James F. Kenefick - Azafran Capital INSIGHTS Vol. 4 by James F. Kenefick

YOUR EYE ON INNOVATIVE MACHINE LEARNING SOLVING REAL WORLD PROBLEMS

Azafran Capital Partners

INSIGHTS issue FOUR The Healthcare Issue

issue four FOCUS

@ The Intersection of Healthcare + Voice & Acoustics + Machine Learning (ML)

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.

The healthcare industry plays a critical role in society for sure, but is also hampered by both inefficiencies and opportunities that are rife for disruption. Issue Four of INSIGHTS focuses a sharp eye on how machine learning, especially driven by voice and acoustics, is and will continue to revolutionize this huge industry and move humanity forward in a positive way. As the chart below highlights, there are a wide range of ways that AI and ML are going to impact healthcare, from the front lines of early detection, to managing and using the incredible mounds of data behind the industry, and enhanced robotics in surgery - which is highlighted as a larger segment opportunity than the other nine categories below. Zeroing in on Azafran Capital’s current focus around voice and acoustics as the growing and primary interface for many of the innovations in healthcare - we are seeing a whole range of companies and technologies both today and on the horizon that will change how we use, administer, apply and think of healthcare going forward. From a high level, some important use cases that we are seeing and looking to invest in the short term, include deep tech looking for vocal patterns such as pitch, tone, rhythm, volume, that serve as powerful data points - "vocal biomarkers." This type of data can help care and clinical teams in their diagnosis of a variety of conditions — from chronic respiratory illnesses to cognitive disorders, early cancer detection (esp. melanoma) and heart attacks. Even simpler areas, such as patient-physician communications, physician’s notes and general patient engagement will be greatly assisted and served by the plethora of ML voice/ acoustic coming onto the market now and the coming months and years.

One of the primary areas of enormous potential we are engaged with is healthcare, the focus of Issue Four of INSIGHTS. Related but through a different lens we look to the area of wellness, which will be the focus of our next issue - our framing and perspective has healthcare as clinical and reactive, wellness more personal and preventative.

The big change in healthcare applications in the future will be the increased use of machine learning techniques. The benefits in, for example, diagnostic applications, are likely to be very significant because machine learning will have a role to play not only in predicting, but also in preventing by early detection, and appropriate treatment by linking known correlations of patient treatment methods. Some medical applications, like the Watson for Oncology system, already uses machine learning, along with other technologies to support cancer detection. The advantages of applications using machine learning technology is their ability to discover new insights from the data, thus moving beyond the traditional limitations of AI. However, the downside of machine learning is that it is predominantly based on black box techniques such as neural networks.” - Keith Darlington on OpenMind

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Healthcare Use Cases The Azafran team has a consistent eye on the healthcare space through our ML+ Voice & Acoustics lens as we see and agree with the prognosticators that healthcare has an endless array of innovation and opportunity ahead. There is hardly any element of the industry that could not stand for a major overhaul when looking from its administration, to services delivery, R&D and patient treatment and follow up. The graph below (Vertical Voice Tech Applications, source: mobihealthnews) shows healthcare as having nearly ½ of all the top voice tech apps out there related to ML and AI. As we do not have hundreds of pages to root out all the possibilities we see and have already engaged with (please connect with us as we’d be happy to share!), following are snapshots of some of the more innovative and interesting areas entrepreneurs and our partner ecosystem are building around healthcare right now: ● ● ● ● ● ●

Diagnosis via patient voice and acoustics monitoring/filtering Patient-provider communication Vocal biomarkers Patient engagement Rapid and more accurate detection across many areas of healthcare Physician notes, patient data from records to visits and treatments

We see these areas as delivering the most value in the short term but the space is advancing exponentially. Healthcare will remain a top priority of Azafran as the industry has such a long way to go to hit escape velocity from its current costly and inefficient operating model.

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

NEWSWORTHY… IDC Projects Explosion in HC ML/AI: Driven by rising consumer expectations, 60% of healthcare providers will make optimizing the digital patient experience a top 3 strategic imperative by 2020. By 2022, 40% of healthcare providers will leverage machine-learning and AI-algorithm advances to improve their cybersecurity capabilities with automated threat detection to thwart ransomware. - 2019 IDC FutureScape Report

Analytics shifts from Big Data to Meaningful Small Data by Hospital Specialty As the healthcare industry gets comfortable with data management workflows, we foresee a high number of specialty-specific analytics solutions will gain prominence among providers striving to investigate drug utilization, treatment variability, clinical trial eligibility, billing discrepancy, and self-care program attribution specific to major chronic conditions. We predict that by end of 2019, 50% of all healthcare companies will have resources dedicated to accessing, sharing, and analyzing real-world evidence for use across their organizations. Source: Forbes - Top 8 Healthcare Predictions for 2019

Azafran Perspective Excerpted from the Azafran Capital White Paper: The Voice of Deep Machine Learning. Why Now? The Azafran Capital Fund One investment thesis is focused now on investing in data driven, machine learning products with voice and acoustics as the user interface. The marketplace, and healthcare in particular, is demanding companies that are providing these transformational solutions and our team is all about market timing. We do this alongside our game-changing partners including Stanford, Carnegie Mellon and MIT, where their research and breakthroughs continue to validate our strategy. The focus of Azafran Capital Fund One on acoustics as the overlay is rooted in the transformational nature of the tech, and that it’s in almost every home and business and the market and is set to grow exponentially. As we choose to be experts instead of generalists, our strategy is already paying off with our incredible early investments, Yobe and Aspinity, with a number of new investments happening soon in transformational companies/technologies.

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Investment Segment Highlight: Embedded Software Component: Application Specific Embedded software is computer software, written to control machines or devices that are not typically thought of as computers, commonly known as embedded systems. It is typically specialized for the particular hardware that it runs on and has time and memory constraints. (source) Manufacturers build embedded software into the electronics of cars, telephones, modems, robots, appliances, toys, security systems, pacemakers, televisions and set-top boxes, and digital watches, for example. This software can be very simple, such as lighting controls running on an 8-bit microcontroller with a few kilobytes of memory with the suitable level of processing complexity determined with a Probably Approximately Correct Computation framework (a methodology based on randomized algorithms), or can become very sophisticated in applications such as airplanes, missiles and process control systems. [source: Wikipedia] The Azafran Take: Embedded software systems use operating systems or bare-metal (no OS) using specialized language platforms to embedded capabilities, particularly where real time operating must be served. As in higher levels of chip capability, such as in System on Chips (SoCs), designers have majorly decided that the systems are fast enough and task tolerant of slight variations in reaction time that nearly real time approach and IoT play an important role. [source: Quora]

IN THE KNOW Voice & Acoustics + Deep Learning + Healthcare = Better Care “Deep learning represents the most promising pathway forward into trustworthy free-text analytics, and a handful of pioneering developers are finding ways to break through the existing barriers. A team from Google, UC San Francisco, Stanford Medicine, and the University of Chicago Medicine, for example, developed a deep learning and natural language processing algorithm that analyzed more than 46 billion data points from more than 216,000 EHRs across two hospitals. The tool was able to improve on the accuracy of traditional approaches for identifying unexpected hospital readmissions, predicting length of stay, and forecasting inpatient mortality.” Source: HealthIT Analytics

Quote of the Month:

Thus far, AGI [Artificial General Intelligence] is fantastical, futuristic and a little bit out of reach. It doesn’t mean that nobody is working on it... But the current practical form of AI is nearly entirely a sub-domain called machine learning.” - Jamie Beach, Top 5 Insights After I Spent 100 Days Learning About Artificial Intelligence - Medium

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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.

voice-techINDUSTRY At a Glance: Top 5 Markets & Global

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