032-Roubort-Advantages and Integration of Multi-vendor LIS Environments by zhangyun

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									Advantages and Integration of
Multi-vendor LIS Environments


                    Pathology Informatics 2010
                    Mark Routbort, MD, PhD
                    University of Texas MD Anderson Cancer Center
                    Houston, Texas




    Disclosures:
    No financial relationships
    Mostly satisfied customer
    Have previously served as a member of IMPAC PowerPath Advisory Board
Anatomy of the laboratory information system




                     General lab
                      resulting
Anatomy of the laboratory information system

                    Inbound integration
                       orders, ADT

                       Phlebotomy/
                    specimen collection

                        General lab
                         resulting

                   Outbound integration
                  EMR, fax/print, outreach

                           Billing
Anatomy of the laboratory information system
                                                       Labs
                    Inbound integration
                       orders, ADT
                                                     Anatomic
                                                     pathology
                       Phlebotomy/
                    specimen collection
                                                Transplant/HLA
                        General lab
                         resulting
                                                    Microbiology

                   Outbound integration
                  EMR, fax/print, outreach   Transfusion

                           Billing                     Cytogenetics

                                             Molecular diagnostics

                                                    Flow cytometry


                                                  Proteomics
Anatomy of the laboratory information system
  Cross-cutting                                              Labs
    features              Inbound integration
                             orders, ADT
    In-lab workflow                                        Anatomic
                                                           pathology
      Digitization           Phlebotomy/
                          specimen collection
                                                      Transplant/HLA
     Data analysis
                              General lab
    Image analysis             resulting
                                                          Microbiology
        QA/QI
                         Outbound integration
                        EMR, fax/print, outreach   Transfusion
   Procedure/EDM

         Rules                   Billing                     Cytogenetics

   Integrated reports
                                                   Molecular diagnostics
     Synoptic data
                         EMR
                                  Radiology               Flow cytometry
  Asset management
                                 Clinical notes
                                                        Proteomics
      Automation
                                  Pharmacy
  The allegorical elephant

• How you define a laboratory
  information system depends to
  some extent on what you are
  trying to do, or what your
  biggest current problems are

• If you want your laboratory
  information system to do “all of
  the above”
    – Very good
    – Very ambitious
A tsunami of clinical diagnostic
and biomedical research data
                                 Example –
                                 Diagnostic bone
                                 marrow biopsy
                          •   Hematologic lab values
                          •   Morphology
                                   •   Clot
                                   •   Core
                                   •   Smears
                          •   Cytochemical/special
                          •   Immunohistochemistry
                          •   Flow cytometry
                          •   Cytogenetics
                          •   Molecular
Dealing with complexity

• Break up problems
  into their constituent
  elements

• Classify and
  subclassify

• Compartmentalize
  and subspecialize
                                            BM report
Test requisition
                                           Mostly hand-
                                          filled, includes
                            Slides from       CBC data
                            hemepath




                                                BM diff

Historical data                               Custom
                   Slides from
                                             application
ClinicStation or    histopath
   PowerPath
                                  Flow
                                 CERNER
However, in support of clinical diagnostic work, data
integration is needed at multiple levels

• Within a single modality over time (historical
  record)

• Across labs for pathologic diagnoses and
  pharmacodiagnostics


• Across the patient record for clinicopathologic
  correlation and optimal diagnostic efficiency
What is an integrated application platform?
• Microsoft Office suite as example

• Consistent “look and feel”
  – From user perspective, ease of use of application is
    enhanced by consistent user interface paradigms
  – From vendor perspective, branding and differentiation
    are considerations as well


• Data communication and updates between
  components
  – Static cut and paste as minimal example
  – Linked objects with dynamic updating
Multi-vendor integration advantages
• Allows a “best of breed” selection process
• Can enable lab-by-lab system upgrades
  – Anatomic versus clinical lab system
  – Transfusion medicine – donor and recipient
• Integration of new or rapidly evolving
  technologies
  – Digital pathology
  – Proteomic/molecular
• Facilitate subspecialty lab data analysis
  – Cytogenetics
  – Flow cytometry
  – Molecular diagnostics
General integration approaches
with multiple systems

•   Cross-system data reports
•   Terminal scripting
•   Health Level 7 interchange
•   XML/Web Services
•   Form based data exporting and importing
•   Application programming interfaces
•   Application integration
    – Simulating a single vendor experience: single sign-on
      and context synchronization
    – Functional integration
Cross system reports

Relational databases enable a granular, extensible
data-centric model of the real world
  Cross system reports




Data from
outside system
(institutional
ADT database)
Terminal scripting
• For terminal/host based LIS integrations
• Programmatically emulate a set of keystrokes
  imitating what a user would do at a terminal
  keyboard
Terminal scripting
Terminal scripting

          Doesn’t have to be (shouldn’t be) “dumb”

• Dumb: timed set of keystrokes played back in
  equal time regardless of host response

• Intelligent
      •   Read host response and react appropriately
      •   Handles branching logic
      •   Handles delays on the part of the host
      •   Handles errors gracefully with logging and alerting
      •   Can abstract data from host windows (“screen scraping”)
Terminal scripting – uses at MD Anderson
• Provide “single sign on” functionality for
  pathologists – lightweight

• Shortcut to flow cytometry test verification
  function for pathologists – lightweight

• Used to automatically update a patient flag in our
  CERNER system based on data from our MAK
  Progesa transfusion medicine system to enable
  intersystem rules based on recent blood typing
  results – much more complex
 MAK Progesa to CERNER Pathnet
 Scripted Updates


• Runs as a
  Windows
  service
   – Unattended
   – Auto start
   – No direct user
     interface
• Incorporates
  logging and
  alerting logic
MAK to CERNER Test Harness
Terminal scripting lessons


• Difficulty of set up is linked to complexity of process being
  automated
   – Branching logic?
   – Errors possible?
   – Interactive or unattended?

• Potentially sensitive to changes in the underlying systems

• Can solve certain problems that can’t be addressed
  effectively in other ways
Information transfer: Health Level 7 (HL7)


  •    Messaging standard for health care inter-systems communication at the
       highest level - application – of the Open Systems Interconnection or OSI
       Model of networking

  •    Founded 1987, versions 2.1, 2.2, 2.3 from 1990-1999, in wide use for
       communicating lab and pathology results (version 2.x)

  •    ANSI standard

 CBC (Supergroup) result message examples - Partial result message

 MSH|^~\&|ESI|LAB|INVISION_PMS|HIS|20050331155000-0600||ORU^R01|2980822|T|2.1
 PID|1||000000000999999|00000|TEST^MICKEY^N||19400313|F||W|||||||UNK|000010501880256|428827901
 PV1|1|O|DICT^DICT|||||||731||||HIS|||0000361^WALTERS, RONALD S. M|R||||||||||||||||||||||||||20050301144200-
 0600|20050402155000-0600
 OBR|1|5500280|01014775200001550550028025032847925032847900000000101|5500312^CBC^COMPLETE
 BLOOD CNT/DIF/PLT|RT|20050331152000-0600|20050331154200-0600|||PCCGS^SO, CELIA
 G.||||20050331154300-0600||0000361^WALTERS, RONALD S.
 M||1||0000509003089|G|||LA|P||^^^200503311520^^RT
 OBX|001|NM|5500009^WBC^WHITE BLOOD CELL COUNT|| 2.4|K/UL| 4.0-
 11.0|L|||F||00000000000000225200|20050331155000.0000-0600|IIM^INSTRUMENT PERFORMED
 ID|PCNDA^ACOSTA, NOEL D.
 OBX|002|NM|5500018^RBC^RED BLOOD CELL COUNT|| 3.03|M/UL| 4.00-
 5.50|L|||F||00000000000000225200|20050331155000.0000-0600|IIM^INSTRUMENT PERFORMED
 ID|PCNDA^ACOSTA, NOEL D.
HL7 version 2.x strengths (weaknesses)

Efficient, well-defined message   Difficult to human-read
model                             Extensions must be through
                                  overloading of fields


Vocabulary independent            Vocabulary independent
Syntactic interoperability        Lack of semantic interoperability


Widely implemented                Widely implemented
Lowest common denominator         Lowest common denominator
Common uses of HL7 to interface lab systems

• ADT interfaces
   – Allow systems to get a direct copy of patient
     demographic data and hospital/outpatient status


• Orders interfaces
   – Allow intersystems direct creation of orders
   – For instance, order entry in the EMR for lab draws with
     transmission to the LIS


• Results interfaces
   – Communication of lab test status and resulting to
     systems connected to the LIS
HL7 between lab information system components

• Can be effective and reliable in the covered domains
   – Uncovered areas of integration out of scope
   – Non-textual data is awkward


• Most common example is incorporation of reference lab
  testing (e.g. Quest Diagnostics, Mayo) into local LIS to
  eliminate manual entry of send-out tests

• Other scenarios are possible but less common
   – Incorporation of lab data stream into pathology system


• HL7 is generally a “push” model for integration
Traditional EMR-centric (push) model for pathology result reporting
HL7-based delivery of pathology reports
converted from editor like Microsoft Word to ASCII


Pathologist
         Self, transcriptionist,
         resident entry
                                        DIAGNOSIS
                    Format conversion
                    to ASCII text        Metastatic adenocarcinoma.



                                                    HL7




      “Native”                               Interface
     pathology                                engine
       report                                                HL7



      Transmission of complex data over                            HIS Viewer
      HL7 generally requires transformation
      (parsing) to ASCII text                                             Custom    Clinician
                                                                          display
                                                                           logic
Report as seen by pathologist
         Report parsed into HL7 and received by the HIS/EMR
Integrity of semantic content is at risk in any transformation process
Push model generally means multiple copies

       Should everyone have their own copy of the data?

• Complexity of the message processing

• Maintenance of the data model

• Maintenance and stewardship of the data,
  including compliance issues

• Multiple potential conflicting sources of truth
An alternative – Service-oriented architecture
  •   A perspective of software architecture that defines the use of services to
      support the requirements of software users.

  •   In SOA, resources such as lab data are made available as independent
      services that can be accessed without knowledge of their underlying
      platform implementation

  •   While SOA does not dictate a specific implementation framework (e.g.
      CORBA, RPC, DCOM, Web Services), Web Services as the
      implementational strategy leverages W3C standards along with
      corresponding deep penetrance of description, analysis, and
      transformational tools

  •   Key features of the SOA/Web Services perspective
       – Schema and documentation is instrinsic to, not extrinisic from, service definition
         (WSDL – web service description language)
       – Schema and data are XSD/XML
       – WSDL permits the automated generation of platform specific proxy classes for
         consuming systems



 Ref: http://en.wikipedia.org/wiki/Service-oriented_architecture
XML


•   eXtended Markup
    Language

•   W3C specification for
    data modeling

•   Human and machine
    readable

•   Self-describing
SPiDR at MD Anderson –
Shared pathology information data repository


 • Middleware service for querying of path & lab data
 • Implementation:
     – HL7 listeners -> population of relational database with normalized
       model of laboratory data
     – For some systems (APLIS - PowerPath), direct database replication
       with implementation of text-indexes for case finding
     – Multiple back-end databases running on multiple servers
          • Supports multiple internal database models integrating data sources over
            time
          • Multiple mirrored servers allows the same data to be queried transactionally
            (get me all the lab data on patient X) or analytically (find me all the patients
            with recent diagnoses of chronic myelogenous leukemia with bcr/abl
            translocation loads above X) without risking transactional performance
 • Web services interface
     – Annotated, streamlined XML schema for LabData
     – Leverages W3C standards
Internal data model


 • Fully relational

 • Process-aware

 • Temporal

 • Multiple data sources; multiple databases
•   Internal data model is complex, normalized, and may vary according to source system
•   Includes temporal elements to support point-in-time state reconstruction (regulatory)
•   Much more complexity than most consumers need!
External (service) model

• Service – oriented question:
   – What are the lab results?


• External model for consumers
   – State but not process aware
   – Significant denormalization to facilitate comprehensibility and
     broad applicability
       • For instance, patient demographic data is represented at the test level
   Service model of lab data
• Tests
   – A lab test, which may be in varying stages of completion (status), and which
     may or may not have associated granular result details (TestDetail) or
     additional metadata about the test itself (TestInfo)
   – Examples: Complete blood count, GI panel, PSA
   – Lab tests include information about the entity on which they were performed -
     generally, a patient - which represents a flattening of the typical HL7 hierarchy
• TestDetails
   – TestDetails are granular data elements representing specific result components
     for a Test
   – Examples: Hematocrit (within CBC test), bilirubin (within GI panel), PSA level
     (within PSA test).
• TestInfo
   – A collection of information about the test itself which does not readily fit into a
     flat Test structure
   – Examples: General result level comments not associated with a specific
     TestDetail, cancellation or other process explanations, order level comments.
Demonstration
Data export and import strategies
• XML is powerful but not often the starting point for non-
  relational data

• How to better get specialty lab diagnostic data in to the
  LIS?
   – Flow cytometry, molecular diagnostics, cytogenetics
       • All share fairly complex workflows (non-linear) and have a high
         degree of dependence on non-integrated analysis tools
       • Data points transcribed in lab from different analysis packages into
         LIS
       • Domain data model is volatile and different than LIS data model
   – It is common for these labs to use worksheets or specialized data
     analysis packages to create summary data reports, which are
     subsequently manually transcribed into the LIS and stored as
     paper support documents
Getting the data in:
Flow cytometric analysis

•   Problems
     – Multiple data analysis packages are required by lab…
       CellQuest, FloJo, Excel, Diva, etc.
     – LIS not designed, nor should it be, for raw list-mode
       data or complex analysis
     – This dichotomy results in separation of the original
       diagnostic data from the LIS and cumbersome and
       error prone transcription from the analysis data to the
       LIS

•   Conclusions
     – Even if acquisition and analysis resides outside the
       LIS, there should be automatic import of both the
       original analysis results and the structured data from
       the analysis
     – The LIS should be the place where the data comes
       together
Sample CellQuest analysis




Multidimensional
scattergrams
Sample CellQuest analysis




Summary front sheet
Steps

• Define a schema for diagnostic flow cytometric analysis
  data

• Define a web service/WSDL (and get our LIS vendor to
  implement it!) for automatic data import using this schema

• Develop an import tool
   – Reading raw PDF files to extract data elements
   – Transformation into schema compliant XML
   – Use web service to import analysis XML as well as an ectronic
     copy of the visual data
FlowAnalysis schema
FlowAnalysis schema
The import tool:
The import tool:
The end result in the LIS:
Pre-vendor integration – electronic flow PDFs
to replace paper printouts
Application programming interfaces (APIs)
• An interface implemented by a software program that
  enables it to interact with other software

• Functional integration is enabled by APIs

• Ideally, well-documented; publicly available

• Can be an extremely powerful paradigm

• It is also possible to create “wrapper” interfaces that use
  techniques such as Windows automation to simulate a
  native API
   – E.g. “LaunchApplication”, “LoginUser”, “OpenCase”
Use of APIs to incorporate digital images
and digital slides in a simple viewer
Application integration



• Simulating a single vendor experience: single sign-on and
  context synchronization

• Functional integration
   – Bar coding support cross-application
   – Automatic initiation of common tasks
      • Accessioning a case
      • Starting a dictation


• Functional build-out
PathStation at MD Anderson –
An enterprise application integration
engine for the laboratorian/pathologist
Design considerations for a unified
multi-vendor environment

• Single sign on for every application
• Intelligent context synchronization
• Use of bar codes to drive workflow in a
  user/station appropriate manner
• Integration with both internal applications
  (CERNER, PowerPath, dictation/transcription,
  Aperio) and external (EMR)
• Platform for functional expansion
Brief demonstration
Conclusions
• Multiple vendor based systems can
  present a relatively integrated end user
  experience if appropriately connected

• This approach can provide some of the
  benefits of incremental or best-of-breed
  implementations with the benefit of a
  unified application

• A robust tool set is needed

• There are many middleware providers,
  developers, and automation toolkits
  available in the marketplace in support

• Don’t take no for an answer – if it seems
  like it should be doable, it almost
  certainly is
Acknowledgements


• Shibu Ninan – PathStation lead developer


• Leslie Nesbitt – project manager


• Trey Elliot, Sanjivkumar Dave, Cathy Price,
  Mohammed Gomah, James Fleming-
  SPiDR


• Mike Riben – Medical Director, Path
  Informatics

								
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