YOUR EYE ON INNOVATIVE MACHINE LEARNING SOLVING REAL WORLD PROBLEMS
Azafran Capital Partners
INSIGHTS issue FIVE well·ness [wel-nis]
issue five FOCUS
noun
At Azafran Capital Partners, we are focused on investing in end to end solutions solving real world problems derived from a scientific or engineering innovation in machine learning. In the following pages we visit trends and predictions in the wellness tech space and in particular inside Azafran Capital’s focus on machine learning with voice & acoustic as the favored user interface to the incredible developments that will shape the data hungry wellness field for the years to come.
1. 2.
the quality or state of being healthy in body and mind, especially as the result of deliberate effort. an approach to healthcare that emphasizes preventing illness and prolonging life, as opposed to emphasizing treating diseases.
Source: Dictionary.com ____________________________________ The last issue of INSIGHTS focused on the clinical intersection of healthcare + voice & acoustics + machine learning, with the Wellness Issue, we get personal. Ask 1,000 people their definition of wellness and you’ll probably get 1,000 slightly different answers, so we thought it best to set the table here courtesy of Dictionary.com. As we take a deep dive into the space here, it is important to keep in mind the definitions above, and the implications of emerging developments in machine learning, voice & acoustics, and deep science and their profound impacts on how we look at and manage our wellness. At a high level, the term wellness is as wide as the ocean and has a total global market worth $4.2T with a CAGR of 12%. Technology is now a primary driver in the space, integrating into all aspects of the market. Dialing this down to the Azafran focus at the intersection of voice & acoustics and machine learning, we see enormous opportunity and progress in the wellness space that is only going to increase over the near and mid term. One of the primary disruptions is putting data that is usually in the hands of a few specialists into the hands of the actual wider base of consumers that want to manage and get control of their health. With a not-too-distant reality of self diagnosis and prevention on a level that was not thought possible even a decade ago. As we have noted over past INSIGHTS issues, this opportunity is the result of the past decades of both technology advances and even more importantly, the collection, management and use of massive amounts of data that feed the machine learning and deep science aspects of the tech we are seeing hitting the market. It is important for us to invest in this space as it will transform how, we as a species, manage and improve our health and well being. It will be good for people. It will save time, untold amounts of money, all while keeping people more healthy. Our thesis is to focus on the entrepreneurs and companies that are solving problems, putting tools in the hands of both the general public and health care providers that change the game. Tools that are driven by the most rich source of data input, voice and acoustics.
In the world of machine learning and healthcare, one of the newest and emerging areas of exploration is understanding how to account for social and behavioral determinants of health (SBDoH) in predictive modeling. In 2015, the National Academy of Medicine (NAM) recommended social and behavioral domains to include in electronic health records which included five main domains of variables: ● ● ● ●
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Sociodemographic (race/ethnicity, education, employment); Psychological (health literacy, stress, depression, anxiety); Behavioral (diet, activity, tobacco or alcohol use); Individual-level social relationships and living conditions domains (social connections, work conditions, exposure to violence); Neighborhoods and communities (neighborhood and community compositional characteristics).
Precise measures of wellness will also contribute to more complete information to inform clinical decision making, personalized care and risk stratification, optimizing care patterns, and real-time course-correcting management of care journeys. By linking, analyzing, and interpreting datasets that may not yet be fully explored in the healthcare space, we can better understand how a more complete historical view of patient care is formed, thereby exposing indications of wellness.” - Dr. Hilary Placzek in MedCity News, March 6, 2019
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Wellness Use Cases Using the framing outlined high level infographic to the right, there is a massive and expanding opportunity for machine learning + deep science driven by voice and acoustics in all four wellness quadrants of mind, space, community and body. The transformational aspect here is the democratization of wellness through technology in the hands of consumers, with the inherent power in the underlying data driven by the new dominant modal input of voice and acoustics. In addition to the vast consumer opportunity and space, we see corporate wellness as a major driver as well in that over 80% of companies today offer wellness programs. Following are snapshots of some of the more innovative and interesting areas entrepreneurs and our partner ecosystem are building around wellness right now: ●
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Sensor and data-driven app platform using voice and acoustics to diagnose potential alarm signs of ailments such as chronic childhood asthma Voice assistants can become companions assisting someone with everyday helpers to handle the basic living functions, which provide a positive change in mental health. Using conversational inputs to enhance multi-modal communications between patient and the care team or with the patient themselves that are analyzed with Deep Learning to create a real time behavioral health vital sign. Using research and machine learning to determine the best behavior change approach and support employees on their health journey.
market PREDICTIONS Healthcare will be a dominant vertical in voice applications Healthcare is at a tipping point with voice specialized players such as Nuance, Orbita and leading tech companies (Amazon, Apple, Google, and Microsoft) are catching on with targeted voice technologies suited for healthcare industry use cases. We anticipate, though out 2019, HIPAA-compliant voice and chatbot applications for healthcare will gain prominence as these tech titans aggressively compete on voice solutions. However, the current maturity of voice technologies makes it suitable for limited voice-enabled applications such as quick medical scribes and transcription speech-based guided interactions, but not well-suited to conveying lengthy pieces of information. Moving forward, bringing voice technology to vetted clinical use cases such as elderly care, chronic condition management, physician’s assistant will provide growth opportunities. Source: Forbes Top 8 Healthcare Predictions for 2019
We see these areas as delivering the most value in the short term but the space is advancing exponentially. Wellness will remain a top priority for the Azafran team, as it becomes a more integral component to the healthcare space, using preventative practices and management to greatly reduce the runaway costs while substantially improving lives.
NEWSWORTHY… USC Launches Virtual Therapist “At the University of Southern California (USC) Institute for Creative Technologies they are conducting research to create what they have dubbed a “virtual therapist” named Ellie. Through a webcam and microphone, the AI is able to process and analyze the emotional cues derived from the patient’s face and the variation in expressions and tone of voice. Used only in research settings, they found that when speaking with Ellie, patients “feel less judged by the virtual therapist and more open [to discussing their feelings].” The institute’s social psychologist, Gale Lucas, explained that “It’s about what’s happening in the moment — having a safe place to talk.” - Dr. Lydia Kostopoulos on Medium Azafran INSIGHTS © Azafran Capital Partners 2019 - All Rights Reserved
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Investment Segment Highlight: Analytics Component: Metrics and Measurements The category of Analytics, Metrics and Measurements is ten miles wide but in the context here we combine it with acoustics and the implications of voice in general as the new frontier of analytics. Improved speech recognition is showing up in new analytics and business intelligence solutions that allow workers to query databases using nothing but their voices. Millions of have been going through the trials of working with the specter of voice search, which sees people speaking into an Amazon Echo or a smartphone app and expecting to hear a verbalized response (the text-to-speech part of the equation is a much easier problem to solve). In fact, Simple voice search is gaining so much traction, that media analytics company comScore predicts that it will account for half of all searches by 2020. One example square in the enterprise/analytics space is iOLAP, based in Frisco, Texas. iOLAP is an analytics company working with Amazon and other platforms to turn their speech recognition tech into the new user interface for enterprise systems. iOLAP voice-enables the corporate dashboard, a tool for managers to get a rundown of customized KPIs. Following one of the ongoing themes and trends we often discuss, incumbents like Amazon and Microsoft supply the algorithms that handle the speech recognition, while startups like iOLAP build all the other pieces needed to create the end to end solution, including turning text payloads into SQL and submitting them to a database, working with APIs, and handling the necessary user access and security controls required in the corporate environment. The Azafran Take: According to PM360, “Artificial intelligence (AI) and its ability to impact data analytics is on everyone’s mind. The percentage of global corporate conference calls that mention AI terms is approaching 20%, according to a Global Investment Outlook report from BlackRock Investment Institute in June 2018. Meanwhile, the McKinsey Global Institute estimates that AI could add $100 billion in value to the life sciences industry annually.” We see the enterprise adoption of analytics, driven by ML, acoustics and the incredible proliferation of voice recognition devices to the edge as a major area of focus for us and where we will be adding portfolio companies for sure in the coming year(s).
IN THE KNOW Wellness: Making Healthcare Data Work Better with Machine Learning (excerpt Patrik Sundberg, Software Engineer and Eyal Oren, Product Manager, Google Brain Team, March 2, 2019): “Over the past 10 years, healthcare data has moved from being largely on paper to being almost completely digitized in electronic health records. But making sense of this data involves a few key challenges. First, there is no common data representation across vendors; each uses a different way to structure their data. Second, even sites that use the same vendor may differ significantly, for example, they typically use different codes for the same medication. Third, data can be spread over many tables, some containing encounters, some containing lab results, and yet others containing vital signs. The Fast Healthcare Interoperability Resources (FHIR) standard addresses most of these challenges: it has a solid yet extensible data-model, is built on established Web standards, and is rapidly becoming the de-facto standard for both individual records and bulk-data access. But to enable large-scale machine learning, we needed a few additions: implementations in various programming languages, an efficient way to serialize large amounts of data to disk, and a representation that allows analyses of large datasets.”
Quotable: “We are entering a new world. The technologies of machine learning, speech recognition, and natural language understanding are reaching a nexus of capability. The end result is that we’ll soon have artificially intelligent assistants to help us in every aspect of our lives.” - Amy Stapleton Azafran INSIGHTS © Azafran Capital Partners 2019 - All Rights Reserved
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Feedback, going forward Thank you for the work you are doing in the world and your continued support of Azafran INSIGHTS’ monthly journey into the intersection of machine learning driven by voice, acoustics, language and image data. Our intention is to use this as a vehicle to open a dialogue with each of you, together as a group, and we strongly encourage and welcome your feedback. We’ve made feedback/comments simple, you can quickly and securely leave us a voice message by clicking here. If you are reading in print, please just visit the contact section of our website at AzafranCapitalPartners.com. In either case, just click on the “Start Recording” button and leave your thoughts and suggestions. Or you can always send us an email to insights@azafranpartners.com - thank you. We will be publishing INSIGHTS each month going forward, exploring the opportunity and intersection of voice tech and AI. We look forward to building this sector together and all the benefits for humanity that are soon coming down the road. From the Azafran team, we wish you all the best and a successful year ahead.
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