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, whanau and healthcare provider participants for qualitative interviews. One-hour, semi-structured interviews were conducted to elicit perspectives on SDM. Data was analysed using Directed Content Analysis. RESULTS: Thirty-one participants were interviewed. SDM conceptualisations primarily concerned the sharing of information. Participants' stories highlighted patients' and whanau willingness to participate in making decisions about their care, to hold authority in this process, and to have their needs and preferences considered beyond the biomedical model. Patients and clinicians identified a range of factors moderating the extent of SDM, creating a gap between SDM expectations and practice. CONCLUSIONS: These data highlight the complexity of information needs in cancer care, and the discrepancy between patients' and their whanau and clinicians' views. This study increases our understanding of cancer stakeholders' expectations of SDM by highlighting various views on the meaning of SDM, informational needs and decision making engagement level. These findings can aid clinicians in creating space for patients to exercise their right to self-determination/rangatiratanga of health and wellbeing. Future work should explore approaches and implementations of SDM to facilitate an equitable experience of cancer care.

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. In this study, we applied the TEHAI framework to the COVID-19 literature in order to assess how well translational topics are covered. Methods: A systematic literature search for COVID-19 AI studies published between December 2019 and December 2020 resulted in 3830 records. A subset of 102 (2.7%) papers that passed the inclusion criteria was sampled for full review. The papers were assessed for translational value and descriptive data collected by 9 reviewers (each study was assessed by 2 reviewers). Evaluation scores and extracted data were compared by a third reviewer for resolution of discrepancies. The review process was conducted on the Covidence software platform. Results: We observed a significant trend for studies to attain high scores for technical capability but low scores for the areas essential for clinical translatability. Specific questions regarding external model validation, safety, nonmaleficence, and service adoption received failed scores in most studies. Conclusions: Using TEHAI, we identified notable gaps in how well translational topics of AI models are covered in the COVID-19 clinical sphere. These gaps in areas crucial for clinical translatability could, and should, be considered already at the model development stage to increase translatability into real COVID-19 health care environments.

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. Currently available evaluation frameworks for AI in healthcare focus on the reporting and regulatory aspects but have little guidance regarding assessment of the translational aspects of the AI systems like the functional, utility and ethical components. METHODS: To address this gap and create a framework that assesses real-world systems, an international team has developed a translationally focused evaluation framework termed 'Translational Evaluation of Healthcare AI (TEHAI)'. A critical review of literature assessed existing evaluation and reporting frameworks and gaps. Next, using health technology evaluation and translational principles, reporting components were identified for consideration. These were independently reviewed for consensus inclusion in a final framework by an international panel of eight expert. RESULTS: TEHAI includes three main components: capability, utility and adoption. The emphasis on translational and ethical features of the model development and deployment distinguishes TEHAI from other evaluation instruments. In specific, the evaluation components can be applied at any stage of the development and deployment of the AI system. DISCUSSION: One major limitation of existing reporting or evaluation frameworks is their narrow focus. TEHAI, because of its strong foundation in translation research models and an emphasis on safety, translational value and generalisability, not only has a theoretical basis but also practical application to assessing real-world systems. CONCLUSION: The translational research theoretic approach used to develop TEHAI should see it having application not just for evaluation of clinical AI in research settings, but more broadly to guide evaluation of working clinical 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 survival analysis using demographic and echocardiographic variables was undertaken using the Cox proportional hazards regression model. RESULTS: Of 862 patients assessed for transplantation, 739 had an echocardiogram and 217 of 739 (29%) died during a mean follow-up of 4.2 years. In a multivariate survival analysis, increased age (P<0.0001), diabetes (P<0.0001), transplant listing status (P<0.0001), severely impaired left ventricular function (P<0.01), pulmonary hypertension and/or right ventricular dysfunction (P=0.01), and regional wall motion abnormalities (P<0.01) were associated with all-cause mortality. Combined in a score where one point was given for the presence of each of the parameters above, these factors were strongly predictive of increased mortality with a hazard ratio of 3.57, 6.80, and 44.47 for the presence of one, two, or more factors, respectively, compared with the absence of any of these factors. CONCLUSIONS: In patients with end-stage renal disease, multiple easily determined echocardiographic parameters, including regional wall motion abnormalities and pulmonary hypertension and/or right ventricular dysfunction, were independently associated with all-cause and cardiovascular mortality. Combining these factors in a simple score may further assist in risk stratifying patients being considered for renal transplantation.

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. The MSSI offers advantages over the standard Straightness Index, and other simple measures of track tortuosity (such as Sinuosity and Fractal Dimension), because it provides multiple characterisations of straightness, rather than just a single summary measure. Thus, comparisons can be made among different segments of trajectories and changes in behaviour can be inferred, both over time and at different temporal granularities. The measure also has an important advantage over several recent and increasingly popular methods for detecting behavioural changes in time-series locational data (e.g., state-space models and positional entropy methods), in that it is extremely simple to compute. Here, we demonstrate use of the MSSI on both synthetic and real animal-movement trajectories. We show how behavioural changes can be inferred within individual tracks and how behaviour varies across spatio-temporal scales. Our aim is to present a useful tool for researchers requiring a computationally simple but effective means of analysing the movement patterns of animals.