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Natural Language Processing (NLP)

M-Scribe uses converting narrative dictation to clinically relevant medical data utilizing natural language processing (NLP) technology. Narrative reports have long been the de facto standard in clinical documentation as it is a clinician’s most natural and efficient form of information capture. natural language processing (NLP) technology bridges the gap between narrative reports and discrete data by delivering contextually structured XML data with SNOMED codified concepts. This breakthrough in technology enables a range of enhancements for data capture, analytics, reporting and research never before possible from narrative notes.

  • Imports text files from any transcription provider, EMR system or other source
  • Populates EMR systems with discrete, reportable data
  • Supports Meaningful Use with information contained in narrative notes
  • Assigns SNOMED codes that map to ICD-9, ICD-10, CPT and other coding standards
  • Provides data for analytics, reporting, alerting and clinical research
  • Structures data with XML to support interoperability standards 

Applications of Natural Language Processing (NLP)

  • Discrete Reportable Transcription (DRT): Converts dictated/transcribed documents into discrete data elements and narrative sections and feeds them into the appropriate placeholders inside an EMR (delivered as XML data using web services)
  • Meaningful Use: Supports reporting compliance for most requirements:
    • Quality Measures
    • Decision Support
    • Clinical Summaries
    • Medication Lists
    • Allergy Lists
    • Problem Lists
    • Vital Signs Tracking
    • Demographics
    • Smoking Status
    • Patients Lists by Condition
    • Medication Reconciliation
    • Patient Summary
    • Electronic Syndromic Surveillance Data
  • Patient Referral Tracking: Captures patient referral statistics to aid administrative controls for tracking lost revenue caused by patient referrals going outside the health system
  • Coding Audit Alerting: Identifies cases for coding auditors or CDI specialists that present as high risk for improper billing levels due to incomplete information documented
  • Core Measures: Identifies adherence to Core Measures treatment standards and extracts data to drive statistics for Core Measure reporting
  • Physcian Quality Reporting System (PQRS) Extracts data from broad sets of narrative notes to compile statistics for any or all of the measures of the Physician Quality and Reporting System
  • Nursing Quality Indicators: Depending on documentation methods, may support automation for data extraction and compilation for nursing quality indicators and reporting to the NDNQI registry
  • Clinical Decision Support: Extracts patient data from clinical notes with rules-based analytics to enable more effective use of CDS systems
  • College of American Pathologists (CAPS) Checklist: Provides required XML structured data for populating pathology registries compliant with the CAP protocol checklists
  • Risk Management support: Identifies conditions such as Present on Admission (POA) or adverse effects which trigger notification alerts to Risk Managers or Case Managers
  • RAC Audit support: Rules-based queries from the narrative notes in medical records deliver data to automate the audit process
  • Accountable Care: Compiles statistical data found in clinical notes required for Accountable Care Organizations (ACOs) reporting
  • Research/Secondary Use: Aggregates anonymized data for warehousing and enables complex queries for Clinical Trials, Comparative Effectiveness and other medical research
  • Data Conversion: With Optical Character Recognition (OCR) technology enables scanned records to be structured as searchable historical data instead of mere PDF image attachments

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Computer Assisted Coding (CAC) >>