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  • 1. Software Engineering of NLP-Based Computer Assisted Coding Applications Mark Morsch, MS; Carol Stoyla, BS,CLA; Ronald Sheffer, Jr., MA; Brian Potter, PhD A-Life Medical, Inc. – San Diego, CA
  • 2. Presentation Overview
    • Introduction and Motivations
    • Background
      • What is CMM (or CMMI)?
      • Can CMM be applied to NLP Software Development?
    • Development Process
      • Key Practice Areas
      • Example Development Schedule
    • Testing Model for NLP-based CAC Software
    • NLP and Scalability
    • Conclusion – Focus on Results
  • 3. Introduction – NLP and CAC
    • Natural Language Processing (NLP) software applications “read” physician notes and extract facts for coding
    • NLP for CAC requires electronic text – human transcription or speech recognition, OCR of typed text also possible
    • Primary applications today – CPT and ICD-9 coding for certain outpatient specialties, for example
      • Radiology -> it’s the biggest
      • Emergency Medicine -> includes E/M coding
      • Pathology
    • Emerging applications
      • Inpatient coding -> work with current workflow, tools?
      • Quality measures -> JCAHO, CMS, etc.
      • Outcomes analysis
  • 4. Motivations
    • Why do we want structured processes for NLP software development?
      • Users should be confident in the results
      • Over time, CAC software should consistently improve
      • Medical coding is difficult and constantly changing, and the amount of prerequisite knowledge is massive
    • What is (medical coding) or (NLP software development) like?
      • “ I see mysteries and complications wherever I look, and I have never met a steadily logical person.” - Martha Gelhorn
    • Structured processes bring order and logic to development
      • Deliver updates on schedule
      • More confidence that what is promised can be delivered
      • Verify that the application works as intended
  • 5. Capability Maturity Model (CMM)
    • Using a ranking system, measures the maturity of an organization’s software development processes
      • Developed by the Software Engineering Institute (SEI) at Carnegie Mellon University
      • Defines best practices for organization involved in product development
      • Started in the 1980s to assess the capability of government contractors
      • Originally published in 1989, updates halted in 1997 in favor of CMMI
    • Capability Maturity Model Integration (CMMI) is the successor to CMM
      • Integrates models from various disciplines – software development, systems engineering, integrated product development and software acquisition
      • Better fit to iterative development methods, versus the traditional waterfall approach
  • 6. Software Hall of Shame (Source – Charette, Robert. Why Software Fails. IEEE Spectrum , September 2005)
  • 7. CMMI Overview
    • Level 1: Initial – Ad hoc processes, results are unpredictable and primarily driven by the skill of the team
    • Level 2: Managed – Core software development activities followed primarily at the project level
    • Level 3: Defined – Development activities are implemented and managed across multiple projects, performance improved through training, verification & validation and integrated project management
    • Level 4: Quantitatively Managed – Measures of business results such as cost, quality and timeliness utilized to improve organization performance, statistical quality control
    • Level 5: Optimized – Continuous, quantitative and proactive process improvement allowing an organization to learn, adapt and improve
  • 8. Applying CMM
    • NLP, like other Artificial Intelligence (AI) software, is often not developed following a software development process
      • Input requirements very difficult to fully specify
      • Complex algorithms require special knowledge
      • Development is often evolutionary or experimental
      • Individuals in NLP development often do not have experience in software engineering
    • Criticisms of CMM
      • Emphasis of process over the individual
      • Lack of emphasis on innovation
      • Emphasis on activities over results
  • 9. Development Process
    • Performance is consistent and continuously improving over time
    • Foster innovative thinking
    • Robust testing model for measuring results
    • Combine efforts of three skill areas:
      • Computer science
      • Linguistics
      • Medical coding
  • 10. Five Practice Areas
    • Requirements Management
    • Rapid Development Cycle
    • Verification and Validation
    • Complete Configuration Control
    • Formal Build and Installation Process
  • 11. 1. Requirements Management
    • Using a defect tracking tool, domain experts file bug reports and enhancement requests
    • Example medical documents and details of the desired output
    • Items are assigned a severity and frequency
    • Priority list is defined at the start of each update cycle
  • 12. 2. Rapid Development Cycle
    • Relatively short time lines between the publication date and the implementation date of coding changes
      • 6 to 12 weeks typically
    • Weekly build and unit test cycles
      • Verification of changes taking place with each unit test
    • For new product development, iterations are extended to accommodate more significant development
  • 13. 3. Verification and Validation
    • Verification ensures the changes work correctly
      • With zero or minimal regressions
    • Validation ensures that the right changes have been done
    • Verification is done at both the unit and system testing levels
      • Unit testing is performed at the component level and is executed by the NLP Development team
      • QA team may assist in the analysis of unit test results
    • Independent quality assurance team, separate from the NLP development team, performs system verification and validation
  • 14. 4. Complete Configuration Control
    • All source code and system knowledge base files are maintained within a configuration control system
      • Examples: VSS, CVS, ClearCase
    • Development Lead is responsible for coordinating source code check-in and setting build checkpoints
    • All changes are recorded and documented, and past build configurations can be recovered
  • 15. 5. Formal Build and Installation Process
    • Installation packages are used with written installation instructions
    • Installation package records all component names and version numbers
    • Used to install releases into both the QA and production environments
    • Greatly reduces the likelihood of errors during the installation process
  • 16. Example Development Schedule
    • Phase 1: Requirements Analysis
      • Weeks 1 -2: Bug reports and enhancement requests analyzed and prioritized
    • Phase 2: Development and Unit Testing
      • Weeks 3 – 9: Changes implemented, may overlap with Phase 1 if final code updates are not known
    • Phase 3: System Testing
      • Weeks 10 – 11: System installation package is built and delivered to the QA team
    • Phase 4: Production Deployment and Documentation
      • Week 12: NLP software installed into the production environment
  • 17. Tracking The Process
  • 18. Testing Model
    • For NLP software, it’s difficult to determine the appropriate level of testing
    • Regression Testing
      • Ensures development does not break current behavior
      • Large scale test with a statistically significant sample (5% to 7%) of monthly production data
      • For A-Life in radiology, over 150,000 documents per batch
    • Progression Testing
      • Verifies changes are functioning as expected
      • Scale is much smaller, hundreds of documents
      • Use examples identified by domain experts during Requirements Analysis phase
  • 19. Test Execution and Analysis
    • Automation of execution and analysis is essential to reach this scale
    • Unit Testing Platform
      • Encapsulates the core NLP processing
      • Used by the NLP development team
    • System Testing Platform
      • Copy of the production system, including all pre- and post-processing stages
    • Analysis Platform
      • Scripts compare differences between any two test runs
      • Visual evaluation tool allows coding experts to score each change
  • 20. Coder Change Statistics
  • 21. Analysis Platform
  • 22. NLP and Scalability
    • Even with large-scale testing, NLP software will encounter new or unfamiliar language
    • Two qualities of graceful behavior:
      • Understand more than patterns of words but also model the underlying semantics – use ontologies
      • Detect situations when the content of the document is not adequately recognized by the NLP software
    • Can NLP CAC software scale across medical domains?
      • Most current applications are focused on medical specialties
      • Verification and validation to address an even larger scale
      • Tighter focus on a single coding system, such as ICD-9, that can be validated in narrower context
  • 23. Conclusion – Focus on Results
    • Structured software development works for NLP
      • Gives confidence to both the developer and user
      • Iterative, results-driven approach is best
    • CAC application require means of verification that are transparent, repeatable and scalable
    • Onus on NLP software developers to verify performance on a large scale
    • Acceptable performance is difficult to quantify
    • Ongoing work - Define a verification process applicable across multiple medical domains
  • 24. More Information
    • Mark Morsch, VP NLP/Software Engineering, [email_address]
    • CMMI Web Site - http://www.sei.cmu.edu/cmmi/
    • Why Software Fails. IEEE Spectrum, Sept 2005 - http://www.spectrum.ieee.org/sep05/1685
    • LifeCode ® NLP technology - http:// www.alifemedical.com/documents/LifeCodeAIMagazine.pdf