How Big Data and Advanced Analytics Can Improve Population Health: Now and In the Near Future

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1 How Big Data and Advanced Analytics Can Improve Population Health: Now and In the Near Future William J. Kassler, MD, MPH Deputy Chief Health Officer Lead Population Health Officer

2 Precision Medicine Completion of Human Genome Project in 2003 led to expansion of research on the contributions of genomics in disease diagnosis, treatment, and prevention Green, ED et al (2011). Charting a course for genomic medicine from base pairs to bedside. Nature 470: Early Discovery What biological or environmental factors are causing disease? Can we design diagnostics and drugs to improve patient outcomes? Clinical Genomics What does my patient s genomic information tell me about the treatment I should select?

3 Precision Population Health

4 Reconciling the Clinical and Community Perspectives Community: Geographically defined Health Systems: Hospitals, ACOs, Health Plans Clinical practices: Patient panels, Racial/Ethnic groups, subgroups with specific chronic diseases Adapted from Kassler et al. N Engl J Med 372; 2015

5 Environment Socioeconomic Status Genetics & Family History Behavior & Habits Healthcare Access & Experience Social Influences McGovern et al. The Relative Contribution of Multiple Determinants to Health Outcomes, Health Affairs, August 21, 2014.

6 A vast amount of untapped data could have a great impact on our health yet it exists outside medical systems 60% Exogenous Factors 12 Terabytes Generated per lifetime Volume, Variety, Velocity, Veracity 30% Genomics Factors 10% Clinical Factors 6 Terabytes Per lifetime 0.4 Terabytes Per lifetime Barbara J. Sowada, A Call to Be Whole: The Fundamentals of Health Care Reform, July 30, 2003, Praeger

7 The Challenges of Big Data Keeping up There are 100,000+ clinical trials running in parallel. A patient will generate >12 TB of personal health data in a lifetime (300 million books). Medline: 424 million published articles in 5600 journals 1.8 million new articles published annually 80% Unstructured A typical high-need patient has a 100+ page electronic health record. Text where meaning is often derived from context Images: X-rays, sonograms, electrocardiograms, magnetic resonance images, and mass spectrometry results Noisy Problems of scale: finding the signal in the noise when its buried in millions of pages across multiple silos Humans must collect, organize data and evaluate evidence Introduces cognitive bias 1. Journal of Clinical Oncology, Talk about Health Blog, Sept

8 Tabulating Systems Era s Programmable Systems Era 1950s Present Cognitive Computing Era 2011 IBM 2017

9 How can AI support health? UNDERSTANDS Reads and understands data both structured & unstructured at a massive scale. REASONS Searches millions of pages of data in seconds and can recognize context and interpret the language of medicine. LEARNS Learns from leading human experts and real world cases and continues to improve over time and experience. EMPOWERS Previously invisible data and knowledge are delivered into actionable insights. AI empowers humans and is transparent.

10 Natural Language Processing Clinical Therapeutics 38:4 2016

11 Natural Language Processing: understanding text 200+ Health & Life-Science knowledge domains trained ML-based annotators Reads unstructured documents, notes, patient history, lab reports Knowledge Graph Index Content Corpus & Training Examples Cognitive NLP Pipelines Content Store Annotation Store IBM 2016

12 Deep Learning: A branch of Machine Learning Deep learning is a branch of machine learning that makes use of multiple processing layers and hierarchical representations to drive the learning process.

13 Cognitive Computing = Knowledge + Data-Driven Insights Knowledge-Driven Insights Scientific papers Evidence-Based Guidelines Reports, Books Articles, Publications Published Knowledge Closing the translational knowledge gap Data-Driven Insights Observational Data Electronic Medical Records Claims, Labs, Images Health risk assessments, Internet of Things Social, environmental, behavioral Delivering real-world evidence 13 Watson Health IBM Corporation 2017

14 Life Sciences Oncology/Genomics Imaging Value Based Care Government Clinical Trials Watson for Drug Discovery Watson for Patient Safety Watson for Genomics Watson for Oncology Clinical Trial Matching for Oncology Imaging for cardiac disease Cardiologists and Radiologists report analysis Imaging for Breast Cancer Population Health Suite Payer Analytics Provider Analytics Social Program Management Health & Human Services Program Integrity IBM WATSON HEALTH CLOUD Image Analytics Analytics / Insights Platform Cognitive Knowledge Platform DATA KNOWLEDGE M+ Claims 100 M+ Patient Records Billions of Images Millions of Medical Documents

15 How can we improve our Population Health with Big Data and Advanced Analytics? Integrate multiple data sources; render insights Risk stratification and predictive analytics Embed measures of population health into practice (assessment, improvement) Support practice infrastructure for value-based payments (MIPS / APMs) Facilitate collaboration and alignment across care team, organizations and sectors Behavioral economics/engagement: provider, patient & consumer nudges

16 Current Applications of Cognitive Analytic Techniques Empowering Individuals Creating personalized nudges Segmentation Analysis Identifying & understanding the population: targeted care delivery and policies Area Insights Identifying & understanding the population: targeted care delivery and policies Trade-off Analytics Personalized shared decision making

17 Empowering People

18 Segmentation Analysis Adapted from: Vuik et al, Health Affairs 35, No 5 (2016) appendix

19 Segmentation Analysis

20 Area Insights: Socio-economic factors linked to poor health outcomes Area Deprivation Index (ADI) Neighborhood-based composite measure consisting of 17 markers of socioeconomic status ADI correlated with: Mortality rates (age and race adjusted) for men and women 30 day readmission rates» increase with worsening ADI» Magnitude equal to COPD and > diabetes Singh, GK Area deprivation and widening inequalities in US mortality, Am J Public Health July 2003 Kind, AJ et al Neighborhood socioeconomic disadvantage and 30-day rehospitalization: a retrospective cohort study Ann Intern Med Dec 2014

21 Area Insights

22 Tradeoff Analytics: Optimize provider selection on service quality, proximity, license held, cost of service, and preferences Narrow down to the best options to meet multiple goals Explore visually Recommend based on the preferred choices

23 Near Future Use Cases for Cognitive Computing Real World Evidence / Patient Similarity Analytics Precision Cohorts: dynamic identification of patients like mine Mental Health and Behavioral Health Computational neuroscience

24 Patient Similarity Analytics: Precision Cohorts of patients like mine

25 Cognitive Assets to Support Behavioral Health Micro-facial expressions to support emotional response tracking Wearable bio-sensors for emotional, physiological tracking

26 Computational Psychiatry to identify risk and changes in health

27 Tone Analyzer Steve Jobs Stanford Graduation Speech

28 Tone Analyzer Steve Jobs Stanford Graduation Speech

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30 Trend: Approaching Human Accuracy Human Error

31 Practice Questions Does it work? Will I eventually be replaced? Will it work in my practice? Will it add to or reduce my administrative overload? How does it impact my patients? Will I get paid? Will I get sued? Watson Health IBM Corporation

32 Principles of AI Transparency = Trust glass box regarding purpose, intent, priorities algorithmic accountability, how trained, data used to train physician input, clinical leadership compliance with existing policy, best practice, ethical values Data Responsibility protection of privacy and personal information data flow and access blockchain data philanthropy Watson Health IBM Corporation

33 Regulatory Framework: Software as a Medical Device Clinical Evaluation International Medical Device Regulators Forum (IMDRF) NXX SaMD WG WD2 June 2016

34 Humans + Cognitive = AI or Augmented Intelligence People excel at: Common sense Dilemmas Morals Compassion Imagination Dreaming Abstraction Generalization Cognitive systems excel at: Natural Language Pattern Identification Locating Knowledge Machine Learning Eliminate Bias Endless Capacity

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36 That it will ever come into general use, notwithstanding its value, is extremely doubtful; because its beneficial application requires much time and gives a good bit of trouble both to the patient and the practitioner; because its hue and character are foreign and opposed to all our habits and associations

37 Let s Work