a sampling of topics covered at the NLM Georgia Biomedical Informatics course, 2014

1. NLM GEORGIA BIOMEDICAL
INFORMATICS COURSE
September 14 – 20, 2014

2. What is informatics?

3. What is biomedical informatics?
• “The field that concerns itself with the cognitive,
information processing and communication tasks of
medical practice, education and research, including the
information science and the technology to support these
tasks.”
Greenes RA, Shortliffe EH.
JAMA 1990 Feb 23; 263(8):1114-20.

4. Burning Platform: Overwhelming Complexity
SetsofFactsperDecision
1000
10
100
5
Human
Cognitive
Capacity
2000 20101990 2020
SNPs, haplotypes,
gene expression
profiles, post-
translational
modification
Decisions by clinical
phenotype
Stead WW. Beyond expert-based practice. IOM (Institute of Medicine). Evidence-based medicine and the
changing nature of health care: 2007 IOM annual meeting summary,(Introduction and Overview, p. 19).
Washington, DC: The National Academies Press 2008.
Socio-cultural
determinants of
health

5. Today’s Presentation
• Medical Vocabularies
• Electronic Health Records, Meaningful Use, and Health
Information Exchange
• Semantic Medline
• Data Visualization
• Other Topics
• Future Research Priorities
• How this applies to my/our work

6. Medical Vocabularies
• MeSH
• CPT
• DRG
• SNOMED-CT
• ICD-10
• NDC
• RxNorm
• LOINC
• FMA
• GO
• NANDA
• NIC
• NOC
• GHHCC
• PCDS
• Omaha
• AORN
• ICNP
• PSY
• MMX
• MDDB
• CSP
• HCDT
• DSM4
• LCH
• NCISEER
• OMIM
• QMR
• PDQ
• VANDF
• ULT
• SOP
• PPAC

7. Desiderata for Controlled Vocabularies
Cimino JJ. Desiderata for controlled medical vocabularies
in the Twenty-First Century. Methods of Information in
Medicine; 1998;37(4-5):394-403.
• Content
• Concept Orientation
• Concept Permanence
• Nonsemantic Concept Identifiers
• Polyhierarchy
• Formal Definitions
• Reject “not otherwise classified”

8. UMLS – A Metathesaurus
“The purpose of the [Unified Medical Language System] is
to improve the ability of computer programs to ‘understand’
the biomedical meaning in user inquiries and to use this
understanding to retrieve and integrate relevant machine-
readable information for users.”
- Donald A.B. Lindberg, 1993

9. UMLS – A Metathesaurus

10. UMLS – A Metathesaurus
Some ways to use it
• Reconstructing source terminologies
• Finding additional synonyms for source terms
• Automated translation

11. Informatics Standards

12. Informatics Standards
Standards here already:
• Billing (ICD-9-CM, ICD-10-CM, CPT4, DRG)
• Data interchange (HL7)
Standards on the way:
• Health data (CDA)
• Terminology
• Infobuttons
Office of the National Coordinator drives adoption
http://www.healthit.gov/sites/default/files/pdf/fact-sheets/onc-office-of-
interoperability-and-standards.pdf

13. Electronic Health Records
2004 – President Bush announces goal to have every
American covered by an EHR by 2014
2009 - Health Information Technology for Economic and
Clinical Health Act (HITECH)

14. Incentive Payments
14

15. Meaningful Use Stages
Data
capture
and
sharing
Advanced
clinical
processes
Improved
outcomes
15
Stage 1
Stage 2
Stage 3

17. What’s Next?
• 3 years: Ensure use,
work on data standards,
address policy/trust
issues
• 6 years: Incorporate
patient-generated data,
improve data
aggregation/analytics,
automate CDS and CQI
• 10 years: the Learning
Health System

18. Using the EHR for Discovery
De-identification
Synthetic Derivative ~ 2 million records
De-identified DNA repository
>170k samples
VanderbiltBioVU
Clinical
Notes
Physician
Orders
Patient and Staff
Messaging
Billing
codes
Labs, Radiology, Test
Results
Electronic Medical Record

19. Health Information Exchange

20. Health Information Exchange
• Health information exchange (HIE) is the electronic
movement of health-related information among
organizations according to nationally recognized
standards.
-Health Resources and Services Administration

21. Health Information Exchange
Why early ones failed
• Funded with temporary investments and grant support
• Health systems perceive risk > benefit
• Old habits

22. nomatterwheremovie.com

23. Semantic Medline
• Typical search technologies rely on similarity…

24. Semantic Medline
• …not meaning.

25. Semantic Medline
• This isn’t really about whether smoking causes asthma…

26. Semantic Medline
• Literature-based discovery and the problem of “mutually
oblivious literatures”
Medical literature as a potential source of new knowledge.
Swanson DR.
Bull Med Libr Assoc. 1990 Jan;78(1):29-37.
PMID: 2403828

28. These literatures never cite each other.

29. Semantic Medline
• SemRep extracts meaningful predications
Tamoxifen Breast carcinoma
TREATS
CDKN1A gene Breast carcinoma
ASSOCIATED_WITH
Aromatase Inhibitors Breast carcinoma
TREATS
CDKN1A gene BARD1 gene
STIMULATES

32. Semantic Medline
• Useful for discovering mechanistic links

33. Semantic Medline
Successes so far
• A closed literature-based discovery technique finds a mechanistic
link between hypogonadism and diminished sleep quality in aging
men [Miller et al., Sleep 2012, PMID 22294819]
• Semantic MEDLINE for discovery browsing: using semantic
predications and the literature-based discovery paradigm to
elucidate a mechanism for the obesity paradox [Cairelli et al., AMIA
Annu Symp Proc 2013, PMID 24551329]

34. Try it yourself
• Request a license to access the UMLS Metathesaurus
Browser, Semantic Medline, and more
https://uts.nlm.nih.gov/home.html

35. Data Visualization
• “From data to insight”
TwinList
https://www.youtube.com/watch?v=YXkq9hQppOw

36. Other Topics
• Genomics
• Mathematical Modeling
• NLM Resources
• Clinical Research Informatics
• Big Data and the Cloud
• Consumer Health/Social Media
• Disaster Informatics
• Public Health Informatics
• Telehealth and Imaging
• Organizational Issues

37. Marching Orders
• Natural language
understanding
• Interactive
publications
• Clinical Trials
• Reproducibility of
results

38. Apply now for 2015 sessions
• Spring dates: April 12-18
• Fall dates: September 27 – October 3
• To
apply:http://gru.edu/library/greenblatt/informaticscourse/appl
y.php