Speech Assisted Radiology System for Retrieval, Reporting and Annotaiton

1. Speech Assisted Radiology System for Retrieval, Reporting and Annotaiton Tim Weninger, Daniel Greene, Jack Hart, William H. Hsu and Surya Ramachandran* Department of Computing and Information Sciences Kansas State University, Manhattan KS *AIdentity Matrix Inc, Elmhurst, IL 2009 IEEE International Symposium on Computer-Based Medical Systems Albuquerque, NM, USA

2. Outline • Introduction • Motivation • Example • Voice Directed Search • Prerequisites • Parsing Spoken Text • Search • Findings and Impressions • Merit Case Client • Experiments • Metrics • Results • Conclusions and Future Work • Demo

3. Introduction Motivation Paradigm: Radiology Healthcare is expensive Why? Errors 2004-2006 Medicare study Errors cost US$8.8 billon University of Baylor study: Out of 113 errors studied Transcription was the base-cause for 46% (Seely et al. 2004) Inefficiencies Medical Transcription Adds cost Adds complexity

4. Introduction Status quo (simplified)

5. Introduction Status quo (simplified)

6. Definitions Define our Terms: Paradigm: Radiology MRI Finding/Impression Medical diagnostic interpretation of particular abnormalities as seen by the radiologist Annotation The expression of a medical opinion related to a specific image. Drawn Arrow Circle Etc Merit Case Client: Speech directed PACS system

7. Voice Directed Search Current PACS systems Example: find men with slipped discs “Search. sex equals male. diagnosis equals herniated disc.” [Search] is a command [male] is a menu option in list [sex] [Herniated disc] is option in list [diagnosis] Disadvantages: Narrow speech scope Voice recognition systems are not foolproof Example: Homonyms “Search. Sex equals mail. Diagnosis equals herniated disk.” Does not compute! Main advantage: Capable of standardizing naturally spoken medical terminologies with significant degrees of variance.

8. Voice Directed Search Example: Find all male patients between the ages of 55 and 60 with a slipped disc in the L4/L5 region with no previous history of disc injury. “Find men with a slipped disc in the L4/L5 region” [Find] is a command along with others [male] is a interpreted to be [male] within [sex] [between 55 and 60] is [55-60] within [age] [slipped disc] is interpreted to be [herniated disc] within [disease] [disc injury] looks for any [disc] within [disease] Moreover: This widens the search scope Voice recognition systems are not foolproof Example: Homonyms and formatting “Find men with a slipped disk in the El four slash El five region.” This works as well. How?

9. Parsing Spoken Text Operating Assumptions: The system maintains a complete list of all ages, sexes, diseases, etc. i.e. type enumeration Valid responses are available in lists Homonyms do not coexist in a list If so, then it’s hard to make a decision Goal Map what is dictated to the appropriate descriptor Sliding window approach: Size Diagnosis Small Disc bulging Herniated disc Small to Moderate Degenerative disc disease There Moderate is moderate disc bulging at L5/S1 Moderate to Large Large There is moderate disc bulging at L5/S1

10. Voice Directed Search Synonym Learning How does the system know: “Slipped Disc” = “Herniated Disc” = “Disc Herniation” The system will make an initial guess. System will not initially recognize “Slipped Disc” System remembers corrections Correction process is easy Learns speakers word choice preference

11. Structured Reporting Image Embedding Findings Impressions Annotations Text, descriptors, drawings Become linked with the image(s)

12. Experiments Data points (1) Text read by the radiologist (2) Text output by speech recognition engine (3) Descriptors filled in by Merit Case Client (4) Correct state of the descriptor (ground truth) Metrics Speech Recognition Metric (SRM) Word-Edit distance between original text (1) and output by the speech recognition system (2). Parsing Engine Metric (PEM) Word-Edit distance between menus filled in by Merit Case Client (3) and the correct answer (4)

13. Experiments Reporting and Analysis Some errors are more costly than others 3 reporting methods: Word distance Weighted errors Disease descriptor= 60% Location descriptor = 20% All others descriptors = 20% All or not Was it completely correct or not? Experiment Radiologist (Dr. Schekall, MD) made 100 dictations based on real-world cases 25 search queries 75 findings and impressions dictations No re-dos allowed Speech recognition system was NOT pre-trained

14. Results Data points and their linear regression lines

15. Results Change in accuracy for each paradigmMethod: (SRM-PEM)/SRM

16. Current domains of implementation (ongoing) • Branded under - Virtual Integrity in Medicine TM (VIM) • Electronic Medical Records • VIM Radiology • PET, CT, MRI, Nuclear, X-Ray, Ultrasound, etc • VIM Cardiology • ECG, Ultrasound, CT, Nuclear, Cath lab, Vitals, Resting, Exercise, Stress, Ambulatory BP and Spirometry • VIM Neurology • From out-patient clinical through surgery • Front & Back Office • Scheduling, Patient profile, Insurance, Rule-outs ICD9/10, Referring Physician, Reporting, Billing & Accounts bridge, Clinical messaging, etc.

17. Demo

18. Questions? Special thanks - Dr. Michael Schekall, MD Deborah Templeton, BS, CNMT, RT(R), LRT Hutchinson Clinic PA, Hutchinson, KS Jeff Barber, Andrew Walters Kansas State University Industry Contact for more information – Surya Ramachandran AIdentity Matrix Medical Inc. surya@aidentitymatrix.com