current and future directions of ai in human …...big transitions in healthcare in the past decade...

Aenor J Sawyer, MD, MSDirector, UCSF Skeletal HealthHealth Innovation &Tech in OrthoUniversity California, San Francisco

Current and Future Directions of AI in Human Performance

@ASawyerMD@ UCSF@UCSFOrthosurg

AI in Human Performance

IntroductionRapid Growth in the SectorBroad Range of Applicability

Current implementation examples§ Occupational, Military, Sports/Fitness, Space, Healthcare

Horizon for Smart Health Informatics

8/3/18TRISH AI WORKSHOP Aenor J Sawyer, MD, MS Confidential and Proprietary UCSF 20182

Intro -Health Data Today

8/3/18

Big transitions in healthcare in the past decade

Clinical Data: • EMRs • Digitized diagnostics

8/3/184 TRISH AI WORKSHOP Aenor J Sawyer, MD, MS Confidential and Proprietary UCSF 2018

Medical Imaging, Monitors, Devices


Remote Medical/Health Monitoring

§ Remote Medical Grade devices –

§ Schools

§ Pharmacies

§ Home

§ Work place

§ Community sites

§ Automobiles

Big transitions in health/care in past decade

• Pt centered m-health revolution: ‒ smartphone and wearables, m-health apps,

patient portals, internet capabilities


Consumer Health devices Over 200k health apps

Validation of Consumer ProductsUCSF Human Performance Center

Orthopaedic Institute

There are many mobile health solutions but how is adoption?

§Consumer uptake slowed from 2015. April Berthene -Design Mobile October 17, 2016

§Hard for consumers and providers to sort out which to use§50% of wearables are in the drawer by 6 months§Not enough reward for the bother§“VALUE/NUISANCE Ratio”, >1 you won!

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Consumer interest in Digital Health:

Phone-centered care

”No Sir, you cant list your iphoneas your Primary Care Doctor”


Big transitions in health/care in the past decade

• Digital Pathology• Genomic capabilities –

‒ affordable, consumer facing• Other Omics


Ubiquitous data sources

Panome

Health Data: On the Front of the wave11

In reality… § Clinical data varies- some structured but much unstructured(Some SUs focus on diag’c data for analytics and algos)

§Vast amount of “real-world” data from over personal health apps and personal tracking devices

§ Accountable for information we cant access or leverage in a meaningful way

§Oceans of under-utilized fragmented data, with much redundancy, instead of Actionable insights


Good News - Exciting gains in Data Fields: Computational Health Science§ Machine Learning § Artificial Intelligence § Deep learning § NLP natural language processing and voice recognition§ OCR, Image recognition, facial recognition§ HAR human activity recognition§ Neural Networks§ Modeling, risk prediction


Analytics§Descriptive Analytics

• what monitoring or episode happened?§Diagnostic Analytics

• why has a specific healthcare event happened?§Predictive Analytics

• what clinical issue is likely (or not) to happen§Prescriptive Analytics

• prescribe to the relevant actions required to mitigate or eliminate problem, preventative measures


Forecast is bright:§“We are now witnessing a new shift in computing: the move from a mobile-first to an AI-first world.” Sundar Pichai CEO Google

§Accenture analysis predicts clinical health AI applications can create $150 billion in annual savings to the US healthcare system by 2026.

§ It is not the first wave of AI excitement but it appears to be the right time –ie hopefull we are not heading into another AI winter.


AI and Human PerformanceRapid Growth in the Sector

8/3/18

Investing


§ AI/ML dollars

and deals

§ The funding

of digital

health

companies

leveraging

AI/ML has

largely

mirrored the

growth in

digital health

funding

overall.

JULY 2018

Explosion of AI

Startups in

Healthcarein past 5

years8/3/1818 TRISH AI WORKSHOP Aenor J Sawyer, MD, MS Confidential and Proprietary UCSF 2018

AI in Healthcare Companies

Field COMPANY

Analytics LumiataDNA NexusSyapse

Cloud Medx Agile MD Atomwise

Core AI/Algos

Ayasdi Digital Reasoning Systems

H20.ai

Drug Discovery

Atomwise Numedii Numeate



Field COMPANY

Oncology Flatiron Health

Pathway Genomics (Oncology

Cyrcadia CureMetrix

SkinVision

Entopsis Smart Healthcare

Specialty focus

Ginger.io(Mental Health)

OmadaHealth(DM, HTN



Field COMPANY

Imaging/Diags

Butterfly network

Imagen iCarbonX

Flatiron Zebra MedicalVision

Enlitic Bay Labs Free-nome

Remote Patient Monitor and Support

Babylon Health

AiCure Sense.ly Sentrian HealthLoop

Force Ther.

Omadahealth

Hinge


AI and Human PerformanceBroad Range of ApplicabilityResearch, Worker, Military, Sports

8/3/18

Huxley, 1957

AI in Basic Science Research Mathematical Modeling of Muscle Contraction:

The intersection between computation and science Kate Jarvis UCDavis

• Our research focuses on developing a molecular-level model of muscle contraction that spans size scales and predicts whole muscle function.

• In particular, we are interested in how small changes at the molecular level scale up and result in muscle fatigue and loss of contractile function.

• By working at the intersection of mathematical modeling and scientific research, we are able to make such connections and provide insight into applications such as heart disease and human motility. (Space Biology Program?)

Katy JarvisUC [email protected]

AI and Human Performance –Sports/Fitness/Wellness

§There are numerous examples of leveraging AI to inform and track training, predict and avoid injury, monitor and enhance rehab

§AL/ML/DL/NN + Sensors • Challenge with intersecting consumer sensors with

healthcare data barriers and poor hardware validation. • We are on a continuum back and forth from wellness to

illness but our data and devices don’t move with us


AI and Human Performance - Occupational settings

§Use of AI to monitor and modify worker risk is expanding rapidly.

§Examples • Heat stress in miners, fire fighters, even workers during

Ebola virus. -core Temp is critical but difficult to measure. ‒ Able to model it by combining data including HR‒ Envtl risks (air quality direct measure on inhalers or

leveraging large data sets from Air Resources Board


AI and Human Performance - Military

§Military use of AI predates much of healthcare activityCombat readiness, Fatigue, Right fit for task, Injury Risk PredictionCognitive decline

§Applying Deep Learning to Human Activity§Mobile Health and Machine Learning§Recognizing Human Activity Using Test Data Sharpening


AI /Human Performance – Sports, Military§Sparta Science – Force plate + AI

• Real time information about injury risk, training errors and training modifications needed


Ocean Rowers: Prisoner of war and AstronautSpace Analogue?

8/3/18

AI and Human PerformanceBroad Range of ApplicabilityHealthcare

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Digitization of Healthcare – includesIn Hospital/Clinic Applications:

§ Electronic Health records

§ Scheduling, Billing

§ Patient Experience

§ Outcomes measures

Beyond Hospital/Clinic Applications:§ Extended or remote care (Telehealth)

§ New infrastructure, new roles

§ Wearables and Home Diagnostics: Remote or “real life” monitoring

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Healthcare Data Utilization

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§ “Data is the new currency of healthcare” - data fluency a must§ Storage/Mgt advances - Shift from Local servers to Cloud based

storage or combinations§ Data virtualization rather than moving or creating redundant data§ Smart processes - Analytics, Algorithms, AI, ML, NLP, Image Recog§ Continuous and Real Time capabilities with contingency plans§ Security/Privacy § Risk prediction and modeling, Dec support§ Audience Specific Dashboards

UCSF Institute for Computational Health SciencesUC Electronic Health Records

build computational infrastructure to link electronic health records (EHRs) and other data from 14 M patient records across the five UC medical centers.

High-Performance Computing

Training Informatics specialists to access, manage, analyze, and use “big data” such as the integrated EHR, as well as other computational tools and resources.

ImmPortThe Immunology and Data Analysis Portal, a NIAID-funded repository of clinical trial data, aims to integrate relevant data sets and accelerate the pace of discovery.

8/3/18

§ CISCO GHAB 2016 Confidential & Proprietary © 2016 UCSF All Rights Reserved

•Informatics in our curriculum

•Informatics and HIT cores in Depts

https://immport.niaid.nih.gov/immportWeb/home/home.do?loginType=full

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MS BioscreenPierre Antoine Gourraud, PhDRiley Bove, MDSteve Hauser, MD

Precision Medicine Platform, Aim is Perscripti Analytics

DNA-Spit KitRemote Labs

The Health eHeart StudyUsing big data and distributed clinical trial model

to reduce heart disease


Remote Medical/Health Monitoring § Remote Medical

Monitoring

Sensors + analytics

DMCHFHTNARRYTMIAEpilepsy -deep brain stim

In Patient Interactive Media WallPatient-centered, interactive, educational,

usage analytics

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Emerge – Hopkins/UCSF

TRISH AI WORKSHOP Aenor J Sawyer, MD, MS Confidential and Proprietary UCSF 2018

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Evid-based or expert consensus driven clinical pathways. Integrate with EMR. Improved outcomes and lower costs. Analytics monitor the adoption and adherence of care standards and allow data-driven conversations about improving patient outcomes and resource utilization, including reductions in avoidable testing, length of stay, and medical errors.

AGILE MD

Biomechanics, video analytics with feedback for training functional assessment.

VERA – Avatar therapist Video/Kinect

8/3/18Presentation Title and/or Sub Brand Name Here39

PREHAB and REHAB Tools

HOSPITAL to HOMEConnected Care Continuum:Pre-emptive messaging, auto-escalation based on response.

Improved patient engagement and activation.

Decrease complications, unplanned care. 29 % dec in Readmissions

Eliminate repetitive education, improve adherence, get actionable information

Pre and post surgical care coordination

TRISH AI WORKSHOP Aenor J Sawyer, MD, MS Confidential and Proprietary UCSF 2018

Attention to workflow, implementation rather than point solutions.

§ Integrated with work flow

§ Integrated with adjacent technologies (other point solutions in E2E journey or with EMR)

§ Integrated with other aspects of DH solutions

• Hardware + Software + Analytics/AI = Actionable Insights

We struggle with point solutions and are looking for combinations or even “compatible with” solutions

Book OR case

Order and template images

Order implant system

from Reps

Schedule OR

Schedule OP Prep

Insurance Auth

Check in Inventory

Sterilize and set up in Room

Ensure team is

trained or Rep avail

Be sure all data is avail

and connected with EMR

PATIENT GENERATED HEALTH DATA (Qualitative and Quantitative)

§Will drive research, clinical care, business intelligence and influence education

§We need to optimize our processes in PGD management.

PRO + SENSOR + CLINICAL DATA8/3/1842 Presentation Title and/or Sub Brand Name Here

Mixed Reality, Haptics, Voice, and AI Adv Center for 3D Technologies:

Education, Simulation and

Real Time Guidanceof Diagnostic and Therapeutic Procedures

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The leg is virtual, the tools are real in OssoVR's orthopedic surgery simulations for medical residents.OSSO VR

AI and Human Performance – Space HealthCIMON a spherical robot using Watson AI to process who will assist astronuats in ISS.CIMON uses Watson AI to process text, speech and images, find specific information and knowledge, and interpret moods and feelingsFully voice-controlled access to documents and media, the astronaut can conveniently navigate through operating and repair instructions and procedures for experiments and equipment. CIMON will thus serve as a complex database of all the necessary information for working on the ISS, and can also be used as a mobile camera for documentation purposes.


AI and Human Performance – Space Health

§As astronauts time and distance in Space increases and move into deep space they will be untethered and need to function autonomously – in health monitoring, maintenance and management.

§Longitudinal monitoring and optimizationMissions are longer, and w/repeat missions, unlikely to fully recover (bone especially)

§ Sophisticated data utilization to optimally track Human Activity/Performance, and provide precision support.


AI and Human Performance – Space HealthNeed for closed loops: “monitoring and managing” for general health maintenance and medical concerns.

• Multiparametric patches or implantable sensors –determine magnitude of variances or combined variances -allow early detection of fatigue or illness

• For Increased acuity – will need intersection of critical data streams as with EMERGE or AGILE MD

• Video with facial, body recognition and AI to detect abnormality of movement patterns, response time, abnlcognition, stress, mood


Better Health Data DaysOn the Horizon and Considerations

8/3/18

Horizon: Better Health Data Days

A hybrid vigor being provided by the

unprecedented intersection between

computational, health and life sciences.


Health Data

Actionable Insights Information from various sources must be contextualized and available to effect change

Requires: INTEROPERABILITYAND Data Availability, Aggregation, Analytics, Algorithms, AI

General needs still:

§IT infrastructure - Integrated Data System

§Validation, recurrent validations are critical

§Translation – take a needs-driven approach

vs fishing, measure value


General needs:

§A Learning system

§Real-world and real-time sensing, Automation of

interpretation with edge analytics

§Need to train up a work force (intersection people)

§Universal Terminology


So we can truly realize…§Real-time Decision Support, based on current data, contextualized with larger datasets

§Rapid, closed loop management of health needs, (centralized as needed)

§Distributed (but not diluted) care models• Extending care providers, distributing expertise, empowering

individuals§Predictive modeling for individuals and populations

• Predictive modeling for incidence of illness or injury, treatment pathways, outcomes, resource allocation

• Modeling of epidemics, new therapeutics8/3/1853 TRISH AI WORKSHOP Aenor J Sawyer, MD, MS Confidential and Proprietary UCSF 2018


Knowledge Network

for Precision Medicine

§ Basic, clinical, social, behavioral sciences

§ Enabled by: Omics,Digital Health,Informatics


INFORMED HEALTHRight info, Right time = Right Choice

Aenor J Sawyer, MD, MSDirector, UCSF Skeletal HealthHealth Innovation &Tech in OrthoUniversity California, San Francisco

THANK YOU

@ASawyerMD@ UCSF@UCSFOrthosurg

To Reference Dr Hood’s remarks§Wellness and disease - A continuum§P4 predictive preventative prescriptive §The Hybrid Vigor created by cross and interdisciplinary work• The importance of modeling, supporting the

Intersection People• Domaine Expertise• OSTEOPOROSIS


Ocean Rowers: Prisoner of war and Astronaut

8/3/18

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Evid-based or expert consensus driven clinical pathways. Integrate with EMR. Improved outcomes and lower costs. Analytics monitor the adoption and adherence of care standards and allow data-driven conversations about improving patient outcomes and resource utilization, including reductions in avoidable testing, length of stay, and medical errors.

AGILE MD

current and future directions of ai in human …...big transitions in healthcare in the past decade...

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