Development of an Automated Language Interpreting Tool for Hispanic Mental Health Patients

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1 Development of an Automated Language Interpreting Tool for Hispanic Mental Health Patients The Way Forward: Advancing Mental Health Equity in California March 30, 2017

2 Colaborators Steven Chan Alberto Odor Alvaro Gonzalez Andrés Sciolla Glen Xiong Michelle Burke Parish Shang Wei Ana-Maria Iosif Peter Yellowlees (PI) Support / Grants Blue Shield of California Virtual Collaborative Health Network Behavioral Health Center for Excellence AHRQ 2 R01 Grants

3 Research Transcultural Telepsychiatry Across Language Barriers (AHRQ R01) Automated Translation System for Transcultural Telepsychiatry (AHRQ - RO1) 2017 Automated Facial Expression Recognition in Telepsychiatry (Pilot) 2017 Augmented Reality to Show Visual and Auditory Hallucinations to Non-Psychotics Need to buy some FTE

4 The Problem 4.7% of the population (over 5 years of age) had limited English-speaking abilities. This is nearly the size of Illinois.

5 The Problem 2010 U.S. Census Language Use 79% 21% 16% 3% 2% English only English "not well" + Other English OK + Other English "not at all" + Other

6 The Problem Sacramento County languages 90+ non-english languages, like Spanish Miao Chinese Vietnamese Tagalog Russian Ukrainian Hindi German Hmong

7 Old Practice Patient Ad hoc Provider

8 Current Practice Patient Provider

9 Current Practice Patient Live interpreter Provider

10 Current Practice Problem: Money, Time Quality Unfunded mandate. Additional $30 - $50/hour Most private insurers do not reimburse interpretation. Except in California. (Wilson 2013, Guntzel 2012) 35% difference in interpreter salary

11 Current Practice Problem Money, Time, Quality Increased encounter time by 57% (live vs phone interpreters) (Locatis 2010). Travel time delays (Masland 2010) Perceived time constraints (Diamond 2009)

staff competency, utilization, or effectiveness of interpreting services (Tschurtz et al.

12 Current Practice Problem Money, Time, Quality Health systems determine what s reasonable for quality of language interpretation (The Joint Commission, 2010) Hospitals often don t test staff competency, utilization, or effectiveness of interpreting services (Tschurtz et al., 2011) Only 100 of 3,500 medical interpreters in Oregon have proper qualifications (Foden-Vencil, 2014)

13 Current Solutions Live interpreters Phone interpreters Videoconferencing interpreters Little information about accuracy of live interpretation Individual variabilities Hypothesis: Machine translation has similar levels of accuracy in a clinical setting.

14 Ultimate Goal Analyze language as data. Create new clinical processes for in-person care and telemedicine. Create a better standard of care with improved interpreting, more efficiently, and less expensively.

15 Asynchronous S&F Telepsychiatry Workflow

16 History (10 years ago) Visalia Tulare

17 History (10 years ago)

18 Current State of Automated Translation

19 Service Access Works with any user without pretraining HIPAA Transcription Translation Handles streaming audio Understands medical terminology Consumer app available to demo? SDKs Assessment Google Translate SaaS API?? Android, ios Nuance online SaaS API Nuance Dragon NaturallySpeaking SDK Client DSC Nuance Dragon NaturallySpeaking SDK Server DSS local software Nuance Dragon Dictate local software local Windows local? Nuance 360 SaaS API third-party apps Nuance NDEV SaaS API Android, ios local Mac OS X Client & Appleoperated servers Microsoft Bing Voice Recognition & Microsoft Translator on Azure SaaS API IBM BlueMix SaaS API SRI SysTran 7 Business Translator local & clientserver local, server, & SaaS English only Windows Phone, web translation web translation N/A iphone, web

20 Transcripts Original language Target language (HUMAN) Target language (COMPUTER)

21 Error Types omission, in which the interpreter (human or machine) did not interpret a word/phrase uttered; addition, in which the interpreter added a word/phrase not uttered by the patient; substitution, in which the interpreter substituted a word/phrase for a different word/phrase; and false fluency, in which the interpreter used an incorrect word/phrase that does not exist in that particular language

22 Interpreter 2 Interpreter 1 Errors in a PTSD Transcript Aggregate counts Transl # Data types J L K M NGrand Total total_added_word_count total_drastic_word_count total_unusual_word_count total_word_count Grand Total Aggregate counts Transl # Data types J L K M NGrand Total total_added_word_count total_drastic_word_count total_unusual_word_count total_word_count Grand Total

23 Automated Evaluation of Languages BLEU (Bilingual Evaluation Understudy), an error-scoring algorithm that has found to have high correlation with human error quality with a score between 0 to 1. Higher scores indicate a higher degree of similarity between human-translated and computer-translated texts. The BLEU formula is the most widely used automated metric for testing MT apps.

24 Implications Language apps can be incorporated in both asynchronous and synchronous telemedicine video encounters and workflows. Not all translation apps are alike: significant differences in the quality between apps. These apps are already being used in commercial industries to replace or augment language interpreters. Expect increased reliability and accuracy in the future.

25 Thank you

Member 1 Member 2 Member 3 Member 4 Full Name Krithee Sirisith Pichai Sodsai Thanasunn

Member 1 Member 2 Member 3 Member 4 Full Name Krithee Sirisith Pichai Sodsai Thanasunn Microsoft Imagine Cup 2010 Thailand Software Design Round 1 Project Proposal Template PROJECT PROPOSAL DUE: 31 Jan 2010 To Submit to proposal: Register at www.imaginecup.com; select to compete in Software