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Role of Clinical NLP in Cardiology
- 1. Role of Clinical NLP, Conversational AI & Virtual Voice Assistants in Cardiology JAI NAHAR, MD, MBA Associate Professor of Pediatrics George Washington University School of Medicine Attending, Division of Cardiology Children’s National Hospital, Washington DC AIMed Cardiology Nov 4th, 2020
- 3. 1. Introductory concepts: NLP 2. Clinical NLP: Application 3. Conversational AI & Virtual Voice Assistants 4. Future Directions 5. Conclusion Agenda
- 4. Natural Language Processing (NLP) • ThisAI methodology allows the computer to understand spoken as well as written human language • NLP = NL Understanding (NLU)+ NL Generation (NLG) Intelligence-Based Medicine 1st Edition. August 2020 Artificial Intelligence and Human Cognition in Clinical Medicine and Healthcare Author: Anthony Chang Chang A. Analytics and Algorithms, Big Data, Cognitive Computing, and Deep Learning in Medicine and Health Care. AI Med Ebook; 2017
- 5. NLP: Functions • Automation: Data Extraction, Entry • Analytics • Prediction • Classification • Risk Stratification • Assistance & Coaching (Virtual Assistant) • Sensing (ASR) • Expression (NLG)
- 6. Clinical NLP: Use cases MD Work Flow Augmentation Care Delivery Revenue Optimization Research Patient Portal UX Conversational AI • Automatic Speech recognition • EHR documentation • Chart Review • Data Mining • Predictive analytics • Phenotypic Classification • Risk Scores/stratification • Clinical decision support • Targeted Intervention • Adverse event prevention • Quality Improvement • Computer assisted coding • Automatic preauthorization • No show prediction • Clinical trial matching • Automated Registry reporting • Analytics Understanding Portal’s Medical Terminology • Conversational agents (Virtual Assistants, Chabots) • Ambient Clinical Intelligence • Voice Biomarker analysis
- 7. Clinical NLP and Care Delivery : Data Mining Input Data Unstructured Data EMR/Other sources Use of NLP Structured Data (Machine Interpretable) ML Analytics Targeted Early Intervention Quality Improvement Data Discovery Advanced Analytics Phenotypic Classification Risk stratification Clinical decision support
- 8. NLP for Data Mining from EHR https://www.healthcareitnews.com/news/how-mercy-using-nlp-its-epic-ehr- improve-analytics-cardiac-care
- 9. Conversational AI Technology which allows Human Machine interaction through the use of Natural conversation, utilizing voice user interface and Machine Intelligence Human Machine Conversational AI Voice Technology NLP ML, Deep Learning Synergistic Convergence Conversation
- 10. Current State Conversational AI Ecosystem Smart Speakers Smart Displays Smart voice enabled devices Chatbots Virtual Assistants Ambient Clinical Intelligence Conversational AI Voice VUI AI Ambient Sensors
- 11. Conversational AI Outpatient clinic Home Customer Service/ Call Center In Hospital Conversational AI touchpoints in Health Care Delivery 1. EHR documentation, navigation 2. Clinical Decision Support 3. Foreign Language Interpretation 1. Appointment Navigation 2. Patient Education 3. Patient Engagement 4. Medication management 5. Chronic care management: bridging the care gap 1. Scheduling appointment 2. Information 3. Assistance 1. Patient Education, inpatient care navigation 2. Discharge preparation 3. Clinical Decision Support 4. Operating suites: Hands free clinical documentation and information retrieval Decreasing Physician Burnout: POC Tasks Home Health Optimization Easing the Hospital Journey Operational Ease
- 12. DigitalTriage: Chatbots Espinoza J, Crown K, Kulkarni O A Guide to Chatbots for COVID-19 Screening at Pediatric Health Care Facilities JMIR Public Health Surveill 2020;6(2):e18808 http://publichealth.jmir.org/2020/2/e18808/ Sample chatbot process map. *: Institutional discretion, follow public health agency guidelines. Clara: CDC’s Coronavirus Self-Checker Chabot Sample Chatbot process map
- 13. Voice EnabledVirtual Health Assistant: Functional Spectrum Assistance Digital Interface Chronic Care Management & Wellness Value & Impact • Screen, Triage • Inform • Assist with care navigation • Connect with providers • Educate • Digital Connection • Remote Monitoring • Risk stratification • Prediction (Voice biomarkers) • Engagement • Adherence • Behavior modification • Patient-Provider partnership • Promotion of wellness Value Pyramid
- 14. Conversational AI solution for decreasing physician burnout ACI: Ambient Clinical Intelligence https://www.nuance.com/healthcare/ambient-clinical-intelligence.html
- 15. ACI: Ambient Clinical Intelligence Comprehensive Voice enabled AI solution • Decreases Physician administrative burden • Decreases Physician burnout • More attention to the patient • Increases patient satisfaction, better outcome ACI Ambient Sensing Technology User Initiated Virtual Assistant Automated Documentation service
- 16. Future Directions • Multimodal data fusion Contextual AI • Smart EHR (NLP and CAI) • Advanced HCI: ProactiveVirtual Assistants with Cognition & Empathy
- 17. Conclusion: KeyTakeaways 1. NLP: Unlocking Actionable Insights from Unstructured data 2. CAI: Intelligent User Interface 3. UX, Smooth Work Flow integration 4. Data Privacy, Security, is integral to successful implementation
Editor's Notes
- Thanks to Anthony and AIMEd team for the invitation and honor to present ________________________________________________________________ With the advancements in Voice technology and Natural language processing, Conversational AI and Virtual Voice Assistants are gaining increasing attention in health care for developing provider, patient and enterprise facing solutions. This workshop will focus on Conversational AI, Virtual voice assistants, and cover the following key points ( next slide)
- My talk today will geared towards application of NLP and CAI in clinical care, including some introductory concepts. In My Talk today I will go over introductory concepts, then focus on applications of Clinical NLP, Conversational AI & Virtual Voice Assistants in health care delivery, then wrap up future directions.
- So what is NLP?
- In clinical world NLP can be used to achieve fgs important functions: Automate: Data Extraction from EHR, Entry/Feed into Registry Analyze -- RTAP-> Intelligence/Insights Predict Classify, Risk Stratify Assist Coach (Virtual Assistant) Sense (ASR) Express (NLG)
- Building upon the NLP functions from previous slide let us look at the use cases of clinical NLP which are highlighted as discrete categories in this slide. Other use cases are: -------- Physician Work flow augmentation: Speech recognition and EHR documentation Care delivery: Clinical decision support, Risk stratification and predictive analytics Revenue optimization: Computer assisted coding, Automatic preauthorization, No show prediction Research: Automation of Registry data entry, Analytics, Registry reporting, Clinical trial matching, Conversational AI: Conversational agents (Virtual agents, chatbots), Voice Biomarker analysis Patient portal UX: NLP tools linking medical terms in portal documents to simple definitions and explanations for the patient, will help to improve patient’s EHR understanding and portal user experience
- Let us look at use of NLP in care delivery. Big challenge of current health care data is that it is trapped in the form of unstructured format. First step shown in this slide is data discover: that is application of NLP tools to the unstructured data from various sources, including EHR thus making this data Machine interpretable. This in turn would facilitate the Second step of advanced analytics using ML to facilitate ----------
- Let us look at one real life use case. Mercy, the St. Louis-based health system, & Medtronics collaboration project: Use of NLP from Linguamatics to mine Epic EHR for HF patients to: Evaluate CRT device performance Help clinicians make better data-driven decisions on treatment Mercy has been using natural language processing technology from Linguamatics to wring out lots of previously inaccessible data from seven years of clinical notes for its cardiac patients. As part of a collaboration agreement with Medtronic, Mercy mines EHR data to evaluate heart failure device performance – letting the manufacturer know how to improve its implantable products and helping Mercy's own clinicians make better data-driven decisions on treatment.
- Foundation of conversational AI is NLP. Conversational AI: Human Machine interaction through the use of conversation, utilizing voice user interface and Machine Intelligence. This is made possible by synergistic convergence of Voice technology, and Artificial Intelligence technology (Natural Language processing, Machine and Deep Learning). It includes smart voice enabled devices, chat bots and virtual assistants Two common applications of Conversational AI are virtual assistants and chatbots, these are also known as virtual or conversational agents. A virtual assistant (e.g. Apple’s Siri, Google Assistant, and Amazon Alexa) is an AI-inspired software agent that is capable of performing certain tasks or services via text or voice. A chat bot is a conversational AI application that is capable of providing information to the user. Virtual voice assistant helps the user in performing simple daily tasks
- There are many ways in we encounter conversational AI in our daily lives. These are in form of smart speakers, Smart displays next evolution of smart speakers and include Voice + touch screen Display : Amazon Echo show, Google home hub, Facebook portal A chat bot is a conversational AI application that is capable of providing information to the user. Virtual voice assistant helps the user in performing simple daily tasks ACI is a newer application of CAI aimed to decrease physician burnout. Two common applications of Conversational AI are virtual assistants and chatbots, these are also known as virtual or conversational agents. A virtual assistant (e.g. Apple’s Siri, Google Assistant, and Amazon Alexa) is an AI-inspired software agent that is capable of performing certain tasks or services via text or voice.
- As highlighted here There are 4 main value adding touch point of CAI in health care delivery. POC to decrease physician burnout; Home for home health optimization, Call center to promote customer service and in hospital to ease the hospital journey. ____________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________ This is a busy slide but highlights how the CV health care delivery system can derive Value from use of conversational AI solutions (including voice assistants, and smart voice enabled devices). Physicians, patients and health care organizations are three important stakeholders who could benefit from this technology There are 4 main touch points in care delivery, which can derive value from CAI, these are the OP clinic, patients home, On the go, and In hospital. The value at the output clinic is in : decrease in physicians burnout by assisting in POC tasks such as: EHR documentation, navigation and CDS The value at pt’s Home is in : Home health optimization by providing --------------------- The value at Hospital is in: Easing the hospital Journey by offering patient education, inpatient care navigation, -------------- On the Go, means away from home and traditional care settings. Here the value is in facilitating care, in mobile health units and ambulances.
- Let us look at one application of Conversational agents that is chat bots. Chat bots can be used to fill the gaps in access to health care. This can be done by using them for patient facing Digital or E triage solutions. Chat bots came very handy during COVID -19 pandemic. Similar solutions can be customized for optimizing Virtual CV care delivery. CDC: in collaboration with Microsoft launched Clara, the Coronavirus Self-Checker The purpose of the Coronavirus Self-Checker is for guidance and to help people decide when to call their physician if they are feeling sick. Referenced on the right of this slide is an excellent recent article on implementation framework for deploying COVID-19 screening chat bots. This is from Omkar Kulkarni and his team from CHLA . The sample Chabot process map illustration which I have shown here has been taken from that article.and shows how chatbot can be used to triage pt to appropriate type of medical care.
- An important component of CAI ecosystem is Virtual assistants. This slide illustrates the Functional Spectrum of Voice Enabled Virtual Health Assistant. There are 3 important functional groups. First is assistance with information, care navigation and education. Second is use as digital interface between pt and provider thus helping for home based remote monitoring which is evolving as an important component of virtual care. Third is Chronic care management and promotion of wellness specially important for high risk patients such as CHF, HTN, diabetes
- Nuance® Dragon Ambient eXperience™ (DAX™) solution, An important, emerging CAI technology is ACI.
- Microsoft and Nuance have partnered to provide Ambient clinical intelligence (ACI) — a comprehensive, AI-powered, voice-enabled solution to decrease the physician workload and improve patient experience It has three components which serve two important functions: 1. Automates patient provider conversation in form of clinical note 2. Help physician get information in and out of EHR using virtual voice assistant. Ambient sensing technology (perception): This involves wall-mounted device which uses a multi-microphone array and integrated computer vision to capture and track patient and provider conversation. 2. User-initiated virtual assistants (Comprehension and execution) : By using voice via Dragon physician can get info in and out of EHR, navigate EHR, saving time. 3. Automated Documentation Services: Physician-pt Conversation is automatically documented as a draft clinical note, which can be edited by the physician _____________________________________________________________________________________ Ambient sensing technology: Clinicians engage in conversation with their patient while a wall-mounted purpose built healthcare device uses a multi-microphone array and integrated machine vision to capture and track audio. 2. User-initiated virtual assistants: Simply say “Hey Dragon” to get information in and out of the EHR. Use natural language to reduce the time it takes to document care, navigate patient charts, and follow up on documentation details. 3. Automated Documentation Services: During an encounter, every spoken word is diarized with speech recognition and automatically translated into a draft clinical note using discrete data that’s delivered to the clinician for authentication directly through the EHR. https://www.nuance.com/healthcare/ambient-c
- In future consideration should be given to 1. Use of NP in multimodal data fusion to develop contextual AI to help in better prediction and prescription. 2. Development of Smart EHR using NLP tools and CAI 3. Development of Advanced HCI solutions such as Proactive Virtual Assistants with Cognition & Empathy.
- In conclusion NLP is an important AI Technology to unlock Actionable Insights from Unstructured data CAI can be used as Intelligent user interface for patients and providers in augmenting care delivery