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Managing 600+ EHR Instances in One of the World’s Most Diverse Cities
Session #65, February 12th, 2018
Ramon Tallaj, M.D., Chairman, SOMOS
Tonguç Yaman, MPH, Former CIO, SOMOS
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Ramon Tallaj M.D.
Tonguç Yaman, M.P.H.
No conflicts of interest to report.
Conflict of Interest
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• Our changing market. Why does the move to value matter for primary care physicians?
• The NY DSRIP program. Where does a PCP belong?
• A different way to DSRIP. At SOMOS, the need to bring together 600+ practices.
• Our implementation. Interoperability means physician engagement and communication, not just technical interfaces.
• Outcomes. Lessons learned as we brought together hundreds of EHR instances in just a year.
• Looking forward. Completing our transformation to a data-driven organization.
Agenda
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• Recognize situations where EHR standardization may not be
optimal for providers
• Design a cloud-based, provider-centric, data integration plan
appropriate for a highly-diverse network
• Develop a physician engagement strategy as part of a data
aggregation and analytics project
• Identify potential barriers to gaining physician trust when rolling
out network-wide reporting
• Plan to roll out care coordination at scale across a multi-EHR,
diverse technical environment
Learning Objectives
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A Patient- and Provider-Centric Approach To Data Integration and
Realtime Analytics For Continuity Of Care Management
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Total health expenditures per capita, U.S. dollars, 2016
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Life Expectancy vs Health Spending
https://www.huffingtonpost.com/2013/11/22/american-health-care-terrible_n_4324967.html
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Health and social services expenditures: Associations with health outcomes
The ratio is calculated by
dividing total expenditures
on social services by total
expenditures on health
services. (OECD
countries) - 2005.
Bradley, E. H., Elkins, B. R., Herrin, J., & Elbel, B. (2011). Health and social services expenditures: Associations with health outcomes. BMJ Quality & Safety, 20(10), 826. doi:http://dx.doi.org.ezproxy.cul.columbia.edu/10.1136/bmjqs.2010.048363
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United States healthcare, relative to other countries…
Highest in cost. And,
costs are rising
unsustainably.
Modest life expectancy.
Limited value for cost
incurred.
Low spend on Social
Determinants of Health,
which have substantial
impact on health outcomes.
Misaligned payment
incentives work against
primary care and care
coordination.
Programs like DSRIP move the market toward a value
based care model that values primary care.
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DSRIP
Program
DSRIP
Program
Active DSRIP Programs, 2018
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New York State DSRIP Program
HOSPITAL-LEDINDEPENDENT
PHYSICIANSFQHC-LED
25 Performing Provider Systems
23 1 1
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What does it take to succeed in DSRIP?(And, what makes that harder for 600+ independent practices?)
• SOMOS Goal: low-cost/high-efficiency infrastructure model without the overheads of costly hospital systems.
– Data: 99% of DSRIP Goals are data-driven
– Transformation: Transition to successful and sustainable Value Based Care
Practice Transformation
• Reduce number of
portals
• Support primary care
physicians
Administrative Efficiencies
• Outreach
• Control medical and administrative costs
• Data management
• Risk management
• Panel growth
• Improved quality scores
• Reduced ER visits and inpatient admissions
Video will be embedded here.
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Where does the PCP belong?
• History and role of the PCP and IPA
• PCP offices today – 50,000 patients visits
everyday
• Strategy for integration
• Demand for data
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Waiting Room
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SOMOS Community Care
Our Population• 700,000 Medicaid members
• Cultural and ethnic diversity
Our Initiatives• System Transformation Projects
• Integrated Delivery System
• Clinical and Population Health
• Transition into VBP - Innovator
• Delegated services
• Practice transformation
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Our (very) diverse network.Patient Cultural and Ethnic Diversity: The population we serve is as diverse
as it gets anywhere in the world (~800 languages and dialects in Queens, NY).
Health IT Diversity: Multitude of EMR Systems with ~600 different workflows.
SOMOS works with:
• 2,500 independent physicians at 600+
practices
• 12+ EMR vendors for PCPs
• Additional specialist EMR vendors
• 6 MCOs serving most of the Medicaid
attributed population (700,000+ lives)
• 8 RHIOs in SHIN-NY (3 cover NYC alone)https://www.businessinsider.com/queens-languages-map-2017-2
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The Demand for Data
Our Needs:
• HEDIS reporting
• CRG/RAPS
• DSRIP program already
underway with
increasing information
requirements
Our entrance into DSRIP created a massive demand for data from our 600+ practices.
Our Challenges:
• 600+ practices to be integrated
• No existing Information Systems
infrastructure
• No bandwidth available in the
healthcare workforce to
implement changes
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A Day in the Life of Dr. Ramirez24 visits / 16 hours / 2,541 clicks
6:33 AM
Dr. Ramirez arrives and
opens the EHR to begin
planning the day.
Support Staff begins a
morning huddle soon
thereafter.
10:42 AM
An unexpected drop-in
with stomach pain
diverts attention.
Dr. Ramirez is left
working through lunch.
2:09 PM
An overly-complex case takes
nearly an hour of Dr. Ramirez’s
time. By the end of the day,
patients are not being seen until 90
minutes after check-in.
8:11 PM
After driving home and
eating dinner, Dr. Ramirez
logs on remotely for 2.5
hours to finish
documentation and close
out charts for the day.
Data visualization used with permission of Arcadia.io. Author: Nick Stepro. Data mined from 32 million EHR HIPAA audit log records.
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Decision: Keep 600+ EHR instances
Option 1: Single EHR.
Move all 600+ practices to a
single EHR platform.
• Not tenable within the DSRIP
performance period
• 600+ unique workflows are part
of a bigger picture: providers
serving communities in their
languages with respect for their
cultures
Option 2: Integration.
Get data from all 600+ EHR
instances into a central data
lake.
• Interoperability challenge
• CIO became Chief Information /
Chief Innovation / Chief
Transformation Officer
• Outsourcing and vendor
selection: vendor needed to be
able to deliver high-quality data
at speed
How to achieve our mission of connecting everyone?
Data visualization used with permission of Arcadia.io. Author: Luke Shulman. Data sourced from combined EHR and Claims data sources for 500 patients.
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SOMOS Implementation
August 2017
Team Assembled
December 2017
3 instances of
EHR #1 in test
environment
January – December 2018
Rapidly increased integration
cadence; 40+ EHR
instances/month coming
online.
Fast, Faster, Fastest
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• SOMOS had unique velocity challenges compared to other
large healthcare systems tackling similar data aggregation
projects
– Difference of magnitude in site counts to be aggregated
– Logarithmically-shorter timeframe for completing
aggregation to support DSRIP performance period
– Need to adapt to “snowflake” EHR instances providers were
accustomed to using
Challenges of Scale and Timeline
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• Majority of providers on “snowflake” instances of a few main EHR types.
• SOMOS implemented a process to increase throughput for each EHR type.
– Identified a “typical” EHR instance as a model for a standard data connection
– Extract data from other instances of that EHR using the “typical” instance model
– Use automated data quality assessment tools to adjust for any unique implementation circumstances or data quality issues
A “Typical” EHR Instance
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Pizza Delivery: Ferrari v. Scooter
VERTICAL INFRASTRUCTURE HORIZONTAL INFRASTRUCTURE
Price: $250,000
Combined Speed: 200mph
Maintenance: Specialized
Provisioning: Shipped from Italy
If the Ferrari crashes on the 3rd delivery, game
over – it affects 9 more customers. If any
customer is slow to answer door and pay,
latency affects downstream customers.
Price: $400 x 12 = $4,800
Combined Speed: 40mph x 6 = 240mph
Maintenance: Basic (throw away)
Provisioning: Shipped from Amazon
If a scooter crashes on the 3rd delivery, it affects
that one customer…and #4 can pick up the pizza
and deliver to customer #3. No losses.
Or, why we chose a horizontal infrastructure.
1 x Ferrari
12 x Scooter
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Best-of-Breed Big Data Platform
• SOMOS partnered with a
data aggregation vendor to
achieve the scale and
agility needed for the
project
• Cadence enabled by
custom-built Mesos OS
and best-of-breed Big Data
platform components
• SOMOS was able to roll out
and run hundreds of data
connections in parallel
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• 3 pilot connectors helped make decision to focus on cadence
• Quality assurance program addresses three sets of gaps that can cause
poor performance on DSRIP measures:
– Workflow Gaps. Data are not being captured and stored in the
EMR, or are stored in an unusual location that is not initially picked
up by the connector.
– Mapping Gaps. Data are not automatically mapped to standard
clinical concepts, because the source system uses unusual
terminology.
– Clinical Gaps. The clinical care being provided to the patient is
insufficient and the clinical workflow needs to be improved.
• Quality improvement efforts focused on critical/necessary data -
Cadence v. Quality?Both are critical for DSRIP success
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• Challenge: EMR vendors make it challenging for physicians to pull out their data for projects like this one – perhaps to ensure customer retention.
• This is a misconception: EMR stickiness does not rely on data portability. None of the 100s of providers we work with entertained the idea of switching EMRs.
• EMR stickiness is related to workflows. And also, to the training of staff and to the presence of existing integrations with partners processing claims and other inbound/outbound transactions.
• Our approach to information blocking: No magic bullet – but constant focus on doctors owning their data. Tenacious, ongoing engagement with vendors; flexibility to move forward whereever we could make progress. Leverage success to convince hold-outs.
EMR Vendor Misconceptions“Stickiness” has nothing to do with the data.
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• Consent: Each of the 600+ practices had to provide consent for
the project.
– BAAs, consent forms, memoranda of understanding
• Education: Physicians required education on:
– The DSRIP model and its reliance on data and documentation
– Importance of using EHR data for performance reporting
– Use of a data lake by SOMOS
– Data quality and clinical quality improvement process – data quality
issues can erode physician trust!
• Network physician leadership: SOMOS physician leaders
worked to gain practice trust.
Physician Engagement StrategyTrust was critical to project success.
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Care Coordination for Complex Cases:
• Data asset used to identify patients with the most complex cases
• Culturally-competent community health workers assess their needs
• Community health workers connect patients with services within and beyond the network
Network Goals, Local InsightsData asset supports population health programs.
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Health Disparities: Targeting ZIP Codes
• Data asset used to identify hotspots of ambulatory-
sensitive inpatient use, disease rates
• SOMOS channeled community health workers into
local blocks to engage most complex patients
• Addressed social determinants of health like
homelessness, lack of access to transportation,
food gaps as well as clinical needs
Network Goals, Local InsightsData asset supports population health programs.
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• NY DSRIP requires SOMOS to accept increasing annual amounts of risk (by 2020, upside risk on 80% of total payments and upside/downside risk on 35% of total payments).
• SOMOS entered risk arrangements with 6 plans willing to align on shared care goals
• SOMOS partnered with large hospital organization that understood importance of primary care
• SOMOS infrastructure allows alerts to practices when patients go to ED, inpatient locations, sharing of information about clinical history and clinical gaps with PCPs.
Partnering for Care Coordination
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Outcomes and Outlook• Outcomes
• 300+ practices integrated in first year
• Quality improvement initiatives
• On target for DSRIP 25% reduced avoidable
hospitalizations goal
• $33M distribution to network after first DSRIP
incentive payment
• Outlook
• Value Based Care for 1M+ New Yorkers
• Participation in Pilot and Innovator Programs (MCO-
like role)
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Recommendations
Cloud-based approach. This technical architecture lets IT teams move quickly to ramp up an interoperability project, then focus on working with providers to improve data quality.
Look beyond the CCD. Getting as much data out of the EHR as possible supports better patient care gap identification and unmet needs of the population.
Collaborative centralized analysis team. This team should coordinate contract performance analysis and reporting – but also collaborate cross-functionally to drive care-coordination improvements.
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Dr Ramon Tallaj, Chairman, SOMOS
Tonguç Yaman, Former CIO, SOMOS
Visit us at somosnyhealth.org
Don’t forget to complete the online session evaluation!
Questions