Continuum Health Alliance

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Transcript Continuum Health Alliance

The Crossroads of Health IT
and Accountable Care
Presented by: Rich Temple, National Practice Director,
Beacon Partners
February 6, 2014
Agenda for
Today
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Quick Background on Beacon Partners
The Fast-Evolving Healthcare Landscape
Why Data Volumes are Exploding
The Impact of Accountable Care on
Healthcare Organizations as a Whole and
Healthcare IT Departments
Particular Challenges from Affordable Care
Population Health
Wrap-Up
Questions / Discussion
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Beacon Partners
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• Consultancy founded in 1989
̵ Exclusive focus is healthcare
• More than 300 consultants in all types of
engagements all over the country and in
Canada
• Focus on strategic advisory to health systems
of all sizes
• Successfully completed more than 2,000
engagements with over 600 healthcare
clients
• HIMSS Platinum member / CHIME
Foundation member
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Timeline of–Health
Over the Decades
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ArielIT40
1990s
Billing
systems
Enterprise
Resource
Planning
systems
Clinical
Ancillary
systems
Early 2000s
Billing—>
Revenue
Cycle systems
Early EMR
systems
Late 2000s
EMRs mature
into EHRs
HIEs enter
the picture
2010->NOW
Meaningful Use
Business Intelligence / Analytics
Moves to the Fore
Affordable Care Act
Value-Based Purchasing
Population Health
Multi-entity provider exchange
Image exchange (VNA)
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What Does
the Future
Hold?
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Genomics /
personalized
medicine
Integration
across providers,
payors, and
regulatory
agencies
Predictive
Analytics
Around Entire
Populations
Sharing of Huge
Diagnostic
Images
Streaming Info
from Home
Devices
Fitbits
Cardiac Monitors
Blood pressure
Monitors,
etc.
Increasing need to capture and mine
unstructured and semi-structured content
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Regulatory
Mandates
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40 Keep Coming
CQM
Clinical
Process of
Care
PQRS
ICD-10
Outcomes
ACO 33
Efficiency
Measures
HCAHPS
Readmits
Clinical
Documentation
improvement,
financial modeling,
code mapping, etc.
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We Knew Data Needs Were Going to Be Overwhelming…
• Prevailing wisdom says that hospital’s data storage
needs will double every 18 months
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Another way of stating this is that data storage needs will increase
tenfold over a five-year period!
There are research papers in circulation that state that this is a very
conservative estimate – that the rate of increase could actually be
many times higher!
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• And this generally doesn’t take into account new
repositories to cover data sources and targets such as:
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Private HIEs with a centralized structure
Enterprise Data Warehouses (for BI/”big data”)
Data structures built to support the data capture and rendering
requirements of Accountable Care Organizations (ACOs)
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How
to Store
and Manage
All This Data
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Cloud Storage (private or public “clouds”)
In-house data storage
Data management strategies
Consider different storage strategies for different types of
data
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Recent, critical data needs to be captured with the lowestpossible latency
Large percentage of data captured is not directly accessed again
– does not need to be in low-latency, easily accessed storage
Archive capabilities for infrequently-used data architected to
minimize bandwidth requirements
Data likely to be required for detailed analytics set up separately
and architected to be accessible via distributed computing tools
such as Hadoop
• De-duplication strategies
• Redundancy strategies for disaster recovery / business
continuity
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Accountable Care and its “parent”,
the “Affordable Care Act”, are
huge drivers of these initiatives
through wide-ranging data
capture, data storage, and through
detailed analysis of a myriad of
clinical and financial data points
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What is an
ACO?
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• Accountable Care Organizations (ACOs) are groups of
doctors, hospitals, and other health care providers,
who come together voluntarily to give coordinated high
quality care to their Medicare patients.
• The goal of coordinated care is to ensure that patients,
especially the chronically ill, get the right care at the
right time, while avoiding unnecessary duplication of
services and preventing medical errors.
• When an ACO succeeds both in both delivering highquality care and spending health care dollars more
wisely, it will share in the savings it achieves for the
Medicare program.
Definition courtesy of CMS: http://www.cms.gov/Medicare/Medicare-Fee-forService-Payment/ACO/index.html?redirect=/ACO/
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What Metrics
Do 40
ACOs Require?
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• For starters, all ACOs need to report on the
“ACO 33” and demonstrate a high level of
quality in the following areas:
̵ Patient/ Caregiver Experience (7 measures)
̵ Care Coordination/ Patient Safety (6 measures)
̵ Preventive Health (8 measures)
̵ At-Risk Populations (12 measures)
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Diabetes (6 measures)
Hypertension (1 measure)
Ischemic Vascular Disease (2 measures)
Heart Failure (1 measure)
Coronary Artery Disease (2 measures)
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What Metrics
Do 40
ACOs Require?
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Patient/Caregiver Experience
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What Metrics
Do 40
ACOs Require?
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Care Coordination / Patient Safety
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What Metrics
Do 40
ACOs Require?
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Preventive Health
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What Metrics
Do 40
ACOs Require?
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At-Risk Populations
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“groups of doctors, hospitals, and other health care
providers, who come together voluntarily to give
coordinated high quality care to their Medicare
patients."
Hospitals
Owned and
affiliated
physician
practices
FQHCs
Subacute /
LTC
facilities
Other providers
Engine to feed data
into in order to
establish quality and
financial metrics and
compare against
moving benchmarks
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“coordinated care is to ensure that patients, especially
the chronically ill, get the right care at the right time”
Capture the right data from
all sources across many
different provider sites and
systems
Patient
Surveys
HIE
Diff
hosp
EHRs
Excel
spreadsheets
Quality
mgmt
systems
•
Claims
Financial
systems
Homedevices
Identity
management across
disparate systems
EMPI
Aggregate allergy,
condition,
medication, and
other clinical info
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•
Diff
physician
EHRs
PAPER
•
Case
mgmt
systems
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Normalize different system
nomenclatures
Capture quality indicators
across disparate systems
Establish compliance with
plans of care
Weed out excess data
points
Track cost of care
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It’s
Not Just
Blindly40
Aggregating Data
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• Pulling data points from widely disparate systems is
challenging enough but…
• Profoundly different workflows exist for how data gets
entered into these different systems
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“Weight” could be entered in many different places even
within the same EHR
Algorithms for computing basic metrics (e.g., Length of
Stay) may vary across providers
Temperature: Fahrenheit versus Celsius
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• Essential to do a detailed workflow analysis and
standardize across all members of the ACO
exactly how data must be entered for proper
reporting
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Corollary to this is building “error reports” to show what
providers are not complying and thus sending over
incomplete or inaccurate key metrics
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Sample ACO
Quality40
Measure Narrative
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One More ACO
Quality
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40 Measure Narrative
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How Do These
Get Built?
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• There are commercial software packages (more
and more every day) that purport to be able to
provide these metrics but rigorous upfront
preparation is necessary to avoid challenges
̵ Different systems feed data into a repository in
different ways
• “HL7” is called a standard, but it is really more of a
suggestion. HL7 feeds vary widely from system to system
• Data normalization
• All the “moving parts” necessary to consider when computing
exceptions, contraindications, etc.
• Data normalization
• Data governance across multiple providers and types of
systems
• Much more…
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How Do These
Get Built?
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• Real-time (or near-real-time) feeds from
multiple sources
̵ Optimally configured interface engines
̵ Connectivity that maximizes uptime and
minimizes latency
̵ Robust error reporting
• BI/Analytics to identify problem areas and
provide appropriate, targeted
interventions, before problems get to the
point of reaching a risk for the entire
“shared savings” model of the ACO
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Population
Health
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40 Management
• Borne out of both Accountable Care and the
Affordable Care Act, as a whole, Population
Health Management (PHM) has become topof-mind for healthcare organizations
• With the financial models for providers
changing to reward truly making patients
better, providers need to have tools to make
sure patients are staying well, even after they
leave the hospital or practice site
• This is where PHM comes in
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Population
Health
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40 Management
• Case managers need to be empowered
with a multitude of different types of data
to identify patients at-risk of readmissions
or complications and intervene right away.
Besides EHR data, critical data will be:
Medication
History Access
(e.g.,
Surescripts)
Case
Management
Systems
Risk
Stratification
Systems
Different types
of Decision
Support
Systems
Payor-type
Actuarial
Systems (to
manage risk)
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Population
Health
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40 Management
• Registries of patients with different types of
potentially risky conditions must be able to be
built
• Processes for automatic reminders, referrals,
etc., need to be in place and adhered to
• PHM involves ongoing communication with
patients and an awareness of their
compliance with post-discharge protocols
• Targeted interventions may be needed (e.g.,
trips to the home) when risks are noted
• Ability to document interventions and their
success or lack thereof is essential
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Wrap-Up– Ariel 40
Heading
• Data storage requirements were increasing exponentially
before Accountable Care moved into our world
• Accountable Care requires a whole new way of thinking
about storing and operating on data and has brought
Business Intelligence and Analytics to the center of
healthcare strategy and operations
• Systems have to speak to each other as never before and
they don’t always speak anything like the same language
• Systems have to support a PHM-type approach and use
both data and targeted interventions to keep patients well
• Healthcare IT does have the tools to pull it all together, but
it requires an understanding of people and process, as well
as technology
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Wrap-Up– Ariel 40
Heading
This is a fact of life now and healthcare
organizations must embrace it if they
want to survive and thrive in this new
world
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Thank You
For more information
please contact:
Rich Temple, National Practice Director,
Beacon Partners
[email protected]
908-705-7108
1.800.4BEACON │ BeaconPartners.com
BOSTON · CLEVELAND
· SAN FRANCISCO · TORONTO
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