Big Data
In The Medical Industry
David Shein, MD
dshein@veriskhealth.com
Medical Director, Verisk Health
Agenda
Verisk Health:
Who we are
What we do
Data management in
the healthcare
environment
Example cases
Data challenges
2
Verisk Analytics – The Science of Risk
Measure, Evaluate and Navigate Risk
P & C Insurance
Healthcare
Mortgage Lending
Supply Chain
3
The Markets We Serve
Understanding Healthcare Risk
Payors
Providers
Employers
Commercial Plans
At-risk Physician Groups
Self-Insured
Third Party Administrators
Integrated Delivery Networks
Benefit Consultants
Disease & Care Management
Provider Hospital Organizations
Brokers
State & Managed Medicaid
Accountable Care Organizations
Medicare Advantage
4
The Healthcare Crisis: On the Rise
~Healthcare spending is projected to reach nearly $4.6 Trillion in the next decade
Employers spent $8,300 on
average per employee per year in
2010…costs are projected to rise
to more than $13,400 in 2019
Today, GM spends more on
healthcare than on steel…
Where do healthcare costs
rank for you?
Today, healthcare spending has reached
$2.5 Trillion…and $3 out of every $4
spent is on chronic conditions
In 2014, when health coverage is
expanded to millions of uninsured
Americans, spending is estimated to
increase by 9.2%
By 2019, healthcare is projected to
account for nearly $1 of every $5 spent,
or about 19.6% of the national GDP
~CDC
~Health Affairs: http://www.healthaffairs.org/press/2010_09_09.php
5
The Healthcare Crisis: A Smarter Way
According to a 2009/2010 Towers Watson Report:
• Companies with effective health benefit programs not only
improved employee health but also experienced superior
human capital and financial outcomes, including:
 Lower medical trends by 1.2%
 1.8 fewer days absent per employee
 Fewer lost days due to disabilities
 11% higher revenue per employee
• These organizations believe in identifying the root causes of
healthcare cost increases
• …and consistently analyze data integrated across programs
to identify opportunities, design programs, and measure
performance
6
Capability & Capacity: Understanding Risk
For…
• Budgeting and Cost
Containment
• Risk adjustment
• Cost & utilization driver
analysis
• High-cost case
identification
For…
• Medical Management
• Identification &
stratification
For…
• Provider Network
Management
• Quality Measurement
• Predictive event modeling
• Employer reporting
• Gaps-in-care
measurement
• NCQA HEDIS reporting
• Trending & reporting
• Program Measurement
• Fraud, waste & abuse
DATA AGGREGATION – ANALYTICS – DECISION SUPPORT - REPORTING – EXPERT INTERPRETATION
7
Data Acquisition and Management is a Core
Operational Strength
Examples of our Data Capabilities
• Data Handling:
• Acquisition: We can accept data in virtually any format
• We currently accept data from over 300 different vendors with over
1,200 mapping and translation schemas
• We process 50 million claims per day and 1.5 billion on a recurring
monthly basis. Average turnaround time less than 10 calendar days.
8
Data Captured Enables Analysis of a
Complete Picture of Health and Productivity
Medical
Worker’s
Comp.
Pharmacy
Incentive
Disability
Wellness
HRA
Absence
Lab
Biometrics
9
Data Elements
Healthcare trend: Progression from
Health  Health and Wellness  Productivity
•
•
•
•
•
Medical claims
Pharmacy claims
Eligibility
Vision
Dental
•
HRA
•
Disability
(Health Risk Appraisal)
•
•
•
Biometric
Lab
EMR
10
The Data Lifecycle at Verisk Health
DATA
Source
Process
Analytics
Access
• Customer
• Payor
• Data
Warehouse
• ETL
• Engine
• ASP
• Service
Bureau
• Warehouse
• Scrub
• Cleanse
• Map
• Quality
• Risk
• HEDIS
11
Data Analytics
Rules Engine
CDF
(Common Data Format)
Enterprise Analytics
Medical Intelligence
Provider Intelligence
DxCG
Performance
Measurement
14
Demographic Analysis
Distribution of spend, health conditions and quality across
membership status, age, sex
Employee
Spouse
Dependent
Age/sex distribution of the
population
Expense distribution
Population
1%
2-5%
6-15%
16-30%
31-60%
61-100%
% of Spend
30+%
< 1%
15
Financial Analysis
Focus on cost drivers
Factors affecting change include: membership, utilization, pricing, intensity
•
Cost trends
•
Norm comparison
•
Modeling
Predicted
•
•
Current
Future
Performance
•
Actual vs Predicted
16
Clinical Analytics
Include disease prevalence, population health status, quality of care
•
Disease Registry
•
List of top conditions
(chronic and acute)
• Prevalence
• New diagnoses
• Cost metrics
•
•
•
Risks:
Disease burden in
population
•
Gaps in Care:
Care delivery / quality
Comparison
• Benchmark Commercial norm
• Period 1 / 2
Quality and risk
measures
•
Clinical deep dives
•
•
Specific consultative
evaluations
Comorbidity analysis
17
Predictive Modeling
• Relative risk score
• Predicts cost and utilization using prior health status and performance
• Regression model fits population around normative “1.0”
• Benchmarked to: - Book of Business
- Norm
•
Concurrent models
Performance
•
Predictive models
Budgeting and
forecasting
•
Likelihood models
Medical management
Intervention
18
Additional Analytics
Understand patterns of utilization and healthcare spending
•
Utilization
•
•
ER
Admissions
•
•
•
•
Specialty services
•
•
•
Acute
Subacute
Med specialties
DME
Non-PBM drugs*
Office
Network utilization
•
•
•
Geography
Pricing / discount differentials
•
Tests
•
•
•
Lab
Imaging
Vascular
19
Pharmacy Analytics
Utilizing PBM and claims data
Examples of analytic capabilities:
• Pricing
•
•
•
Brand
Generic
Fill location
• Mail order
• Prescribing patterns
•
•
•
Brand
Generic
Opportunities for generic conversion
• Medication possession ratio (MPR)
• Measure of Rx compliance
Ratio of: # Days filled
# Days Rx
• Non-PBM drug
• Costs by location
• e.g. office, outpatient hospital
20
Case #1: Employer
• Large multi-state employer
High rates of chronic disease
Benefit strategy includes
•
•
• High deductible health insurance and HMO options
• Disease management through outside vendors
• Challenges:
•
•
•
Manage health care costs
Provide insight into vendor performance
Budgeting and forecasting
21
Case #1: Cost Drivers
• Answers questions including:
•
•
•
•
What is the overall performance across all carriers?
How do specific subgroups perform?
What is driving changes in medical spend?
Are there areas to focus for controlling cost?
22
Case #1: Cost Drivers
• Aggregate carrier information to common format for overall analysis
• Analyze cost trends and determine drivers
• Report on demographic and membership patterns across all carriers
• Analyze patterns of health spend by cuts across membership
• Influences include health policy, general economy
• Impacts on membership changes (e.g. hiring or layoffs)
• Monitor changes
23
What Are Key Cost Drivers?
Change in Member
Months (Thousands)
-5%
352
Total Expenses
$ Millions
335
+1%
128
285
300
P1
P2
129
P1
P1
Change in Medical
PMPM
+5%
$
P2
P2
Change in Total
PMPM
+7%
$
364
390
Change in Pharmacy
PMPM
$
+9%
89
P1
97
P2
Sep 08 – Aug 10: Calculations are based on Total expenses and membership
P1
P2
24
Medical Cost Drivers
Change in Unit Pricing
$ / Event
+2%
552
564
P1
P2
Change in Medical PMPM
$
+5%
285
300
Change in Utilization
Events / Member Months
+4%
P1
P2
0.56
P1
Full Cycle - Demo
0.59
P2
25
Case #1: Health Analytics
• Answers questions including:
• What are the key conditions driving health care spend in the population?
• What is the quality of the care delivered to the population?
• Overall
• Subgroup analysis
• What approaches can be taken to address the health issues?
• How to track interventions?
26
Case #1: Health Analytics
• Disease Registry
• Identifies key conditions
• Prevalence and change in prevalence
• Cost by member (PMPM/PMPY)
• Comparison to norm
• Future capabilities will include industry-specific norm (NAICS)
• View by subgroup
• Business unit
• Individual carriers
• Insight into significant population health conditions and patterns of change
• Provides a focus for disease management and wellness
27
Disease registry key findings: PMPY cost
Condition
Prevalence
Per 1000
VH Norm
• PMPY for the top 4
prevalent conditions
is higher than the VH
Norm.
$11,799
Hypertension
202
107
Hyperlipidemia
$10,401
175
81
Diabetes
$15,111
85
56
Coronary
Artery Disease
51
24
Co #1
VH Norm
Analysis Period. Based on current members
$10,781
$21,602
$17,393
• Members with
Coronary Artery
Disease and
Diabetes contribute
to highest costs.
• Opportunities for
wellness and
disease
management
programs
28
Case #1: Health Analytics Cont’d
• Quality and Risk Measures
• Detail on health status of overall population
• Compared to BOB and norm
• Quality measures – gaps in care
• Baseline evaluation and comparison
• Evaluate range of quality across multiple conditions
• Enable monitoring over time for changes
• Evaluation can be done for overall population or subgroup analysis
• Carrier
• Business unit (location, division)
• Demographic cut (membership, geographic)
29
Quality of care comparison: Coronary Artery Disease
29.0%
26.8%
Patients diagnosed with CAD and without
a lipid profile test in the last 12 months
38.9%
1.6%
Patients diagnosed with CAD and without
an office visit in the last 12 months
3%
• No apparent issues
with access to care
2.2%
36.8%
Patients diagnosed with CAD and without
antihyperlipidemic drugs in the analysis period
Patients diagnosed with CAD and HTN without
antihypertensive drugs in the analysis period
• Plan performance
is similar
42.5%
29.2%
7.7%
8.1%
13.6%
• Disease
management to
improve lipid
treatment may
lower future CAD
costs
VH Norm
Plan A
Plan B
30
Case #1: Risk Modeling
• Answers questions including
• How does the health risk for population of interest compare to other populations?
• How do specific areas compare?
• How to budget for next year’s health costs?
31
Case #1: Risk Modeling
• Normative benchmark for overall population risk assessment
• Normalized to BOB for division analysis
• Carrier
• Business unit (location, division)
• Demographic cut (membership, geographic)
• Concurrent models
• Predictive models
32
Quality comparison:
Risk-adjusted spend and utilization by carrier
Well managed sick pop.
Carrier
Lives
RRS
PMPM
(unadjusted)
PMPM
Admissions
(RRS-adjusted)
Index
ER
Index
Imaging
Index
1
3,180
1.45
285.12
196.63
1.02
0.98
0.92
2
8,327
0.71
85.97
121.08
0.72
0.80
0.95
3
16,784
1.00
200.41
200.41
1.01
0.95
1.05
4
1,903
2.12
532.86
251.35
1.32
1.41
1.21
5
1,201
0.86
189.22
220.02
1.1
1.22
0.85
Poorly managed “healthy”
population pop.
Analysis for demonstration purposes
33
Case #2: Provider Organization
Large single-state provider organization
Accepts risk from insurance contracts
Looking to perform at the forefront of healthcare with innovative reimbursement approaches
•
•
•
Alternative Quality Contract
•
Experience with local clinical information (EMR)
Own data warehouse
•
•
•
Challenge: Rising costs and medical expense management
•
•
•
Tools for financial analysis, modeling and forecasting
Monitor network utilization and cost factors
Practice pattern variation: Provider dashboard
•
•
•
Quality
Efficiency
Utilization patterns
34
Case #2: Clinic and Physician
Manager and Dashboard
• How does performance compare across physicians and clinic sites?
• “My patients are sicker than yours”
• Who are the top performing providers and clinics?
• Efficiency
• Quality of care
• Where to focus for performance improvement?
• How to evaluate utilization patterns…
•
•
•
•
Drug
ER
Specialists
DME
… and take action?
35
36
Case #2: Clinic and Physician
Manager and Dashboard
• Metrics for comparison based on quality, volume, risk and efficiency
• Quality (gaps in care)
• Relative risk models
• Generic utilization rates
• Drill down functionality across drug class to dose
• Readmission rates
• Efficiency scores:
•
•
•
•
actual utilization
concurrent predicted
Total admissions
Potentially avoidable admissions
ER visits
Imaging
• Drill down capability to view individual physician or clinic
• Comparison to BOB or norm
37
Case #2: Outside Utilization
• How to evaluate referral patterns:
• Where are outside referrals going?
• What diagnoses are being treated outside?
• What procedures are being done outside?
39
Case #2: Outside Utilization
• Define “events”
• Typical views by claim or unit vs episode group
• Includes ancillary claims
• Capture true event cost
• Evaluate utilization patterns
• Cost and quality
• View events by:
•
•
•
•
•
Provider: Professional
Location: Facility
Specialty
Group (similar events)
Code level
40
Case #2: Outside Utilization
• Evaluation of referral patterns:
41
Case #3
Large benefit management company
•
Needs to provide information on wellness and augment disease management for clients
•
• Chronic disease
Clients are capturing HRA (health risk appraisal)
•
•
Challenge
•
•
Create HRA report
Understand implications of self-reported health outcomes
42
Augmenting Member Level Data for Analysis: HRA
Health Risk Appraisal
Claims
data
• Self-reported data
• History of disease
• Lab results (glucose)
• Biometrics (height, weight)
 Example conditions with claims and HRA data
Diabetes
Tobacco use
Obesity
43
Diabetes* by Claims and HRA
(30%)
Claims
(66.6%)
HRA
(33.3%)
 Individuals with
claims for Diabetes
have a relatively low
rate of diagnosis
reporting on HRA
 Adding HRA will
increase the
population of
diabetic individuals
by about 5%
*Diabetes claims: Medical Intelligence disease registry criteria
*Diabetes HRA: Blood glucose value, blood glucose range, or diabetes
history
44
Compliance Rates in Diabetes:
Identification by Claims vs HRA
Highest compliance
Medium compliance
Lowest compliance
HbA1c in
last 12
months
Renal disease
Eye exam in screening in Lipid profile ACE/ARBs in
last 12
last 12
in last 12
last 12
Statins in last
months
months
months
months
12 months
Overlap (Claims and
HRA)
Claims
HRA only
• Individuals identified by HRA only have a lower compliance rate
• Individuals identified by claims and report having diabetes in HRA (overlap) have the
highest compliance rate
45
Data Hosting Platform - ASP
Primary Site
•
Physically hosted at Class A Datacenter, SAS
70 Type II certified
•
Industry-leading managed services and
enterprise infrastructure provider
•
Located in downtown Boston with alternate
sites throughout the US
•
Direct connection to Tier 1 Sprint backbone,
MCI, Verizon backup
•
Raised floor, redundant grid power, climate
control, smoke detection, fire suppression
systems
Secondary Site
• Duplicate attributes of Primary Site
• Located 30 miles northwest of Boston
• Direct connections to multiple Tier 1
backbones (Level 3, Global Crossing)
• End User Data Warehouse migrated to
secondary site during Final QC to Production
Post phase
• Transactional updates migrated nightly in
batch
47
Security
•
•
•
•
•
•
•
FTP
HTTPS
Password change 90 days
Idle time out
Lock out after 3 unsuccessful login attempts
Client admin for all users
User-level rights access
• PHI
• Data level
• Clinical vs financial
48
Challenges
• Processing
• Normative data set
• 10 mil lives
• Clinical quality measures
• 500+
• Cloud
• Private vs public
• Maintaining accuracy of imported data
• Security
• Managing PHI
49
Challenges
• Meeting the needs of a changing industry
• PPACA
• ACOs
• Changing paradigms for care, reimbursement and reporting
• Clinical progress
• New drugs and diagnoses
• Changing clinical guidelines
• Coding evolution (ICD-10)
• New reimbursement policies
• Readmissions
• Episodes of care
• New and evolving reporting needs
• ACO
50
Questions
51