1. CAC with Natural Language Processing (NLP) Solves ICD-10 Conundrum James M. Maisel, MD Chairman, ZyDoc jmaisel@zydoc.com

2. Paradigm Shift toward Data-Centric Health Care Old ParadigmNew Paradigm Little coded data requiredLarge 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 2

3. 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 3

4. 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 4

5. The ICD-10 Challenge S82.51Displaced fracture of medial malleolus of right tibia S82.51XA…… initial encounter for closed fracture S82.51XB…… initial encounter for open fracture type I or II S82.51XC…… initial encounter for open fracture type IIIA, IIIB, or IIIC S82.51XD…… subsequent encounter for closed fracture with routine healing S82.51XE…… subsequent encounter for open fracture type I or II with routine healing S82.51XF…… subsequent encounter for open fracture type IIIA, IIIB, or IIIC with routine healing S82.51XG…… subsequent encounter for closed fracture with delayed healing S82.51XH…… subsequent encounter for open fracture type I or II with delayed healing S82.51XJ…… subsequent encounter for open fracture type IIIA, IIIB, or IIIC with delayed healing S82.51XK…… subsequent encounter for closed fracture with nonunion S82.51XM…… subsequent encounter for open fracture type I or II with nonunion S82.51XN…… subsequent encounter for open fracture type IIIA, IIIB, or IIIC with nonunion S82.51XP…… subsequent encounter for closed fracture with malunion S82.51XQ…… subsequent encounter for open fracture type I or II with malunion S82.51XR…… subsequent encounter for open fracture type IIIA, IIIB, or IIIC with malunion S82.51XS…… sequela How to select the correct fracture from a drop-down menu? 5

6. Problem: No additional time to produce richer documentation Dictation & Natural Language Processing Produce richer documentation with more structured data in same amount of time 6

7. 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 7

8. Natural Language Processing Generates structured data from unstructured text 8 8

9. June 14, 2012 Presented by James Maisel, MD 2012 NJHIMA Annual Meeting 9 9 9

10. EHR Paradigm Dictation  Transcription  Auto Coding  Import to EHR Current Paradigm Physician Enters Data in EHR 10 minutes 2 minutes 10

11. ICD-10 Extraction from Text with NLP 11

12. Billing Needs Thorough coding supports maximal billing Coder productivity Appropriate coding for correct reimbursement Traceable coding Reproducible coding RAC Audit Risk reduction 12

13. 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 13

14. 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 14

15. 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 15

16. 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 16

17. 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 17

18. 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 18

19. Secondary Use: Risk Reduction 19

20. Thank You James M. Maisel, MD Founder and Chairman MediSapien Natural Language Processing Medical Transcription Clinical Data ZyDoc 20