A Rapid-Learning Health System Using in silico research Lynn Etheredge Wolfram Data Summit - September 9, 2010

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

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

A Rapid-Learning Health System

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)

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))

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

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 – test multiple theories Iteratively use in silico, in vitro, & in vivo methods, w/ database development Create a national “rapid learning” culture