Bmc Med
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Risk prediction models are commonly used in practice to inform decisions on patients' treatment. Uncertainty around risk scores beyond the confidence interval is rarely explored. We conducted an uncertainty analysis of the QRISK prediction tool to evaluate the robustness of individual risk predictions with varying modelling decisions. ⋯ Risk prediction models that use routinely collected data provide estimates strongly dependent on modelling decisions. Despite this large variability in patient risk, the models appear to perform similarly according to standard performance metrics. Decision-making should be supplemented with clinical judgement and evidence of additional risk factors. The largest source of variability, a secular trend in CVD incidence, can be accounted for and should be explored in more detail.
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Big data, coupled with the use of advanced analytical approaches, such as artificial intelligence (AI), have the potential to improve medical outcomes and population health. Data that are routinely generated from, for example, electronic medical records and smart devices have become progressively easier and cheaper to collect, process, and analyze. In recent decades, this has prompted a substantial increase in biomedical research efforts outside traditional clinical trial settings. ⋯ This article collection provides concrete examples of how "big data" can be used to advance healthcare and discusses some of the limitations and challenges encountered with this type of research. It primarily focuses on real-world data, such as electronic medical records and genomic medicine, considers new developments in AI and digital health, and discusses ethical considerations and issues related to data sharing. Overall, we remain positive that big data studies and associated new technologies will continue to guide novel, exciting research that will ultimately improve healthcare and medicine-but we are also realistic that concerns remain about privacy, equity, security, and benefit to all.
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Medication management support in diabetes: a systematic assessment of diabetes self-management apps.
Smartphone apps are becoming increasingly popular for supporting diabetes self-management. A key aspect of diabetes self-management is appropriate medication-taking. This study aims to systematically assess and characterise the medication management features in diabetes self-management apps and their congruence with best-practice evidence-based criteria. ⋯ A large proportion of diabetes self-management apps lacked features for enhancing medication adherence and safety. More emphasis should be given to the design of medication management features in diabetes apps to improve their alignment to evidence-based best practice.
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Meta Analysis
Hospitalisation rates and predictors in people with dementia: a systematic review and meta-analysis.
Hospitalisation is often harmful for people with dementia and results in high societal costs, so avoidance of unnecessary admissions is a global priority. However, no intervention has yet reduced admissions of community-dwelling people with dementia. We therefore aimed to examine hospitalisation rates of people with dementia and whether these differ from people without dementia and to identify socio-demographic and clinical predictors of hospitalisation. ⋯ People with dementia are more frequently admitted to hospital than those without dementia, independent of physical comorbidities. Future interventions to reduce unnecessary hospitalisations should target potentially modifiable factors, such as polypharmacy and functional ability, in high-risk populations.