Artificial intelligence service in healthcare

Business Consulting Center (BCC) Korea

Global Startup Case Study

Artificial Intelligence Service in Healthcare

June, 2018 Sung-Bin Yoon & Art Choi

Background It is no secret that artificial intelligence is shaping new business landscapes in every industries. As one of emerging convergence technologies, Artificial Intelligence (AI) creates new products and services, finally innovating business models. Especially, it has been noted by industry experts and researchers that healthcare sector has the biggest potential of AI convergence. In fact, major technology companies including Google, Microsoft and IBM have invested in AI in healthcare sector. Thousands of AI startups are active launching innovative services related to healthcare.

The main objective of this study is to enhance understanding of AI based service innovation. The study analyzes AI convergence services created by startups as multi-dimensional modes of service innovation. Accordingly, the main research questions are:

How AI innovates service model in healthcare sector?

This study will provide a framework for analyzing the role of AI in convergence service innovation and how it can lead to business model innovation.

This study monitored more than 100 AI startups in healthcare sector (sourced from CB Insights, Crunchbase and etc). With the literature review about AI applications, our research team categorized AI convergence healthcare service into four segments and thirteen sub-segments.

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Conceptual framework for case study This study will adopt the theoretic framework about convergence service innovation process argued by former research (Yoon, 2017). It assumed service innovation typologies are applicable to the analysis of convergence service. In this regard, the study proposed four dimensional service innovation model including convergence technology, service product innovation, service process innovation and business model innovation.

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AI healthcare service segmentation Patient data analytics

Medical Research

Re-engineer human genome Drug discovery

Medical imaging/diagnostics Clinical Care

Screening neurology Robot assisted surgery Virtual nursing assistant

AI

Patient feedback manage

Patient experience manage Hospital Management

In-patient clinical monitor Healthcare provider Chatbot & applications

Personal Healthcare

Recognizing deteriorating patients Wearable applications 4

Segment 1

Patient Data Analytics

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AI startups in patient data analytics Company

Service

Ayasdi

Use AI to automate business processes, enables intelligent applications that augment and even surpass human capabilities, make new discoveries in big data and high dimensional data.

Apixio

AI Coding Solution for massive health records.

Jvion Lumiata Flatiron Health Wellframe

AI software to predict and prevent patient-level diseases and financial losses. Big-data enabled predictive analytic software solutions that combine clinical intelligence with machine learning.

Real-time AI predictive analytics that help hospital networks and insurance carriers to provide care to more patients in less time.

Develops AI software that connects community oncologists, academics, hospitals, life science researchers, and regulators on a shared technology web platform.

AI personalized care protocols for every patient and communicates them via their mobile app. Delivers a set of todo lists and allows patients to directly interact with the caregiver.

Welltok

A reliable health optimization software that leverages personalized, data-driven approaches to connect all consumers with the relevant benefits, rewards, and resources.

A.I Med

AI Software platform to help practices to manage billing, electronic records, electronic claims, document imaging, speech dictation, and paperless scheduling.

Healint

Deep analytics and machine learning to uncover insights from wealth of data to improve patient outcomes and expedite clinical trials.

CloudMedX

H2O.AI Health Catalyst

Clinical AI to improve patient records and use of machine learning organize patient data.

Use of AI platforms for patient care, preventative care, and patient outcomes.

Use of AI to automate machine learning and deep learning solution for massive medical records AI-Powered Healthcare Benchmarking and Performance Improvement Solution 6

Case study - Apixio Company Overview Year founded

HQ location

Key service

Website

2009

San Mateo, CA, USA

AI coding solution for massive health records

www.apixio.com

AI Tech

 Apixio uses AI to analyze massive sets of medical documents and coded data  It uses machine learning, natural language processing (NLP), and neural networks

 It gathers PDF’s, wellness data, claims, flat files, and other documents to compile and organize it into its data acquisition platform

Service Innovation

 Apixio solution help code more accurately in less time- up to four times faster than traditional chart review methods  For Hierarchical Condition Category (HCC) coding, on average, a person can do 24 charts per day with 48 HCCs, but with Apixio, one would be able to code 160 HCCs opportunities per day

Business model Innovation

 Apixio has implemented its business model to offer its systems to healthcare providers, and insurers to be subscription based, pay as you go, and performance based.  Apixio now boasts 33 customers, including 5 national health plans and 9 Blue Cross Blue Shield Association plans

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Case study - Apixio “Health plans are striving for greater accuracy in their chart review and risk adjustment processes than ever before. However, traditional coding processes are no match for tod ay’s increasing workloads and strict industry standards,” James P. (Jim) Bradley, Board Chairman, Apixio

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Case study - Ayasdi Company Overview Year founded

HQ location

Key service

Website

2008

Palo Alto, CA, USA

Clinical variation management

www.ayasdi.com

AI Tech

 With EMR system, hospitals get tremendous amount of patient data  Typically, managing variation is highly manual and labor intensive  Ayasdi’s solution using machine learning simplified clinical variation management. It helps hospitals to identify areas of unwarranted variation and surfaces new best practices

Service Innovation

 Ayasdi’s clinical variation management solution can offer below benefits; 1) Discover what’s going on in the hospital 2) Identify best care practices 3) Build new care paths for different patient groups 4) Implement best practices into care coordination systems 5) Provide continuous improvement care

Business model Innovation

 Ayasdi’s software is licensed on an annual subscription basis,  It can be deployed via centralized cloud service, or via an onpremise, private cloud installation

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Segment 2

Re-Engineering in Human Genome

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AI startups in genomics

Company

Service

Deep Genomics

 Uses AI and machine deep learning to trace potential genetic causes for disease.  Apply AI techniques to medicine and drug development driven by the emergence of powerful new algorithms, but also by cost-effective new ways of sequencing whole genomes, the entire readout of a person’s DNA.

Desktop Genetics

 AI that aids in experimental design and in data interpretation.  DESKGEN AI powers DESKGEN CRISPR Library product range, which enables the work of pharma, biotech and academic customers working in drug discovery, functional genomics, and cell therapy

Pathway Genomics

 Genetic Screening for patient care  The program is developing a smartphone app that merges artificial intelligence and deep learning with personal genetic information. The app provides users with personalized health and wellness information based on the individual’s health history

Freenome

 AI genomics company seeking to empower everyone with the tools they need to detect, treat, and ultimately prevent their diseases.

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Case study - Deep Genomics Company Overview Year founded

HQ location

Key service

Website

2015

Toronto, Canada

Develop genetic medicines

www.deepgenomics.com

AI Tech

 It uses deep learning, or very large neural networks, to analyze genomic data.  Identifying one or more genes responsible for a disease can help researchers develop a drug that addresses the behavior of the faulty genes.

Service Innovation

Deep Genomics provide therapy and medicine discovery platform which combines advanced biological knowledge and data with AI system  It enables to efficiently find drug candidates targeting the genetic determinants of disease at the level of RNA or DNA.

Business model Innovation

 Deep Genomics provides the platform with pharma companies on drug development  On September 25, 2017, it received a USD $13 million equity investment led by Khosla Ventures, accompanied by early stage investment firm True Ventures

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Case study - Freenome Company Overview Year founded

HQ location

Key service

Website

2015

South San Francisco, CA, USA

Early cancer detection

www.freenome.com

AI Tech

 It uses AI for decoding the vast complexity of the cell-free genome  By training on thousands of cancer-positive blood samples, AI genomics platform learns which biomarker patterns signify a cancer’s stage, type, and most effective treatment pathways

Service Innovation

Freenome’s artificial intelligence (AI) platform is poised to detect cancer at its earliest stages and help clinicians optimize the next generation of precision therapies  Freenome’s AI allows it to process all of the cfDNA in the blood

Business model Innovation

 Freenome is conducting the first clinical validation study of an AIGenomics Blood Test  It plans to bring a blood-based cancer test to market in 2018  The product will make earlystage detection and treatment of colorectal cancer, a reality for millions of patients

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Segment 3

Drug Discovery

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AI startups in drug discovery Company

Service

Atomwise

 Atomwise created its own neural network called AtomNet, which uses deep learning to simulate the creation of new drug molecules. The AI also makes predictions about the compounds, including potential side effects, toxicity, effectiveness and so on.

MedAware

 Using AI to eliminate prescription errors. MedAware’s machine-learning algorithms mine data gathered from millions of electronic medical records to detect outliers in prescription behavior and flag them in real-time to healthcare providers.

NuMedii

 NuMedii has built AIDD(Artificial Intelligence for Drug Discovery) that harnesses Big Data and AI to rapidly discover connections between drugs and diseases at a systems level.

Numerate

 Using the power of cutting-edge AI to revolutionize small molecule drug design

twoXar

 The convergence of big data, cloud computing, and artificial intelligence has allowed twoXAR to build a drug discovery platform that is order of magnitudes faster, cheaper, and more accurate than traditional wet-lab based approaches.

Recursion Pharmaceuticals

 It combines AI with automation to conduct experimental biology at scale — testing thousands of compounds on hundreds of cellular disease models in parallel.

BenevolentAI

 BenevolentTech is developing an advanced AI platform that helps scientists make new discoveries and redefines how scientists gain access to, and use, all the data available to them to drive innovation. It is built upon a deep judgement system that learns and reasons from the interaction between human judgement and data.

Cyclica

 Cyclica harnesses biophysics, bioinformatics and AI to help pharmaceutical companies navigate the drug discovery pipeline by assessing the safety and efficacy of drugs.

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For more information and on demand research, please contact U.S.A. Office Mark Liu Tel:+1-6262952442 E-mail: liujunjie@qyresearch.com China Office Simon Lee(Zhang Dong) Tel : +86-1082945717 E-mail : zhangdong@qyresearch.com South Korea Office Sung-Bin Yoon, Ph. D Tel : +82-10-7551-1278 E-mail : yoon@qyresearch.com

Japan office Tang Xin Tel:+81-9038009273 E-mail: tangxin@qyresearch.com