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The Hive Think Tank: Unpacking AI for Healthcare

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In this The Hive Think Tank talk, Ash Damle, CEO of Lumiata takes a deep dive into Lumiata’s core technological engine - the Lumiata Medical Graph, which applies graph-based machine learning to compute the complex relationships between health data in the same way that a physician would, and how this medical AI engine powers personalization and automation within risk and care management.

Veröffentlicht in: Technologie

The Hive Think Tank: Unpacking AI for Healthcare

  1. 1. #healthpredicted Unpacking AI for Healthcare to Automate Risk and Care Management @ashdamle | Hive Think Tank Image from http://bryanchristiedesign.com/
  2. 2. our health is complex 37+ Trillion Cells
  3. 3. And today, Healthcare feels like a unwinnable game of Tetris nocontrol&coordination withimprecise outdatedsystems.
  4. 4. this is how visible tomorrow’s health is today Image from http://bryanchristiedesign.com/
  5. 5. Because, healthcare has one of the most complex data sets in existence High volume . High dimensionality .Heterogeneous Varied formats . Multi-faceted relationships .Noisy And why?
  6. 6. healthcare lacks visibility, predictability, and precision which results in a failure of Timeliness . Alignment . Coordination And because of this extreme data complexity,
  7. 7. Many organizations face challenges in cleaning, standardizing, normalizing and making sense of longitudinal data. This leads to an incomplete, outdated view of patients’ health. Challenge 1 Inability to combine multi-sourced data efficiently and at scale In 2012, 500 petabyes  by 2020, 25,000+ petabytes. Effective big data solutions could result in annual industry savings of $300 billion.
  8. 8. Healthcare institutions & individuals are taking more financial risk. But they fail to minimize underlying health risk because they cannot predict what care is needed for whom, when and why. Challenge 2 An asymmetry between financial and health risk 30% of providers are in risk-sharing agreements, and that figure will double by 2020.
  9. 9. Today’s care management processes are costly, labor-intensive, imprecise, and do not align payers, providers and patients. And, they have failed to reduce hospitalization for those with chronic illnesses. Challenge 3 Outdated care management processes Only 15% of administrative costs on care management, though it impacts 75% of costs Effective coordination could reduce hospital readmission rates by 10 to 15%.
  10. 10. So, why not healthcare? voice recognition, image recognition, natural language processing, deep learning & machine learning Over the last 3 years, AI has helped many other industries achieve unprecedented levels of efficiency in overcoming data complexity
  11. 11. $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) And healthcare’s problem that AI is best positioned to address is fixing the precision of risk & care management AI surfaces the signal from the noise in health data allowing us to understand what to do, for whom, when, and why so we can improve efficiency, reduce costs and deliver precise personalized care. +
  12. 12. And we believe within the next 3 years, AI will do so same across the healthcare continuum Automated information processing 45% of routine, manual tasks that can cost up to $90 million can be automated by adapting current AI technologies (McKinsey). 1 Precise disease management Machine learning could increase patient outcomes at by 50% at about half the cost (Indiana University). 2 Efficient provider- patient encounters Virtual health apps can save physicians 5 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
  13. 13. 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 However, unpacking AI for risk and care management demands
  14. 14. Introducing Lumiata, an example of unpacking AI for Healthcare #healthpredicted
  15. 15. Lumiata leverages Medical AI to precisely predict and manage risk at the individual level, and drive the personalization and automation needed to make health predictable.
  16. 16. We want to help healthcare institutions Lumiata is on a mission to power a virtuous cycle of predictable health through AI outsmart disease with data-driven precision making health predictable in real-time empowering everyone to act with control & confidence #healthpredicted
  17. 17. And for that we must build real-time machine-based systems that enable us to surpass our limits of precision & timeliness, so we can deliver high-value personalized care at scale We need to fast-track healthcare into the ”Fourth Industrial Revolution”
  18. 18. 18 Data Scientists Utilize the latest in AI & deep learning to evolve Lumiata’s Medical Graph Design & deploy new models for targeted use cases Clinical Scientists Adjudicate ongoing clinical inputs into Lumiata’s Medical Graph Ensure clinical relevance of predictive analytics & rationale D S C S To build Lumiata, we combine deep domain expertise
  19. 19. to augment our AI’s ability to identify and capture value in data by automating risk adjustment, quality metric, & care coordination activities (currently finding about +$600 on average of additional revenue per patient) • For those who have bear risk and have data, these activities directly improve both top and bottom line • Most risk bearing organizations have care management programs which are ripe for automation • This gets us the data we need to learn and embedded into workflows for feedback
  20. 20. We seek to orchestrate proactive real-time personalized care by being the interpretive interface between all actors & data to automate care management activities data gathering + data synthesis + analysis + planning + messaging + decision + fulfill
  21. 21. All towards building a virtuous cycle of AI to create an end-to-end system that transforms data into insights, and insights into action. Data Model Accuracy Communicability Distribution Usage/Feedback 10s of Millions of Patient Records Every article in PubMed 38K Physician Hours Medical Graph (39M+ Edges) 80%+ PPV across major conditions Clear chain of medical reasoning for each prediction and suggested action Analytic & Conversational API to communicate tasks Active Supervised Learning Continuous improvements to our models Data Insight Action
  22. 22. 330M+ data points describing the relationships between… 3TB+ unstructured data || 10s of millions patient records || 36K+ physician curation hours • 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
  23. 23. symptoms diagnoses labs Images therapy procedure s meds environ. factors, seasonalit y lifestyle + demo. profile geograph y past medical history genetics family history vitals complaint s ∫(age, gender, duration, ethnicity, …) ∫(age, gender, sensitivity, specificity, …) This enables us to generate models on an individual patient level. which maps multi-dimensional relationships to handle the complexities of health
  24. 24. and by mapping out the relationships of health data, the Medical Graph address many of the data complexities in systematic scalable way Demographics Lumiat a 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
  25. 25. Our first step in making health predictable is the Risk Matrix: Time-based, real- time, personalized predictions on an individual’s risk of chronic disease & events Lumiata Risk Matrix Clear clinical rationale provides the confidence to act Currently, models are available for: • Atrial fibrillation • Bipolar disease • Chronic kidney disease • Congestive heart failure • COPD • Coronary heart disease • Dementia • Depression • Diabetes Mellitus Type 2 • Obesity • Primary hypertension • Rheumatoid Arthritis
  26. 26. PUBMED References where each prediction is supported with clinical rationale with highly specific data and links to medical literature through the Medical Graph with over 39 million edges Past Medical Hx Abnormal Labs Procedures Medications Clinical Rationale Diagnoses Predicted Diagnosis #1 PUBMED References
  27. 27. 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 Powering end-to-end, clinically relevant value
  28. 28. that addresses tangible challenges across the entire healthcare spectrum 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
  29. 29. Identify True Clinical State and 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
  30. 30. Population Health Vendor > 80% PPV across multiple conditions over >800K patients Today,Lumiata’s“L”isembeddedinworkflowsofFortune1000customers Large Payer Used in Gaps of Dx, Optimal Coding, NLP, & Risk Adjustment 200 Health Coaches >850K members ~$300-$1K on average identified Large ACO > 1,100 Users 290K+ Patients 87,231+ Measures closed in 2 months and proof points on the value of AI powered automation in care management
  31. 31. distributing precise opportunities per patient in real-time with action taken 60%-70% of the time because each opportunity is backed by clear medical rationale
  32. 32. 100K feet view Lumiata Cloud Raw Data/Partial Updates CSV, JSON, PDF, CCDA, HL7, API (Claims, Labs, EHR, sensors, genetics, …) Per Patient FHIR Bundle of Input Data (Data per patient transformed into FHIR, stnadardized, normalized, and temporally ordered) … … Lumiata Risk Assessment FHIR Resource Risk Matrix + Clinical Rationale developer.lumiata.com
  33. 33. unifies knowledge & machine learning combining 4TB of text, 37K doc hours & 61M patient records with deep learning to power hyper-personalized (per patient) models Differentiated from other approaches through the Medical Graph Stats: 330M+ data points, 4.2M nodes, 37M edges 100K+ Diagnoses, 70K+ Labs, 10K+ Procedures, 500K+ Meds, 45K+ Symptoms
  34. 34. We are humbled to be recognized today as a leader in Medical AI
  35. 35. We believe AI’s most transformative impact will be toward a #healthpredicted world and Lumiata is building the AI to make health predictable Image from
  36. 36. #healthpredicted Unpacking AI for Healthcare to Automate Risk and Care Management The Hive Think Tank Ash Damle, Founder & CEO of Lumiata ash@lumiata.com; @ashdamle