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Predicting risk of hospital admission and readmission of Scottish population Scottish Patients at Risk of Readmission and Admission (SPARRA) is a risk prediction tool initially developed by NHS National Services Scotland in 2006 to predict an individual鈥檚 risk of emergency hospital inpatient admission over the next twelve months. The current version of SPARRA (released in 2012) uses hospital inpatient admissions data to calculate a risk score for those patients who have had an emergency admission in the previous three years. Scores are calculated quarterly and disseminated to NHS Goards, Community Health Partnerships (CHPs), and GP Practices. Currently SPARRA is mainly used for 鈥渃ase finding鈥� i.e.helping to identify patients with complex care needs who are likely to benefit most from anticipatory health care. Risk scores are calculated for around 4.2m patients monthly and accessed by authorised healthcare professionals. This talk looks at updating the underlying machine learning model. Measuring Sample Quality with diffusions Standard Markov chain Monte Carlo diagnostics, like effective sample size, are ineffective for biased sampling procedures that sacrifice asymptotic correctness for computational speed. Recent work addresses this issue for a class of strongly log-concave target distributions by constructing a computable discrepancy measure based on Stein鈥檚 method that provably determines convergence to the target. We generalize this approach to cover any target with a fast-coupling Ito diffusion by bounding the derivatives of Stein equation solutions in terms of Markov process coupling times [Gorham et al., 2016]. As example applications, we develop computable and convergence-determining diffusion Stein discrepancies for log-concave, heavy-tailed, and multimodal targets and use these quality measures to select the hyperparameters of biased samplers, compare random and deterministic quadrature rules, and quantify bias-variance tradeoffs in approximate Markov chain Monte Carlo. Our explicitmultivariate Stein factor bounds may be of independent interest.