This document discusses how AI can improve research and care models in healthcare. It begins by defining key AI concepts like machine learning and deep learning. It then discusses how data quality is important for AI applications. The document notes that access to data, computing power, and an experimentation mindset are enabling new AI solutions. It provides examples of leaders in the AI field and discusses select use cases for AI in areas like life sciences, care delivery, payors, and consumers. Overall, the document suggests that while AI is in early stages, it has potential to transform healthcare research and delivery models.
Artificial Intelligence in Medical Imaging: An Analysis of Funding for Start-upsSimon Harris
There are over 50 start-up companies developing artificial intelligence solutions for medical imaging. Combined, these companies have raised over $100 million in funding. This short report from Signify Research shows the trends in capital funding for these companies and highlights how funding breaks down by company, by region and by clinical application.
Have Data—Need Analysts. Lessons Learned From The Woodworking IndustryHealth Catalyst
The document discusses lessons learned from the woodworking industry that can be applied to healthcare analytics. It notes that both require the right tools, skilled people, and analytic workflows. The woodshop layout places importance on how stations are arranged to efficiently flow materials through the process. Similarly, the layout of analytic work streams should reflect skills, tools, and experience to optimize analytic workflow. It also stresses the importance of data analysis skills over other technical skills and of having the right skills matched to descriptive and prescriptive analytic work.
Gain insights from data analytics and take action! Learn why everyone is making a big deal about big data in healthcare and how data analytics creates action.
This document discusses machine learning approaches for detecting healthcare fraud, waste, and abuse. It begins by outlining the scope of fraud in the US healthcare system and the large volume of healthcare data available for analysis. It then describes different types of fraud, waste, and abuse and analytical approaches used, including supervised learning models, unsupervised anomaly detection techniques, and generating provider-level features from claims data. Specific challenges in detecting healthcare fraud like imbalanced data and evolving fraud schemes are also discussed.
AI for Healthcare Leaders: The New AI Frontier for Improved Leadership Decisi...Health Catalyst
A new frontier is expanding AI from artificial intelligence to augmented intelligence. Traditional AI has focused on improving analytics efficiency and effectiveness. Augmented Intelligence is about improving the decision-making ability of healthcare leaders.
Our goal is to support leaders in driving systemwide outcomes improvement—do we have more opportunity in readmission or depression, how should we staff the ED on weekends, how long does a nurse manager need to improve safety culture, and so on. There is an opportunity to include AI to assist in decision making in new and innovative ways. In this webinar, you will see specific frameworks and tools to use AI to close the information gap for leaders to drive outcomes improvement.
Big Data = Big Headache? Using People Analytics to Fuel ROItalent.imperative
• Interpret trend information to understand the business case for Big Data in HR.
• Examine your fears and assumptions about Big Data.
• Learn from best practice case studies how to demonstrate HR’s contributions to ROI.
• Understand how to engage key stakeholders as part of your organization’s people analytics journey.
This document is a presentation by Raymond Gensinger on data analytics in healthcare. It discusses examples of analytics used in baseball to improve performance, the different types of analytics including descriptive, predictive, and prescriptive. It also covers how analytics have evolved, organizational readiness for analytics, and key factors for analytics success including data, enterprise integration, leadership, targets, and having the right analysts. The presentation provides a framework for healthcare to apply analytics and examples of how different types of analytics could be used.
This document discusses the evolution of people analytics and how measuring social interactions and communication patterns can provide insights into team performance. It introduces the sociometric badge, a device that can measure interactivity, speech patterns, and collect digital data to analyze communication behaviors. This data is aggregated and anonymized to ensure individual privacy. Metrics like cohesion, exploration, and collective intelligence may provide new ways to understand team effectiveness. Lastly, it advocates taking a blended approach to change management by addressing structural, social, and individual levels to create sustainable organizational impact.
Artificial Intelligence in Medical Imaging: An Analysis of Funding for Start-upsSimon Harris
There are over 50 start-up companies developing artificial intelligence solutions for medical imaging. Combined, these companies have raised over $100 million in funding. This short report from Signify Research shows the trends in capital funding for these companies and highlights how funding breaks down by company, by region and by clinical application.
Have Data—Need Analysts. Lessons Learned From The Woodworking IndustryHealth Catalyst
The document discusses lessons learned from the woodworking industry that can be applied to healthcare analytics. It notes that both require the right tools, skilled people, and analytic workflows. The woodshop layout places importance on how stations are arranged to efficiently flow materials through the process. Similarly, the layout of analytic work streams should reflect skills, tools, and experience to optimize analytic workflow. It also stresses the importance of data analysis skills over other technical skills and of having the right skills matched to descriptive and prescriptive analytic work.
Gain insights from data analytics and take action! Learn why everyone is making a big deal about big data in healthcare and how data analytics creates action.
This document discusses machine learning approaches for detecting healthcare fraud, waste, and abuse. It begins by outlining the scope of fraud in the US healthcare system and the large volume of healthcare data available for analysis. It then describes different types of fraud, waste, and abuse and analytical approaches used, including supervised learning models, unsupervised anomaly detection techniques, and generating provider-level features from claims data. Specific challenges in detecting healthcare fraud like imbalanced data and evolving fraud schemes are also discussed.
AI for Healthcare Leaders: The New AI Frontier for Improved Leadership Decisi...Health Catalyst
A new frontier is expanding AI from artificial intelligence to augmented intelligence. Traditional AI has focused on improving analytics efficiency and effectiveness. Augmented Intelligence is about improving the decision-making ability of healthcare leaders.
Our goal is to support leaders in driving systemwide outcomes improvement—do we have more opportunity in readmission or depression, how should we staff the ED on weekends, how long does a nurse manager need to improve safety culture, and so on. There is an opportunity to include AI to assist in decision making in new and innovative ways. In this webinar, you will see specific frameworks and tools to use AI to close the information gap for leaders to drive outcomes improvement.
Big Data = Big Headache? Using People Analytics to Fuel ROItalent.imperative
• Interpret trend information to understand the business case for Big Data in HR.
• Examine your fears and assumptions about Big Data.
• Learn from best practice case studies how to demonstrate HR’s contributions to ROI.
• Understand how to engage key stakeholders as part of your organization’s people analytics journey.
This document is a presentation by Raymond Gensinger on data analytics in healthcare. It discusses examples of analytics used in baseball to improve performance, the different types of analytics including descriptive, predictive, and prescriptive. It also covers how analytics have evolved, organizational readiness for analytics, and key factors for analytics success including data, enterprise integration, leadership, targets, and having the right analysts. The presentation provides a framework for healthcare to apply analytics and examples of how different types of analytics could be used.
This document discusses the evolution of people analytics and how measuring social interactions and communication patterns can provide insights into team performance. It introduces the sociometric badge, a device that can measure interactivity, speech patterns, and collect digital data to analyze communication behaviors. This data is aggregated and anonymized to ensure individual privacy. Metrics like cohesion, exploration, and collective intelligence may provide new ways to understand team effectiveness. Lastly, it advocates taking a blended approach to change management by addressing structural, social, and individual levels to create sustainable organizational impact.
There Is A 90% Probability That Your Son Is Pregnant: Predicting the Future ...Health Catalyst
In this webinar, which is geared for managers and executives, Dale Sanders provides a new version of a very popular lecture he presented at this year’s Health Analytics Summit in Salt Lake City. Attendees will gain an understanding of:
What to expect from predictive analytics as it relates to human behavior
A general overview of predictive analytics models, and the contexts in which those various models should and should not be used
The scenarios in which predictive models in healthcare are effective and when they are not, given that 80% of population health outcomes are determined by socio-econonic factors, not healthcare delivery
The relationship between predictive analytic accuracy and topics of data management such as data quality, data volume and patient outcomes data
The use of predictive analytics to identify patients who are on a trajectory for poor, as well as good, outcomes
How current predictive analytics strategies are overlookng the cost of intervention and “Return on Engagement”, ROE— the cost per unit of healthcare improvement for patient populations
The cultural, philosophical, and legal conundrums that predictive analytics will create for healthcare, notably healthcare rationing
The success of predictive analytics will not be defined by the simple risk stratification of patient populations for care management teams. Success will depend on the costs of intervention to reduce the risks that are identified by predictive analytics, which boils down to this two-part question: Now that we can predict a patient’s risk for a bad healthcare outcome, “What’s the probability of influencing this patient’s behavior towards a better outcome?” And, “How much effort and cost will be required for that influence?”
AI for Precision Medicine (Pragmatic preclinical data science)Paul Agapow
This document summarizes a presentation on using data science approaches like machine learning for precision medicine and biomedical research. It notes that biomedical data sets are often small, which limits the use of deep learning techniques that require large amounts of labeled data. It advocates combining multiple smaller datasets together using standards to create larger datasets for analysis. It also emphasizes using multiple data types (e.g. omics data, electronic health records, social media) together through integrated analysis to provide more context than any single data type alone. It provides examples of applying these approaches to problems like classifying texts for systematic reviews and discovering asthma subtypes through multi-omics analysis.
Data Is the New Strategic Asset in M&As: Is Ripping and Replacing EHRs Really...Health Catalyst
In this webinar, Tim and Dale, who worked together at Northwestern Medicine to establish an early-on and leading enterprise data warehouse solution for the hospital, physicians and medical school, will present their unique perspectives creating a thoughtful environment of comparison and contrast. This won’t be a typical corporate dozer—rather it will provide an opportunity for you to think deeply about the novel nature of your organization’s data. Historically, hospital expansion by building a larger footprint was the way to scale and capture market share. While those things still matter, attention has shifted to the expansion of the distribution of care through virtual and physical access points that embody a far more consumer friendly means to deliver care. It is in those entities that enriched data can be used to deliver care outreach that actually makes a difference for patients. That is where the new margins exist.
Healthcare and Life Sciences organizations are leveraging Big Data technology to capture data in order to get a better insight into patient centric and research centric information. Combining these two requires extreme computing power. We will discuss use cases where Big Data technology was instrumental ; Merging Genomic and Clinical Data in order to advance personalized Medicine
Las expectativas en la inteligencia artificial (IA) son muy altas, pero ¿qué están haciendo las empresas ahora? El objetivo de este informe es presentar una línea base realista que permita
empresas comparar sus ambiciones y esfuerzos en inteligencia artificial. Basándose en datos en lugar de
conjetura, la investigación se basa en una encuesta global de más de 3.000 ejecutivos, gerentes y analistas de todas las industrias y entrevistas en profundidad con más de 30
expertos en tecnología y ejecutivos.
This document discusses artificial intelligence (AI) in healthcare, including key challenges and best practices for implementation. Some common challenges with AI implementation include not having enough high quality data for training models and ensuring the models align with real-world problems that can change over time. It is important to have a planned strategy for AI, carefully select partners, and ensure ethical and transparent use of data that complies with regulations. When implemented properly, AI has potential to improve healthcare through applications like personalized patient experiences and optimizing operations.
MBA Presentation for "Innovation: The Future of Healthcare" that focuses on the Big Data and Precision Medicine and how leveraging these powerful concepts will move healthcare delivery from provider-centric to patient-centered care.
HR analytics provides valuable insights for organizations by analyzing employee-related data using statistical tools. It has two main components: descriptive analytics which measures past performance, and predictive analytics which provides insights into future outcomes. The increased focus on HR analytics stems from both necessity and opportunity. Necessity arises from the growing importance of human capital to organizational value creation. Opportunity comes from the vast amounts of employee data now available that can be transformed into useful insights using analytics. When done effectively, HR analytics can help organizations improve performance, better link business objectives to workforce strategies, and increase returns on investment in human resources.
P 01 advanced_people_analytics_2016_04_03_v11Vishwa Kolla
Vishwa Kolla presented at the Predictive Analytics World for Workforce conference on applying advanced analytics to workforce issues. He discussed how employees are a company's biggest asset and focusing analytics on acquisition, nurture and retention can improve productivity, engagement and performance. Network analysis of employee interactions was highlighted as a way to better understand engagement issues. Careful data collection and modeling over time was emphasized as critical to successfully implementing people analytics initiatives.
This document discusses how big data can be used in the healthcare sector to improve outcomes and reduce costs. It begins by defining big data and describing how large corporations have been using big data for years. It then draws a parallel between how big data helped answer what advertising worked for companies like Google, and how big data can help determine which medical treatments are effective. The document outlines some key characteristics of big data in healthcare, such as different types of data silos and the 4 Vs of big data. It also discusses drivers for adoption of big data in healthcare and provides examples of how big data can enable quality improvement and cost cutting. Challenges to adoption are outlined as well as some leading big data companies in healthcare. The document
Artificial Intelligence in Restaurants and Food ServicesDaniel Faggella
The document provides an overview of AI applications in restaurants and food services by Dan Faggella, CEO of TechEmergence. It discusses the current state of AI adoption in SMBs, highlights some potential uses of robotics, kiosks, chatbots, and consumer platforms, and emphasizes that AI will likely impact SMB food services indirectly through consumer applications in the near future rather than requiring direct implementation by restaurants themselves. It cautions against "toy" AI projects and recommends SMBs focus on understanding emerging consumer trends and business intelligence uses of AI.
Precision medicine has profound implications for patient care and clinical outcomes, and is already beginning to impact everyday medical practice. However, implementation faces several obstacles, including overstated claims, resistance among clinical medicine thought leaders and providers, and concerns about costs, data overload, and interoperability. This webinar will address five key concerns, challenges, and barriers among clinicians and IT professionals struggling to determine the value and limitations of implementing precision medicine, and offer tangible recommendations to help drive toward precision medicine adoption.
Learning Objectives:
Identify obstacles that impede the implementation of precision medicine in clinical practice.
Contrast population-based medicine and precision medicine.
Demonstrate the real world benefits of precision medicine in today's healthcare setting.
Big Data: The Force That’s Good for Consumers and SocietyExperian_US
Craig Boundy, CEO of Experian North America, discusses how big data is being used as a force for good. Good for consumers, good for business, and good for society. He shares his perspective how Experian’s work in data and analytics has real-life applications.
Qinsight™ is so much more than just another search engine. Qinsight is rooted in Artificial Intelligence, which provides superior results with ease and powers visual analytics of the actual text content. Here, we highlight three key aspects of Qinsight versus traditional tools.
Storytelling for analytics | Naveen Gattu | CDAO Apex 2020Gramener
Chief data and analytics officers (CDAO) Apex Winter 2020 conference in Orlando was an intimate gathering of analytics leaders from across the world.
Gramener's Co-founder and COO, Naveen Gattu, spoke about the importance of storytelling on easy data consumption and impact on data-driven decision making for business users.
The robots are near. But they are at a disadvantage when it comes to interpersonal sensitivity. As long as there are humans involved in Talent Acquisition, it remains a highly relationship-driven process. Whether it be candidates themselves, hiring managers, hiring teams, vendor partners or senior leadership, you need to know how each of these groups is thinking about how you are engaging with them, how to earn their trust, and how to get what you need from each relationship you create.
This presentation examines the evolving recruiting relationship ecosystem, encourages you to identify your own blind spots, and leaves you with actionable steps to create effective collaboration across key stakeholder groups. In this discussion you will:
• Better understand the evolving recruiting relationship ecosystem
• Discover your own relationship blind spots
• Learn how to take action to more effectively collaborate with various recruiting stakeholder groups
This document discusses how unstructured data like text can be analyzed using natural language processing techniques. It provides an example of analyzing the Hindu epic Mahabharata, which contains 1.8 million words, to extract insights. Specific techniques mentioned include sentiment analysis of the text to understand relations between characters, and visualizing closeness of characters. The document advocates augmenting available data with metadata like keywords, sentiment, visual recognition from images and transcripts from audio to structure unstructured data for analysis.
HealthXL: How Artificial Intelligence (AI) Can Improve Research & Care Models...Maeve Lyons
Artificial Intelligence in Pharma and Care Delivery Delivering on the Promise.
Earlier this week we published a blog about the state of AI in pharma and care delivery, and we also mentioned we’d be launching an accompanying report. We’re happy share our initial report presentation as part of HealthXL’s Big Data & AI Working Group above.
In the future, HealthXL’s Working Group will go deeper into use cases and discuss other relevant industry topics such as best practices for acquiring quality data, regulatory guidelines for AI solutions, leading academic centers, and much more!
https://healthxl.co/report-artificial-intelligence-pharma-care-delivery-delivering-promise/
Digital and technological advancements and how they have impacted health. From data, IoT & wearables, 3D printing, personalized medicine, all of these trends can be levers to help with demographic shifts, increased access to healthcare and rising costs.
The document provides an overview of digital healthcare and some of the anticipated legal issues. It was written by Professor Yoon Sup Choi of Sungkyunkwan University, who is also the director of the Digital Healthcare Institute. He has experience investing in startups and advising various digital health companies. The document discusses how artificial intelligence is rapidly transforming the conservative medical system and some of the challenges this poses for medical professionals. It also briefly introduces the author's background and perspectives on digital healthcare innovation.
There Is A 90% Probability That Your Son Is Pregnant: Predicting the Future ...Health Catalyst
In this webinar, which is geared for managers and executives, Dale Sanders provides a new version of a very popular lecture he presented at this year’s Health Analytics Summit in Salt Lake City. Attendees will gain an understanding of:
What to expect from predictive analytics as it relates to human behavior
A general overview of predictive analytics models, and the contexts in which those various models should and should not be used
The scenarios in which predictive models in healthcare are effective and when they are not, given that 80% of population health outcomes are determined by socio-econonic factors, not healthcare delivery
The relationship between predictive analytic accuracy and topics of data management such as data quality, data volume and patient outcomes data
The use of predictive analytics to identify patients who are on a trajectory for poor, as well as good, outcomes
How current predictive analytics strategies are overlookng the cost of intervention and “Return on Engagement”, ROE— the cost per unit of healthcare improvement for patient populations
The cultural, philosophical, and legal conundrums that predictive analytics will create for healthcare, notably healthcare rationing
The success of predictive analytics will not be defined by the simple risk stratification of patient populations for care management teams. Success will depend on the costs of intervention to reduce the risks that are identified by predictive analytics, which boils down to this two-part question: Now that we can predict a patient’s risk for a bad healthcare outcome, “What’s the probability of influencing this patient’s behavior towards a better outcome?” And, “How much effort and cost will be required for that influence?”
AI for Precision Medicine (Pragmatic preclinical data science)Paul Agapow
This document summarizes a presentation on using data science approaches like machine learning for precision medicine and biomedical research. It notes that biomedical data sets are often small, which limits the use of deep learning techniques that require large amounts of labeled data. It advocates combining multiple smaller datasets together using standards to create larger datasets for analysis. It also emphasizes using multiple data types (e.g. omics data, electronic health records, social media) together through integrated analysis to provide more context than any single data type alone. It provides examples of applying these approaches to problems like classifying texts for systematic reviews and discovering asthma subtypes through multi-omics analysis.
Data Is the New Strategic Asset in M&As: Is Ripping and Replacing EHRs Really...Health Catalyst
In this webinar, Tim and Dale, who worked together at Northwestern Medicine to establish an early-on and leading enterprise data warehouse solution for the hospital, physicians and medical school, will present their unique perspectives creating a thoughtful environment of comparison and contrast. This won’t be a typical corporate dozer—rather it will provide an opportunity for you to think deeply about the novel nature of your organization’s data. Historically, hospital expansion by building a larger footprint was the way to scale and capture market share. While those things still matter, attention has shifted to the expansion of the distribution of care through virtual and physical access points that embody a far more consumer friendly means to deliver care. It is in those entities that enriched data can be used to deliver care outreach that actually makes a difference for patients. That is where the new margins exist.
Healthcare and Life Sciences organizations are leveraging Big Data technology to capture data in order to get a better insight into patient centric and research centric information. Combining these two requires extreme computing power. We will discuss use cases where Big Data technology was instrumental ; Merging Genomic and Clinical Data in order to advance personalized Medicine
Las expectativas en la inteligencia artificial (IA) son muy altas, pero ¿qué están haciendo las empresas ahora? El objetivo de este informe es presentar una línea base realista que permita
empresas comparar sus ambiciones y esfuerzos en inteligencia artificial. Basándose en datos en lugar de
conjetura, la investigación se basa en una encuesta global de más de 3.000 ejecutivos, gerentes y analistas de todas las industrias y entrevistas en profundidad con más de 30
expertos en tecnología y ejecutivos.
This document discusses artificial intelligence (AI) in healthcare, including key challenges and best practices for implementation. Some common challenges with AI implementation include not having enough high quality data for training models and ensuring the models align with real-world problems that can change over time. It is important to have a planned strategy for AI, carefully select partners, and ensure ethical and transparent use of data that complies with regulations. When implemented properly, AI has potential to improve healthcare through applications like personalized patient experiences and optimizing operations.
MBA Presentation for "Innovation: The Future of Healthcare" that focuses on the Big Data and Precision Medicine and how leveraging these powerful concepts will move healthcare delivery from provider-centric to patient-centered care.
HR analytics provides valuable insights for organizations by analyzing employee-related data using statistical tools. It has two main components: descriptive analytics which measures past performance, and predictive analytics which provides insights into future outcomes. The increased focus on HR analytics stems from both necessity and opportunity. Necessity arises from the growing importance of human capital to organizational value creation. Opportunity comes from the vast amounts of employee data now available that can be transformed into useful insights using analytics. When done effectively, HR analytics can help organizations improve performance, better link business objectives to workforce strategies, and increase returns on investment in human resources.
P 01 advanced_people_analytics_2016_04_03_v11Vishwa Kolla
Vishwa Kolla presented at the Predictive Analytics World for Workforce conference on applying advanced analytics to workforce issues. He discussed how employees are a company's biggest asset and focusing analytics on acquisition, nurture and retention can improve productivity, engagement and performance. Network analysis of employee interactions was highlighted as a way to better understand engagement issues. Careful data collection and modeling over time was emphasized as critical to successfully implementing people analytics initiatives.
This document discusses how big data can be used in the healthcare sector to improve outcomes and reduce costs. It begins by defining big data and describing how large corporations have been using big data for years. It then draws a parallel between how big data helped answer what advertising worked for companies like Google, and how big data can help determine which medical treatments are effective. The document outlines some key characteristics of big data in healthcare, such as different types of data silos and the 4 Vs of big data. It also discusses drivers for adoption of big data in healthcare and provides examples of how big data can enable quality improvement and cost cutting. Challenges to adoption are outlined as well as some leading big data companies in healthcare. The document
Artificial Intelligence in Restaurants and Food ServicesDaniel Faggella
The document provides an overview of AI applications in restaurants and food services by Dan Faggella, CEO of TechEmergence. It discusses the current state of AI adoption in SMBs, highlights some potential uses of robotics, kiosks, chatbots, and consumer platforms, and emphasizes that AI will likely impact SMB food services indirectly through consumer applications in the near future rather than requiring direct implementation by restaurants themselves. It cautions against "toy" AI projects and recommends SMBs focus on understanding emerging consumer trends and business intelligence uses of AI.
Precision medicine has profound implications for patient care and clinical outcomes, and is already beginning to impact everyday medical practice. However, implementation faces several obstacles, including overstated claims, resistance among clinical medicine thought leaders and providers, and concerns about costs, data overload, and interoperability. This webinar will address five key concerns, challenges, and barriers among clinicians and IT professionals struggling to determine the value and limitations of implementing precision medicine, and offer tangible recommendations to help drive toward precision medicine adoption.
Learning Objectives:
Identify obstacles that impede the implementation of precision medicine in clinical practice.
Contrast population-based medicine and precision medicine.
Demonstrate the real world benefits of precision medicine in today's healthcare setting.
Big Data: The Force That’s Good for Consumers and SocietyExperian_US
Craig Boundy, CEO of Experian North America, discusses how big data is being used as a force for good. Good for consumers, good for business, and good for society. He shares his perspective how Experian’s work in data and analytics has real-life applications.
Qinsight™ is so much more than just another search engine. Qinsight is rooted in Artificial Intelligence, which provides superior results with ease and powers visual analytics of the actual text content. Here, we highlight three key aspects of Qinsight versus traditional tools.
Storytelling for analytics | Naveen Gattu | CDAO Apex 2020Gramener
Chief data and analytics officers (CDAO) Apex Winter 2020 conference in Orlando was an intimate gathering of analytics leaders from across the world.
Gramener's Co-founder and COO, Naveen Gattu, spoke about the importance of storytelling on easy data consumption and impact on data-driven decision making for business users.
The robots are near. But they are at a disadvantage when it comes to interpersonal sensitivity. As long as there are humans involved in Talent Acquisition, it remains a highly relationship-driven process. Whether it be candidates themselves, hiring managers, hiring teams, vendor partners or senior leadership, you need to know how each of these groups is thinking about how you are engaging with them, how to earn their trust, and how to get what you need from each relationship you create.
This presentation examines the evolving recruiting relationship ecosystem, encourages you to identify your own blind spots, and leaves you with actionable steps to create effective collaboration across key stakeholder groups. In this discussion you will:
• Better understand the evolving recruiting relationship ecosystem
• Discover your own relationship blind spots
• Learn how to take action to more effectively collaborate with various recruiting stakeholder groups
This document discusses how unstructured data like text can be analyzed using natural language processing techniques. It provides an example of analyzing the Hindu epic Mahabharata, which contains 1.8 million words, to extract insights. Specific techniques mentioned include sentiment analysis of the text to understand relations between characters, and visualizing closeness of characters. The document advocates augmenting available data with metadata like keywords, sentiment, visual recognition from images and transcripts from audio to structure unstructured data for analysis.
HealthXL: How Artificial Intelligence (AI) Can Improve Research & Care Models...Maeve Lyons
Artificial Intelligence in Pharma and Care Delivery Delivering on the Promise.
Earlier this week we published a blog about the state of AI in pharma and care delivery, and we also mentioned we’d be launching an accompanying report. We’re happy share our initial report presentation as part of HealthXL’s Big Data & AI Working Group above.
In the future, HealthXL’s Working Group will go deeper into use cases and discuss other relevant industry topics such as best practices for acquiring quality data, regulatory guidelines for AI solutions, leading academic centers, and much more!
https://healthxl.co/report-artificial-intelligence-pharma-care-delivery-delivering-promise/
Digital and technological advancements and how they have impacted health. From data, IoT & wearables, 3D printing, personalized medicine, all of these trends can be levers to help with demographic shifts, increased access to healthcare and rising costs.
The document provides an overview of digital healthcare and some of the anticipated legal issues. It was written by Professor Yoon Sup Choi of Sungkyunkwan University, who is also the director of the Digital Healthcare Institute. He has experience investing in startups and advising various digital health companies. The document discusses how artificial intelligence is rapidly transforming the conservative medical system and some of the challenges this poses for medical professionals. It also briefly introduces the author's background and perspectives on digital healthcare innovation.
eyeforpharma Philadelphia 2018 was all about delivering real value for patients. While there, A. J. Triano, SVP of Engagement Strategy at Syneos Health Communications, spoke to the audience about sideways thinking and how non-traditional players and new technologies are increasing value and control for consumers. A. J. talked about three macro trends: The Changing Interface of Healthcare, AI is Becoming the Interaction Engine, and Redefinition of Point-Of-Care.
The document summarizes the future of healthcare and digital healthcare. It introduces Professor Yoon Sup Choi, the director of the Digital Healthcare Institute at Sungkyunkwan University. It discusses how artificial intelligence is reshaping the conservative medical system and how quickly AI is developing and influencing healthcare. The convergence of information technology, biotechnology, and medicine is creating innovation that will transform medical education and clinical practice.
The document discusses the future of healthcare and digital healthcare. It introduces Professor Yoon Sup Choi, the director of the Digital Healthcare Institute at Sungkyunkwan University. It also discusses artificial intelligence in medicine and how AI is revolutionizing the traditionally conservative medical system. However, the fast development and wide influence of medical AI is difficult for modern medical experts to understand. The document provides case studies and insights into the current state and future of medical AI.
When it comes to AI use for prediction, diagnosis and treatment of medical conditions, reality is often replaced with a hype. Limitations should be known. A review of AI failures and challenges in healthcare showing why it is not likely for algorithms to replace physicians in the nearest future.
How to implement digital medicine in the futureYoon Sup Choi
by Yoon Sup Choi, PhD
yoonsup.choi@gmail.com
Professor, SAHIST, Sungkyunkwan University
Director, Digital Healthcare Institute
Managing Partner, Digital Healthcare Partners
The document discusses global trends in the digital healthcare industry and regulation. It notes that in 2018, a record $14.6 billion was invested globally in digital health, continuing a trend of annual increases since 2015. However, Korea does not have any of the 38 digital health unicorn startups valued over $1 billion that exist globally. It defines key terms like digital health, mHealth, and personal genomics. It also discusses regulatory issues and the increasing role of artificial intelligence. The future of digital medicine is that it will become integrated into ordinary medicine.
CLGPPT FOR DISEASE DETECTION PRESENTATIONYashRajput82
This document summarizes a project presentation submitted by three group members - Aanchal Rastogi, Kapil Gangwar, and Shahnavaj - to their department of computer science engineering on the topic of "Disease Prognostication & Prevention Using Soft Computing". The presentation includes an introduction, explanations of artificial intelligence and machine learning, the problem statement, evolution of the topic, challenges and limitations, implementations, future work, and a conclusion.
The Role of Artificial Intelligence in Revolutionizing Healthcare A Comprehen...ijtsrd
A breakthrough era that holds enormous promise for increasing patient care, lowering healthcare costs, and improving overall healthcare outcomes has arrived with the integration of Artificial Intelligence AI in healthcare. This in depth analysis examines the several ways in which AI is transforming healthcare, including diagnosis, treatment, drug research, patient management, and administrative procedures. To lay a strong foundation for understanding AI, machine learning, and deep learning applications in healthcare, the examination begins with clarifying their core principles. It explores how AI might be used to analyze large scale, intricate medical datasets including electronic health records EHRs , medical imaging, and genomes, enabling the early detection of disease, precise diagnosis, and tailored therapy recommendations. Additionally, AI driven technologies like natural language processing NLP have demonstrated considerable potential in extracting important insights from unstructured clinical notes and research literature, supporting clinical decision support and medical research. AI powered robotics and automation have also begun to play crucial roles in rehabilitation and minimally invasive surgery, lowering the invasiveness of operations and speeding up patient recovery. The review emphasizes the efforts that are still being made to create AI driven drug discovery systems that hasten the identification of new treatments and enhance the layouts of clinical trials. By examining trends and patterns in healthcare data, it also examines AIs function in predictive analytics, predicting disease outbreaks, and enhancing population health management. Furthermore, in the context of optimizing healthcare operations and lowering administrative duties, the contribution of AI to administrative tasks such as medical billing, fraud detection, and resource allocation is considered. The review emphasizes the significance of privacy, transparency, and responsible AI deployment while highlighting the ethical and regulatory concerns involved with AI in healthcare. In order to fully realize the potential of AI, it also analyzes potential adoption barriers and the necessity of interdisciplinary cooperation between healthcare experts, data scientists, and legislators. In conclusion, this in depth analysis offers a complete overview of how AI is altering healthcare and provides insights into its present successes and potential in the future. This effort intends to spur innovation, educate stakeholders, and open the door for a more effective, patient centered, and accessible healthcare ecosystem by shedding light on the revolutionary effects of AI on healthcare. Kajal Gohane | Roshini S | Komal Pode "The Role of Artificial Intelligence in Revolutionizing Healthcare: A Comprehensive Review" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-5 , October 2023, URL: https://www.ijtsrd.com/pa
1) The document discusses digital healthcare in Korea, focusing on medical artificial intelligence.
2) It introduces Yoon Sup Choi, a leading expert in digital healthcare and medical AI in Korea. He is a professor and director of a digital healthcare institute.
3) The document provides endorsements of Choi's book on medical AI from other doctors and professors, praising its overview of the current state and future of medical AI.
Artificial Intelligence in Healthcare.pdfMedTechBiz
Artificial Intelligence (AI) has emerged as a powerful ally in the healthcare sector, revolutionizing the way medical professionals diagnose, treat, and manage diseases. By integrating advanced machine learning algorithms and natural language processing, AI has demonstrated its ability to swiftly and accurately analyze vast medical datasets, providing valuable insights that can lead to more precise diagnoses and effective treatments.
From interpreting medical imaging to real-time patient monitoring, the applications of AI in healthcare are vast and multifaceted, promising to significantly enhance the quality of medical care and ultimately save lives.
In this Artificial Intelligence in Healthcare: Top 100 Startups, Use Cases" you will gain a comprehensive overview of how startups are leading the way in applying artificial intelligence (AI) in the healthcare sector. These companies are at the forefront of innovation, exploring AI's potential to improve healthcare worldwide.
By examining these startups, we can learn much about AI's potential in healthcare and how it is shaping the future of the industry. Through ongoing research and development efforts, these companies are helping to pave the way for more effective, accessible, and personalized healthcare for all.
EBOOK AMAZON
https://www.amazon.com/dp/B0CW1D4F1C
AI in Healthcare: From Hype to Impact (updated)Mei Chen, PhD
This document summarizes a workshop presentation on AI in healthcare. It begins by discussing the hype around AI and how it has not yet delivered many results. It then outlines some challenges to using AI in healthcare like a lack of understanding of what AI can do, poor implementation strategies, and a shortage of trained workforce. The objectives of the workshop are then stated as understanding AI's real potential and how to invest wisely. Various AI technologies like machine learning, natural language processing, and voice technology are described. Key requirements for successful AI include understanding its limitations and developing a strategy to bring real value.
Pravir Ishvarlal- Artificial Intelligence in Healthcareitnewsafrica
Pravir Ishvarlal, Data Scientist at Netcare, on Artificial Intelligence in Healthcare, at Healthcare Innovation Summit Africa 2023 hosted by IT News Africa. #HISA2023 #Healthcare #Healthtech #HealthInnovation
Recent advances in artificial intelligence (AI) are transforming healthcare in several ways:
1) AI is being used to detect diseases like cancer more accurately and at earlier stages by analyzing medical images and data.
2) Health monitoring tools using AI, like wearable devices and apps, are helping encourage healthier behaviors and allow remote monitoring by doctors.
3) AI systems are improving clinical decision-making by analyzing large amounts of medical data to customize treatment and support precision medicine approaches.
AI – Opportunities and Challenges in Transforming the Biopharma Value ChainEY
These slides were presented by Pamela Spence, EY Global Life Sciences Industry Leader, at the annual BIO International Convention on 20 June 2017. Pamela led a panel discussion on Artificial Intelligence (AI) and the opportunities and challenges it presents in transforming the biopharma value chain. The panelists included Dr. Attul Butte, Director of the Institute for Computational Health Science at the University of California – San Francisco, Iya Khalil, Chief Commercial Officer and Co-founder of GNS Healthcare, Nathan Price, Associate Director of the Institute for Systems Biology and co-founder of Arivale, and Jackie Hunter, CEO of Benevolent AI
Similar to HeathXL report on use cases for Big Data and AI (20)
Patient engagement in clinical trials Martin Kelly
The document discusses improving patient engagement in clinical trials through digital methods. Only 2-5% of cancer patients currently participate in clinical trials. The top 3 solutions identified to engage patients were: 1) a clinical trial finder for patients to inform them of available trials, 2) virtual clinical trials conducted entirely online, and 3) tools to personalize clinical trials to individual patient needs/preferences. The document reviews several companies providing these types of solutions and proposes an experiment partnering with a CRO to test if a digital intervention increases patient enrollment and retention in a clinical trial compared to a control group without a digital intervention.
HealthXL is a leading global platform for digital health collaboration that connects over 700 innovators and 300 advisors across 25 global brands. It provides evidence from leading healthcare innovators and allows users to build relationships within the healthcare industry. The invite-only community focuses on common problems through curated partnerships.
The HealthXL proposal describes a global digital health collaboration platform that connects over 700 innovators and advisors across 25 brands. The platform aims to build relationships in healthcare through evidence-based partnerships and invite-only collaborations focused on common problems. Key features include a global community of clinicians, executives, investors and founders, as well as events, market insights, and resources to inform, engage and connect global innovators to drive collaborations.
HealthXL is a leading global platform for digital health collaboration that connects over 300 advisors, 700 innovators, and 25 global brands. It hosts exclusive quarterly retreats for global health leaders and uses its platform to share insights from these leaders. HealthXL actively works with thought leaders to solve common challenges and drive collaborations around key issues such as new models of care, consumer engagement, and precision medicine.
The document summarizes a HealthXL event held in Boston on September 21st, 2016. It provides an agenda for the event including sessions on clinical trials and patient engagement, new care models for seniors, and heart health. It also summarizes the afternoon workshops held on various health topics and the attendees of the event. The partnership between HealthXL and Ranked Health to evaluate digital health applications is described. Upcoming HealthXL events and how to connect with the HealthXL community are outlined.
Ranked health health xl webinar ank presentation v2Martin Kelly
This document discusses digital health and patient safety. It introduces HealthXL, a platform for digital health collaboration, and Ranked Health, which provides evaluations of mobile health apps. Ranked Health uses a peer review process with clinical experts to provide standardized, transparent reviews. It focuses on areas like mental health, diabetes, and sleep. The document also discusses partnerships between HealthXL and Ranked Health to scale evaluations of digital health technologies.
This document summarizes a workshop hosted by HealthXL to discuss new models of care. It notes that increasing healthcare costs are driving the need for alternative delivery methods. The workshop brought together 20 professionals to brainstorm opportunities for transitioning care from hospitals to communities and homes. Key areas for new care models that emerged were in-home care for seniors, chronic disease management, and improved health management. Barriers to new models included difficulties proving cost savings and technology infrastructure challenges. The group prioritized collaborating with community groups to deliver care and reduce health risks. Next steps included further exploring digital health solutions and collaborating to address challenges raised.
HealthXL is a digital health platform that connects over 200 advisors, 600 innovators, and 20 global brands to collaborate on solving big healthcare problems. The platform offers challenges to drive collaborations between members, shares insights through its informational resources, and hosts exclusive quarterly events to facilitate connections. Since launching, HealthXL has run workshops, webinars and meetings bringing together experts. Challenges on issues like aged care, medication adherence and sleep have engaged members and led to the sharing of case studies and resources through the platform.
This document outlines the agenda and activities for a workshop aimed at exploring how to build business value for organizations in the connected health sector. The workshop will involve participants discussing challenges, envisioning collaborative ecosystems between stakeholders, and identifying how HealthXL challenges could help enable partnerships by minimizing costs and maximizing benefits. Groups will present their ecosystem models and lists of enabling factors to the other participants. The goal is to reinforce existing collaborations and expand networks to support integrated care through connected health.
The document announces and provides details about the Solve Sleep Weekend event taking place from October 23rd-25th, 2015 in Cleveland, Ohio. It aims to address challenges in diagnosing and treating sleep disorders through collaborative workshops and panels with clinical experts, patients, and startup companies. The agenda includes design thinking exercises on the first day to understand challenges from both patient and provider perspectives. On the second day, teams will develop potential solutions to prioritized challenges and pitch them for feedback and judging. The event is hosted by HealthXL in collaboration with other organizations and aims to bring together diverse stakeholders to spur innovation around solving sleep health issues.
The HealthXL mission is to accelerate innovation in healthcare by bringing together startups, corporations, and leaders from around the world to collaborate on solving global health challenges. Through a 2-year program, HealthXL organizes participants into focus areas like big data, remote monitoring, and behavior change. Entrepreneurs apply to gain access to decision-makers from large healthcare firms and investors. Partners get involved to help set the digital health agenda, find new innovations, and gain market insights without the fees of venture capital. Projects have clear objectives and are measured against goals to improve and scale solutions globally.
Enhancing Hip and Knee Arthroplasty Precision with Preoperative CT and MRI Im...Pristyn Care Reviews
Precision becomes a byword, most especially in such procedures as hip and knee arthroplasty. The success of these surgeries is not just dependent on the skill and experience of the surgeons but is extremely dependent on preoperative planning. Recognizing this important need, Pristyn Care commits itself to the integration of advanced imaging technologies like CT (Computed Tomography) and MRI (Magnetic Resonance Imaging) into the surgical planning process.
CHAPTER 1 SEMESTER V COMMUNICATION TECHNIQUES FOR CHILDREN.pdfSachin Sharma
Here are some key objectives of communication with children:
Build Trust and Security:
Establish a safe and supportive environment where children feel comfortable expressing themselves.
Encourage Expression:
Enable children to articulate their thoughts, feelings, and experiences.
Promote Emotional Understanding:
Help children identify and understand their own emotions and the emotions of others.
Enhance Listening Skills:
Develop children’s ability to listen attentively and respond appropriately.
Foster Positive Relationships:
Strengthen the bond between children and caregivers, peers, and other adults.
Support Learning and Development:
Aid cognitive and language development through engaging and meaningful conversations.
Teach Social Skills:
Encourage polite, respectful, and empathetic interactions with others.
Resolve Conflicts:
Provide tools and guidance for children to handle disagreements constructively.
Encourage Independence:
Support children in making decisions and solving problems on their own.
Provide Reassurance and Comfort:
Offer comfort and understanding during times of distress or uncertainty.
Reinforce Positive Behavior:
Acknowledge and encourage positive actions and behaviors.
Guide and Educate:
Offer clear instructions and explanations to help children understand expectations and learn new concepts.
By focusing on these objectives, communication with children can be both effective and nurturing, supporting their overall growth and well-being.
English Drug and Alcohol Commissioners June 2024.pptxMatSouthwell1
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This is a presentation on the overview of the role of monitoring and evaluation in public health. It describes the various components and how a robust M&E system can possitively impact the results or effectiveness of a public health intervention.
The Importance of Black Women Understanding the Chemicals in Their Personal C...bkling
Certain chemicals, such as phthalates and parabens, can disrupt the body's hormones and have significant effects on health. According to data, hormone-related health issues such as uterine fibroids, infertility, early puberty and more aggressive forms of breast and endometrial cancers disproportionately affect Black women. Our guest speaker, Jasmine A. McDonald, PhD, an Assistant Professor in the Department of Epidemiology at Columbia University in New York City, discusses the scientific reasons why Black women should pay attention to specific chemicals in their personal care products, like hair care, and ways to minimize their exposure.
The facial nerve, also known as cranial nerve VII, is one of the 12 cranial nerves originating from the brain. It's a mixed nerve, meaning it contains both sensory and motor fibres, and it plays a crucial role in controlling various facial muscles, as well as conveying sensory information from the taste buds on the anterior two-thirds of the tongue.
This particular slides consist of- what is Pneumothorax,what are it's causes and it's effect on body, risk factors, symptoms,complications, diagnosis and role of physiotherapy in it.
This slide is very helpful for physiotherapy students and also for other medical and healthcare students.
Here is a summary of Pneumothorax:
Pneumothorax, also known as a collapsed lung, is a condition that occurs when air leaks into the space between the lung and chest wall. This air buildup puts pressure on the lung, preventing it from expanding fully when you breathe. A pneumothorax can cause a complete or partial collapse of the lung.
Mental Health and well-being Presentation. Exploring innovative approaches and strategies for enhancing mental well-being. Discover cutting-edge research, effective strategies, and practical methods for fostering mental well-being.
VEDANTA AIR AMBULANCE SERVICES IN REWA AT A COST-EFFECTIVE PRICE.pdfVedanta A
Air Ambulance Services In Rewa works in close coordination with ground-based emergency services, including local Emergency Medical Services, fire departments, and law enforcement agencies.
More@: https://tinyurl.com/2shrryhx
More@: https://tinyurl.com/5n8h3wp8
The Ultimate Guide in Setting Up Market Research System in Health-TechGokul Rangarajan
How to effectively start market research in the health tech industry by defining objectives, crafting problem statements, selecting methods, identifying data collection sources, and setting clear timelines. This guide covers all the preliminary steps needed to lay a strong foundation for your research.
"Market Research it too text-booky, I am in the market for a decade, I am living research book" this is what the founder I met on the event claimed, few of my colleagues rolled their eyes. Its true that one cannot over look the real life experience, but one cannot out beat structured gold mine of market research.
Many 0 to 1 startup founders often overlook market research, but this critical step can make or break a venture, especially in health tech.
But Why do they skip it?
Limited resources—time, money, and manpower—are common culprits.
"In fact, a survey by CB Insights found that 42% of startups fail due to no market need, which is like building a spaceship to Mars only to realise you forgot the fuel."
Sudharsan Srinivasan
Operational Partner Pitchworks VC Studio
Overconfidence in their product’s success leads founders to assume it will naturally find its market, especially in health tech where patient needs, entire system issues and regulatory requirements are as complex as trying to perform brain surgery with a butter knife. Additionally, the pressure to launch quickly and the belief in their own intuition further contribute to this oversight. Yet, thorough market research in health tech could be the key to transforming a startup's vision into a life-saving reality, instead of a medical mishap waiting to happen.
Example of Market Research working
Innovaccer, founded by Abhinav Shashank in 2014, focuses on improving healthcare delivery through data-driven insights and interoperability solutions. Before launching their platform, Innovaccer conducted extensive market research to understand the challenges faced by healthcare organizations and the potential for innovation in healthcare IT.
Identifying Pain Points: Innovaccer surveyed healthcare providers to understand their difficulties with data integration, care coordination, and patient engagement. They found widespread frustration with siloed systems and inefficient workflows.
Competitive Analysis: Analyzed competitors offering similar solutions in healthcare analytics and interoperability. Identified gaps in comprehensive data aggregation, real-time analytics, and actionable insights.
Regulatory Compliance: Ensured their platform complied with HIPAA and other healthcare data privacy regulations. This compliance was crucial to gaining trust from healthcare providers wary of data security issues.
Customer Validation: Conducted pilot programs with several healthcare organizations to validate the platform's effectiveness in improving care outcomes and operational efficiency. Gathered feedback to refine features and user interface.
Sectional dentures for microstomia patients.pptxSatvikaPrasad
Microstomia, characterized by an abnormally small oral aperture, presents significant challenges in prosthodontic treatment, including limited access for examination, difficulties in impression making, and challenges with prosthesis insertion and removal. To manage these issues, customized impression techniques using sectional trays and elastomeric materials are employed. Prostheses may be designed in segments or with flexible materials to facilitate handling. Minimally invasive procedures and the use of digital technologies can enhance patient comfort. Education and training for patients on prosthesis care and maintenance are crucial for compliance. Regular follow-up and a multidisciplinary approach, involving collaboration with other specialists, ensure comprehensive care and improved quality of life for microstomia patients.
1. HOW AI CAN IMPROVE
RESEARCH & CARE MODELS
August 2017
H E A L T H X L B I G D A T A & A I W O R K I N G G R O U P
2. About HealthXL
The HealthXL Platform brings together key market stakeholders in digital health and empowers them
to collaborate and learn from each other. HealthXL engages leading companies such as …
T H E L E A D I N G P L A T F O R M F O R C O L L A B O R A T I O N
4. First, what do
we mean by
AI?
Defining AI and its various methods is a
subject of high scrutiny and debate. At the
risk of being overly simplistic, we’ve taken a
practical approach for this report.
Further, we’ve focused the report on select
AI applications in the following areas: life
sciences, care delivery, payor & consumer.
Artificial intelligence (AI) is that activity
devoted to making machines intelligent.
Machine learning refers to a process in
which computers use algorithms to analyze
large data sets in non-linear ways, identify
patterns, and make predictions that can be
tested and confirmed.
Deep learning is the application of artificial
neural networks to learning tasks that
contain more than one hidden layer.
A R T I F I C I A L
I N T E L L I G E N C E
M A C H I N E
L E A R N I N G
D E E P
L E A R N I N G
Source: "Artificial Intelligence and Life in 2030." One Hundred Year Study on Artificial Intelligence: Report of the 2015-2016 Study
Panel, Stanford University (2016), UCSF & GE White Paper: Big Data, Analytics & Artificial Intelligence (2016)
5. Data quality is
of the utmost
importance
Source: "Artificial Intelligence and Life in 2030." One Hundred Year Study on Artificial Intelligence: Report of the 2015-2016 Study
Panel, Stanford University (2016), UCSF & GE White Paper: Big Data, Analytics & Artificial Intelligence (2016)
Source: The AI Hierarchy of Needs (http://bit.ly/2wI8oMW)
In pharma and care delivery applications in
particular, understanding the context and
setting of data collection can provide clarity in
how the data should be utilized or interpreted.
While access to many data types is increasing,
often times data remains filled with gaps and
lacks a level of completeness necessary for
analysis.
AI projects should ideally incorporate a
prospective data collection methodology to
ensure the appropriate type of data is
collected from the onset.
6. Why
now?
Source: Internet Association, Jeff Bezos Fireside Chat (May, 2017)
• Data access and computing power are enabling AI solutions that were unimaginable
in years prior, improving both research processes and care delivery. Access to high
quality, “complete” data remains a challenge however in many instances.
• Tech giants alongside innovative AI startups are diving head first into various
applications - ranging from general platforms (IBM) to niche application areas
(cancer imaging). There remains a desire for increased transparency into the
algorithm development process.
• Early results from validation studies and initial use cases indicate AI is augmenting
human intelligence instead of replacing it. As result, individuals are becoming more
efficient and able to focus on more creative tasks.
• A strong commitment, dedication, and a mindset of deep partnerships is needed at
this time to maximize the value of AI approaches. Stakeholders are similarly
experimenting with collaboration models to better reach partnership objectives.
- J E F F B E Z O S
C E O , A M A Z O N
“
”
“AI [artificial intelligence] … this is a
renaissance, this is a golden age … ML [machine
learning] and AI is a horizontal enabling layer, it
will empower and improve every business -
every government organization, every
philanthropy - there is no institution in the world
that cannot be improved with ML.”
7. A N D R E W N G
Former Chief Scientist
Baidu
R A Y K U R Z W E I L
Founder & Futurist
Multiple Companies as a serial
entrepreneur
A T U L B U T T E
Director of the Institute of
Computational Health Sciences
UCSF
Received $10M from Mark
Zuckerburg & Priscilla Chan to
advance health research.
Raising a $150M fund for AI
startups; established Coursera
Deep Learning course.
Continues to advance
understanding of natural
language at Google.
Leaders in the field continue to make
progress in applying AI methods
Visit HealthXL.co for access to leaders in the field of Big Data & AI.
8. AI has a long history, but today’s enablers
are distinct from years prior
E X P E R I M E N T A T I O N
M I N D S E T
Ecosystems and centers of excellence
are emerging, facilitating pilot
opportunities and novel research
partnerships.
C O M P U T I N G
P O W E R
The gaming industry has enabled
computing power to increase,
particularly companies like Nvidia’s
GPUs (graphical processing units).
D A T A
A C C E S S
New technologies and biological
discoveries are expanding the
available pool of data without
traditional access challenges.
- A T U L B U T T E
I N S T I T U T E O F C O M P U T A T I O N A L H E A L T H S C I E N C E S , U C S F
“ ”
“Hiding within those mounds of data is knowledge that could change the life of a patient, or change the world.”
9. AI has broad utility across a
number of use cases
B U S I N E S S P R O C E S S
O P T I M I Z A T I O N
S E L E C T U S E C A S E S
L I F E S C I E N C E S
• Disease Understanding
• Drug Repurposing
• Drug Discovery
C A R E D E L I V E R Y
• Care Management Plans
• Treatment Selection
• Remote Monitoring
P A Y O R
• Risk Stratification
• Patient Engagement
• Customer Service
C O N S U M E R S
• Nutrition
• Care Management
• Novel Experiences
T h e r e a r e a n u m b e r o f u s e
c a s e s t h a t h e l p b u s i n e s s e s
i m p r o v e t h e i r c o r e
o p e r a t i o n s .
S u c h u s e c a s e s i n c l u d e
p r e d i c t i v e i n v e n t o r y
m a n a g e m e n t , a u t o m a t e d r i s k
& s e c u r i t y a s s e s s m e n t s , a n d
m e t h o d s t h a t i m p r o v e d a t a
s t a n d a r d i z a t i o n , a m o n g m a n y
o t h e r s .
T h e s e t y p e s o f a p p l i c a t i o n s
w i l l b e f u r t h e r d i s c u s s e d i n
t h e f u t u r e w i t h i n H e a l t h X L ’ s
B i g D a t a & A I W o r k i n g G r o u p .
Note: While imaging is a major application, other use cases are starting to gain traction.
10. Flourishing startup scene
A C R O S S M A N Y M A R K E T S E G M E N T S
Note: The companies listed above are meant to be representative, not exhaustive. Visit HealthXL.co for more detailed company information including partners, funding, and publications.
C A R E D E L I V E R Y / P A Y O R
R E S E A R C H
C O N S U M E R
11. Investments
into AI
companies
Estimates vary, but total VC investment in health
or research related AI companies is in the billions,
in part fueled by projections of the AI in healthcare
market surpassing $6 billion by 2021.
Some funds invest in a number of industries and
prioritize robustness of tech approach; in other
cases, funds are focused on healthcare and see
their AI investments as an extension of their
thesis.
$30
$75
$493
$292
$748
2012 2013 2014 2015 2016
A N N U A L F U N D I N G H I S T O R Y
( $ M , U S D )
A C T I V E V C s
Source: CB Insights, Accenture, Company Websites
A C R O S S A D I V E R S E
I N V E S T O R B A S E
12. News headlines vary, but leaders believe
we’re still in early phases of AI
- S T E P H E N K R A U S
B E S S E M E R V E N T U R E
P A R T N E R S
“
”
“It’s all for real - this isn’t about putting out
vaporware in order to boost stock prices.
This is hard. It’s not happening today, and
it might not be happening in five years.
And it’s not going to replace doctors.”
HOW MACHINE LEARNING, BIG DATA AND AI ARE
CHANGING HEALTHCARE FOREVER
FDA ASSEMBLES TEAM TO OVERSEE AI REVOLUTION IN
HEALTH
NHS MEMO DETAILS GOOGLE / DEEPMIND’S FIVE YEAR
PLAN TO BRING AI TO HEALTHCARE
MICROSOFT ANNOUNCES NEW AI-POWERED HEALTH
CARE INITIATIVES TARGETING CANCER
IN SURVEY ACROSS EMEA, UK MOST SKEPTICAL OF
ROBOTS, AI FOR HEALTHCARE
- A N D R E W N G
S T A N F O R D ( F O R M E R L Y
B A I D U )
“
”
“We still have work ahead to get these
algorithms into the healthcare system's
workflow. But I think health care 10 years
from now will use a lot more AI and will
look very different than it does today.”
HEADLINES
13. Imaging has been the focus of many
innovators, however use cases are growing
- D R . A N D R E W B E C K
P R E S I D E N T & C E O ,
P A T H A I
“
”
“The implications of this work are large,
suggesting that in the future we’ll see
more examples of AI being used with
traditional pathology to make diagnoses
more accurate, standardized and
predictive”
PathAI is engineering and applying
proprietary deep learning technology to
massive aggregated sets of pathology data
to help physicians and scientists more
effectively understand, diagnose and treat
disease. Its models have been improved
through trained experts in pathology,
and have now surpassed
human accuracy.
P A T H O L O G I S T A L O N E
E R R O R R A T E 3 . 5 %
A I M O D E L A L O N E
E R R O R R A T E 2 . 9 %
C O M B I N E
P A T H O L O G I S T S
+ A I M O D E L
E R R O R
R A T E 0 . 5 %
BREAST CANCER
Source: PathAI Website, Deep Learning Drops Error Rate for Breast Cancer Diagnoses by 85% (Nvidia Blog, 2016)
15. Life Sciences
Source: Company Websites. Visit HealthXL.co for more detailed company information including partners, funding, and publications.
- B R E N D A N F R E Y
U N I V E R S I T Y O F T O R O N T O
( A N D F O U N D E R O F D E E P
G E N O M I C S )
“
”
“There’s going to be this really massive shake-
up of pharmaceuticals. In five years or so, the
pharmaceutical companies that are going to
be successful are going to have a culture of
using these AI tools.”
R E D E F I N I N G B I O L O G I C A L
U N D E R S T A N D I N G O F
D I S E A S E
D I S E A S E U N D E R S T A N D I N G
Breaking down biochemical processes and
physiology to better map natural history of
health, disease, and diagnostic process.
D R U G R E P U R P O S I N G
Mapping relationships between known drugs to
novel indications by creatively leveraging
compound libraries.
D R U G D I S C O V E R Y
With an understanding of structural biology,
creating new classes of drug categories and
interventions.
16. Care Delivery
Care delivery can be viewed as a complex
process with many interdependencies, AI
approaches can help streamline the delivery of
care and how clinical insights are discovered.
G A I N I N G 3 6 0 º V I E W O F
P A T I E N T N E E D
C A R E M A N A G E M E N T P L A N S
Optimizing care management plans and creating
guidelines to manage follow ups, intakes,
readmissions, and more.
T R E A T M E N T S E L E C T I O N
Identifying methods to provide better treatments,
early switch rates, and improve adherence.
R E M O T E M O N I T O R I N G
Medical grade sensors and clinical algorithms
track high-risk patients beyond facility walls
S E L E C T H E A L T H S Y S T E M S
W I T H A I I N I T I A T I V E S
Source: Company Websites. Visit HealthXL.co for more detailed company information including partners, funding, and publications.
17. S E L E C T P A Y O R S
W I T H A I I N I T I A T I V E S
Payors
Payors are aiming to strike a balance between
broad population coverage and meeting
member expectations how they expect to
interact with technology. Further, AI
approaches can help facilitate value-based
reimbursement strategies.
R E T H I N K I N G R I S K
S T R A T I F I C A T I O N &
P O P U L A T I O N H E A L T H
R I S K S T R A T I F I C A T I O N
Applied analytics to predict patient outcomes
and inform treatment recommendations.
P A T I E N T E N G A G E M E N T
Machine learning to tailor member outreach
based on clinical, claims, and contextual data.
C U S T O M E R S E R V I C E
Chatbots to help members navigate their
benefits quickly and efficiently.
Source: Company Websites. Visit HealthXL.co for more detailed company information including partners, funding, and publications.
18. Consumers
N O V E L C H A T
I N T E R F A C E S & V I S U A L
E X P E R I E N C E S
N U T R I T I O N
Chatbots, food image analyses, and personalized
nutrition based on microbiome and other
biological determinants.
C A R E M A N A G E M E N T
Enabling personalized medicine, often through
use of genomics and research models.
N O V E L E X P E R I E N C E S
Interactive technology and new engagement
models via robotics.
Consumer-facing applications of AI are
emerging across every major health segment.
Advances in natural language processing
(NLP), sensors, voice recognition, augmented
reality (AR), sentiment analysis, and more are
raising the sophistication of digital interaction
and reshaping consumer experiences.
Source: Company Websites. Visit HealthXL.co for more detailed company information including partners, funding, and publications.
19. Prevention & timely intervention
I S R E S H A P I N G O V E R A L L H E A L T H M A N A G E M E N T
( E X : D I A B E T E S )
Source: Company Websites
C A R E D E L I V E R Y / P A Y O R
R E S E A R C H
C O N S U M E R
A R T I F I C I A L
P A N C R E A S
Closed-loop insulin dosing and
blood glucose management.
S M A R T P O P U L A T I O N
M A N A G E M E N T
Real-time insulin pump adjustments
based on patient-specific care plans.
C O G N I T I V E P A T I E N T
E N G A G E M E N T
Patient decision-making aids based
on insulin, diet, lifestyle.
S M A R T E A T I N G
A S S I S T A N T S
Foster healthy diets by turning
knowledge into know-how with AR,
NLP, decision support tools.
20. IBM Watson Health is a large ecosystem player, with
dozens of partners spanning oncology, pharma, payers,
medical device, and health systems. Partnerships largely
focus on ingesting partners’ proprietary data to train
Watson to strengthen applied cognitive computing tools.
Tech giants with deep pockets
A R E N O W S I G N I F I C A N T P L A Y E R S
Seamless workflow integration into complex settings.
B A C K G R O U N D
C H A L L E N G E S
DeepMind, Google’s AI company, signed a 5-year deal with
the UK’s National Health Service for access to 1.6M patient
records. Goals include workflow automation and
optimization to enable the detection and intervention of
avoidable conditions like sepsis or acute kidney failure.
Privacy and security concerns in the public dialogue.
B A C K G R O U N D
C H A L L E N G E S
Source: Company Websites
21. Within research, strategic multi-stakeholder
partnerships are becoming the norm
Source: Company Websites
iCarbonX has created the Digital
Health Alliance, bringing together
various technologies, proprietary
data sources, patient access, and
drug development capabilities into a
comprehensive research ecosystem.
WuXi Next Code and AbbVie
entered 15 year partnership to
sequence the genomes of 45,000
participants across Ireland to
identify novel targets of disease.
GSK and Exscientia partnered to
accelerate small molecule drug
discovery. The deal could total
upwards of $42.7 million (USD).
22. Partnerships are similarly common in care
delivery, often with a focus on specific diseases
Source: Company Websites
IBM Watson Health has a growing
ecosystem, followed by Microsoft
& UPMC, and GE & Partners
Healthcare.
Specialization across diseases
such as Ginger.io’s coaching
platform in behavioral health,
Flatiron’s oncology focus across
care and research, and Cyft’s
precision care platform across
diseases.
Payers play a key role in
experimenting with new benefit
design, Aetna particularly around
substance abuse (top) and
outcomes based reimbursement
for insulin pumps (bottom).
23. Future thought
R E D E S I G N I N G S O C I E T Y F O R H E A L T H Y L I V I N G
Thinking beyond generally defined “health
data” and including novel data sets will
increase our understanding of biology,
behaviors, and outcomes management.
As AI methods become more sophisticated, there’s a real opportunity to ensure
we keep tackling the problems that really matter to society.
HealthXL looks forward to enabling global collaborations
between leading players to create a better future.
As predictive capabilities increase,
ensuring that as a society we’re creating
the incentives where avoidance of risk is
economically rewarded is crucial.
With novel methods, we can begin to solve
social and structural problems, in part by
better understanding social determinants
of health and everyday living conditions.
25. Authors
C A R L O S
R O D A R T E
Founder & Managing Director
Volar Health, LLC
+ HealthXL Advisor
N A V E E N
R A O
Founder & Managing Partner
Patchwise Labs, LLC
+ HealthXL Advisor
J U L I E
C A R T Y
Chief Operating Officer
HealthXL
26. HOW AI CAN IMPROVE
RESEARCH & CARE MODELS
August 2017
H E A L T H X L B I G D A T A & A I W O R K I N G G R O U P