Obstacles to Adding Measures to EHRs
and Ways to Overcome these for the
Patient, Provider, System, and Society:
General Principles and Real-World
Experience
Presented to the IOM Committee on Recommended
Social and Behavioral Domains and Measures for
Electronic Health Records
April 8, 2014
Shaun J. Grannis, MD MS FAAFP FACMI
The Regenstrief Institute
Indiana University School of Medicine
Adding EHR data and functionality is
costly
• Potential for increased data gathering burden
– Additional time
– Altered workflow
• Necessary Design/Development costs
– Systems must be re-configured to accommodate
new data/functionality
• Who absorbs these costs?
“Why, What, How, and By Whom”
• Why:
– Well-articulated Problem/Need/Goal ... to which
additional information/functionality are a clear
solution
• What:
– Identify information needed to accomplish the goal
• How:
– Define process. Re-use existing resources before
asking for more: Identify and leverage potential preexisting measures that address the need.
“Why, What, How, and By Whom”
• Who:
– Contemplate who may be optimally positioned
to gather/supply new information
• Physician
• Nurse
• Registration Clerk
• Patient
• Other?
Examples
Rwanda: Maternal Child Health
Community
Clinic
Hospital
Community
Clinic
Hospital
Define Metrics to Assess Progress
Toward Goal
Integrating Community level
and Geospatial data
“Why”: Integrating Community level
geospatial data
• Socio-behavioral factors are important, often unrecognized
determinants of health outcomes
• US healthcare system is oriented to acute, hospital based,
disease treatment.
• Responding to the health needs of both non-hospitalized and
hospitalized patients with chronic diseases is proving difficult.
• Improving population level health problems like healthcare
disparities is also challenging, in part because of the complex
interplay of socio-behavioral, community and biologic factors
within the context of the current healthcare system.
• Just as IT enabled advances in sub-molecular medicine,
behavioral and population sciences are on the verge of an IT
based revolution.
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
Frederickson K, Grannis SJ, Dixon B, Bodenhamer DJ, Wiehe S. Incorporating geospatial
capacity within clinical data systems to address social determinants of health. Public
Health Reports. 2011 126. Suppl 3: 54.
Population-Level Analytics:
Surveillance, Patient trends,
Predictive Modeling
GI Event
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)
Multi-stream 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
Population Trends
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.
Predicting Frequent ED Users
84% PPV for predicting which patients who
will use ED > 16 times in two years.
Wu J, Xu H, Finnell JT, Grannis SJ. A Practical Method for Predicting Frequent Use of
Emergency Department Care Using Routinely Available Electronic Registration Data.
AMIA Annu Symp Proc. 2013:1524.
Thank You!