1 Speech Recognition & The Persistence of Narrative Documentation in the EHR Environment Don Fallati VP-Senior Advisor

2 A Union of Speech Recognition Industry Leaders

3 CPR The Clinical Information Challenge: How Do We Automate Narrative? CLINICAL AUTOMATION Clinical Information Benefits: Cost Savings/Efficiency Quality/Patient Safety CPOE Patient Documentation Patient Documentation Devices/ Dept Systems

4 Voice: Fastest Information Entry Modality “Speech Recognition’s record breaking accuracy improvements make using speech recognition easier than ever before.” Input DeviceAverage Time Mobile phone keypad 1:57 Mobile phone keypad (T9 predictive text) 1:15 RIM Blackberry (QWERTY keyboard) 0:57 RIM Blackberry 7100 (predictive text) 0:59 Desktop PC (QWERTY keyboard) 0:22 Voice (Mobile dictation) 0:12 Average text-entry time using various input mechanisms (expert users) Text segment used:

5 Narrative Paradigm Medical reporting is capturing stories Narrative will persist for a long time and there will be a lot of it EMR documentation paradigm flawed Need to change the game Need Narrative-based structure

6 Persistence of Narrative Narrative will stay because: –Natural form of communication –Comfortable to create –Voice friendly –Provides nuance, detail, context –Supports comprehensive documentation –Not just habit Narrative remains. Transcription, as we know it today, may not.

7 Three Solutions taking aim at transcription Outsource EMR Transcription Speech

8 Dictaphone Documentation Automation

9 Speech Recognition Editing Styles Provider-drivenTranscription-assisted Speech Recognition with NLP

10 Integrating Speech Recognition Into Workflow Platforms Workflow Platform Server Based Speech Recognition EHR Desktop Based Speech Recognition

11 State of Speech Recognition Today Over 1200 systems sold Covering broad range of medical specialties Inpatient and outpatient usage Large and small institutions Being applied to high percentage of daily volume

12 Gartner Healthcare Technology Hype Cycle SR Transcription SR Once and Done

13 Productivity Gains - Radiology 900 systems sold Elimination of hundreds of thousands of $ annual transcription cost hour turnaround times slashed to a few minutes

14 Productivity Gains Transcription-Assisted SR Name Overall Productivity Top MT Number of Dictators Monthly SR Volume SR % of Total Volume Affinity Health Systems 54%217%10517,60035% Advanced Healthcare 30%156%1018,00075% Brooks Memorial 43%85% % Reading Hospital 29%388%661,69527% Outagamie Health 59%361%622,59287% Rush Foundation Hospital 40%41% % AthletiCo 41%99%3008,00078% Regions 29%68%982,00014% Dean Medical Center 43%141%35012,90022%

15 Self-Edit Non-Radiology Case Study Self-Edit UsersReports Camino Healthcare 1807,665

16 The Physician Adoption Challenge – User Interface

17 The Mobile Vision Any Device, on Any Network Mobile Phones Telephones Computers Laptops PDAs The Physician Adoption Challenge – User Interface

18 The Physician Adoption Challenge – Time Breakthrough Dictaphone technology creates new time savings: – Reuse of previously dictated material Multiplier effect with other speech recognition features

19 Dictated Medical Report Clinical Data Natural Language Processing Natural Language Processing What does it do? Sectional Tagging

20 “Jump Start” Technology

21 from H&P Report from Normals “Jump Start” Technology Discharge Report Automation from HIM Database

22 Report Completion by Shortcuts and Reuse

23 “Natural Language” Patient Record Dictation The Physician Adoption Challenge – Speech & the EMR Data Quality & Re-usability Physician Usability High Low High Low Install Cost & Complexity EMR with structured data

24 Speech Can Address the Various Hospital/Clinic Scenarios 1.No EMR Interest or Intent 2.Actively Considering EMR 3.Implementing EMR –Concerned with Adoption 4.Deployed and Using

25 Speech Enable the EMR

26 Migrating the Physician Traditional dictation The “Natural Language” Patient Record Background MT Speech Rec SR & NLP structured report / data creation Phys self-edit Speech Rec Natural Language Bridge to Clinical Automation