Achieving Data
Standardization in
Health Information
Exchange and
Quality Measurement
Amy Sheide
Clinical Informaticist
3M Health Information Systems
USA
Abstract
This presentation reviews
the benefits and challenges of achieving
and maintaining interoperability.
Specifically, it showcases successful implementation of a
centralized terminology server in health information
exchange, biosurveillance and quality measurement.
Background
“Interoperability describes the extent to which systems and
devices can exchange data, and interpret that shared data.
For two systems to be interoperable, they must be able to
exchange data and subsequently present that data such that it
can be understood by a user.”
http://www.himss.org/library/interoperability-standards/what-is
Share
Exchang
e
Interpret
Benefits of interoperability
Delivery of High Quality Cost Effective Care
Process Improvement
Providing care
within clinical
guidelines
Coordination Across
Care Settings
Medical Error Reduction
A complete
health history
Accurate
medication
Lists
Allergy and
adverse
reaction lists
Vaccine history
Examining
variation in
physician
practice
“The complexity of patient data in electronic
medial records, coupled with expectations that
these data facilitate clinical decision making,
healthcare cost effectiveness, medical error
reduction, and evidence based medicine,
makes obvious the role of standardized
terminologies as a foundation for comparable
and consistent representation of patient
information.”
-Pathak and Chute, Division of Biomedical Statistics and Informatics,
Mayo Clinic
Pathak, J., & Chute, C. G. (2010). Analyzing categorical information in two publicly available drug terminologies:
RxNorm and NDF-RT. Journal of the American Medical Informatics Association, 17(4), 432-439.
Drivers for interoperability in the US
•The 2009 Health Information
Technology for Economic and
Clinical Health (HITECH) Act
has the goal of using certified
electronic health record
technology (CEHRT) to promote
patient safety and
interoperability between and
within health care systems.
•The initiative in HITECH Act are
also known as Meaningful Use
(MU).
Collecting
Exchanging &
Reporting
EHR
Reaching the interoperability target
Current
State
• Governing bodies have defined
structured data requirements with
standard terminology
• Limitations and challenges exist in
adopting these standards
• How do you obtain and implement the standards?
• Are the current standards robust to function in
current clinical workflows?
• Standard terminology is free but how much does it
cost to implement and understand?
• How is your organization going to share data
elements that don’t have a standard code?
Challenges in interoperability
Standardization
Integration
Organization
Multiple Standard
Terminologies
Variable Release
Formats
Flat Lists of Codes
Variable Versions
Synonymous concepts
with different Identifiers
Variation in Granularity
Gaps in Standard
Terminologies
Unidirectional Data
Translation
Significant effort to
maintain mappings
Centralized Terminology Server (CTS)
solution
• Metadata repository which
enables the translation and
integration of healthcare data
• Standardized terminology
vocabulary compliance
• Knowledge Base to
understand how data is
represented and structured
across the organization
Addresses the simple questions that are hardest to manage,“ What does
it mean, where is it from, and how does it relate to everything else!”
CTS components
• Delivery of standard
terminologies in a consistent
consumable format
• Update to the standards content
• Local content
• Translation between source
and target system
• Browsing and Runtime Services
• Terminology and mapping
container
• Search and browse content
• Mapping tools
Content
Software
Web
Service
APIs
Mapping
Services
• Terminology consulting
• Integration of local codes
• Interoperability
Health Information Exchange (HIE) use case
Goal: Make patient lab results available to
any provider regardless of performing
laboratory
Standardization:
Map labs to
Standard
Terminology
Integration:
Translate Standard
Terminology back
to Source Lab
Organization:
Location to Store
the Mappings
HIE without a CTS
Requires mapping
from each source
system. Each
change at one site
require a remap
across systems.
The amount of variability
results in difficulty maintaining
translation and consumption
to source systems
Plasma
Hemoglobin
Updates require a remap
across all systems
Facilitating HIE with a CTS
Economies of
scale in
Centralized
Mapping
Bidirectional Data
Exchange
Updates applied
once and
automated
Plasma across systems
Hemoglobin
Mapping storage
and retrieval via
the CTS
Biosurvalence use case
Goal: Automate the identification and
tracking of reportable diseases
Standardization:
Identify LOINC, ICD or
SNOMED CT codes for
reportable diseases
Integration:
Group standard
terminology codes by
disease group
Organization:
Store the requirements
from the county, state
and federal level
Biosurvalence without a CTS
• Intensive data mining effort to find
diagnosis and lab information that
meet the reportable criteria (due to
the use of multiple code systems
required)
• Resources to manage updates
from the reporting agencies as well
as updates to the code system
• Maintaining the lists at each level of
reporting (county, state, federal)
Mumps Virus Antibodies,
Serum, Semi-Quantitative
LOINC 31503-6
Local Code: A
008.43
Campylobacter Species
Identified, Stool Culture
LOINC 6331-3
Campylobacter Species
SNOMED CT 116457002
Campyloba
Campylobac
cter coli
ter jejuni
SNOMED CT
SNOMED CT
40614002
66543000
Campylobacteriosis
SNOMED CT 86500004
Mumps
SNOMED CT 36989005
$
ICD-9-CM Coding:
008.43
ICD-10-CM Coding:
A04.5
ICD-9-CM Coding:
072.9
ICD-10-CM Coding:
B26.9
Facilitating biosurvalence with a CTS
• Centralized location to
manage code sets
• Add groupings across
terminologies
• Allows instantiation of
reports to different
agencies
• Enterprise wide
structured data integration
Utah Reportable
Conditions
US Nationally
Reportable
Conditions
County
Reportable
Conditions
Mumps
Campylobacteriosis
Problems
SNOMED CT 36989005
SNOMED CT 86500004
ICD-9-CM Coding:
ICD-9-CM Coding:
Has Associated
072.9
008.43
Disease
ICD-10-CM Coding:
ICD-10-CM Coding:
B26.9
A04.5
Campylobacter Species
Mumps Virus Antibodies,
Labs
Identified, Stool Culture
Serum, Semi-Quantitative
LOINC
6331-3
LOINC 31503-6
Has Analyte
Local Code: A
Local Code: B
NCID
76770
008.43
008.43
Campylobacter jejuni
Campylobacter Species
SNOMED CT 66543000
SNOMED CT 116457002
Campylobacter coli
SNOMED CT 40614002
Clinical Quality Measure (CQM) use case
Goal: Identify groups of patients receiving or
eligible for treatment
Standardization:
Over 20 different
code systems
required to
calculate CQMs
Integration:
Manage multiple
versions of value
sets and code
systems
Organization:
Link local codes to
CQM data elements
and measures
Clinical Quality Measure without a CTS
•Simple CQMs
require multiple data
elements
•Each CQM data
element can have
multiple value sets
•Value set and code
set versioning cause
a high level of
variability
Facilitating CQM with a CTS
•Cost and process
benefits in managing
the complexity of data
value sets and values
•Technical benefit in
accessing CQM
content with APIs and
runtime services
•Versioning reduces
variability of content
Achieving enterprise intelligence with a scalable
CTS
Accelerates
implementation of
electronic health
records
• Longitudinal patient
care record
• Personal health
records
Semantically
interoperable data for
exchange, analytics,
decision support, alerts
and reminders
• Lower total cost of
ownership
• Maximized
consistency, quality
and efficiency of Enterprise
mapping
Enables structured
clinical data capture,
queries and analytics
• Data mining
• Complex secondary
data use
Intelligence
Questions