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B14 Fast, Structured Clinical Documentation via Contextual Autocomplete

Divya Gopinath
Research Engineer at TruEra
+ 5 speakers
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Machine Learning for Healthcare
August 7, 2020, Online, Los Angeles, CA, USA
Machine Learning for Healthcare
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About the talk

00:00 Intro

00:34 Natural language and free-text

01:01 Contextual autocomplete

01:51 Deconstructing the Language Model

02:12 The keystroke burden of data

02:25 Labeled clinical data

About speakers

Divya Gopinath
Research Engineer at TruEra
Monica Agrawal
Software Engineering Intern at Flatiron Health
Luke Murray
Research Intern at Microsoft
Steven Horng
Associate Director, Multi-disciplinary Fellowship in Clinical Informatics at Beth Israel Deaconess Medical Center
David Karger
Advisory Board Member at Instabase
David Sontag
PhD, Associate Professor of Electrical Engineering and Computer Science at MIT

Divya Gopinath grew up in a household that might seem familiar to many first-generation South Asians – one where math and science were highly valued. Her father, an engineer, and mother, a neuroscientist, inspired her young mind as she grew up in Chappaqua, New York, in an insular community with few other girls who looked like her. Gopinath went from being a high school valedictorian to MIT graduate, her resolve and perfectionism seeing to it that she became a machine learning expert addressing the nuance of machine-based bias, and who is now working at Truera, a startup in the Bay Area. She was accepted to many top universities, but ultimately chose MIT for its focus on STEM subjects, and quickly gravitated to computer science, specifically machine learning and artificial intelligence. Her research project in her junior year combined computing and neuroscience to build machine learning models to replicate human hearing. She went on to earn her master’s degree there. At Truera, a small startup in the Bay Area, she grapples with the challenge of safely deploying machine learning. Her master’s thesis, for example, focused on how to leverage machine learning to make clinical note-writing easier for doctors. She is proud of that moment because it set her up for later experiences at MIT.

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I am a third year PhD student at MIT CSAIL, advised by David Sontag in the Clinical Machine Learning group. In my research, I tackle various challenges within clinical natural language processing, including: Clinical entity normalization Extraction from longitudinal clinical notes Creation of smarter electronic health records Previously, I earned a BS and MS in Computer Science at Stanford. While there, I researched in the SNAP Group under Jure Leskovec and Marinka Zitnik, where I used biological networks to predict disease-gene associations and polypharmacy side effects. I've also had internship stints at Google (2015, 2017) and Flatiron Health (2016, 2018). I am passionate about issues surrounding diversity in computer science. I recently completed my term as an officer for GW6 (Graduate Women in Course 6 at MIT), and I'm on the Dean of Engineering's graduate advisory committee for Diversity, Equity, and Inclusion.

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Dr. Horng is dual board-certified in emergency medicine and clinical informatics with degrees in computer science and biomedical informatics. He specializes in translational clinical informatics and focuses his research on novel methods of artificial intelligence to improve the quality and efficiency of emergency care. Specifically, he works on applying state-of-the-art machine learning methods to implement automated information retrieval and targeted decision support at the bedside. He also oversees clinical decision support and computerized order entry as part of his operational responsibilities. The Division of Informatics is also a live clinical test-bed which fast tracks innovative technologies such as machine learning and Google Glass to bring them to the bedside for clinical use.

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David Karger is a Professor of Computer Science and a member of the Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology. Karger splits his research between algorithms and information retrieval. His work in algorithms has focused on applications of randomization to optimization problems and led to significant progress on several core problems. He has also researched applications of theoretical ideas to applied areas such as compilers and networks. He recently received the National Academy of Science's 2004 Award for Initiative in research. David leads the Haystack group at CSAIL, which researches many facets of information management including capture, organization, retrieval, sharing, and visualization.

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David Sontag joined the MIT faculty in 2017 as Hermann L. F. von Helmholtz Career Development Professor in the Institute for Medical Engineering and Science (IMES) and as Associate Professor in the Department of Electrical Engineering and Computer Science (EECS). He is also a principal investigator in the Computer Science and Artificial Intelligence Laboratory (CSAIL). Professor Sontag’s research interests are in machine learning and artificial intelligence. As part of IMES, he leads a research group that aims to transform healthcare through the use of machine learning. Prior to joining MIT, Dr. Sontag was an Assistant Professor in Computer Science and Data Science at New York University’s Courant Institute of Mathematical Sciences from 2011 to 2016, and postdoctoral researcher at Microsoft Research New England from 2010 to 2011. Dr. Sontag received the Sprowls award for outstanding doctoral thesis in Computer Science at MIT in 2010, best paper awards at the conferences Empirical Methods in Natural Language Processing (EMNLP), Uncertainty in Artificial Intelligence (UAI), and Neural Information Processing Systems (NIPS), faculty awards from Google, Facebook, and Adobe, and a NSF CAREER Award. Dr. Sontag received a B.A. from the University of California, Berkeley.

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My name is Vivian today. I'll be talking about our paper. If a structured clinical documentation by a contextual autocomplete which is work out of both Beth Israel, Deaconess Medical Center and MIT Conditions currently spend more time, documenting information in electronic, health records and they actually spend communicating with patience. And this time sink is positive to be a leading cause of what position stress and burnout clinicians of a dr. That's why using natural language in free text to create their notes. The resulting clinical documentation has been quite messy in that its

narrative and unstructured. It contains domain-specific jargon and it also has interests in definitions and overloaded acronyms. Overall EHR data is unstructured, noisy, incomplete in ambiguous in this primarily affects three parties. Patients who can't understand, medical jargon Physicians who have trouble retrospectively disambiguation between these overloaded terms of acronyms. And finally, learned algorithms, that Reliance structured data to make their intelligence predictions. We propose contextual automobile, which is a method of capturing clinical Concepts, at the point of care of

Viola. Learn suggestions. As you can see here, as the doctor is creating his, or her note, we are creating a semi-structured representation of the note at the point of care. While decreasing documentation birth is typing less to achieve the same note, concepts are normalized ontology, like you, Use multiple sources of information in architectural autocomplete models, including prior notice from the medical history, as well, as if we are just estimate and chief complaint. Permissions can document multiple types of Concepts using the language that there used to be documenting

conditions, medications symptoms and lab. Using the language that they traditionally would like a candidate. They've been other abbreviations for common Concepts or language model, is a two-step process. What refers to the local context of what the doctor has just type to predict when we want to show the autocomplete drop down as well as what concept type is about to be documented, then we use the terms to autocomplete for each concept. Display overall, we dramatically reduce

the keystroke burden or the amount. The doctor needs to type to create a concept of data entry in live settings across all concept types, Metro Auto complete its labeled clinical data at the point of care. While simultaneously, decreasing documentation burden and their Myriad future directions to take the Sun. But one particularly promising one is how we can use the Stanton Mabel better clinical decision support that could mean after getting a lab Trend once a doctor has toggle a blood glucose in a note, or can mean using already documented information, like hypertension, two free, porcelain

later sections, like the medication section and reduce redundancy. Overall, we believe that contextual autocomplete can act as a Cornerstone of an intelligent HR that finally works for doctors. And if you're interested in learning more, about our poster and paper, and let us know. Thank you.

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Divya Gopinath
Monica Agrawal
Luke Murray
Steven Horng
David Karger
David Sontag