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THE SKY’S THE LIMIT: BIG DATA IN TODAY’S HEALTHCAREAdele Allison, Director of Provider Innovation StrategiesSeptember 20, 2017

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© 2016 DST Systems, Inc. All rights reserved.

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• Data and Economics• Perspective on Data• Measurement Considerations• Role of Population Health Management• Questions

AGENDA

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1. Condition-Specific Population-Based Payment

2. Comprehensive Population-Based Payment

1. Alternative Payment Models (APMs) with Upside Gainsharing

2. APM with Upside Sharing & Downside Risk

1. Pay for Infrastructure & Operations

2. Pay-for-Reporting

3. Pay-for-Performance

4. Performance Rewards and Penalties

4 CATEGORIES OF VALUE-BASED PAYMENT (VBP)

Category 4Population-Based Payment (PBP)

Category 3Alternative Payment Built on FFS Architecture

Category 2FFS Linked to Quality & Value

Category 1FFS No Link to Quality & Value

You Are Here

Advancing Provider Alignment Creates Data and Operational ComplexitiesSource: HHS Health Care Payment Learning & Action Network, Financial Benchmarking White Paper, Feb. 2016

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PREDOMINANT PAYMENT REFORM MODELS

• Medical Home Incentives

• Care Management Fees

• Value-Based Payment Modifier (VBPM)

• Pay-for-Performance/Incentives

• Shared-Savings with PCMH / ACOs

• Accountable Care Organizations

• Bundled Payments

• Episode-Based Payment (e.g., OCM)

• Full/Partial Capitation + Performance

FFS

+ Q

ualit

y M

easu

res

Ris

k-B

earin

g

Category 2

Category 3

Category 4

Transform

ation from

Productivity M

gmt. to H

ealth-Value M

gmt.

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MACRA BY THE NUMBERS

• 95 – Pages long

• 31 – “Reasonable Cost Reimbursement”

• 18 – Risk

• 27 – EHR or Technology to Manage, Measure and Report

• 8 – Meaningful Use

• 38 – Quality Measures

• 19 – Resource Use or Efficiency

• 171 – “Measures” or “Measurement”

• 103 – Data

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PREDOMINANT PAYMENT REFORM MODELS

FFS

+ Q

ualit

y M

easu

res

Ris

k-B

earin

g

Category 2

Category 3

Category 4

MA

CR

AQ

uality Paym

ent Program

(QP

P)

Merit-Based Incentive Payment System (MIPS)(2017 Perform, 2019 Payment)

Advanced APM (A-APM)

• Medical Home Incentives

• Care Management Fees

• Value-Based Payment Modifier (VBM)

• Pay-for-Performance/Incentives

• Shared-Savings with PCMH / ACOs

• Accountable Care Organizations

• Bundled Payments

• Episodes of Care Groupers

• Full/Partial Capitation + Performance

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MIPS COMPOSITE PERFORMANCE SCORE

CMS, Medicare Program; Merit-Based Incentive Payment System (MIPS) and Alternative Payment Model (APM) Incentive under the Physician Fee Schedule, and Criteria for Physician-Focused Payment Models, Final Rule, Released to Office of Federal Register, October 14, 2016.

Performance Year / 

Application Year

Quality MeasuresResource Use 

or CostImprovement Activities

Advancing Care Information

DescriptionReplaces CMS Physician Quality Reporting System (PQRS)

Replaces ACA Value‐based Payment Modifier

New category of measurement; Medical Homes and NCQA PCSR receive full credit; 93 activities available

Replaces CMS EHR Incentive Programs f/k/a Meaningful Use; 

Reporting Methods

Claims, CSV, Web Interface (for group reporting), EHR, Qualified Clinical Data Registry (QCDR)

ClaimsAttestation, QCDR, Qualified Registry, EHR Vendor 

Attestation, QCDR, Qualified Registry, EHR Vendor, Web Interface (groups only)

2017 / 2019 60% 0%* 15% 25%

2018 / 2020 50% 10% 15% 25%

2019 / 2021 30% 30% 15% 25%*Measured for feedback only in 2017

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VBP INDUSTRY TRENDS

MACRA – MIPS

• 676,722 clinicians $199-$321 million in ±adjustments

• $500 million in “exceptional perform.”

MACRA – Advanced APMs

• 70,000-120,000 clinicians in 2019

• $333-$571 million APM incentives

CMS Policy

• Mandatory Bundles →Ortho and Cardio

Aetna

• Merck – Januvia and Janumet rebates for T2DM

• Driven by treatment outcomes

Cigna

• Sanofi and Amgen –Praluent and Repatha –Cholesterol PCSK9 inhibitors ~ $14K/year

• Discounts linked to LDL reduction benchmarks

2017 High Target Drugs

• Hep C and Oncology therapies

BCBS Plans VBP• 1:5 dollars spent of

$65BN directed towards VBP

• Anthem (14 states), 58% VBP – 75% shared-savings contracts, 159 ACO contracts

• BCBSMI – 1,500 PCMHs, 4,500 MDs, “Organized Sys. Of Care”

UnitedHealth Group

• $49BN/year through VBP contracts (33%)

• Goal to raise to $65Bn by 2018

Medicare Advantage• Seeking data on 4

categories of VBP

• Included in MACRA A-APMs thresholds PY2019

Managed Medicaid

• 5 state approaches

− MCOs used state developed VBP model

− % of payments must be VBP

− Evolving VBP over years

− Multi-payer VBP alignment

− State approved VBP pilots

Sources: CMS MACRA Final Rule; Forbes UHC Article, Aug. 4; Aetna Press Release, Oct 11, 2016; Fortune, Jun 21, 2016; Forbes, Anthem BC, Apr. 11, 2017; AIS Health, 2017 Blues Outlook, Dec. 29, 2016; UHC website, May 16, 2017; MA Call Letter; CHCS Brief, Feb. 2016

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• Data and Economics• Perspective on Data• Measurement Considerations• Role of Population Health Management• Questions

AGENDA

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BITS, NIBBLES AND BYTES

• Bit = 1 or 0 (on / off) → Binary Digit

• Nibble = 4 Bits of Data

• Byte = 8 Bits of Data

• Kilobyte (KB) = 1,024 Bytes

• Megabyte (MB) = 1,048,576 Bytes or 1,024 KB

• 1 MB = 873 Pages of Plain Text (1,200 characters)

• 800 MB = Human Genome (2001) → (700,000 pages of data)

Source: doi:10.1093/bioinformatics/btn582

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GIGABYTES (GB) AND TERABYTES (TB)

• 1 GB = 1,024 Megabytes

− 1 GB = 7 Minutes HD‐TV Video

− 2 GB = 20 Yards of Books on a Shelf

• 1 TB = 1,024 GBs

− 1 TB = All X‐rays in large hospital

− 7 TB = Amount of Tweets/Day

− 10 TB = All Printed Materials of U.S. 

Library of Congress

− 45 TB = Data Amassed by Hubble 

Telescope first 20 years (launched 1990)

Source: www.mozy.com

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PETABYTE (PB)• 1 PB = 1,024 TB

• 1 PB = 20 Million, 4‐drawer filing cabinets of text

• 1 PB = DNA of U.S. population

• 1.5 PB = Size of Facebook photos → 10 Billion

• 20 PB = Data processed by Google EVERY DAY!

• 50 PB = ALL Mankind’s written works from Beginning of Recorded 

History (All Languages)

• 100 PB = Facebook data storage before IPO (2.1.2012)

• 300 PB = Facebook data today (600 TB/day)!

Sources: www.mozy.com and Computer Weekly

‐ and then clone them 2x

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ROLE OF HEALTH IT

PrescriptiveHow can we make it happen?

PredictiveWhat will happen?

DiagnosticWhy did it happen?

DescriptiveWhat happened?

Val

ue a

nd D

iffic

ulty

Con

tinuu

m

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• Data and Economics• Perspective on Data• Measurement Considerations• Role of Population Health Management• Questions

AGENDA

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HIPAAMIPPATRCHAARRAPPACAMARCA ERA!

AnxietyChangeChaossClutterComplexityComplicationDistasteDisorderDoubtFearfulJumbleMessMuckSnafuPickleNightmarePredicamentMuddl

Healthcare is overwhelming!

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INFORMATION OVERLOAD

We have to move to

Value-BasedPayment

I don’t understand

my condition

Our CEO says the future is in documenting

with structured data (?)

We need a new

server

We don’t like the word

“Bundled” We must contain costs

UDS Reports

are almost due

I can’t afford my

meds

I’m not hitting my

performance measuresThe

Internet is down

We need to issue the

reg by July

The Federal Marketplace is imploding!

I can’t afford

coverage!

Our Hospital revenues

are declining

We cannot sustain

Medicare

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(Mis-)INFORMATION CAN IMPACT PERCEPTION

8 out of 10Doctors

Recommend

Its DoctorRecommended

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DEALING WITH THE COMPLEXITIES

Ready,

Set,

HOW?

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3 WAYS TO ENTER DATA• Narrative Text

‒ Examples: Cut/Paste Dictation, Voice Recognition, Typing‒ Pro: Personalizes patient encounter information, “Say it the way you want”‒ Con: Not machine readable, no conducive to research and reporting

• Structured, User-Defined Fields‒ Examples: Customizable Drop-down Lists‒ Pro: Customizable, reportable within organization‒ Con: Not conducive to aggregated research and reporting

• Codified, Object-Oriented Data‒ Examples: ICD, CPT, SNOMED, LOINC‒ Pro: Machine readable, consistent across country, very researchable/reportable‒ Con: Rigid structure, hard to personalize to individual patient

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RULE #1 – TRANSPARENCY / DATA-SHARING MATTERS

• Future is about managing “health” not “healthcare” → Alignment

• Advanced Value‐Based Payment (VBP) is Population Driven

• Provider “population” view through EHR is limited 

• CMS EHR Incentive Programs – Meaningful Use

– 2015 Edition EHR supports Pop Health

– Pro: Can be clinically‐driven (e.g., A1c result)

– Con:  Organizational data only

– Limits longitudinal view of patient

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RULE #2 – DATA MEANS A BIGGER WORLD FOR YOU

• When payers complain, we get movement

• When patients complain, we get movement

• When the government issues regs, we get movement

• Proactive vs. Reactive

• Understanding the whole system is a challenge

• You must work with your Community Partners!

• 3 keys to success:

– Gov’t & Industry → Monitoring + leadership + advocacy

– Infrastructure → Data modelling and advanced analytics 

– Education, education, education

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RULE #3 - DATA CAN HELP ADVANCE GOALS• How do we move forward with our goals?

• Scientific problem‐solving approach → Observations vs. Inference

• Observations can be Qualitative (descriptive) or Quantitative (numeric/measurable)

• Which is better?  Science uses both, for example:

Patient has Pain

OBSERVATION

Rate Pain Severity – Scale of 1‐10

Qualitative Quantitative

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RULE #3 - DATA CAN HELP ADVANCE GOALS

• Inferences explain observations, based on:

– Past experiences

– Knowledge

• Is the solution broader than you?

Patient has Pain

OBSERVATION

Rate Pain Severity – Scale of 1‐10

Qualitative Quantitative

INFEREN

CE

Post‐Surgery Post‐Trauma

Cancer Post‐Surgery

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PAYMENT BASED ON HEALTH-VALUE MANAGEMENT

Managing “Healthcare”(Resource-Based)

Old

New

Managing “Health” (Outcomes-Based)

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PERFORMANCE MEASUREMENT

Meaningful Use, UDS, PQRS, HIPQR, HOPQR, HEDIS Data

Triple Aim

Rewards / Penalties

Care Delivery Redesign

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CMS RESOURCES

Help!URL:  https://qpp.cms.gov/

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• Data and Economics• Perspective on Data• Measurement Considerations• Role of Population Health Management• Questions

AGENDA

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• MACRAnomics→ Population‐Based Payment (PBP)

− Performance is Foundational

− Expands Care Continuum

− Outcomes‐based

− Incentivizes Strong Care

• Assessing Risk of Attributed Patient Populations

OWNING RISK

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THE BASICS OF RISK

Attributed Population’s

Inherent Risk

Control

Exposure

Options: Accept Risk or Take Action

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MEASURING THE TRIPLE AIM

Lower Costs Better Care Better Health

Population Health ManagementEssential Component

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Low/No Risk

Moderate Risk

High

Risk

BUILDING PHM PROGRAMS

Attributed Population

Health Assessment

Intervention

Risk Stratification

Incr

easi

ng In

tens

ity

Who?

What? How?

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COMMUNITY LEVEL RISK

Health Plans have been in Community Level Risk Management for Years

… but not care delivery

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MANAGING “HEALTHCARE”

Analytics

• Benefits & Plan Design• Enrollment Data• Prevalence / Utilization Data• Burden of Disease

• Network Adequacy• Performance Data• Conditions by Specialty• Patient Capacity

• Administration• Compliance by Business Line• Provider Reimbursement Rates• Patient Out‐of‐Pocket• Inbound Revenue (e.g., premium)

Members

Providers Plan Operations

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E.G. #1 – NETWORK ADEQUACY

ProvidersABC County Primary Care

ABC County Oncology

ABC County Cardiology

Minimum Providers

14.0000 2.0000 3.0000

Maximum Providers

96.0000 38.0000 99.0000

Total Providers 78.0000 20.0000 84.0000

Network Adequacy

14 2 3

Overall ProvidersAvail.

200 50 100

1

23 4

5

6

7

8

9

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E.G. #2 – RISK MANAGEMENTJOHNS HOPKINS ACGS – POPULATION DECISION TREE

The Whole Population

Non‐Users Single Morbidity (either acute or 

chronic)

Commonly occurring morbidity 

combinations

Complex morbidity 

combinations

PregnantWomen

Infants (<12 months of age)

• No utilization, No or Invalid diagnoses

• Invalid Age

• Acute Minor• Acute Major• Likely to Recur• Asthma• Chronic Medical• Chronic Specialty• Eye• Dental• Psycho‐social• Preventive/ 

Administrative

• Acute: Minor and Acute: Major

• Acute: Minor and Likely to Recur

• Acute: Minor and Chronic Medical: Stable

• Acute: Minor and Eye/Dental

• Acute: Minor and Psychosocial

• Acute: Major and Likely to Recur

• 2‐3 morbidities• 4‐5 morbidities• 6‐9 morbidities• 10+ morbidities

• Further differentiated by age, sex and major morbidities

• 0‐1 morbidities• 2‐3 morbidities• 4‐5 morbidities• 6+ morbidities

• Further differentiated by major morbidities and delivery status

• 0‐5 morbidities• 6+ morbidities

• Further differentiated by major morbidities and low birthweight

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E.G. #2 – RISK MANAGEMENTIndividual Population Health Intervention

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E.G. #3 – HEDIS REPORTING

100,000 Lives

HEDIS

1 Measure ~ Millions of Dollars

20-25Revenue Linked

MeasuresConsiderable

Revenue

% HEDIS Met STAR Ratings

Government Revenue

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TODAY’S LIMITATIONS

• Under PBP → Plans must manage health over healthcare

• Potential Limits– Data-Sharing → Plan-Provider-Patient

– Technology Availability / Implementation

– Workflow Redesign → Competing Priorities

– Information at Point-of-Decision →Interoperability

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CODIFYING PATIENT INFORMATION• Health Plans →

Inch Deep, Mile Wide− Medical, Dental

& Pharmacy Claims

− Eligibility files− Provider Files

• EHRs → Mile Deep, Inch Wide

Common MU2 

Data Set

Objective‐Specific 

Data Requirements

*Defined

Vocabularies Patient Name

Sex

DOB

Race*

Ethnicity*

Preferred Language

Care team member(s)

Allergies*

Medications*

Care plan

Problems*

Lab test(s)*

Lab value(s)/result(s)*

Procedures*

Smoking Status*

Vital Signs

Provider Name and 

Office Contact 

Information (Ambulatory 

Only)

Reason for Referral 

(Ambulatory Only)

Encounter Diagnoses*

Cognitive Status

Functional Status

Discharge Instructions 

(Inpatient Only)

Immunizations*

OMB Standards for race, 

ethnicity

ISO 639‐2 alpha‐3 codes 

limited to those that also 

have corresponding alpha‐2 

codes in ISO 639‐1 for 

preferred language

SNOMED CT for Smoking 

Status

ICD or SNOMED CT for 

Problems

HCPCS and CPT for 

Procedures

RxNorm for Medications and 

Medication Allergies

LOINC for Lab tests, values 

and results

CVX for Immunizations

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ENVIRONMENTAL ASSESSMENT – HEALTH IT

Leading EdgeBleeding Edge

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PROVIDER HEALTH IT

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U.S. HEALTHCARE PAYERS HEALTH IT

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THANK YOU

Adele AllisonAMAllison@DSTHealthSolutions.com

@Adele_Allison