James M. Maisel, M.D. Chairman, ZyDoc jmaisel@zydoc
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Transcript of James M. Maisel, M.D. Chairman, ZyDoc jmaisel@zydoc
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Natural Language Processing An innovative, disruptive technology for ICD-10 Coding, Secondary Data Use,
and EHR Data Capture James M. Maisel, M.D.
Chairman, [email protected]
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Natural Language ProcessingGenerates structured datafrom unstructured text
June 14, 2012 Presented by James Maisel, MD2012 NJHIMA Annual Meeting 22
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NLP Generates ICD-10
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Paradigm Shift toward Data-Centric Health Care
Old Paradigm New ParadigmLittle coded data required Large amount of coded data requiredLittle detail required in documentation
Increasingly granular documentation required
Coding personnel responsible for billing only
Coding personnel responsible for billing, documentation quality, and data for secondary use
Minimal structured data entered manually into EHR by physician
Rich structured data captured using dictation with natural language processing and edited by coders
Manual coding with “lookup” software
EHR, CAC or Natural language processing and automated coding necessary
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NLP as a part of a Billing Solution• Empowers better documentation with dictation
allowing full charge capture
• Faster, more accurate, more reliable, more thorough than manual coding alone
• Works for both in-patient and ambulatory records for all specialties
• ICD-10 capability
• Effective educational platform
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EHR ParadigmDictation Transcription Auto Coding Import to EHR
Current ParadigmPhysician Enters Data in EHR
10 minute
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2 minute
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NLP Systems Perform 3 Functions
Capturing Data
Structuring Data
Facilitating Exchange of Data
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NLP Enables Coordination of Care
Data currently in silosin various formats
NLP systems create aconsolidated record
Providers access the record through an HIEand address issues holistically & efficiently
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June 14, 2012 Presented by James Maisel, MD2012 NJHIMA Annual Meeting 99
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Thank YouJames M. Maisel, MDFounder and Chairman
MediSapien Natural Language Processing
Medical Transcription
Clinical Data
ZyDoc
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The ICD-10 Challenge
S82.51Displaced fracture of medial malleolus of right tibiaS82.51XA…… initial encounter for closed fractureS82.51XB…… initial encounter for open fracture type I or IIS82.51XC…… initial encounter for open fracture type IIIA, IIIB, or IIICS82.51XD…… subsequent encounter for closed fracture with routine healingS82.51XE…… subsequent encounter for open fracture type I or II with routine healingS82.51XF…… subsequent encounter for open fracture type IIIA, IIIB, or IIIC with routine healingS82.51XG…… subsequent encounter for closed fracture with delayed healingS82.51XH…… subsequent encounter for open fracture type I or II with delayed healingS82.51XJ…… subsequent encounter for open fracture type IIIA, IIIB, or IIIC with delayed healingS82.51XK…… subsequent encounter for closed fracture with nonunionS82.51XM…… subsequent encounter for open fracture type I or II with nonunionS82.51XN…… subsequent encounter for open fracture type IIIA, IIIB, or IIIC with nonunionS82.51XP…… subsequent encounter for closed fracture with malunionS82.51XQ…… subsequent encounter for open fracture type I or II with malunionS82.51XR…… subsequent encounter for open fracture type IIIA, IIIB, or IIIC with malunionS82.51XS…… sequela
How to select the correct fracture from a drop-down menu?
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ICD-10 Conundrum• Challenges
• Greater documentation needs• Training requirements for 155,000 ICD-10 codes • Temporary loss in productivity• Dual data storage systems during implementation
• Boon • Increased reimbursements• >POA, >SOI
• Bust • Denials
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Problem: No additional time toproduce richer documentation
Dictation & Natural Language Processing
Produce richer documentation with more structured data in same amount of time
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NLP Systems Can Accept• Dictated, transcribed, voice-recognized, or scanned
patient encounter notes regardless of source
• Semi-structured patient data from any ONC-certified EHR
NLP Systems Can Output• Fully coded structured data that can be shared
cross-platform • e.g. in HL7 Level 3 CDA R2 documents
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Benefits of Improved Coordination of Care
• Avoid unnecessary tests and/or adverse drug reactions• Reduce preventable hospital admissions or readmissions• Enable informed treatment plans for better health
outcomes• Enable reporting and tracking for quality measurement and
audit functionality• Increased efficiency in gathering correct documentation
more time for patient care and education
• Especially for patients with multiple physicians • i.e. patients with chronic conditions or multi-systemic
diseases
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Secondary Use: 2010 Death Rate US• Heart disease: 616,067• Cancer: 562,875• Stroke : 135,952• Chronic lower respiratory diseases: 127,924• Accidents :123,706• Alzheimer's: 74,632• Diabetes: 71,382• Influenza and Pneumonia: 52,717
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Secondary Use: Risk Reduction
June 14, 2012 Presented by James Maisel, MD2012 NJHIMA Annual Meeting 1717