PowerPoint Presentation

#healthpredicted

Unpacking AI for Healthcare to Automate Risk and Care Management

@ashdamle | Hive Think Tank

Image from http://bryanchristiedesign.com/

End with We need to make a leap

our health is complex37+ Trillion Cells

Sources: https://www.siam.org/meetings/sdm13/sun.pdfhttp://www.mckinsey.com/industries/healthcare-systems-and-services/our-insights/the-big-data-revolution-in-us-health-care

And today, Healthcare feels like a unwinnable game of Tetrisno control & coordination with imprecise outdated systems.

Sources: FierceHealth Payer ReportSurvey of 300 executives by Numerof and Associates and Jefferson College of Population Health

this is how visible tomorrows health is today

Image from http://bryanchristiedesign.com/

Because, healthcare has one of the most complex data sets in existence

High volume . High dimensionality . HeterogeneousVaried formats . Multi-faceted relationships . NoisyAnd why?

Sources: FierceHealth Payer ReportSurvey of 300 executives by Numerof and Associates and Jefferson College of Population Health

healthcare lacks visibility, predictability, and precision

which results in a failure of

Timeliness . Alignment . Coordination

And because of this extreme data complexity,

Sources: https://www.siam.org/meetings/sdm13/sun.pdfhttp://www.mckinsey.com/industries/healthcare-systems-and-services/our-insights/the-big-data-revolution-in-us-health-care

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This leads to an incomplete, outdated view of patients health.Challenge 1Inability to combine multi-sourced data efficiently and at scaleIn 2012, 500 petabyes by 2020, 25,000+ petabytes.

Effective big data solutions could result in annual industry savings of $300 billion.

Sources: https://www.siam.org/meetings/sdm13/sun.pdfhttp://www.mckinsey.com/industries/healthcare-systems-and-services/our-insights/the-big-data-revolution-in-us-health-care

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 2An asymmetry between financial and health risk30% of providers are in risk-sharing agreements, and that figure will double by 2020.

Sources: FierceHealth Payer ReportSurvey of 300 executives by Numerof and Associates and Jefferson College of Population Health

Todays 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 3Outdated care management processesOnly 15% of administrative costs on care management, though it impacts 75% of costs

Effective coordination could reduce hospital readmission rates by 10 to 15%.

Sources: 1. JAMA Study, Accenture report2. https://hbr.org/2015/09/what-has-the-biggest-impact-on-hospital-readmission-rates

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

$6B

$2BThe 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 healthcares problem that AI is best positioned to addressis fixing the precision of risk & care managementAI surfaces the signal from the noise in health dataallowing us to understand what to do, for whom, when, and why

so we can improve efficiency, reduce costs and deliver precise personalized care. +

Sources: FierceHealth Payer ReportSurvey of 300 executives by Numerof and Associates and Jefferson College of Population Health

And we believe within the next 3 years, AI will do so same across the healthcare continuumAutomated information processing45% of routine, manual tasks that can cost up to $90 million can be automated by adapting current AI technologies (McKinsey).

1Precise disease management Machine learning could increase patient outcomes at by 50% at about half the cost (Indiana University).

2Efficient provider-patient encountersVirtual health apps can save physicians 5 mins per patient encounter (Accenture)

3Social robots for patient engagement Robots like PARO have been found to reduce patient stress and interaction with caregivers(World Economic Forum)

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Sources: JAMA Study, Accenture report

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 workflowsHowever, unpacking AI for risk and care management demands

Sources: FierceHealth Payer ReportSurvey of 300 executives by Numerof and Associates and Jefferson College of Population Health

Introducing Lumiata,an example of unpacking AI for Healthcare

#healthpredicted

End with We need to make a leap

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.

We want to help healthcare institutions Lumiata is on a mission to power a virtuous cycle of predictable health through AI outsmart diseasewithdata-driven precisionmaking healthpredictable in real-timeempowering everyoneto act with control & confidence#healthpredicted

End with We need to make a leap

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

Sources: https://www.weforum.org/pages/the-fourth-industrial-revolution-by-klaus-schwab/

18Data Scientists

Utilize the latest in AI & deep learning to evolve Lumiatas Medical GraphDesign & deploy new models for targeted use casesClinical Scientists

Adjudicate ongoing clinical inputs into Lumiatas Medical GraphEnsure clinical relevance of predictive analytics & rationale

DSCSTo build Lumiata, we combine deep domain expertise

to augment our AIs 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 lineMost risk bearing organizations have care management programs which are ripe for automationThis gets us the data we need to learn and embedded into workflows for feedback

We seek to orchestrate proactive real-time personalized careby being the interpretive interface between all actors & data to automate care management activities

data gathering + data synthesis + analysis + planning + messaging + decision + fulfill

All towards building a virtuous cycle of AI to create anend-to-end system that transforms data into insights, and insights into action.

DataModel AccuracyCommunicabilityDistributionUsage/Feedback10s of Millions of Patient RecordsEvery article in PubMed38K Physician Hours

Medical Graph (39M+ Edges)80%+ PPV across major conditionsClear chain of medical reasoning for each prediction and suggested action Analytic & Conversational APIto communicate tasksActive Supervised LearningContinuous improvements to our models

Data

Insight

Action

End with We need to make a leap

330M+ data points describing the relationships between3TB+ unstructured data || 10s of millions patient records || 36K+ physician curation hoursHundreds of protocols & guidelines40K+ Symptoms & Signs4K Diagnoses3K Labs, Imaging, Tests3K Therapeutic Procedures7K Medications across age, gender, durations, lifestyleOur AI is powered by a learning probabilistic Medical Graph

Overcoming the key challenge in unifying machine learning and clinical knowledge

symptomsdiagnoseslabsImagestherapyproceduresmedsenviron. factors,seasonalitylifestyle + demo. profilegeographypast medical historygeneticsfamily historyvitalscomplaints(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

and by mapping out the relationships of health data, the Medical Graph address many of the data complexities in systematic scalable wayDemographicsLumiata Medical Graph

ProceduresPhysical Exam & TestsMedical & Social HxSensors & WearablesGenomics

High volumeHigh dimensionalityHeterogeneousVaried formatsMulti-faceted relationshipsNoisyMultiple Coding SystemsGraphs not Trees/DAGs

Our first step in making health predictable is the Risk Matrix: Time-based, real-time, personalized predictions on an individuals risk of chronic disease & events

Lumiata Risk MatrixClear clinical rationale provides the confidence to act

Currently, models are available for: Atrial fibrillationBipolar diseaseChronic kidney diseaseCongestive heart failureCOPDCoronary heart disease

DementiaDepressionDiabetes Mellitus Type 2ObesityPrimary hypertensionRheumatoid Arthritis

PUBMED Referenceswhere each prediction is supported with clinical rationale with highly specific data and links to medical literaturethrough the Medical Graph with over 39 million edgesPast Medical HxAbnormal LabsProceduresMedicationsClinical RationaleDiagnosesPredicted Diagnosis #1PUBMED References

36,000+Physician Curation Hours

Clinical Integration EngineClinical Analytics EngineAPI & Web PlatformReal-Time Data ClinicalFinancialSocialEnvironmental

DescriptiveIntrospectivePredictivePrescriptiveDiscovery

Operationalize Data

Data UnificationInsight & Action GenerationData & Action DistributionPowering end-to-end, clinically relevant value

that addresses tangible challenges across the entire healthcare spectrumAutomated risk stratification to drive population health management

Precise & personalized care management interventions

Clinical alignment and agreement between payers and providersReduced costs by removing labor-intensive, redundant tasks

Identify True Clinical State and Risk EvolutionDifferential Diagnosis and TriageMissing DiagnosisData Driven GuidelinesClinically Right Coding (ICD, HCC)Risk AdjustmentQuality MaximizationPredict High Cost Claimants Utilization PredictionCare Coordinationwith clear practical use cases available via an API or web app

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Population Health Vendor> 80% PPV across multiple conditions over >800K patients

Today, Lumiatas L is embedded in workflows of Fortune 1000 customers

Large Payer Used in Gaps of Dx, Optimal Coding, NLP, & Risk Adjustment200 Health Coaches>850K members~$300-$1K on average identified Large ACO> 1,100 Users290K+ Patients87,231+ Measures closed in 2 months

and proof points on the value of AI powered automation in care management

distributing precise opportunities per patient in real-timewith action taken 60%-70% of the time because each opportunity is backed by clear medical rationale

100K feet view LumiataCloud

Raw Data/Partial UpdatesCSV, 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 ResourceRisk Matrix + Clinical Rationaledeveloper.lumiata.com

End with We need to make a leap

unifies knowledge & machine learning

combining 4TB of text, 37K doc hours

& 61M patient records with deep learning

to power hyper-personalized (per patient) modelsDifferentiated from other approaches through the Medical GraphStats:330M+ data points, 4.2M nodes, 37M edges100K+ Diagnoses, 70K+ Labs, 10K+ Procedures, 500K+ Meds, 45K+ Symptoms

34We are humbled to be recognized today as a leader in Medical AI

We believe AIs most transformative impact will be toward a#healthpredicted world

and Lumiata is building the AI to make health predictableImage from http://bryanchristiedesign.com/

#healthpredicted

Unpacking AI for Healthcare to Automate Risk and Care Management

The Hive Think Tank

Ash Damle, Founder & CEO of Lumiataash@lumiata.com; @ashdamle

End with We need to make a leap