HIGH COUNTRY NUCLEAR

MEDICNE CONFERENCE

February 27, 2016

BIG DATA AND PERSONALIZED MEDICINE

Robert C. Hendel, MD, FACC, MASNC

Professor of Medicine and Radiology

Associate Chief Medical Officer

Director, Cardiac Care Unit

University of Miami Hospital

BIG DATA IN HEALTH CARE

• Of the people, for the people, by the

people…..

• “Transition of data from refuse to riches” JAMA 2013; 309: 1351

A FEW EXAMPLES

• Astronomy

• Engineering

• Retail sales

• Entertainment

• Politics

• Medicine

–E. Coli outbreaks

–Cancer screening

–Drug surveillance

BIG DATA SUPPORTS IOM’S TRIPLE AIM

• Improve the health of the

population

• Enhance the patient

experience of care (including

quality, access, and reliability)

• Reduce, or at least control, the

per capita cost of care.

BIG DATA AND POTENTIAL IMPACT

ON HEALTHCARE

• Stimulate change that can improve patient

outcome

• Define further areas of investigation

• Better monitoring of safety and effectiveness

• Permit development of personalized medicine

leading to improved outcomes and efficiencies

THE FIVE “V’s” OF BIG DATA

• Volume –Massive amounts of data, with anticipated increase of 20-50 fold within

5 years [zetttabyte (1024 gigtabytes)]

–Strain capacity and capability for storage, management and retrieval

• Variety –Linkage of disparately formatted data

• Velocity –More flexibility and more rapidly using big data infrastructure

• Veracity –Data assurance (error-free)

• Value

COMPONENTS OF BIG DATA IN HEALTHCARE

• Notes

• Prescriptions

• Imaging

• Laboratory

• Pharmacy

• Insurance

• Administrative data

• EHR data

• Social media

• Monitoring feeds

–Vital signs

–HF data

APPLICATIONS OF BIG DATA IN HEALTHCARE

• Population characteristics

• Particularly value for evalution of social determinants

of health

• Identification of risk factors

• Development of predictive models

–Diagnostic

–Prognostic

• Comparison of different interventions (observational)

• Variation among providers

–Systems

–Individuals

• Fuel for research BJS 2015; 102: e93

JACC 2015; 66: 1898

POTENTIAL HEALTHCARE SAVINGS WITH BIG DATA ANALYTICS

$300 Billion

• Lost through waste and inefficiency

–Clinical operations: comparative effectiveness; CE strategies

–R & D: modeling, predictions, clinical trial design

–Public health: disease patterns,

• Additional contributions of big data analytics

–Evidence-based medicine

–Geonomic analytics

–Fraud analysis

–Device monitoring

–Patient profile analytics

Raghupathi W, Raghupathi V

HISS 2014; 2: 3

CLINICAL DATABASES

• Administrative

–Routinely collected

–Limited patient information

–Clinical management: usually episode of care

–Health insurance: claims data

• Clinical

–Specific collection process

–Relevant clinical data collected

–More data on outcomes and co-morbidities

–Disease-based: Primary focus on clinical quality

–Treatment-based: Value of intervention

BIG DATA APPLICATIONS

FOR HEALTH CARE QUALITY

• Generation of new knowledge

–Allows for observational evidence for clinical questions

not possible with RCT

–Enhance generalizability of trial findings

• Knowledge dissemination

–Dashboards, clinical alerts

• Translation into personalized medicine

• Empowerment of patients

Murdoch TB, Detsky AS

JAMA 2013; 309: 1351

BIG DATA AND CLINICAL RESEARCH

Need for New Thinking, Training and Tools

(Harlan Krumholtz, MD)

• THE PROBLEM

–Current medical research cannot keep pace with

the information needs of patients, clinicians,

administrators, and policy makers

• THE SOLUTION

–Data generated every day could serve as an

inexhaustible source of knowledge to fuel a

learning health system

BIG DATA CHALLENGES FOR CLINICAL RESEARCH

• Data quality

–Completeness: representative and comprehensive

–Accuracy: Valid and reliable •Data entry error

•Systemic inconsistencies and omissions

•Misclassification

–May be enhanced by incentives (i.e. reimbursement)

BIG DATA CHALLENGES FOR CLINICAL RESEARCH

• Application of causality (causal interpretation)

–Must use caution, as with all observational studies

–Situations of excessive variation •Concern with administrative database

• Incomplete data, patient differences, violation of modelling

assumptions, secular trends, chance

–Comparison of outcomes •Variation of treatment groups beyond treatment received (bias)

–Analytics •Multiple regression

•Propensity scores

BIG DATA CHALLENGES FOR CLINICAL RESEARCH

• Application of causality (causal interpretation)

–Must use caution, as with all observational studies

–Situations of excessive variation •Concern with administrative database

• Incomplete data, patient differences, violation of modelling

assumptions, secular trends, chance

–Comparison of outcomes •Variation of treatment groups beyond treatment received (bias)

–Analytics •Multiple regression

•Propensity scores

Whenever a causal interpretation is applied to data from

complex observational studies, such as those with big

data, “it cannot be overemphasized that ….data need to

be interpreted with a high degree of caution”

Ejerhed J et al, 2014

Lancet: 384: 303

BARRIERS FOR APPLICATION OF BIG DATA

• Privacy

–Cultural and ethical issues related to patient privacy

–Need for robust, integrated security for databases

–Blurred boundaries of research (consent required) and quality

improvement (exempt form consent)

–Concern raised in English Parliament caused suspension of

big data collection (BMJ 2015; 35: 351)

• Lack of expertise (data scientists, IT, etc)

• Time and expense

• Lack of acceptance

–Scientific community

–Clinicians

–Regulators

PERSONALIZED MEDICINE

The Wikipedia Definition

Medical model that separates patients into different

groups with medical decisions, practices, interventions

and/or products being tailored to the individual patient

based on their predicted response or risk of disease.

The terms personalized medicine, precision medicine,

stratified medicine and P4 medicine are used

interchangeably to describe this concept though some

authors and organizations use these expression

separately to indicate particular nuances,

PRECISION MEDICINE INITIATIVE (PMI)

• Announced during State of the Union

address on January 20, 2015

• $215 million support in FY 2016

–$70 for cancer genomics

–$130 M for cohort program

• Involves 1,000,000 participants

• EHR, genomics, patient engagement

• Disease focus but also prevention

• Goal of understanding complex

mechanisms of a patient’s health, disease

or condition and better predict which

treatments will be most effective.

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Opportunity to be

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Opportunity to

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Implementing Tools at

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SLH 2013

• Predicted Bleeding Rate in 2013: 6.5%

• Observed Bleeding in 2013: 2.4%

• A third of predicted!

Impact of Accountability

The Benefits of Precision Medicine

Incidence of PCI-Related Bleeding Incidence of PCI-Related Acute Kidney Injury

Incidence of Rarely Appropriate Elective PCI

Total Cost Savings over 1 Year @

BJH

Savings from a reduction in PCI complications $1,235,000

Increased revenue from improved outcomes

(QHIP) $884,000

Increased revenue from Medicare (VBP) program $600,000

Savings from Same Day Discharge (SDD) program $452,000

Savings on PCI medications / Pharmacy savings $494,000

Estimated total savings $3,665,000

USING TO IMPROVE CARE

Prospective Data

Collection

Creation of

Predictive Models

Prospectively

Improve Health

Requires a IT Solution

Periodic Benchmark

Reports for Quality

Assurance

Courtesy of J Spertus

PRECISION MEDICINE (Wikipedia)

• Precision medicine….proposes customization of

healthcare, with medical decisions, practices and/or

produces being tailored to the individual patient

• Diagnostic testing...for selecting appropriate and optimal

therapies based on the content of a patient’s genetic

content and other molecular or cellular analysis.

• Much overlap wtih the term “personalized medicine”

• An approach to discover and develop treatments for

disease prevention and deliver superior outcomes by

integrating “big data”, clinical, molecular, environmental

and behavior informaotni, thereby leading to better

selection of theraeputic targets and novel prevention

strategies

ADVANCED

ANALYTICS Deals with smaller datasets but

uses advanced techniques to

analyze the impact of future

scenarios.

BASIC

ANALYTICS Relies on historical observations to

help avoid past mistakes and

duplicate past success.

BIG DATA

COMPUTING From a systems perspective, data

becomes more consolidated while

analytic workflows are more

streamlined and automated.

BIG DATA

ANALYTICS Can fuse different data types on a

massive scale resulting in

predictive and real-time analysis

capabilities.

An

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Size of Data

Small amounts of data or samples

(megabytes to gigabytes)

Large (or all) Amounts of data

(gigabytes to petabytes)

Predictive and real-time analytics capabilities

Accurate historical observations

Open Data: Examples Of Recently Released Data • Provider Utilization and Payment Data

• Public use files and interactive dashboards at the

state, HRR, and county level

– Chronic conditions

– Geographic variation

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– Hospital Inpatient

– Hospital Outpatient

– Physician and other Supplier

Data-Sharing Programs: Virtual Research Data Center

ACCESS(

PRIVACY(

SECURITY(• (No(shipping(of(data(on(external(media((• (Users(only(see(data(files(with(the(data(they(need(to(conduct(their(project((• (CMS(can(track(and(monitor(use(of(the(data(

• (Users(can(perform(their(own(analyses(and(data(manipulaGon((in(

the(virtual(environment(

• (Secure(File(Transfer(System(allows(users(to(upload(their(own(data(and(download(output(files(efficiently(and(securely(

DATA(&(ANALYSIS(

• (Researchers(use(own(laptop(to(securely(access(data(remotely((

• (Increases(efficiency(of(data(sharing(and(reduces(infrastructure(costs(for(data(users((

• (Users(may(only(remove(aggregated(output(files;(no(granular(idenGfiable(output(may(be(taken((

Out(of(the(VRDC(• (CMS(encrypts(all((beneficiary(idenGfiers(

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PATIENT-POWERED RESEARCH NETWORKS

PCORI

• PCORnet launched in 2014

• Goal: Improve speed, efficiency and cost-

effectiveness of clinical research by engaging

patients and leveraging EHR

• Better evidence will lead to improved patient

health outcomes