined with advertisement and snowball recruitment identified patient, whānau and healthcare provider participants for qualitative interviews.

Application of a Comprehensive Evaluation Framework to COVID-19 Studies: Systematic Review of Translational Aspects of Artificial Intelligence in Health Care.

Background: Despite immense progress in artificial intelligence (AI) models, there has been limited deployment in health care environments. The gap between potential and actual AI applications is likely due to the lack of translatability between controlled research environments (where these models are developed) and clinical environments for which the AI tools are ultimately intended.

Objective: We previously developed the Translational Evaluation of Healthcare AI (TEHAI) framework to assess the translational value of AI models and to support successful transition to health care environments.

Lessons learned from developing a COVID-19 algorithm governance framework in Aotearoa New Zealand.

Aotearoa New Zealand's response to the COVID-19 pandemic has included the use of algorithms that could aid decision making. Te Pokapū Hātepe o Aotearoa, the New Zealand Algorithm Hub, was established to evaluate and host COVID-19 related models and algorithms, and provide a central and secure infrastructure to support the country's pandemic response. A critical aspect of the Hub was the formation of an appropriate governance group to ensure that algorithms being deployed underwent cross-disciplinary scrutiny prior to being made available for quick and safe implementation.

Evaluation framework to guide implementation of AI systems into healthcare settings.

Objectives: To date, many artificial intelligence (AI) systems have been developed in healthcare, but adoption has been limited. This may be due to inappropriate or incomplete evaluation and a lack of internationally recognised AI standards on evaluation. To have confidence in the generalisability of AI systems in healthcare and to enable their integration into workflows, there is a need for a practical yet comprehensive instrument to assess the translational aspects of the available AI systems.

Do echocardiographic parameters predict mortality in patients with end-stage renal disease?

Background: Left ventricular function predicts cardiovascular mortality both in the general population and those with end-stage renal disease. Echocardiography is commonly undertaken as a screening test before kidney transplantation; however, there are little data on its predictive power.

Methods: This was a retrospective review of patients assessed for renal transplantation from 2000 to 2009.

A new multi-scale measure for analysing animal movement data.

We present a new measure for analysing animal movement data, which we term a 'Multi-Scale Straightness Index' (MSSI). The measure is a generalisation of the 'Straightness Index', the ratio of the beeline distance between the start and end of a track to the total distance travelled. In our new measure, the Straightness Index is computed repeatedly for track segments at all possible temporal scales.