CAC with Natural Language Processing (NLP) Solves ICD-10 Conundrum

James M. Maisel, MD

Chairman, ZyDoc

jmaisel@zydoc.com

Paradigm Shift toward Data-Centric Health CareOld Paradigm New Paradigm

Little 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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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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Increasing Incentives for producing richer documentation

• Precision of ICD-10, which necessitates detailed documentation

• Value-based medicine requirements

• Incentives for reporting severity of illness (SOI), present on arrival (POA), PQRS, etc

• Fraud & abuse detection tools getting stronger (esMD)

• RAC audits

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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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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 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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Natural Language ProcessingGenerates structured datafrom unstructured text

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June 14, 2012 Presented by James Maisel, MD2012 NJHIMA Annual Meeting 999

EHR ParadigmDictation Transcription Auto Coding Import to EHR

Current ParadigmPhysician Enters Data in EHR

10 minute

s

2 minute

s

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ICD-10 Extraction from Text with NLP

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Billing Needs

• Thorough coding supports maximal billing

• Coder productivity

• Appropriate coding for correct reimbursement

• Traceable coding

• Reproducible coding

• RAC Audit Risk reduction

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Reducing RAC Audit Risk• FUTURE: Government will audit ALL records using

Natural Language Processing (esMD program)

• Natural Language Processing reduces audit risk

• Thorough coding supports more appropriate billing

• Reproducible coding from source text

• Verifiable coding

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How NLP Can Help (1 of 4)

Documentation Improvement

• Apply NLP to current documentation

• Identify deficiencies in documentation (omissions, lack of specificity)

• Educate caregivers

• Dictation captures more data than standard EHR entry for POA, SOI, $, quality measures, meaningful use, PQRS, reporting, analytics, and better care

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How NLP Can Help (2 of 4)

Coder Productivity

• Apply NLP to narrative or semi-structured documentation

• Enable approximately 20% increase in productivity

• Reduced coding-related overtime payments• Decreased costs to collect and days in accounts

receivable • Improved coder job satisfaction

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How NLP Can Help (3 of 4)

Coder Training

• Code single documents in ICD-9 and ICD-10

• Enable trainees to learn or be tested

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How NLP Can Help (4 of 4)

EHR Preparation

• Generate ICD-10 codes from legacy EHR data• Enable clinical and financial analysis straddling

October 2014

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Secondary Data Use Medical Knowledge Management

• Data automatically extracted from documentation process• Empower more individuals• New applications and capabilities• Better measurement and outcomes• Better outcomes at lower cost

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Secondary Use: Risk Reduction

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Thank YouJames M. Maisel, MD

Founder and Chairman

MediSapien Natural Language Processing

Medical Transcription

Clinical Data

ZyDoc

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