1. 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

2. Natural Language Processing Generates structured data from unstructured text June 14, 2012 Presented by James Maisel, MD 2012 NJHIMA Annual Meeting 2 2

3. NLP Generates ICD-10 3

4. 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 4

5. 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 5

6. EHR Paradigm Dictation  Transcription  Auto Coding  Import to EHR Current Paradigm Physician Enters Data in EHR 10 minutes 2 minutes 6

7. NLP Systems Perform 3 Functions Capturing Data Structuring Data Facilitating Exchange of Data 7

8. NLP Enables Coordination of Care Data currently in silos in various formats NLP systems create a consolidated record Providers access the record through an HIE and address issues holistically & efficiently 8

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

10. Thank You James M. Maisel, MD Founder and Chairman MediSapien Natural Language Processing Medical Transcription Clinical Data ZyDoc 10

11. 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? 11

12. 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 12

13. Problem: No additional time to produce richer documentation Dictation & Natural Language Processing Produce richer documentation with more structured data in same amount of time 13

14. 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 14

15. 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 15

16. 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, MD 2012 NJHIMA Annual Meeting 16

17. Secondary Use: Risk Reduction June 14, 2012 Presented by James Maisel, MD 2012 NJHIMA Annual Meeting 17