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SUMMARY:Computing High Resolution Health(care) - Professor Iain Buchan\, P
 rofessor in Public Health Informatics and leads the Centre for Health Info
 rmatics at the University of Manchester
DTSTART:20170816T110000Z
DTEND:20170816T120000Z
UID:TALK76181@talks.cam.ac.uk
CONTACT:48007
DESCRIPTION:The quest to harness “big data” for better healthcare is d
 riving new computational approaches to both discovery science and actionab
 le analytics. Precision medicine for example\, seeks actionable biomarkers
  of disease mechanisms. This approach is useful if the mechanisms uncovere
 d explain a large proportion of the variation in clinical outcomes. Common
 ly\, however\, the real-world variation in outcomes involves many mechanis
 ms\, biomarkers and phenomarkers – some known\, some yet to be known\, a
 nd some unlikely to be known. Informaticians therefore seek to build compu
 tational approaches from the ‘middle out’\, linking ‘bottom up’ co
 mputational biology with ‘top down’ (clinical) epidemiology. Professor
  Buchan will give examples of endotype discoveries made with biostatistica
 l and machine learning approaches designed to extract more detailed phenom
 arker information from longitudinal (birth cohort) data. He will then take
  a similar approach to clinical and patient-reported data\, putting the ca
 se that healthcare evidence needs to be generated in ways that more easily
  shrink onto local contexts. Looking toward a future of more predictive an
 alytics around personal health information\, Professor Buchan will conside
 r the point at which an individual’s health ‘avatar’ might say “no
 ” to a healthcare provider’s care pathway – a collision of equipoise
  – and how health systems might prepare for this. He will argue that cur
 rent ‘low resolution’ healthcare maximises information utility for epi
 sodes of care\, and that future data-intensive health systems could offer 
 ‘high resolution’ approaches that focus on patient trajectories\, supp
 orting: more timely\, preventive and tailored interventions\; and continuo
 us experimentation/adaptivity/learning.
LOCATION:Seminar Room\, Strangeways Research Laboratory
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