Natural Language Processing An innovative, disruptive technology for ICD-10 Coding, Secondary Data U...
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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 [email protected] Natural Language Processing Generates structured data from unstructured text. June 14, 2012.

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James m maisel m d chairman zydoc jmaisel zydoc

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

[email protected]


Natural language processing generates structured data from unstructured text

Natural Language ProcessingGenerates structured datafrom unstructured text

June 14, 2012

Presented by James Maisel, MD

2012 NJHIMA Annual Meeting

2


Nlp generates icd 10

NLP Generates ICD-10

3


Paradigm shift toward data centric health care

Paradigm Shift toward Data-Centric Health Care


James m maisel m d chairman zydoc jmaisel zydoc

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


James m maisel m d chairman zydoc jmaisel zydoc

EHR Paradigm

Dictation  Transcription  Auto Coding  Import to EHR

Current Paradigm

Physician Enters Data in EHR

10 minutes

2 minutes

6


Nlp systems perform 3 functions

NLP Systems Perform 3 Functions

Capturing Data

Structuring Data

Facilitating Exchange of Data

7


Nlp enables coordination of care

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


James m maisel m d chairman zydoc jmaisel zydoc

June 14, 2012

Presented by James Maisel, MD

2012 NJHIMA Annual Meeting

9


Thank you

Thank You

James M. Maisel, MD

Founder and Chairman

ZyDoc

MediSapien Natural Language Processing

Medical Transcription

Clinical Data

10


James m maisel m d chairman zydoc jmaisel zydoc

The ICD-10 Challenge

How to select the correct fracture from a drop-down menu?

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

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James m maisel m d chairman zydoc jmaisel zydoc

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


James m maisel m d chairman zydoc jmaisel zydoc

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


Nlp systems can accept

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


Benefits of improved coordination of care

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


Secondary use 2010 death rate us

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


Secondary use risk reduction

Secondary Use: Risk Reduction

June 14, 2012

Presented by James Maisel, MD

2012 NJHIMA Annual Meeting

17


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