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A Rapid-Learning Healthcare System In silico research A national HIT infrastructure of computable data Adopting best practices Lynn Etheredge Wolfram Data.

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Presentation on theme: "A Rapid-Learning Healthcare System In silico research A national HIT infrastructure of computable data Adopting best practices Lynn Etheredge Wolfram Data."— Presentation transcript:

1 A Rapid-Learning Healthcare System In silico research A national HIT infrastructure of computable data Adopting best practices Lynn Etheredge Wolfram Data Summit 2011 September 8, 2011

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. Advances translational science & evidence-based medicine. Key concept: in silico research -- using large computerized databases and research networks for 21 st century science –Complements in vitro (lab) and in vivo (experimental) methods –Enables study of many more patients, richer & longitudinal data, many more researchers, more, different & faster studies –Researchers: multi-year data collection  log-on to & use the world’s evidence base of computable data

3 A Rapid-Learning Health System RL = R + C + D + Apps We have: R (great researchers), C (very fast computers) D (databases) = a national system of high quality, clinical research registries & 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 data- rich environment, a computable world clinical evidence base Apps: user-friendly software to acquire, share, analyze & use computable data, quickly & efficiently

4 A Rapid-Learning Health System

5 Toward A Learning Healthcare System Recent initiatives: –Comparative effectiveness research (CER) ($1.1B). A national system of “gap-filling” registries and networks for sub-populations usually not included in clinical trials: children, seniors, minorities, persons with multiple chronic conditions, rare diseases. Independent CER agency (PCORI); –A national EHR system for all Americans ($27B+); – FDA Sentinel Network (100 M patient records); –A Medicare/Medicaid Innovation Center for national pilots and implementation of best practices ($10 B);

6 Toward A Learning Healthcare System Recent initiatives: –A national biobank + bio-repository (Kaiser, NIH, RWJF), 500,000 patient EHRs w/ genetic & environmental database, bio- specimens; –NIH eEMERGE network for genetic & clinical studies, personalized medicine, with shared “open science” data; –NIH “Collaboratory” with HMO Research Network (19 HMOs, 14 M patients (  28M) w/ EHRs, Virtual Data Warehouse w/ standardized core data elements; –VA “one million veterans” initiative w EHRs, genetic information; –EHR4CR: European universities & industry (33 partners)

7 Toward A Learning Healthcare System Recent initiatives: –Physician societies & specialists building rapid-learning systems: Real-time oncology learning network (ASCO) – Sept 16 National cardiovascular research infrastructure & registries (ACC) Pediatrics studies networks (PEDSNet, PTN, PQMP) National quality registry network (NQRN) – Nov. 2011 –Advances in predictive models, e.g. Archimedes-ARCHeS & IndiGo (40% fewer strokes/heart attacks, 70% less cost) (NYT, 5/19/2011) –New statistical & research methods: VA adaptable trials, accelerated multi-cohort/rapid cycle strategies, Bayesian & decision-theory statistics, mixed-methods, multiple-source research, randomized cluster designs –National Center for Accelerating Translational Science (NCATS): new NIH institute (Oct. 2011), CTSA Consortium (60 academic centers)

8 EHRs, Registries & Apps National goal: EHRs for all Americans; $27 B in subsidies for federally-qualified EHRs; a learning healthcare system –EHR Status: rapid-learning leaders w/ EHRs & research programs in organized care systems (Kaiser, VA, Geisinger, HMORN) ~ 35 M patients; larger multi-specialty practices & institutions. Elsewhere fragmentary (25% physician practices, 10% hospitals). An evolving HIT strategy for computable data –National EHR standards, with a core dataset & data exchange capabilities –A national system of research registries for all patients & conditions –A vibrant Apps marketplace to acquire, share & use data (aka modules, plug-ins, links, downloads, add-ons)

9 EHRs, Registries & Apps Rationale for basic EHRs + Apps –Vast differences between leaders & average healthcare, economy, politics, more reasons to purchase & use EHRs; vastly expand potential for acquiring & using computable data Growing use of private sector EHR standards, registries, modular/specialized HIT & Apps: –HMO Research Network, its Virtual Data Warehouse, EPIC systems –Specialty & disease-focused learning networks (cancer, cardiology, pediatrics, medical devices, rare diseases) –Patient-focused Apps: Health 2.0, M-Health (500 m users by 2015? Star- Trek “tricorder” medicine?) –NIH’s Apps-development challenge (300 databases, 8/31/2011), FDA Sentinel Apps-standard algorithms, CDC flu & reportable diseases Apps

10 Rapid-Cycle Learning & Adoption of Best Practices Health Reform legislation: $10 billion & models to be tested by CMS & rolled-out, if successful (examples): accountable care organizations, pay-for-performance, medical home, bundled payment, community- based care transitions, independence at home, hospital re-admission reductions, global safety net system payment, national quality reporting system, all-payer systems, integrated care for “dual eligibles” payment incentives for evidence-based cancer care, improved post- acute care through continuing care hospitals, comprehensive payments for “innovation zones”, payment reforms to advance care coordination, collaborations of high-quality, low-cost health care institutions, patient decision-support tools, etc.

11 Rapid-Cycle Learning & Adoption of Best Practices HHS new $ 1B Medicare initiative to produce $50B in 10 yr savings from improved safety, fewer re-admissions (April 2011) –Partnership For Patients uses research and data to identify “best practices” and interventions –Financial incentives through payment reforms, pilot projects –Target: 60,000 lives saved over next 3 years Recently announced: Accountable care organizations (ACOs), bundled payments Many more rapid-cycle learning initiatives, coming soon, e.g. –Maternal & infant care –Cardiovascular disease - 1 million heart initiative

12 Key Messages US national policy is adopting a new way of thinking -- a learning healthcare system The key idea is to learn, as quickly as possible, about “best practices” – and then to have these adopted throughout the healthcare system. And to create a continuous learning cycle, and a continuously learning system All parts of this new system are now open for discussion & creation – a great time for innovators, computable-data people, and do-gooders !!


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