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A Rapid-Learning Health System

A Rapid-Learning Health System

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A Rapid-Learning Health System

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  1. A Rapid-Learning Health System Usingin silico research Lynn Etheredge Wolfram Data Summit - September 9, 2010

  2. A Rapid-Learning Health System • Objective: a health system that learns as rapidly as possible about the best treatment for each patient • Addresses personalized medicine, major health system problems: clinical practice variations, poor quality, lack of comparative effectiveness research (CER), high & rising costs, ineffective markets, inefficient regulation • Key concept: in silico research (using large computerized databases and research networks) • Complements in vitro (lab) and in vivo(experimental) methods • Enables study of many more patients (e.g. millions), much richer & longitudinal data (e.g. EHRs w/ genetics), many more researchers, many more & different studies, much faster studies • Researchers: multi-year data collection  log-on to world’s evidence base

  3. A Rapid-Learning Health System • RL = R + C + D • We have: R (great researchers), C (very fast computers) • D (databases) = a national system of high quality, clinical research databases, pre-designed, pre-built, and pre-populated for RL • Most RL questions are applied research – treatment “A” vs treatment “B” for (sub) population “C”: can specify data that will answer the question • Electronic health records (EHRs) enable RL databases & registries

  4. A Rapid-Learning Health System

  5. National RL Initiatives • Recent (examples): • Comparative effectiveness research ($1.1B including database(s) and network development); FDA Sentinel Network (100 M patient records); national EHR system ($40B+); independent CER agency; NCI’s Biomedical Informatics Grid (CaBIG); NIH’s genetic databases (dbGaP), Medicare/Medicaid Innovation Center ($10 B); IOM workshops/reports & consensus committee • Future (examples): • A NIH “Collaboratory” with HMO Research Network (16 HMOs, 11M patients/EHRs, Virtual Data Warehouse (standardized, distributed data network)

  6. National RL Initiatives • Future (examples): • A NIH-Kaiser Permanente-RWJF national biobank + bio-repository, 500,000 patient EHRs w/ genetic & environmental database, bio-specimens • A national system of “gap-filling” registries and networks for sub-populations usually not included in clinical trials: children, pregnant women, seniors, minorities, persons with multiple chronic conditions, rare diseases, surgery, e.g. Medicare’s Chronic Disease Warehouse (CDW), PEDSNet, NIH rare diseases registry & bio-repository • A national RL system for cancer (Health Affairs (2009), IOM report (2010), IOM-NCI conference (October 2010))

  7. National RL Initiatives • Future (examples): • A new generation of predictive models that will integrate clinical studies & research databases, enable physicians and patients to input patient characteristics, receive state-of-the-art analyses of treatment options. Archimedes/ARCHeS predictive model (Kaiser Permanente, Robert Wood Johnson Foundation (2010)) • A national database for effectiveness research studies All publicly-funded clinical studies to file complete, standardized databases for “open access” science, e.g. • A national RL system for new technologies (Rx, devices, diagnostics, radiology, surgery), with research plans/registries at market entry

  8. Turbocharge Rapid Learning • Build “everything included” databases ( rate of scientific progress = F (how smart we are at using computers !!) • Develop first-rate predictive models for all conditions • Invent new mathematics/statistics/analytic tools for data-rich environments & studying complex, adaptive systems • Evolve biological theory – testmultiple theories • Iteratively use in silico,in vitro, & in vivo methods, w/ database development • Create a national “rapid learning” culture