Leveraging Big Data & Emerging Artificial Intelligence ...Leveraging Big Data & Emerging Artificial...
Transcript of Leveraging Big Data & Emerging Artificial Intelligence ...Leveraging Big Data & Emerging Artificial...
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Leveraging Big Data & Emerging Artificial Intelligence Techniques to Stratify a
Patient PopulationPatient Population
Global Population Health was valued at $12.8 billion in 2013 and is poised to grow at a CAGR of 26% from 2013 to 2018, to reach
$40.6 billion by 2018.
Sitaramesh Emani, MD
Darren Selsky, MS, MHA
Disclosures
S Emani Thoratec/St. Jude – consultant, grant funding Abiomed – travel reimbursementAbiomed travel reimbursement CareDx – Advisory Board
D Selsky VP, Marketing & Business Development, Capsenta Consultant, Heart Rhythm Society
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Epidemiology Heart Failure in the U.S.
Major public health problem
5 million Americans with heart failure
650 000 di d h 650,000 new cases diagnosed each year
Lifetime risk of having HF is 20% (> 40 yo)
Most frequent cause of hospitalization in patients older than 65 years
1-Year mortality rate is about 10-15%
5-Year mortality rate approaches 50%y pp
Epidemiology of Heart Failure
Despite current therapies and disease management approaches, the rate of heart failure hospitalization remains unacceptably high
> 1.1 million heart failure hospitalizations annually
#1 cause of hospitalization for those ≥ 65 years
#1 cause of hospital readmission
> 25% readmission rate at 1 month
$18 billi i l di t t f h it li ti > $18 billion in annual direct costs of hospitalization
Current methods for monitoring and managing heart failure patients have not adequately addressed this problem
Aghababian RV. Rev Cardiovasc Med 2002; 3:S3; Jong P, et al. Arch Intern Med 2002; 162:1689; Jencks and Williams, NEJM 2009; 360:1418; www.hospitalcompare.gov
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Natural History of Heart Failure
Normal ♥ Chronic HF Death
Initial
Fun
ctio
nal A
bilit
y
Initial myocardial injury
First ADHF admission
Acute Exacerbations Cause Progressive HF
Time
Late ADHF – ICU admission and rescue therapy
Epidemiology
Miller & Guglin, JACC 2013
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HF Topography
NYHA I
1 year mortality of NYHA III HF is 10-15%
A HF hospitalization is a strong predictor of mortality (NYHA IIIb-IV)
NYHA II
NYHA IVNYHA I No limitation of activity
NYHA II Slight limitation of activity; normal activity causes fatigue, dyspnea
NYHA III Marked limitation of activity; less than ordinary activity causes fatigue, dyspnea
NYHA IV Symptoms at rest; unable to perform even minimal levels of activity
Scrutenid et al, EHJ 1994Gheorghiade et al, JACC 2013
The danger of late referrals
Profile Description
1 Critical cardiogenic shock / “Crash & Burn”
o k? 2 Progressive decline on inotropes
3 Stable but inotrope dependent
4 Resting symptoms on home oral therapies
5 Exertion intolerant
6 Exertion limited
Too
sick
Not sick
enough?
7 Advanced NYHA III symptoms
Stevenson et al, JHLT 2009
k ?
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Outcomes by Profile
Kirklin et al, JHLT 2013
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Treating End-Stage Heart Failure
Ventricular Assist Devices (VADs) are used to treat end-stage heart failure
End-Stage Heart Failure Persistent severe symptoms of HF despite optimal Persistent, severe symptoms of HF despite optimal
medical therapy Limited life-expectancy due to underlying cardiac
disease
Not all patients with end-stage HF are candidates Therapy has complications Will not help certain cardiac conditions Will not help non-cardiac co-morbidities
Axial Flow Pumps
Courtesy of Thoratec Corp.
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Centrifugal Flow Ventricular Assist Device
CXR – Pre-VAD
Pre-VAD Post-VADExplant Heart
w/ VAD
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Left ventricular assist devices (LVAD) therapy: expenses and gains.
Leslie W. Miller et al. Circulation. 2013;127:743-748
Left ventricular assist devices (LVAD) early costs are comparable with other life-saving therapies.
Leslie W. Miller et al. Circulation. 2013;127:743-748Copyright © American Heart Association, Inc. All rights reserved
• Cost effectiveness of VAD therapy as DT is improving but has yet to achieve the goal of <$100 000 USD/QALY
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Rehospitalizations
Setoguchi S, et al, AHJ 2007
Project Charter – LVAD StratificationChart Reviews just don’t work…
OSU HF Chart Review:
18/ 47 HF readmissions were “missed” due to readmission onto non-cardiology services.onto non cardiology services.
30 instances of high-risk medications (i.e. inotropes) ordered by non-cardiac services during that time.
Conservative estimates would suggest ~ 20 patients every 60 days that are “high-risk” 120 pts/yr (~ 30 pts – LVAD)
$6.75 M
FY 2013 CMS Median Payment for MS-DRG 1 ≈ $202,000 High cost cases may qualify for outlier payments
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High Risk HF Characteristics
HF Sx that fail to respond to medical therapy
Intolerance to HF meds (esp new intolerance)Hypotension Hypotension
Renal dysfunction Bradycardia
Frequent hospitalizations 2 in 3 months 3 in 6 months Need for inotropes during hospital stay
Adapted from J Stehlik, Univ of Utah
Heart Failure
HF Charcteristics
• HF Sx that fail to respond to medical therapy
• Intolerance to HF meds (esp new
Simpler Referral Triggers
• NYHA III-IV & ≥1 or NYHA II & ≥ 2 of the following:
• Intolerance to HF meds (esp new intolerance)• Hypotension• Renal dysfunction• Bradycardia
• Frequent hospitalizations• 2 in 3 months• 3 in 6 months• Need for inotropes during
• SBP ≤ 90 mmHg• Hgb ≤ 12 mg/dl• Cr ≥ 1.6• Not on RAAS inhibition• Not on β-blocker
Adapted from J Stehlik, Univ of Utah
p ghospital stay
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Example – Colorectal CancerWho is at greater risk?
Patient 1 Patient 2
Risk calculators can assist with assessments
• 65 male• No family history• Diet with vegetables• Non-obese• No previous polyps
• 55 male• Strong family history• Poor diet• Obese• No previous
colonoscopy
Risk calculators can assist with assessments
Time consuming/manual entry
Require data elements to be available
Can available stored data (i.e., EHR data) be accessed to help calculate risk?
How can screening tools be implemented?
Version 1 Patient is in office leading to an alert when chart accessed
Version 2 Automatic alerts generated electronically when certain criteria are
triggered Cholesterol panels Lab work for certain medications Flu shots
Associated with improved adherence to recommend care processes
Hi h f h lth t i Higher performance on healthcare process metrics
Data analytics can help healthcare delivery
Ancker, JS et al, JAMIA, 2015, vol 22: 4, 664-671
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Rudimentary nature of current technology
Use basic data structures
Look at simple/singular metrics
R l “ i l” li bilit Rely on “universal” applicability
Do not capture complex situations or assimilate complex data
How can we leverage our EMR to help
Identification
• Patients who may have “fallen through the cracks”
Perfect EMR
• Search well-defined parameters
• Exclude “noise” and
Real Life EMRs
• High degree of variability in parameters
• Search parameters can yield results
• But, may be prone to erroneous or missed identification
inaccurate parameters• Return simple, easy to
interpret results
• Chronic systolic HF• Acute systolic HF• Acute on chronic
systolic HF• Acute exacerbation
of HF• Cardiogenic shock
• In OSU system, EF not il h bleasily searchable
May not be equipped with internal search algorithms
Garbage In… Garbage Out
Updated medical histories?
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The Goal
Identify patients who could benefit from advanced therapies early
Try not to use advanced therapies as “salvage therapy”
Challenges
Data is stored in many different places
Data has many synonymsData has many synonyms
Data may live in notes, dictations & scanned reports
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Just for fun…
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How does LinkedIn know that someone has a 3rd generation connection?How does Facebook build their footprint of friends and friendship tags?How does Google summarize data on their search screen?
Works for
Spouse of
Father of
Mo
the
r of
Brother of
Colleague of
Alumni of
Works forMother of
Alumni ofFather of
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Works for
Spouse of
Father of
Mo
the
r of
Brother of
Colleague of
Alumni of
Works forMother of
Alumni ofFather of
Identify Heart Failure Patients indicated for a LVAD
Echo Mgmt
(Siemens)CDM Data
(MySQL)Epic
(Mumps)
(MySQL)
Goals Driven
Patient Stratification
Health Outcomes
Use Existing Infrastructure
Use Industry Standards
• Identify at-risk patients for timely interventions • Improve care management delivered at point
of care• Aggregate data for population analytic
reasoning. • Search HF Patient Population to understand
patient cohort
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Taking it one step further…LVAD Use Case
LHA - Left heart assist operationLVAD - Implantation of left ventricular assist deviceImplantation of left ventricular assist device (procedure)
Left Ventricular Assist DeviceLVAdEnd Diastolic Area
SNOMED HCPCS CMO ICD
SEMANTICS
device (procedure)ALVAD - Aorta-left ventricular assist device
LVDaCardiac Assist Pump
VAD DRIVELINE, MAINTENANCE KIT CM#DT18355
VAD DRIVELINE, MANAGEMENT TRAY CM#DT15340
VAD DRIVELINE MAINTENANCE KIT MED#DYNDC1488
ICD 9: Insertion of percutaneous external heart assist device (37.68)ICD 10 - Presence of heart assist device (Z95.811)
Why technology is ripe for population health
Community outreach to improve adherence.
Immunization registries
Clinical Trial opportunitiesClinical Trial opportunities
Retrospective and real time analysis of patient outcomes
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Value of Data Virtualization for Healthcare
Simplified Data Access.
Continued ROI of Existing Infrastructure.
Data Storage and Cost Reduction.
Improved Care Quality.
Compliance and Data Governance Support.
Assay Experiment
Help Me Fail Faster
Genomics
External
Patent
NDF, CHEMBL
G l D i
Flexibility Dashboarding
Analysis Adhoc Query
Clinical Discovery Reporting
Goals Driven1. Stop unnecessary clinical research2. Are there patent infringements?3. Has this already been done?4. Help me speed time to Stage II Clinical Trials
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Medical Device IdentificationImplants
ER
Medtronic
Boston Scientific
St Jude Medical
Biotronik
Sorin
Nursing Home
Clinic
OR
Nursing HomeHospital Inpatient
Master Provider Management
Hospital A
Hospital B
Hospital C
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Patient Stratification - AF• What Classifies as a Cardiac Arrhythmia?
• What are the common conditions (CV and non-CV)?
• What are the AF Contraindications?
• What are the indicated meds?What are the indicated meds?
• What are Meds that “May be Treating AF?”
• What are the biomarkers/ Risk Factors?
Multiple Theories of the etiology of AF
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Privacy/HIPAA Concerns
IRB Approval – research or “not” research
BAA
Follow-up directed to PCP or primary cardiologist
Conducting quality assessment and improvement activities, including outcomes evaluation and development of clinical guidelines, provided that the obtaining of
generalizable knowledge is not the primary purpose of any studies resulting from such activities; patient safety activities (as defined in 42 CFR 3.20); population-based activities
relating to improving health or reducing health care costs, protocol development, case management and care coordination, contacting of health care providers and patients
with information about treatment alternatives; and related functions that do notwith information about treatment alternatives; and related functions that do not include treatment; §164.501 of HHS Regulations (January 2013)
Thank you
Sitaramesh Emani, MDAssistant Professor of MedicineAdvanced Heart Failure & Cardiac TransplantThe Ohio State University Wexner
Darren SelskyVP, Business Development5525 Fossil Rim RoadAustin, TX 78746
The Ohio State University WexnerMedical Center473 W. 12th Ave, Suite 200 DHLRIColumbus, OH 43210P: 614-293-4967 | F: [email protected]
Mobile: 484-356-6037