Can learning health systems help organisations deliver personalised care?

There is increasing international policy and clinical interest in developing learning health systems and delivering precision medicine, which it is hoped will help reduce variation in the quality and safety of care, improve efficiency, and lead to increasing the personalisation of healthcare. Although reliant on similar policies, informatics tools, and data science and implementation research capabilities, these two major initiatives have thus far largely progressed in parallel. In this opinion piece, we argue that they should be considered as complementary, synergistic initiatives whereby the creation of learning health systems infrastructure can support and catalyse the delivery of precision medicine that maximises the benefits and minimises the risks associated with treatments for individual patients. We illustrate this synergy by considering the example of treatments for asthma, which is now recognised as an umbrella term for a heterogeneous group of related conditions

Was reduced pollen viability in Viola tricolor L. the result of heavy metal pollution or rather the tests applied?

We used different tests to assess the effect of high soil concentrations of heavy metals on pollen viability in plants from metallicolous (MET) and nonmetallicolous (NONMET) populations. The frequency of viable pollen depended on the test applied: MET plants showed no significant reduction of pollen viability by acetocarmine, Alexander, MTT and X-Gal dye testing, but a drastic reduction of pollen viability in MET flowers (MET 56% vs 72% NONMET) by the FDA test. There was no correlation between pollen viability estimated in histochemical tests and pollen germination in vitro or in vivo. We discuss the terminology used to describe pollen viability as determined by histochemical tests

Data from: Education research: simulation training for neurology residents on acquiring tPA consent: an educational initiative

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supplement_fig - Clinical Decision-Making for Thrombolysis of Acute Minor Stroke Using Adaptive Conjoint Analysis

<p>supplement_fig for Clinical Decision-Making for Thrombolysis of Acute Minor Stroke Using Adaptive Conjoint Analysis by Ava L. Liberman, Daniel Pinto, Sara K. Rostanski, Daniel L. Labovitz, Andrew M. Naidech, and Shyam Prabhakaran in The Neurohospitalist</p