Transcript Data Aggregation, Liquidity, and the Learning Healthcare System: Perspectives from the Indiana Experience Shaun J.

Data Aggregation, Liquidity, and
the Learning Healthcare System:
Perspectives from the Indiana
Experience
Shaun J. Grannis, MD, MS, FACMI, FAAFP
Biomedical Informatics Research Scientist,
The Regenstrief Institute
Associate Professor of Family Medicine,
Indiana University School of Medicine
What We’ll Cover
• The Context
• The Problem
• Potential Solutions
Regenstrief Institute
• Endowed by Sam Regenstrief Inventor of the low cost frontloading dishwasher
• Supported the creation of the
Institute to apply process
improvement to medicine
• A medical informatics
“skunkworks”
Regenstrief Informatics - What We Do
• Build medical information systems
• Study systems and supporting technologies
• Rationalize, organize and standardize health
care data
• Pragmatists - needs driven, create solutions to
real-world problems
• Describe what works and what doesn’t
High Costs and Inefficiencies
• Very large and inefficient information enterprise
that still operates with substantial amounts of
paper
• Costs are rising
– $2.4 Trillion in 2009, ~$8,000/person
– Growth outpacing inflation
– Now $2.7 Trillion in annual spending (2012 est.)
– May reach $4 Trillion by 2020 (!)
1. RAND Study, Hillestad
2. Social Transformation of American Medicine, Starr
Healthy Life Expectancy versus
Expenditure per capita
Total Healthcare Expenditures per Capita $USPPP, 2006 or Latest
Source: OECD Health Database, June 2008 version; WHO World Health Data 2008; EU-15 average is the GDP weighted average
Infant Mortality versus Expenditure
per capita
Total Healthcare Expenditures per Capita $USPPP, 2006 or Latest
Source: OECD Health Database, June 2008 version; WHO World Health Data 2008; EU-15 average is the GDP weighted average
Variation in Medicare Reimbursement Rates
Copyright © 2014, The Regenstrief Institute, Inc.
Healthcare Labor Productivity
Kocher R, Sahni NR. Rethinking Health Care Labor.
N Engl J Med 2011; 365:1370-1372. October 13, 2011
Copyright © 2014, The Regenstrief Institute, Inc.
INPC Data Management and Services
Data Management
Data Access & Use
Hospitals
Hospital
Payers
Health
Information
Exchange
Physicians
Labs
Labs
Data
Repository
Network
Applications
Outpatient RX
Physician Office
Results delivery
Secure document transfer
Shared EMR
Credentialing
Eligibility checking
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Results delivery
Secure document transfer
Shared EMR
CPOE
Credentialing
Eligibility checking
• Results delivery
Public
Health
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Payer
• Secure document transfer
• Quality Reporting
Public Health
Ambulatory Centers
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Researchers
Surveillance
Reportable conditions
Results delivery
De-identified, longitudinal
clinical data
• De-identified, longitudinal
clinical data
Clinical Abstract
Overhage JM, Dexter PR, Perkins SM, Cordell WH, McGoff J, McGrath R, McDonald CJ. A
randomized, controlled trial of clinical information shared from another institution. Ann
Emerg Med. 2002 Jan;39(1):14-23.
Notifiable Condition Detection
System Overview:
Notifiable Condition Detector
Realtime
Inbound
Message
Compare to Dwyer I
Potentially
Reportable
E-mail
Summary
Abnormal flag,
Organism name
in Dwyer II, Value
above threshold
Reportable
Condition
Daily Batch
To Public
Health
Reportable
Conditions
Databases
To Infection
Control
Record Count
as denominator
Copyright © 2014, The Regenstrief Institute, Inc.
Print
Reports
ELR Completeness
4,785 total reportable cases
INPC– 4,625 (97%)
Health Dept – 905 (19%)
Hospitals – 1,142 (24%)
Timeliness
ELR identified cases 7.9 days earlier
than did spontaneous reporting.
Prepopulated Reporting Forms
Clinical Messaging/Public Health
Communication
Sample
Pre-populated
Reporting Form
Reporting Form
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Patient
Demographics
Clinical Data
Provider
Demographics
Understanding Reporting Workflow
Pre-populated form Information flow
Outcome measures
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Time-to-treatment
Timeliness of reporting to public health
Completeness of reporting data
Level of communication among PH and clinical
providers
Syndromic Surveillance
Over 110 Indiana
Emergency Departments
contribute 2.1 million
visits to the system each
year.
Copyright © 2014, The Regenstrief Institute, Inc.
Information Flow: Clinical
Network
Connection
HL7 ADT
message
Hospital ED
Registration
Hospital
Interface
Engine
(Routing)
Imported
into Clinical
Repository
Clinical
Repository
Message
Processor
Hospital Firewall
(Encryption)
Message
Listener
Firewall
(Decryption)
Information Flow: PH Surveillance
Network
Connection
HL7 ADT
message
Hospital ED
Registration
Public Health
Hospital
Interface
Engine
(Routing)
Batched,
delivered
to ISDH
every 3
hours
Message
Processor
Hospital Firewall
(Encryption)
Message
Listener
Firewall
(Decryption)
Neuro Event
GI Event
Natural Disaster
H1N1 Surveillance
H1N1,Oct 2009
H1N1, April 2009
Flu ICD9
Flu CC
Pneumonia ICD9
Pneumonia CC
ILI ICD9
ILI CC
All Flu Tests
Positive Flu Tests
Positive Rate
All Health Care is Not Local: An
Evaluation of the Distribution of
Emergency Department Care
Delivered in Indiana
All Health Care is Not Local
• Over 3 years, 2.8 million patients totaled 7.4 million visits for an
average of 2.6 visits per patient.
• More than 40% of ED visits during the study period were for
patients having data at multiple institutions.
• This population analysis suggests a pull model is necessary, and
helps inform the ongoing dialog regarding the merits of peer-topeer (push) and federated aggregate HIE (pull) NwHIN
architectures.
Leveraging Analytics to Enable
Accountable Care
• Patients receive healthcare
from multiple providers and
across organizations
• More than 40% of ED visits
are for patients having data
at multiple institutions
A network diagram illustrating the connectedness among Indiana EDs that participate in PHESS. Circular nodes
represent EDs; node size indicates the visit volume; node color indicates the centrality of the ED. The gray edges
connecting nodes indicate where patient crossover occurs. EDs that share proportionally larger number of patients are
clustered together. While general clusters of “medical trading areas” emerge, the myriad gray edges clearly illustrate
how interconnected all EDs are to one another.
Distribution of patients stratified by the total number of ED visits. Note that six patients
visited the ED more than 300 times and a single patient accumulated 385 visits for the 3year study period.
• Shifting nonurgent visits from ED to primary care: During the 6month trial with 9 Central Indiana hospitals, the 320,000member managed health plan reduced nonurgent ED visits
among members served by these hospitals by 53 percent, while
simultaneously increasing primary care office visits by 68
percent.
• Cost savings: The shift from ED to primary care visits that
occurred during the pilot test saved the health plan an
estimated $2 to $4 million over the 6-month period.
Copyright
© 2014, The Regenstrief Institute, Inc.
http://www.innovations.ahrq.gov/content.aspx?id=3988
84% PPV for predicting which patients who will use ED > 16 times
in two years.
Copyright © 2014, The Regenstrief Institute, Inc.
Supporting ACO Services
• Care management support
– HIE Information matched to CMS defined ACO
population
– Patient care summaries extracted
– Delivered to ACO care management systems via
CDA documents
• Readmission risk stratification (LACE model)
– Adaptation underway
Integrating socio-behavioral determinants of health
using geospatial information
Patient
Address
Change
1
ADT
Processor
2
Update person_address table
with new address information
6
person_address
table
post_processing
table
Geo-Coding
Application
Call Polis Center
web service
which returns 5
geo-coded
addresses
In real-time, Address
3 Update Detector detects
and writes address changes
to the post_processing table
Address Update
Detector
4
Geo-Coding app reads
the post_processing
table
Polis Web
Service
Comer KF, Grannis S, Dixon BE, Bodenhamer DJ, Wiehe SE. Incorporating
geospatial capacity within clinical data systems to address social determinants
of health. Public Health Rep. 2011 Sep-Oct;126 Suppl 3:54-61.
Improving Efficiency of Data Integration
A Cautionary Note: the Era of “Big
Medical Data”, Analytics, and Data
Quality
Aggregate Data Example:
Diabetes and Obesity Cohort
“Coders should pay attention to the BMI
because it makes a difference in terms of
reimbursement […]. A BMI of 40 or higher diagnosis code V85.4 - is considered a
complicating condition, meaning higher
reimbursement when reporting this code along
with the appropriate principal diagnosis.”
(http://medicalcodingpro.wordpress.com/page/2/)
Data often reflect financial
incentives, not the true population
distribution.
“Using Information
Entropy to Monitor
Chief Complaint
Characteristics and
Quality”
Data Quality
• Supplemental data is often necessary to enhance
practice based population health processes
Missing data rate for a sample of
clinical transactions received by the
INPC in 2008.
Copyright © 2014, The Regenstrief Institute, Inc.
Copyright © 2014, The Regenstrief Institute, Inc.
Conclusion