NLP as a Data Integration Tool for Your CIO James M. Maisel, MD Chairman, ZyDoc

Overview Business Needs Billing EHR Meaningful Use Reporting & Analytics Interoperability The NLP Solution Important NLP Technical Attributes 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 3

NLP Can Generate: For Billing CPT-4® – Procedures ICD-9-CM – Diagnoses ICD-10-CM/PCS – Diagnoses/Procedures RxNorm - Medications Beyond Billing SNOMED-CT® – Clinical Codes & Section Headers LOINC® – Laboratory Results RxNorm – Medications 4

How is NLP the Solution? Billing EHR Meaningful Use Interoperability Reporting & Analytics 5

Natural Language Processing Generates structured data from unstructured text 6

How Natural Language Processing Works Automatic generation of codes from unstructured text, semi-structured EHR data, or structured EHR data. Can generate very specific codes = Accuracy 7

June 14, 2012 Presented by James Maisel, MD 2012 NJHIMA Annual Meeting 8 8

NLP Producing SNOMED-CT 9

NLP Producing ICD-10-CM 10

NLP as a part of a Billing Solution Faster, more accurate, more reliable, more thorough than manual coding alone Works for both in-patient and ambulatory records ICD-10 capability Effective educational platform 11

NLP as part of a Billing Solution (cont.) (Reducing RAC Audit Risk) Traceable coding Reproducible coding Thorough coding supports more appropriate billing 12

Billing Needs (ICD-9, CPT-4, RxNorm and ICD-10 Codes) Coder productivity ICD-10 Coder education Appropriate coding for correct reimbursement RAC Audit Risk reduction 13

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? 14

EHR Paradigm Dictation  Transcription  Auto Coding  Import to EHR Current Paradigm Physician Enters Data in EHR 10 minutes 2 minutes 15

EHR Meaningful Use (SNOMED-CT, ICD-9, LOINC, and RxNORM Codes) Data capture using dictation & transcription with NLP Faster EHR adoption Increased EHR usability Less training and customization More efficient data entry More thorough coding Easier to read Health Story + codes Manual coding infeasible for SNOMED-CT 16

NLP as part of an EHR Meaningful Use Solution 17

Business Needs 18

NLP as a Reporting Solution Standardized codes can be used for numerous secondary uses ORYX, SOI, POA, PQRI Quality measures Health care analytics Data can be stored in a clinical data repository and is accessible even if the EHR cannot do reporting 19

Example of NLP as a Reporting Solution: Clinical Core Measures for Diabetes 1.Hemoglobin A1C poor control 2.Hemoglobin A1C (<8.0%) 3.LDL Management and Control 4.BP Management 5.Eye Exam 6.Diabetic Retinopathy: Severity of Retinopathy 7.Diabetic Retinopathy: Communication 8.Urine screening 9.Foot Exam 20

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 21

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 22

NLP Systems Perform 3 Functions Capturing Data Structuring Data Facilitating Exchange of Data 23

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 24

Interoperability Metcalfe’s Law HIE, RHIO, data exchanges CDA-standards for interoperability Security and Privacy Audit trails 25

NLP Facilitates Interoperability Metcalfe’s Law EHRs require ICD-9 or SNOMED codes for diagnoses and allergies and RxNorm for medications for interoperability certification and Meaningful Use All EHRs must receive data from external systems that can contain standardized data (i.e., CCDs and CCRs - IHE Interoperability at HIMSS) EHR acceptance of data from multiple unstructured sources, including transcribed dictation, legacy text, scanned documents (CDA4CDT) All EHRs must generate CCD and CCR documents that contain semi-structured data that can be further coded 26

NLP as an EHR Meaningful Use & Interoperability Solution (for data transmission) Meet interoperability standards by generating the right codes Interface with external systems due to standardization of data and delivery format (i.e., CDA) 27

Important NLP Technical Attributes Security, Confidentiality, Privacy Cloud-Based SaaS Systems Browser-Independence Flexibility Data Accessibility 28

Cloud-Based SaaS Systems Benefits Low Implementation Costs Low Ongoing Costs Professionally managed Off-site Internet access Upgrades for all users 29

Browser-Independence Benefits User-Friendliness Low Implementation Costs Low Ongoing Costs 30

Security, Confidentiality, Privacy Security in transit Security in storage Automatic log-off Off-site long term storage Tracking and Monitoring –Audit trail –Time tracking for productivity –Accuracy monitoring 31

Flexibility Should the software fit the provider or the provider fit the software? Adjustability to fit various work flow models Customizability 32

Thank You James M. Maisel, MD Founder and Chairman MediSapien Natural Language Processing Medical Transcription Clinical Data ZyDoc 33