Presentation to the University of Michigan Taubman Health Sciences Library March 2016. Includes an addendum of slides on ICD-10.

1. March 28, 2016

2. Federal
Mandates
Advances in
Technology
Wealth
of Data

3.  Federal mandates/incentive programs have
not created an interoperable healthcare
system
 Lack of consistent standards and systems
 Lots of data that is hard to analyze
 Example – mapping data from Centricity to
Clarity to get a comprehensive picture of a
surgical encounter

4.  We do not lack for standards
 UMLS and its children:
◦ SNOMED
◦ CPT
◦ LOINC
◦ RxNorm
◦ ICD9/10

5.  International Classification of Diseases
 International standard for classifying
mortality data
 Used in US for health claims
 Used at almost every point of clinical care
 ICD-9 used in US for 30+ years
◦ Outdated
◦ Not Expandable
 Mandatory transition to ICD-10 occurred
October 1, 2015
 Larger, expandable, and more specific than
ICD-9

6.  V91.07 - Burn Due to Water-Skis on Fire
 V97.33XD - Sucked into Jet Engine,
Subsequent Encounter
 W22.02xD - Walked Into Lamppost,
Subsequent Encounter
 R46.1: Bizarre Personal Appearance
 Y92.241 - Hurt at Library

7. Rheumatoid Arthritis:
 One ICD-9 code - 714.0
 190 1CD-10 codes, with variants for:
◦ Specific joints
◦ With/without organ system involvement
◦ With/without rheumatoid factor
◦ Bursitis
◦ Nodules
◦ Codes recommended for review
ICD-9 ICD-10
Pro Only have to look for
one code
Can automate greater
precision by excluding
population members at the
code level
Con May require manual
exclusion of the patient
population
Must review and understand
all the codes

8.  Includes:
◦ Financial
◦ ADT
◦ Demographic
 Advantages:
◦ Structured and consistent
◦ Mature data source
◦ Simplifies complex care to a single code
 Drawbacks:
◦ Simplifies clinical complexity
◦ Coding bias to get a higher DRG
◦ No temporal granularity (Except POI)

9. Includes:
 Unstructured
◦ Concepts and content mixed together
◦ Requires parsing or manual review
◦ NLP tools can help
 Structured
◦ Distinct clinical concepts in separate areas
◦ Codified – list of possible answers with definitions – pick
list
◦ Easier to map to different lexicons
◦ Quality depends on data accuracy
 Automated data collection
◦ Lab results, device data
◦ No human interaction, easy to collect
◦ Lacks clinical context

10.  For quality improvement, benchmarking,
research
 Manually collected
 Have a point of view
Example:
 NSQIP – National Surgical Quality
Improvement Program
◦ 200 participating hospitals
◦ 137 data elements for every case
◦ Consistent, accurate data
◦ Excellent source of 30 day outcomes

11.  National Registries:
◦ Society of Thoracic Surgeons
◦ National Registry of Cardiopulmonary
Resuscitation
 Nationwide Inpatient Sample – 10% sample
of all acute care facilities in the US, UMHS
has a license
 Medicare National Data – all patients, de-
identified
 Payor Data – BCBS and other, de-identified
 Social Security Death Master File (DMF)

12. 1. Understand data resources already available
or in development at UM and facilitate their
use.
Examples:
 DataDirect - https://datadirect.med.umich.edu/
◦ All UM Patents
◦ For Cohort Discovery
 COMPASS Data Set Catalog -
http://umms-ckan.umms.med.umich.edu/ckan/
◦ Directory of data assets available for research
◦ Metadata about the assets

13. 2. Make UM resources more accessible.
Example:
 At least 148 health science registries
 No single place to locate them
 Create a data set catalogue for all health
science data registries, with metadata for
◦ Data description
◦ Data history
◦ Data owner
◦ Access requirements

14. 3. Form partnerships to better understand
data needs and provide guidance and
support.
Example:
 Learn more about issues related to openly
sharing data to support journal articles, and
help develop solutions

15. 4. Promote data usability, interoperability,
and literacy by creating resources for better
understanding of health science data.
Example:
 Develop traditional and online courses on
a variety of topics:
◦ Different types of data sources, pros and cons, and how to
access them
◦ Different health science vocabularies and ontologies
◦ Standards and best practices for the collection and storage of
health science data

16. 5. Understand data resources available
outside UM and make them visible.
Examples:
 Gather in one place and describe the
various national sources of health science
data
 Get involved at the national level in the
advancement and development of standards
that enhance interoperability

17. 6. Promote increased awareness of data needs
related to diversity and cultural competence.
Example:
 Share information regarding the relationship
between healthcare disparities and data
collection
 Provide training about data collection best
practices developed to address disparities in
quality and outcomes for various
populations

18.  Lynne Frederickson
 lrago@med.umich.edu

19. Policy
 Health IT Legislation -
https://www.healthit.gov/policy-researchers-implementers/health-it-legislation
 Federal Health Data Initiative:
http://www.healthdata.gov/blog/health-data-initiative-strategy-execution-plan-released-
and-ready-feedback
 Interoperability Standards Advisory - https://www.nlm.nih.gov/research/umls/
 Interoperability Roadmap –
https://www.healthit.gov/sites/default/files/hie-interoperability/nationwide-
interoperability-roadmap-final-version-1.0.pdf
Data Sources
 Society of Thoracic Surgeons (Registry)- http://www.sts.org/national-database
 National Registry of Cardiopulmonary Resuscitation (Registry) -
http://www.ncbi.nlm.nih.gov/pubmed/19213296
 National Library of Medicine list of data sources:
https://www.nlm.nih.gov/hsrinfo/datasites.html
 AHIMA “Understanding Publicly-Available Healthcare Data Sources” –
http://library.ahima.org/xpedio/groups/public/documents/ahima/bok1_050345.hcsp?dDoc
Name=bok1_050345

20. ICD-10
 GEMS –
https://www.cms.gov/medicare/coding/icd10/downloads/gems-crosswalksbasicfaq.pdf
 General Information - http://www.cdc.gov/nchs/icd/icd10cm_pcs_background.htm
Other Coding Systems
 UMLS - https://www.nlm.nih.gov/research/umls/
Healthcare Disparities
 AHRQ Quality and Disparities Report -
http://www.ahrq.gov/research/findings/nhqrdr/index.html
 Healthcare Research Disparities Toolkit - www.hretdisparities.org
Tools
 3M Code Translation Tool - http://www.med.umich.edu/i/icd10/conversion_tools.html
 Healthcare Data Visualization - http://www.vizhealth.org/about/

21. Tools
 ARMIS (HIPAA-aligned supercomputing cluster) –
http://arc-ts.umich.edu/using-armis/
 COMPASS Data Set Catalog - http://compass.medicine.umich.edu/
 DataDirect (cohort discovery) - https://datadirect.med.umich.edu/
 EMERSE (Natural Language Processing) - http://project-emerse.org/index.html
Support Services
 Center for Healthcare Outcomes and Policy –
https://umchop.org/data.html
 Medical School Office of Research - https://medicine.umich.edu/medschool/research/office-research
 MICHR (Michigan Institute for Clinical and Health Research) - https://www.michr.umich.edu/home
 MIDAS (Michigan Institute for Data Science) - http://midas.umich.edu/
 Office of Clinical Informatics –
https://medicine.umich.edu/dept/office-clinical-informatics
 Data Office for Clinical and Translational Research –
https://medicine.umich.edu/medschool/research/office-research/data-office-clinical-and-translational-
research
Data Sources
 CQIs (BCBSM Clinical Quality Initiatives) - http://www.med.umich.edu/multi-hospitalQI/
 Dr. Kheterpal’s presentation on data sources - https://www.mpogresearch.org/content/lecture-two-data-
sources-available-observational-research

22.  History
 Structure Breakdown
 Comparison of ICD-9 to ICD-10
◦ Structure
◦ Functionality
 ICD-9 to ICD-10 Mapping Complexities
 Benefits for Different areas of Health Science
 Considerations for Research
 Conversion of Quality Measurement Systems to
ICD-10
 CTT Translation Tool
 General Equivalence Mappings (GEMs)

23.  Linnaeus, the father of ICD
 ICD-1, late 1800s – 44 causes of death
 ICD-9-CM and PCS in use in US since the
late 70s
◦ revised (annually?)
◦ not expandable
 Mandatory transition to ICD-10
10/1/2015
◦ Diagnosis coding for all claims
◦ Procedure coding of inpatient facility
claims

24. ALPHA NUMERIC NUMERIC
ALPHA or
NUMERIC
ALPHA or
NUMERIC
ALPHA or
NUMERIC
ALPHA or
NUMERIC
CATEGORY
ETIOLOGY, ANATOMICAL SITE,
SEVERITY
EXTENSION

25. ICD-9 CM ICD-10 CM/PCS
Procedure 3824 codes 71,924 codes
Diagnosis 14,025 codes 69,823 codes
Diagnosis
Structure
3-5 characters
First character
numeric or alpha
Characters 2-5
numeric
3-7 characters
Character 1 alpha
Character 2 numeric
Characters 3-7
alpha or numeric
Procedure
Structure
3-4 characters
All characters
numeric
Minimum of 3
characters
7 characters
Any mix of alpha
and numeric
No letters I or O

26. ICD-9-CM Procedure Codes ICD-10 PCS Procedure Codes
Based on outdated technology Reflects current usage of medical
terminology
Lacks laterality Has laterality (right and left)
Lacks detail and precision Very specific regarding methodology,
approach, body part, device used, and
qualifying information
ICD-9-CM Diagnosis Codes ICD-10-CM Diagnosis Codes
Lacks laterality Has laterality (right and left)
Lacks detail Very specific
Difficult to analyze data due to
non-specific codes
Richness of data for analysis
Limited space for adding codes Flexible for adding new codes
Does not support interoperability
- no longer in use by other
countries
Supports interoperability and the
exchange of health data between other
countries and the U.S.

27. http://library.ahima.org/xpedio/groups/public/documents/ahima/bok1_049636.hcsp?dDocName=bok1_049636

28. Quality
Measurement
• Data availability to assess quality standards,
patient safety goals, mandates and compliance
• Higher quality information for measuring
healthcare service quality, safety, and efficiency
Public Health • Improved disease and outbreak information
• Improved ability to track and respond to
international public health threats
Research • Better data mining for increased analysis of
diagnosis, treatment efficacy, prevention
• More precise identification of study populations
Organizational
Monitoring and
Performance
• Recognition of advances in medicine and
technology
• Enhanced ability to identify and resolve problems
and ability to differentiate payment based on
performance

29.  Living in two worlds requires a mapping
strategy
◦ Longitudinal studies
◦ EMRs required to change, but strictly clinical systems not
required (EMR, CRF, CDMS)
◦ Protocols may require amendments
 Common procedure names not used in ICD-
10 PCS, e.g:
◦ ICD-9: 45.23 – Colonoscopy
◦ ICD-10 : 0DJD8ZZ - Inspection of Lower Intestinal Tract, Via Natural or Artificial
Opening, Endoscopic
 Complex mapping relationships

30.  Coordinated conversion of quality measurement systems:
◦ Outreach
◦ Inventorying
◦ Tools assessment
◦ Conversion
◦ Validation
◦ Support and training
 Impacted groups:
◦ CIDSS (now PACE)
 Peer Review
 Clinical Dashboards
◦ Performance Improvement
◦ CQI Registries
◦ QMP
 Biggest challenges:
◦ Identifying needs
◦ Complex mapping relationships
◦ Trending

31. 
Imports ICD-9 codes from several file formats
 Translates codes to ICD-10 based on GEMs
 Identifies ICD-9 codes with no ICD-10 equivalent
 Identifies ICD-9 codes that are replaced by clusters of ICD-
10 codes
 Exports code translations to a text file or spreadsheet
 Provides built-in reference data from MedPar and OSHPD
 Provides lists of related clinical concepts (age, gender-
specific)
 Includes codebook lists based on chapters and sections of
the ICD-10 CM and PCS books
 Free within UMHS
 For access -
http://www.med.umich.edu/i/icd10/conversion_tools.html

32.  Public domain mapping reference
 Developed by CMS, NCHS, AHIMA, 3M
 Published and maintained by CMS
 Guides for translating between ICD-9 and
ICD-10 codes (and vice versa)
 Useful for linking data in long-term clinical
studies
 For reimbursement- not always clinically
accurate