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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

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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