Data Driven Care:
The Key to Accountable Care Delivery
from a Physician Group Perspective
Dr. Greg Spencer & Luke Skelley
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Today’s Agenda
Why a regional physician group is heavily investing
in analytics and data warehousing
Crystal Run Healthcare’s strategy to turn data into
improved care as well as financial viability in the
future
How Crystal Run manages across its patient
population who are covered by 24 payer entities
Some of the preliminary challenges and successes
engaging clinicians in the use of data
The importance of an adaptive data architecture to
turn clinician questions into actionable results
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POLL QUESTION #1
What best describes the group you belong to?
Health Plan
Physician Group
Provider Organization
Vendor
Other
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Crystal Run Healthcare
• Physician owned MSG in NY
State, founded 1996
• 300+ providers, 16 locations
• Joint Venture ASC, Urgent Care,
Diagnostic Imaging, Sleep
Center, High Complexity Lab,
Pathology
• Early adopter EHR (NextGen®)
1999
• Accredited by Joint Commission
2006
• Level 3 NCQA PCMH
Recognition 2009, 2012
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Crystal Run Healthcare
• Single entity ACO
• April 2012: MSSP participant
• December 2012: NCQA ACO
Accreditation
• 25,000 commercial lives at risk
• MSSP
• 10,400 attributed beneficiaries
• 82% primary care services
within ACO
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Crystal Run Healthcare
The mission of Crystal Run
Healthcare is to improve the
quality and availability of, and
satisfaction with, health care
services in the communities we
serve. To accomplish this goal,
the practice emphasizes both
traditional medical excellence
as well as responsiveness to
consumer needs through
service excellence and patient
empowerment.
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The Goal: The Triple Aim
Improve the health of the population
Enhance the patient experience of care
Reduce, or at least control, the per capita cost of care
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Crystal Run Strategy and Objectives
Embrace goals of Triple Aim
Physicians play a crucial role in driving change in healthcare
Focus on providing coordinated care
Population health management is critical
Competition from hospitals and health plans is occurring
Coverage area is expanding, and needs to expand further
Physicians and their teams need to work together for the best of
their patients
A strategic pillar is to be the practice of choice for physicians,
patients, and employees
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Crystal Run Governance Model
JOINT CLINICAL AND FINANCIAL GOVERNANCE MODEL
Hal Teitelbaum, MD, JD, MBA
Managing Partner & CEO
EDW EXECUTIVE
SPONSORS
Michelle A. Koury, MD
Chief Operating Officer
Greg Spencer, MD
Chief Medical &
Chief Medical Info Officer
Mary DeFreitas
Chief HR Officer
Erlene Washington
Senior VP of
Finance & Accounting
Establish data warehouse
priorities
Set policies for data
access, information
security and privacy
Develop process for
setting data definitions and
standards
EDW GOVERNANCE
Greg Spencer, MD
CMO & CMIO
Jonathan Nasser, MD
Medical Director
Miguel Hernandez
Technology Director
Lou Cervone
BI Director
Coordinate with Partners
eCare leadership
EDW Steering Committee
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Crystal Run Care Management
Strategies
Embedding Care Managers at different offices,
medical homes and hospitals
Identify high-risk patients from registries and PCP/
team referral
Implement evidence based protocols
Use EHR and mobile / home devices
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Crystal Run Quality Structure
27 divisions each headed by its own physician specialist
Manage quality efforts and information
Work with Best Practice Council (quality committee) to
define registries
Report to practice-level committee for quality and patient
safety
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Why Crystal Run is heavily investing in
analytics and data warehousing
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12
POLL QUESTION #2
If you are a health plan, physician group, or provider organization, do
you currently exchange clinical and claims data with these other
constituents?
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Crystal Run Data Analytics Strategy
Implementing formal quality improvement methodology
Implementing EDW with multiple data sources
Implementing analytical applications
Daily financial reporting
Order tracking: In-house vs. Sent out
Claims Data Integration
RVUs Standardization
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Crystal Run Analytics Current State
Quality improvements heavily dependent upon data
Using simple analytical tools – Excel, Access, Tableau
Time and effort spent on manual data entry and
extraction is excessive and poorly scalable
Decisions about what data to use based on amount of
disruption vs. value
Data entry/analysis not done at Top of Licensure
Reporting quality metrics resource intensive – kept it
simple
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Physician Dashboard
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Physician Dashboard
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Turning data into improved care &
ensuring financial viability in the future
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Physician Variation Analysis
Mean Cost per Case = $20,000
Dr. J.
15 Cases
$60,000 Avg. Cost Per Case
$40,000 x 15 cases =
$600,000 opportunity
$35,000 x 25 cases =
$875,000 opportunity
Total Opportunity = $600,000
Total Opportunity = $1,475,000
Total Opportunity = $2,360,000
Total Opportunity = $3,960,000
Cost Per Case, Vascular Procedures
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Crystal Run Results
Reduced hospital admissions 4+% in one year
Improved mammogram rates from 60-65% to greater
than 75%
Achieved less than 9% rate of A1Cs > 9
Blood pressure control in hypertensive patients
improved to greater than 75%.
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Improved Quality
Breast Cancer Screening
Mammography
76.0%
75.0%
74.0%
73.0%
72.0%
71.0%
CRHC Results
70.0%
NCQA Goal
69.0%
68.0%
67.0%
66.0%
1st
2nd
3rd
4th
1st
2nd
3rd
4th
1st
2nd
3rd
Quarter Quarter Quarter Quarter Quarter Quarter Quarter Quarter Quarter Quarter Quarter
2011
2011
2011
2011
2012
2012
2012
2012
2013
2013
2013
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Outcomes: Avoidable Admissions
Monthly Quality Trend
# Avoidable Admissions
40
30
20
17%
10
0
Nov-12
Dec-12
Jan-13
Feb-13
Mar-13
Apr-13
May-13
Jun-13
Jul-13
Aug-13
Sep-13
Oct-13
Avoidable Admissions
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Outcomes: Readmissions
20.00%
30 Day Readmission Rate for Medicare
19.50%
19.00%
18.50%
CRHC
Linear (CRHC)
18.00%
17.50%
17.00%
16.50%
Q2-2012
Q3-2012
Q4-2012
Q1-2013
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Q2-2013
Q3-2013
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Total cost difference
(equalized as cost per patient treated)
PEG-filgrastim use in Breast cancer patients
2012 pre-pathway
2013 post-pathway
791 patients
817 patients
$595,920
$368,160
TOTAL COST
SAVINGS
$227, 760
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Reducing Pharmaceutical Costs
PEG Filgrastrim cost per patient before and
after breast cancer pathway
$14,000
$12,000
$10,000
$8,000
$6,000
$4,000
$2,000
$0
Physician A
Physician B
Physician C
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Physician D
Average
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A Culture Of Efficiency: Improving
Access
• 41,823 fewer visits
• 30,206 more patients
• “Created” 12 physicians
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Variation Reduction Spread
Reduction in Charges
DIAGNOSIS
CHF
Diabetes
Thyroid Nodule
Otitis Externa
GERD
Cholelithiasis
COPD
HTN
Hyperlipidemia
HA/Migraine
Breast Cancer
Lateral Epicondylitis
Asthma
Asthma
Renal Mass
TOTAL
DEPARTMENT
Cardiology
PCP/Endocrine
Endocrinology
ENT
GI
General Surgery
Hospitalists
Primary Care
FP/IM
Neurology
Oncology
Orthopedics
Pediatrics
Pulmonology
Urology
% CHANGE PP
TOTAL $$ CHANGE
-6%
-17%
-26%
-2%
-20%
-7%
-20%
-16%
-19%
-10%
-7%
-8%
-10%
+3%
-4%
-$53,457
-$844,755
-$304,224
-$2,373
-$178,381
-$11,408
-$9,215
-$943,002
-$1,150,376
-$208,054
-$393,622
-$27,647
-$24,570
+$26,238
-$62,812
-$4,187,658
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Variation Reduction Spread
Improving Access
DIAGNOSIS
CHF
Diabetes
Thyroid Nodule
Otitis Externa
GERD
Cholelithiasis
HTN
Hyperlipidemia
HA/Migraine
Breast Cancer
Lateral Epicondylitis
Asthma
Asthma
Renal Mass
TOTAL
DEPARTMENT
Cardiology
PCP/Endocrine
Endocrinology
ENT
GI
General Surgery
Primary Care
FP/IM
Neurology
Oncology
Orthopedics
Pediatrics
Pulmonology
Urology
CHANGE IN VISITS
-722
-3,051
-1,971
+70
-143
-12
-3,013
-2,966
-550
-278
-84
-92
-66
-11
-12,889
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CHANGE IN PATIENTS
+213
+41
+132
+65
+266
+59
+339
-561
+225
+16
-4
-134
+1,132
-6
+1,783
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Managing patient populations across
payer entities
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Crystal Run’s payer mix
24 Payer Entities
No dominant payer, so little to no leverage for
discounts, etc.
No dominant payer, so payers need to contract with
Crystal Run to effectively do business in the area
Complicates data analysis due to limited
population/statistics by payer
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Payer Challenges
Having data-focused payer conversations about
shared savings
Need claims data to support risk contracting
Multiple payers limits ability to do valid statistical
modeling
Collaborating with multiple plans to develop shared
savings model
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Living in Two Worlds
Improvements in quality reduces costs to benefit of
payer
Reduction in patient visits offset by increase in
patient volume
Hospitals acquisition of physicians not based on
value but to protect referrals
Triple Aim is a threat to hospitals
Hospitals have to align optimal patient care vs.
optimal reimbursement
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Clinician Engagement:
Challenges & Successes
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Clinician Engagement
Data used in physician recruitment and retention
Data also used to support alignment and/or acquisition
decisions
Sharing physician performance data helps affect
behavior even if no penalty or not tied to a quality effort
• Sharing physician data makes outliers come to
consensus
• Not all physician practices focus on value
• Younger physicians sometimes avoid change more
than older ones
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It’s All About the Data
The limitation is data.
The doctors need performance data
They have metrics to measure care
~ 80% of Business Intelligence’s time spent gathering
versus analyzing data
90/10 of data capture time to analysis time.
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Crystal Run EDW Requirements
Fast to implement and fast to ROI
Capable of easily expanding to add new data sources
Library of analytical applications
Vendor with healthcare experience and expertise
Data model conducive to healthcare data
Ability to become self sufficient
Long term business partner
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Adaptive Data Architecture:
Turning clinician questions into
actionable results
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Provider-Payer Collaboration
Clinical data
Claims data
Case mix analysis
Risk stratification
Utilization review
Prior
authorizations
Utilization review
Care management
Care gap notification
Prevent readmissions
Provider
Physician profiling
Regulatory measures
Admission notification
Physician profiling
Discharge notification
Case management
Evidence based guidelines
Consumer transparency
Wellness programs
Payer
Contracting
Regulatory measures
Case management
Evidence based guidelines
Consumer transparency
Wellness programs
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Provider Payer Data Sharing
Case mix analysis
Clinical data
Utilization review
Risk stratification
Prior
authorizations
Utilization review
Care management
Care gap notification
Prevent readmissions
Provider
Physician profiling
Regulatory measures
Payer
Contracting
Admission notification
Physician profiling
Discharge notification
Regulatory measures
Case management
Evidence based guidelines
Case management
Claims data
Consumer transparency
Wellness programs
Evidence based guidelines
Consumer transparency
Wellness programs
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Crystal Run EDW Architecture
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Catalyst Apps and Claims Data
Key Process Analysis
Executive Dashboard Integration Tool
Comorbidity Analyzer
Claims Data
Cohort Builder
Readmissions Explorer
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Population Explorer
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Catalyst Data Warehouse Advantages
• Driven by business and clinical need
• Rapid development and deployment of data sources
• Built incrementally (i.e., less expensive)
• Ownership transferred to client with technical support as needed
• Align with access roles and data stewardship jurisdictions
• Applications support Healthcare Analytics Adoption Model
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Healthcare Analytic Adoption Model
Level 8
Personalized Medicine
& Prescriptive Analytics
Tailoring patient care based on population outcomes and genetic
data. Fee-for-quality rewards health maintenance.
Level 7
Clinical Risk Intervention
& Predictive Analytics
Organizational processes for intervention are supported with
predictive risk models. Fee-for-quality includes fixed per capita
payment.
Level 6
Population Health Management
& Suggestive Analytics
Tailoring patient care based upon population metrics. Fee-for-quality
includes bundled per case payment.
Level 5
Waste & Care Variability Reduction
Reducing variability in care processes. Focusing on internal
optimization and waste reduction.
Level 4
Automated External Reporting
Efficient, consistent production of reports and adaptability to
changing requirements.
Level 3
Automated Internal Reporting
Efficient, consistent production of reports and widespread availability
in the organization.
Level 2
Standardized Vocabulary
& Patient Registries
Relating and organizing the core data content.
Level 1
Enterprise Data Warehouse
Collecting and integrating the core data content.
Level 0
Fragmented Point Solutions
Inefficient, inconsistent versions of the truth. Cumbersome internal
and external reporting.
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POLL QUESTION #3
On a scale of 1-5, with 5 being very advanced, how far along is your
organization in using data to guide your quality and cost initiatives?
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Organizational Structure Goals
SENIOR EXECUTIVE
LEADERSHIP TEAM
Provides overall
governance and
prioritization of
initiatives
GUIDANCE
TEAM
Supports
development
of clinical
content and
analytics
feedback
ENSURE
THAT…
CONTENT AND
ANALYTICS
TEAM
CLINICAL
IMPLEMENTATION
TEAM
WORK
GROUP
Provides
steady
state domain
oversight
Refines Work
Group output
and leads
implementation
Provides clinical
forum to develop
clinical content and
analytics feedback
• Workgroups are created with institutional priority
• Appropriate leadership is engaged in prioritization
• Organizational barriers between team members are
removed
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Organizational Teams
Subject Matter Expert
Data Capture
Women & Children’s Clinical Program Guidance Team
Pregnancy
MD Lead
RN SME
Normal
Newborn
MD Lead
RN SME
Gynecology
MD Lead
RN SME
Data Provisioning & Visualization
Data Analysis
Guidance Team MD lead
(e.g., Pregnancy MD Lead)
RN, Clinical Ops Director
Pregnancy
SAM
Normal Newborn
SAM
Gynecology
SAM
Knowledge
Manager
Data
Architect
Application
Administrator
Permanent Teams
Integrated Clinical and Technical members
Supports Multiple Care Process Families
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Workgroup Roles
Subject Matter
Expert
Knowledge
Managers
Data Capture
Workflow
Analysis
Data
Provisioning
DATA CAPTURE
• Acquire key data elements
• Assure data quality
• Integrate data capture into
operational workflow
Application Administrators
(e.g., EMR Administrators,
Financial System Administrators)
Data Analysis
Knowledge
Managers
DATA ANALYSIS
DATA PROVISIONING
• Interpret data
• Discover new information in the data
Data Architects
(data mining)
(Analysis)
• Evaluate data quality
• Move data from transactional
systems into the EDW
• Build visualization for use by
clinicians
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Data Architects
(infrastructure)
Data Architects
(Visualization)
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Crystal Run EDW Teams
EDW data acquisition
●
Systems programmers
●
Database administrator
●
Clinical SME’s
EDW data architecture and integration services
●
BI director
●
Data architects
●
Business development
●
Project manager
●
Clinical SME’s
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Catalyst Resource Deployment
Installation
Improvement
Independence
Primary owner
Secondary owner
Technical Director
Level of
Engagement
Engagement Executive
Support
SOW#
1
SOW#
2
SOW#
3
SOW#
4
SOW#
5+
Engagement Time
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The Analytic Organization’s Journey
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AHIP Institute 2014
June 11-13, 2014 in Seattle, WA
AHIP’s Data Analytics Forum will provide valuable insights on how stakeholders in the
health care system utilize big data to enhance care quality, reduce costs, make better
business decisions, and streamline operational processes.
Please join Luke Skelly and Health Catalyst at Booth #911
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Questions?
Proprietary and Confidential
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Seed Questions
What are some of the barriers you’ve run across in acquiring claims from payers?
What internal challenges do you see payers or providers facing in developing a data driven
culture?
How does Health Catalyst support a population health management approach using claims data
from non-acute care settings (home health, skilled nursing facilities, etc.)
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Contact Information
Dr. Greg Spencer, CMO
Crystal Run Healthcare
www.crystalrunhealthcare.com
Luke Skelley, VP
Health Catalyst
luke.skelly@healthcatalyst.com
www.healthcatalyst.com
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Survey Questions
On a scale of 1-5, with 5 being the highest, how satisfied are you overall with the quality of this
webinar?
What do you wish the presenter had spent less time on?
What do you the presenter had spent more time on?
What topics would you like to see in future webinars from Health Catalyst?
On a scale of 1-5, how interested are you in a demonstration of Health Catalyst Solutions?
What additional comments do you have?
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