Standards

1
Standards Vocabulary
2
Standards in Informatics

• Why use standards?
• Exchange data and share information efficiently
  (HIE within RHIOs/NHIN)
• Correctly interpret data (Males1 or 0)
• Improve data quality (Allows for easy grouping)
• Collaboration opportunities (Multi-state
  outbreaks)
• Handle more complex information (Laboratory
  reports)

3
Standards in Informatics

• Everyone benefits from a common approach to
  representing and exchanging public health data
• Those who collect it from outside sources
• Those who enter it into electronic format
• Those who analyze it
• Those who verify the findings
• Those that communicate the information for public
  health interventions

4
A Figure of Speech

• Conversation
• Vocabulary
• The words you choose to use
• Content standards
• Grammar
• The way you put the words together
• Format standards
• Context
• The environment where you have the conversation
• Software, hardware, and resources required for
  data exchange

5
Vocabulary

• Big tables of codes that describe things
• Numbers as county codes (FIPS)
• Reportable diseases as numbered codes
• ICD-9, ICD-9 CM, ICD-10 codes for underlying
  cause of death
• Vocabularies can be
• Locally-defined vs universally-defined
• Lumpers vs splitters

6
Vocabulary Examples

• LOINC-Logical Observation Identifiers Names and
  Codes (www.regenstrief.org/loinc)
• Developed for billing purposes in early 1990s
• Gathers concepts into a single code
• 13951-9 Serum EIA for Hep A Antibody
• PHIN standard for reportable disease test
  requests
• Codes not assigned in systematic or hierarchical
  way the actual number is meaningless

7
Vocabulary Examples

• SNOMED CT-Systematized Nomenclature of Medicine
  Clinical Terms (www.snomed.org)
• Pathologist developed mid 1960s
• Teases out concepts into atomic elements
• Enzyme immunoassay, Serum, Hepatitis A
  Virus, Antibody
• PHIN standard for reportable disease test results

8
Vocabulary Examples - UMLS

• UMLS - Unified Medical Language System
  (1994-present) (http//www.nlm.nih.gov/research/um
  ls/)
• Goal To integrate systems by allowing the
  mapping of concepts to different standardized
  vocabularies and the development of vocabularies
  in biomedicine and health that have not been
  previously developed

9
Vocabulary Examples - UMLS

• Metathesaurus - Very large, multi-purpose, and
  multi-lingual vocabulary database that links all
  included vocabularies
• Lexicon A dictionary-like database organized by
  concept or meaning with attributes that help to
  define its meaning
• Semantic Network A database of biomedical and
  health related concepts, their various names, and
  the relationships among them

10
Grammar

• The way you put words together is important
• The disinfectant is contaminated with blood
• The blood is contaminated with disinfectant
• Critical in Natural Language Processing (NLP)
• Can be
• Storage (database) or Message (transmissions from
  one database to another over a network)
• Flat files or Relational databases

11
Grammar Examples

• Storage comma-delimited, tab-delimited, MS
  Access, SAS
• Message HL7-Health Level Seven (www.hl7.org)
• Clinical and administrative data
• Standardizes format and protocol
• Defines the sequence in the message for data
  elements as well as the data type
• Currently implemented in immunization and cancer
  registries, emergency department reporting, and
  lab reporting

12
Grammar Example

• HL7 allows multiple patients per message,
  multiple orders per patient, multiple tests per
  order, multiple results per test, etc.
• MSH\LABMED-SOUTHWEST68D089677CLIA
• PID178893565DOEJOHN490 Elm StPhoenixAZ
• OBR105099409000220738STD SCREENL
• OBX1CE5292-8RPR-SYPHILISLNG-A200POSITIVES
  NM
• OBX2CE6487-3GONNORRHEA ANTIGENLNG-A201NEGA
  TIVE
• OBX3CE14468-3CHLAMYDIA ANTIGENLNG-A201NEGA
  TIVE

13
Context

• Along with the vocabulary and format, there needs
  to be an infrastructure to support the data
  exchange
• Data model
• Communication (business rules, protocols, etc.)
• Security software
• Support staff
• Standard approaches to implementation of
  interoperable systems are needed

14
Context Example

• What it the NHIN?

15
Nationwide Health Information Network

• Nationwide system
• Interoperable Exchange of Data, Information,
  Knowledge (ultimately)
• Secure
• Containing multiple Regional Health Information
  Organizations (RHIOs)

16
NHIN - RHIOs

• RHIOs at the regional / local level
• Provide
• Leadership
• Oversight
• Governance
• Funding Coordination

17
NHIN

• Overall Issues that must be (are) being
  addressed
• Organization and Business Framework
• Privacy and Security
• Legal Issues
• Management and Operational Considerations
• Standards and Policies to Achieve
  Interoperability

18
Privacy, Security Confidentiality
19
Background

• Technology versus people/practices?
• Technology
• Always a moving target
• Excellent track record overall
• People
• Human errors in judgment
• Social engineering
• Lack of standardization in laws across states
• Good record in public health

20
NHIN Privacy Security Project

• AHRQ, ONC (HIT) collaborative initiative with 34
  states/territories
• Goals
• Identify both best practices and challenges
• Develop consensus-based solutions for
  interoperable electronic health information
  exchange (HIE) that protect the privacy and
  security of health information, and
• Develop detailed implementation plans to
  implement solutions.
• Nationwide report due early summer 2007

21
HIPAA

• Health Insurance Portability and Accountability
  Act (1996) (www.hipaa.org)
• Improved efficiency and effectiveness of
  healthcare through standardization of shared
  electronic information including financial and
  administrative data
• Improved privacy of personal information
• Public health exemption

22
HIPAA

• Two rules
• Security rule
• Need for hardware security, access security, and
  transmission security
• Privacy rule
• Need for de-identified data to provide for
  patients privacy of personal information

23
HIPAA

• Personal identifiers that cannot be included in
  shared data
• Names, address, zip codes
• Telephone numbers, Fax numbers, Email addresses
• Birth date, admission date, discharge date, date
  of death, age
• Social security numbers
• Medical record numbers, Health plan beneficiary
  numbers
• Account numbers, Certificate/license numbers
• License plate numbers
• Device identifiers and serial numbers
• Web URL, IP address numbers
• Biometric identifiers
• Full face photographic images
• Any other unique identifying number,
  characteristic, or code