Challenges of Integrating a Laboratory System with Clinical Computing System in a Health Care Enterp

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Challenges of Integrating a Laboratory System
with Clinical Computing System in a Health Care
Enterprise

• Satellite Meeting 8
• Focus on Information Technology for Laboratory
  Medicine Bologna, Italy
• REED M. GARDNER, Ph.D.LDS Hospital University
  of Utah
• Reed.Gardner_at_hsc.utah.edu
• Saturday 5 June 1999 1800 to 1830

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• When it comes to gathering information, medicine
  has not evolved much from the hunting and
  gathering stages of social evolution.
• Clement J. McDonald, MD
• June 1994

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Transcribed Dictation
ICU/SURGERY
Nurse Care Plans
Pulse Oximeters
Infectious Disease
Nursing Procedure Charting
Ventilators
KNOWLEDGE DATABASE
Pharmacy
Mixed Venous Saturations
IV Pumps
ECG Lab
DECISION MAKING PROCESSOR, DATA AND TIME DRIVER
MIB Data
Bedside Monitors
INTEGRATED CLINICAL DATA BASE
Surgery Anesthesia Charting
Physiologic Data
X-Ray
Laboratory
Blood Gas Lab
Data Review Alerts Computations Interpretations Pr
otocols
Pathology
Surgery Schedule
Blood Bank
Admitting
Catherization
Medical Records
Respiratory Therapy
OUTPUT
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Healthcare Enterprise Management System
Int er f ace Eng i ne
Clinics Home Health
gt
Case Management Disease Management Protocol
Management
DD Mapp i ng
Clinical Decision Support
Primary Care Providers
Clinical Data Repository
gt
Clinical Workstation
HIS System
Ancillary Systems
Outcomes Measurement CQI Studies Research
SQL Reporting
gt
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Healthcare Data Dictionary
A database whose content establishes the clear
definition of each data element and its
significance to the management of care
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Healthcare Data DictionaryCharacteristics

• Provides a road map to the content and structure
  of the patient database
• Supports data exchange between independent
  computer systems
• Supports the encoding of data to remove the
  ambiguities that may exist in natural language
• Cross-references data concepts with nationally
  internationally accepted coding schema

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Characteristics of a Good Computer
VocabularyJames J. Cimino, M.D., Columbia
University

• Comprehensive content
• Concept based
• Concept permanence
• Synonyms, homonyms
• Meaningless identifiers
• Multiple hierarchies
• Formal definitions
• Aggregation-decomposition
• Multilingual

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Instantiators
Database
(
NLP Transcription Structured Interfaces Instrum
ent Interfaces
Events
Information Model
Controlled Medical Vocabulary
Knowledge Base
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Why Use Encoded Data?

• Execution of decision logic
• Flexible data entry and reporting
• Data analysis and query
• Clinical research
• Administrative research
• Many Computerized Patient Records are now only
  scanned image files, as a result you
• Cant do logic with these
• Nor search within them

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Common Test Identifiers

• Not a usual practice with stand alone computer
  systems
• When adding a new test easiest to just assign a
  new code and not consider older codes
• Crucial for computerized decision-support and
  longitudinal record keeping
• Recent Salt Lake City experience had over 5,600
  test identifiers for just 4,500 tests

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Why use HL7 Vocabulary?

• Decrease time and cost of implementations
• Approaches plug-and-play
• Enable data sharing
• Mergers due to managed care
• Regional, national international clinical
  studies
• Disease prevention and control
• Enable sharing of decision support modules
• Alerts
• Protocols
• Clinical pathways

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LOINC

• Name Logical Observation Identifier Names and
  Codes
• Organization LOINC Committee
• Purpose Names for observations in HL7 messages
• Content Lab procedures, clinical measurements
• Information
• http//www.mcis.duke.edu/standards/termcode/loinc.
  htm

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Guidelines for HL7 Vocabulary

• compliant with the HL7 message structure
• contributed to the UMLS Metathesaurus
• organization committed to timely maintenance
• comprehensive for the intended domain of use
• oversight by nonprofit health care professional
  organization (preferred)
• properly versioned (concept identifiers do not
  change meaning between versions)
• willing to accept new terms from HL7

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LOINC Usage Example
OBRA000520LYTESSerum Electrolytes OBX1NM
2951-2 Serum Sodium1138mmol/L
LOINC (Logical Observation Identifier Names and
Codes) 2948-8 SODIUM SCNC PT CSF
QN 2949-6 SODIUM SCNC PT DIAF
QN 2950-4 SODIUM SCNC PT FLU
QN 2951-2 SODIUM SCNC PT SER QN
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Uniform Patient Identifiers

• Currently NO national standard is USA
• Up to 40 of computerized enterprise patient
  records may be duplicates
• Longitudinal record systems require excellent
  patient identifiers
• Maintaining clean is a difficult process
• HIPAA legislation mandates in USA

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Interfaces
Admit Discharge Transfer Orders
Enterprise System
Lab System
Results
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College of American Pathologists Q-Probe

• Median Downtime 28.7 min/day 98.1
  uptime
• Some (10 of labs) uptime of only 89.2
• More complex systems serving 2 or more sites were
  more likely to have unscheduled downtime
• Arch Pathol Lab Med 1996120626-632

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Think out of the box

• THOUGHTS OPINIONS
• 1. Computer and communications technology is
  advancing rapidly
• 2. Moores Law tells us that in 18 months
  we will have 2 times greater compute capability
  for the same cost
• 3. We are rate limited by creative ideas and
  organizational inertia

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Laboratory Pathology Opportunities

• 1. Must think beyond their boundaries
• 2. Must help erase boundaries
• 3. Have opportunity knowledge to lead
• 4. Must put the individual patient at the top
  of their priority
• 5. Have much to gain from development of
  integrated longitudinal record systems