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Artificial Intelligence Service in Healthcare

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.

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Artificial Intelligence Service in Healthcare

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  1. Business Consulting Center (BCC) Korea Global Startup Case Study Artificial Intelligence Service in Healthcare June, 2018 Sung-Bin Yoon & Art Choi

  2. 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.

  3. 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.

  4. 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 AI Virtual nursing assistant Patient feedback manage Patient experience manage Hospital Management In-patient clinical monitor Healthcare provider Chatbot & applications Personal Healthcare Recognizing deteriorating patients Wearable applications

  5. Patient Data Analytics Segment 1

  6. AI startups in patient data analytics

  7. Case study - Apixio Company Overview AI Tech Service Innovation Business model 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 • 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 • 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

  8. 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 today’s increasing workloads and strict industry standards,” James P. (Jim) Bradley, Board Chairman, Apixio

  9. Case study - Ayasdi Company Overview AI Tech Service Innovation Business model Innovation • 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 • 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 • Ayasdi’s software is licensed on an annual subscription basis, • It can be deployed via centralized cloud service, or via an on-premise, private cloud installation

  10. Re-Engineering in Human Genome Segment 2

  11. AI startups in genomics

  12. Case study - Deep Genomics Company Overview AI Tech Service Innovation Business model Innovation • 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. • 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. • 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 

  13. Case study - Freenome Company Overview AI Tech Service Innovation Business model Innovation • 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 • Freenome’sartificial 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 • Freenome is conducting the first clinical validation study of an AI-Genomics Blood Test • It plans to bring a blood-based cancer test to market in 2018 • The product will make early-stage detection and treatment of colorectal cancer, a reality for millions of patients

  14. Drug Discovery Segment 3

  15. AI startups in drug discovery

  16. For more information and on demand research, please contact U.S.A. Office MarkLiu Tel:+1-6262952442 E-mail: liujunjie@qyresearch.com ChinaOffice SimonLee(Zhang Dong) Tel : +86-1082945717 E-mail : zhangdong@qyresearch.com South Korea Office Sung-Bin Yoon, Ph. DTel : +82-10-7551-1278 E-mail : yoon@qyresearch.com Japanoffice TangXin Tel:+81-9038009273 E-mail: tangxin@qyresearch.com

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