Unpacking AI for Healthcare

1. #healthpredicted Unpacking AI for Healthcare @ashdamle Image from http://bryanchristiedesign.com/

2. We have very little control over health and care. From doctors to insurers to patients – we are all struggling with making sense of health.

3. our health is complex 37+ Trillion Cells

4. Image from http://bryanchristiedesign.com/ We have have no control, and very little visibility into how health evolves

5. As a result, care management and coordination is broken & imprecise, leading to: higher and higher costs of care with little improvement in health outcomes.

6. We have an opportunity. High quality data and analytics can drive precision into healthcare, reducing costs of medical care while improving health outcomes.

7. The challenge: Healthcare has one of the most complex data sets in existence. High volume. High dimensionality . Heterogeneous. Varied formats. Multi-faceted relationships. Noisy.

8. And yet, we are still using 19th century solutions for a 21st century problem!

9. Why not healthcare? voice recognition, image recognition, natural language processing, deep learning & machine learning AI has helped many other industries achieve unprecedented levels of efficiency in overcoming data complexity

10. $6B $2B The AI market in healthcare will hit $6 billion by 2020 (Frost and Sullivan) $2 billion can be saved annually with a tech-enabled processes (Accenture) AI is best positioned to solve the health data challenge AI surfaces the signal from the noise in health data allowing us to understand what to do, for whom, when, and why +

11. giving everyone more control and precision over health and care Automated information processing 45% of routine, manual tasks that can cost up to $90 million can be automated by adaptingcurrent AI technologies (McKinsey). 1 Precise disease management Machine learning could increase patientoutcomes at by 50% at about half the cost (Indiana University). 2 Efficient provider-patient encounters Virtual health appscan save physicians5 mins per patient encounter (Accenture) 3 Social robots for patient engagement Robots like PARO have been found to reduce patient stress and interaction with caregivers (World Economic Forum) 4

12. What if we could use AI to predict future health with precision, timeliness and speed? Could we significantly reduce costs of care while creating more improving outcomes: less complex, real-time feedback loops, more personalized?

13. How do we get there? We need real-time machine-based systems that leverage data to predict health with precision, timeliness and confidence, so we can deliver high-value personalized care at scale.

14. It requires… 1.Deep domain expertise in medicine to build robust, clinically- relevant models Data science expertise to handle complexity of health data and apply advanced machine learning techniques Access to large data sets for supervised and unsupervised training of models Infrastructure that can prepare terabytes of data for analysis with speed Industry collaboration to build solutions that can be seamlessly applied into clinical workflows

15. Introducing Lumiata: an example of Medical AI that handles the complexity of health data

16. We want to radically transform the way health data is put to work. 1. Power data-driven precision in predicting health to reduce costs and improve health outcomes 2. Bring clarity, control and confidence to all health actors

17. Lumiata leverages Medical AI to precisely predict and manage risk at the individual level. We drive the personalization and automation needed to make health predictable.

18. Data Scientists Utilize the latest in AI & deep learning to evolve Lumiata’s MedicalGraph Design & deploy new models for targeted use cases Clinical Scientists Adjudicate ongoing clinical inputs into Lumiata’sMedical Graph Ensure clinical relevance of predictive analytics& rationale DS CS To build Lumiata, we combine deep domain expertise

19. 330M+ data points describing the relationships between… • Hundreds of protocols & guidelines • 40K+ Symptoms & Signs • 4K Diagnoses • 3K Labs, Imaging, Tests • 3K Therapeutic Procedures • 7K Medications across age, gender, durations, lifestyle Our AI is powered by a learning probabilistic Medical Graph & Deep Learning 3TB+ unstructured data 175M+ patient record years 39K+ physician curation hours

20. that predicts individual health risks, and helps embed personalization and automation in risk management operations. Input (Data) Analyses (FHIR+AI) Output (Insights) Delivery (API) ImpactAction Risk Matrix + Clinical RationaleRISK MATRIX & CLINICAL RATIONALE MEDICAL GRAPH

21. It augments our ability to identify and capture value in data by bringing clinical precision, giving everyone the confidence to act with precise health predictions by automating labor- intensive risk management operations to reduce costs (data gathering + data synthesis + analysis + planning + messaging + decision + fulfill) &

22. symptoms diagnoses labs Images therapy procedures meds environ. factors, seasonality lifestyle + demo. profile geography past medical history genetics family history vitalscomplaints ∫(age, gender, duration, ethnicity, …) ∫(age, gender, sensitivity, specificity, …) Generating per patient models of health, making healthcare delivery predictable and personalized. Our Medical Graph maps multi-dimensional relationships to handle the complexities of health data

23. and by mapping out the relationships of health data, the Medical Graph address many of the data complexities in systematic, scalable way Demographics Lumiata Medical Graph Procedures Physical Exam & Tests Medical & Social Hx Sensors & Wearables Genomics High volume High dimensionality Heterogeneous Varied formats Multi-faceted relationships Noisy Multiple Coding Systems Graphs not Trees/DAGs

24. PUBMED References PUBMED References Lumiata Risk Matrix Condition 1 2 3 4 5 6 7 8 … 0-‐1 Year Y N N Y Y N N N … 1-‐2 Years Y N N Y Y Y N N … 2+ Years Y N N Y Y Y N Y … Clinical Rationale Clinical Rationale Past Med History Diagnoses Abnormal Labs Procedures Medications where each prediction is supported with medical evidence, bringing confidence, control and clarity to health operations

25. 36,000+ Physician Curation Hours Clinical Integration Engine Clinical Analytics Engine API & Web Platform Real-Time Data Clinical Financial Social Environmental Descriptive Introspective Predictive Prescriptive Discovery Operationalize Data Data Unification Insight & Action Generation Data & Action Distribution and transforms data to insight to action

26. Fast-tracking healthcare toward value-based care Automated risk stratification to drive population health management Precise & personalized care management interventions Clinical alignment and agreement between payers and providers Reduced costs by removing labor-intensive, redundant tasks +

27. True Clinical State & Risk Evolution Differential Diagnosis and Triage Missing Diagnosis Data Driven Guidelines Clinically Right Coding (ICD, HCC) Risk Adjustment Quality Maximization Predict High Cost Claimants Utilization Prediction Care Coordination with clear practical use cases available via an API or web app

28. Through AI, we are giving everyone the confidence to act on data in a way that improves care, automates processes and reduces costs. Health plans become more cost-effective and collaborative. Caregivers deliver more precise and timely care. Patients get personalized treatment plans.

29. Image from http://bryanchristiedesign.com/ powering clear, predictable health outcomes

30. #healthpredicted Unpacking AI for Healthcare @ashdamle