iHT2 Health IT Summit San Francisco 2013 - Christopher Chute, Division of Biomedical Informatics, Mayo Clinic, Presentation “Strategies for the ICD-10 Transition”

Biomedical Informatics

Strategies for the ICD-10 Transition
with Comments on Meaningful Use
Christopher G. Chute, MD DrPH
Professor Biomedical Informatics
Vice-Chair, Data Governance
Mayo Clinic

Chair, International Classification of Disease Revision
Member, HIT Standards Committee, HHS/ONC
Chair, ISO Technical Committee 215 on Health Informatics

IHT2, San Francisco
26 March, 2013


Declarations

• No real or apparent financial conflicts of interest
• All products are open-source
• Comments represent beliefs of the author
• He has an odd sense of humor


Key Learning Objectives
(they made me do this slide)
• How Meaningful Use standards impact ACO
operations and success
• What role will the new ICD-10 play in providers
understanding their practice and outcomes
• What are the future directions in ICD-11 and
beyond Meaningful Use Phase 3

© Mayo Clinic College of Medicine 2010 3


Health Care Is
An Information Intensive Industry
• Control of Health Care Costs ...
• Improved Quality of Care ...
• Improved Health Outcomes ...
• Appropriate Use of Health Technology...
• Compassionate Resource Management...
... depend upon information
… Ultimately Patient Data
4


Biocomputing
Unimaginable potential, next 50 years
• Moore’s law still reigns
• 1012 fold increase in computing power / 50 yrs
• World-class Supercomputers of 1990
• Game platforms and cell phones of 2012 (GPUs)
• Extensions to networks, memory, & storage
110 baud to 100Gb backbone (109 / 50 yrs)
Vacuum tubes to 100Gb RAM (1011 / 50 yrs)
Paper-tape to Pb drives (1015 / 50 yrs)
Cloudy computing (103 / 3 yrs)
• Synergistic Summary – 1050 / 50 yrs
5


Big Science Collaboration
and Semantics
• Communication of findings and results
• Human publication
• Sharing of data resources as building blocks
• Foundation for incremental, big-science
• Harnessing computing requires formalization
• Data format and structures
• Discrete terms, vocabulary, ontology
• Non-ambiguous concepts, non-overlapping terms
• Information models and problem architectures
• Standards, conventions, and shared context
6


Comparable and Consistent Data

• Inferencing from data to information requires
sorting information into categories
• Statistical bins
• Machine learning features
• Accurate and reproducible categorization
depends upon semantic consistency
• Semantic consistency is the vocabulary
problem
• Almost always manifest as the “value set” problem
7


From Practice-based Evidence
to Evidence-based Practice
Clinical
Registries et al.
Data Databases
Shared Semantics
Inference

Medical Knowledge
Patient Standards
Encounters
Terminologies & Data Models
Decision Expert Clinical Knowledge
Guidelines
support Systems Management
Foundations for Learning Health System 8


World Health Organization; ICD-11 9


World Health Organization; ICD-11 10


Meaningful Use
13 January, 2010

Reporting Interoperability
Requirements Standards
169pp 35pp 11


The Challenge

• Most clinical data in the United States is
heterogeneous – non-standard
• Within Institutions
• Between Institutions
• Meaningful Use is mitigating, but has not yet
“solved” the problem
• Achieving standardization in Meaningful Use is
sometimes minimized


ACO Operations and Success

• Learning Health System
• Commitment to discovering best practice
• Critical examination of practice
• ACO rationale
• Align incentives for reimbursement
• Practice integration, communication, exchnage
• Quality measurement, ultimate improvement
• Best Practice Discovery, monitor adherence
• Improve health, wellness, disease management
• Reduce overall costs


What Does It Take To Become
Learning Health System
• Culture, Culture, and Culture
• Data issues
• Comparable and consistent data representation
• Comprehensive and longitudinal EMR/EHR
• Data quality, reliability, and completeness
• Curation, monitoring, management
• Near real-time warehouse
• Analytic capacity
• First order Decision Support implementation


What Does It Take To Become
Learning Health System
• Culture, Culture, and Culture
• Data issues

Comparable and consistent data representation

• Comprehensive and longitudinal EMR/EHR
• Data quality, reliability, and completeness
• Curation, monitoring, management
• Near real-time warehouse
• Analytic capacity © Mayo Clinic College of Medicine 2010 15


Meaningful Use and Comparable Data

• Phase I – an into
• Phase II – dawn of effective standards
• Phase III – deployment of useful comparability
• Biannual follow-ons – ratcheting consistency

• Learning Health System cannot be supported
by Meaningful Use standards alone
• Not yet sufficient granularity/specificity


Clinical Terminology and Classification

• SNOMED-CT – evolving into utility
• Not a human entry terminology
• Increasingly comprehensive and rational
• Platform for data comparability
• Meaningful Use Phase II requirement (problem
list)
• Consolidated CDA
• Vast improvement over CCD
• Specifies Value Sets
• NLM National Value-set Center


So, Where’s ICD10 in All This?



Short Summary of Those Reasons
for delaying ICD-10-CM
1. Meaningful Use phase I
2. Meaningful Use phase II
3. Meaningful Use phase III
• Requirement to implement SNOMED CT
• Phase II for problem lists
• Limited clinical benefit
• Substantial cost
• ICD-10 is already 25 years old


Some Observations on American Clinical
Modifications of the ICD
ICD-9-CM Diagnoses codes ICD-10-CM Diagnoses codes
• Roughly 13,000 codes • Roughly 68,000 codes
• Injury codes: 15% • Injury codes: 60%
• Disease codes: 8,500 • Disease codes: 18,000
• Permuted by:
• Right, Left, Both,
Unspecified
• First Episode, subsequent
• Adds up to 6 codes per
disease
• Done for a bit more than
half disease codes 21


How Well do Clinical Classifications Work?
• Study of Coding efficacy to measure content
capture Chute et al., JAMIA 1996;3(3):224-233
• Text reports from four academic medical centers
• Data parsed into 3,061 clinical observations
• Coded twice by national panel of experts
• Updated re-coding using most current versions
of ICD-10-CM and ICD-9-CM - 2010
• Coding over-read by CDC/NCHS
• National Center for Health Statistics


Scoring Example
ICD-9-CM
2 <Primary Diagnosis>:melanoma
0 <Extent>:Clark’s level 2
0 <Quantitative>:0.84 mm [depth of invasion]
2 <Histology>:superficial spreading
1 <Anatomy>:thigh
0 <Topology>:left

Code: 172.7 | Malignant melanoma of skin, lower limb
Code: M8743/3 | Superficial spreading melanoma


Comparison of Clinical Content Coverage
between ICD-9-CM and ICD-10-CM


ICD-10 – The good part
Roughly 2% of Expansion Attributable to
• Administration
• Imaging
• Radiation Oncology
• Physical Rehabilitation
• Diagnostic Audiology



International Classification of Disease
ICD11 Use Cases
• Scientific consensus of clinical phenotype
• Public Health Surveillance
• Mortality
• Public Health Morbidity
• Clinical data aggregation
• Metrics of clinical activity
• Quality management
• Patient Safety
• Financial administration
• Case mix
• Resource allocation
27


Traditional Hierarchical System
ICD-10 and family

28


Addition of structured attributes to concepts
Concept name
 Definition
Language
translations
Preferred string
Language
translations
Synonyms
Language
translations
Index Terms

29


THE CONTENT MODEL
Any Category in ICD is represented by:
1. ICD Concept Title 7. Causal Properties
1.1. Fully Specified Name 7.1. Etiology Type
1.2 Preferred Name
7.2. Causal Properties - Agents
1.3 Synonyms
7.3. Causal Properties - Causal Mechanisms
7.4. Genomic Linkages
2. Classification Properties 7.5. Risk Factors
2.1. Parents
2.2 Type 8. Temporal Properties
2.3. Use and Linearization(s) 8.1. Age of Occurrence & Occurrence Frequency
8.2. Development Course/Stage
3. Textual Definition(s) 9. Severity of Subtypes Properties
10. Functioning Properties
4. Terms 10.1. Impact on Activities and Participation
4.1. Base Index Terms 10.2. Contextual factors
4.2. Inclusion Terms 10.3. Body functions
4.3. Exclusions
5. Body System/Structure 11. Specific Condition Properties
5.1. Body System(s) 11.1 Biological Sex
5.2. Body Part(s) [Anatomical Site(s)] 11.2. Life-Cycle Properties
5.3. Morphological Properties 12. Treatment Properties
6. Manifestation Properties 13. Diagnostic Criteria
6.1. Signs & Symptoms
6.2. Investigation findings


Addition of semantic arcs - Ontology
Relationships
Logical Definitions
Etiology
Genomic
Location
Laterality
Histology
Severity
Acuity

31


Serialization of “the cloud”
Algorithmic Derivation

32


Linear views may serve multiple use-cases
Morbidity, Mortality, Quality, …

33


Relationship with IHTSDO
SNOMED content
• IHT (SNOMED) will require high-level nodes that
aggregate more granular data
• Use-cases include mutually exclusive, exhaustive,…
• Sounds a lot like ICD
• ICD-11 will require lower level terminology for
value sets which populate content model
• Detailed terminological underpinning
• Sounds a lot like SNOMED
• Memorandum of Agreement – July 2010!
• WHO right to use for authoring and interpretation 34
ICD11 and SNOMED


Potential Future States
SNOMED
ICD-11

Ghost ICD

Ghost SNOMED

35


Joint
Alternate Future
ICD-IHTSDO
Effort SNOMED
ICD-11

36


SHARP Area 4:
Secondary Use of EHR Data
• Agilex Technologies • Harvard Univ.
• CDISC (Clinical Data Interchange• Intermountain Healthcare
Standards Consortium) • Mayo Clinic
• Centerphase Solutions • Mirth Corporation, Inc.
• Deloitte • MIT
• Group Health, Seattle • MITRE Corp.
• IBM Watson Research Labs • Regenstrief Institute, Inc.
• University of Utah • SUNY
• University of Pittsburgh • University of Colorado


Themes & Projects


Mission

To enable the use of Leverage health informatics to:
EHR data for secondary •generate new knowledge
purposes, such as
clinical research and •improve care
public health. •address population needs

To support the community •open-source tools
of EHR data consumers by •services
developing: •scalable software


Modes of Normalization

• Generally true for both structured and un-
structured data
• Syntactic transformation
• Clean up message formats
• HL7 V2, CCD/CDA, tabular data, etc
• Emulate Regenstrief HOSS pipeline
• Semantic normalization
• Typically vocabulary mapping


Clinical Data Normalization

• Dr. Huff on Data Normalization Stanley M. Huff,
M.D.; SHARPn Co-Principal Investigator;
Professor (Clinical) - Biomedical Informatics at
University of Utah - College of Medicine and
Chief Medical Informatics Officer Intermountain
Healthcare. Dr. Huff discusses the need to
provide patient care at the lowest cost with
advanced decision support requires structured
and coded data.


That Semantic Bit…

• Canonical semantics reduce to
Value-set Binding to CEM objects
• Value-sets should obviously be drawn from
“standard” vocabularies
• SNOMED-CT and ICD
• LOINC
• RxNorm
• But others required: HUGO, GO, HL7


NLP Deliverables and Tools
http://informatics.mayo.edu/sharp/index.php/Tools
•cTAKES Releases
• Smoking Status Classifier • cTAKES Side Effects module
• Medication Annotator • Modules for relation extraction
•Integrated cTAKES(icTAKES)
• an effort to improve the usability of cTAKES for end users
•NLP evaluation workbench
• the dissemination of an NLP algorithm requires performance benchmarking. The
evaluation workbench allows NLP investigators and developers to compare and
evaluate various NLP algorithms.
•SHARPn NLP Common Type
• SHARPn NLP Common Type System is an effort for defining common NLP types used
in SHARPn; UIMA framework.


High-Throughput Phenotyping
• Phenotype - a set of patient characteristics :
• Diagnoses, Procedures
• Demographics
• Lab Values, Medications
• Phenotyping – overload of terms
• Originally for research cohorts from EMRs
• Obvious extension to clinical trial eligibility
• Quality metric Numerators and denominators
• Clinical decision support - Trigger criteria


Drools-based Architecture
Jyotishman Pathak, PhD.
discusses leveraging
open-source tools such as
Drools.
Clinical Data Access
Element Layer
Databas
e
Business Logic

Transformation Inference
Layer Engine
(Drools)

Service for List of
Transform physical Creating Diabetic
representation Output (File,
 Normalized logical Patients
representation (Fact Model) Database, etc)


SE MN Beacon – ONC – Community HIT
High-value community-based care delivery model


http://semnbeacon.com/
http://informatics.mayo.edu/beacon

47


SHARP and Beacon Synergies
• SHARP will facilitate the mapping of comparable and
consistent data into information and knowledge

• SE MN Beacon will facilitate the population-based
generation of best evidence and new knowledge
discovery
• SE MN Beacon will allow the application of Health
Information Technology to primary care practice
• Informing practice with population-based data
• Supporting practice with knowledge 48


Where is This Going?
• Biomedical practice and research are data,
information, and knowledge intensive
• Comparable and consistent data
representation are pre-requisite for efficient
clinical analytics
• Learning Health Systems will underpin
successful ACOs
• Meaningful Use is a positive move toward
establishing a data framework for Learning
Health Systems
49

iHT2 Health IT Summit San Francisco 2013 - Christopher Chute, Division of Biomedical Informatics, Mayo Clinic, Presentation “Strategies for the ICD-10 Transition”

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iHT2 Health IT Summit San Francisco 2013 - Christopher Chute, Division of Biomedical Informatics, Mayo Clinic, Presentation “Strategies for the ICD-10 Transition”

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