Natural Language Processing An innovative, disruptive technology for ICD-10 Coding, Secondary Data Use,

and EHR Data Capture James M. Maisel, M.D.

Chairman, ZyDoc

jmaisel@zydoc.com

Natural Language ProcessingGenerates structured datafrom unstructured text

June 14, 2012 Presented by James Maisel, MD2012 NJHIMA Annual Meeting 22

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 required

Little 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

s

2 minute

s

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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

Thank YouJames M. Maisel, MD

Founder 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

June 14, 2012 Presented by James Maisel, MD2012 NJHIMA Annual Meeting 1616

Secondary Use: Risk Reduction

June 14, 2012 Presented by James Maisel, MD2012 NJHIMA Annual Meeting 1717