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5 annoying Alexa and Amazon Echo settings you can change

FOX News

Amazon announced that their voice assistant Alexa can now sign business agreements with health providers under the Health Insurance Portability and Accountability Act, or HIPAA. Third-party health developers can meet the rules that govern how sensitive health information is shared, and major health providers and companies have launched a number of voice programs to help users manage chronic conditions. If you have an Amazon Echo at home, you need to dive into the privacy settings. There are a few important things to lockdown. Don't forget to turn off voice purchasing if you never use that feature, or at least set up a PIN.


Knowledge Graph and Machine Learning: 3 Key Business Needs, One Platform Registration

#artificialintelligence

Connect internal and external datasets and pipelines with a distributed Graph Database - UnitedHealth Group is connecting 200 sources to deliver a real-time customer 360 to improve quality of care for 50 million members and deliver call center efficiencies. Xandr (part of AT&T) is connecting multiple data pipelines to build an identity graph for entity resolution to power the next-generation AdTech platform.


Amazon is cozying up in all corners of the healthcare ecosystem--AI is its next frontier

#artificialintelligence

Amazon Web Services (AWS) launched Amazon HealthLake--a new HIPAA-eligible platform that lets healthcare organizations seamlessly store, transform, and analyze data in the cloud. The platform standardizes unstructured clinical data (like clinical notes or imaging info) by in a way that makes it easily accessible and unlocks meaningful insights--an otherwise complex and error-prone process. For example, Amazon HealthLake can match patients to clinical trials, analyze population health trends, improve clinical decision-making, and optimize hospital operations. Amazon already has links in different parts of the healthcare ecosystem--now that it's taking on healthcare AI, smaller players like Nuance and Notable Health should be worried. Amazon has inroads in everything from pharmacy to care delivery: Amazon Pharmacy was built upon its partnerships with payers like Blue Cross Blue Shield and Horizon Healthcare Services, Amazon Care was expanded to all Amazon employees in Washington state this September, and it launched its Amazon Halo wearable in August.


RPA - 10 Powerful Examples in Enterprise - Algorithm-X Lab

#artificialintelligence

More and more enterprises are turning to a promising technology called RPA (robotic process automation) to become more productive and efficient. Successful implementation also helps to cut costs and reduce error rates. RPA can automate mundane and predictable tasks and processes leaving employees to focus more on high-value work. Other companies, see RPA as the next step before fully adopting intelligent automation technology such as machine learning and artificial intelligence. RPA is one of the fastest-growing sectors in the field of enterprise technology. In 2018 RPA software soared in value to $864 million, a growth of over 63%. In the course of this article, we clearly explain exactly what RPA really is and how it works. To help our understanding we will also explore the potential benefits and disadvantages of this technology. Finally, we will highlight some of the most powerful and exciting ways in which it is already transforming enterprises in a range of industries. Robotic Process Automation, or RPA for short, is a way of automating structured, repetitive, or rules-based tasks and processes. It has a number of different applications. Its tools can capture data, retrieve information, communicate with other digital systems and process transactions. Implementation can help to prevent human error, particularly when charged with completing long, repetitive tasks. It can also reduce labor costs. A report by Deloitte revealed that one large, commercial bank implemented RPA into 85 software bots. These were used to tackle 13 processes interacting with 1.5 million requests in a year.


Buoy Health Raises $37.5M to Expand AI-Powered Healthcare Navigation Platform

#artificialintelligence

Buoy Health, a Boston, MA-based AI-powered healthcare navigation platform, today announced the completion of a $37.5 million Series C funding round. Cigna Ventures and Humana led the funding round and were joined by Optum Ventures, WR Hambrecht Co, and Trustbridge Partners. To date, Buoy has raised $66.5 million. Today, hospitals and insurance companies are increasingly investing in digital health innovations like Buoy to solve problems related to accessing the healthcare system and helping patients to get to the right care setting on the first attempt. Founded in 2014 by a team of doctors and computer scientists working at the Harvard Innovation Laboratory, Buoy Health uses AI technology to provide personalized clinical support the moment an individual has a health concern.


How Might Artificial Intelligence Applications Impact Risk Management?

#artificialintelligence

Artificial intelligence (AI) applications have attracted considerable ethical attention for good reasons. Although AI models might advance human welfare in unprecedented ways, progress will not occur without substantial risks. This article considers 3 such risks: system malfunctions, privacy protections, and consent to data repurposing. To meet these challenges, traditional risk managers will likely need to collaborate intensively with computer scientists, bioinformaticists, information technologists, and data privacy and security experts. This essay will speculate on the degree to which these AI risks might be embraced or dismissed by risk management.


AI, Health Insurance, And Data Harmonization: Interview With Shiv Misra, CVS Health

#artificialintelligence

Over the last decade, data and analytics have grown to be more than just a quantitative support function. Many organizations have traditionally used data to win customers and market share. However they are now also leveraging data to re-design future products based on evolving customer needs and macro trends. While significant progress has been made in the field of machine learning, as well as artificial intelligence –there is one critical element to making this all work: having the right data. Business decisions that are built using flawed data can cause an organization significant revenue loss, increased expenses, compliance issues, possible legal issues and even more severe ramifications.


Your AI May Be Ethical, But Is It Prudent?

#artificialintelligence

Do you remember a story about an irate father who marched into a Target to complain that his teenage daughter received maternity coupons, only to find out a few days later that she was pregnant? The story came from a 2012 New York times article and it signaled the arrival of predictive analytics. Despite reasonable skepticism over whether the story was real, it helped initiate an ethical debate over consumer privacy that has only intensified. Today, we live in a world with more powerful predictive capabilities and more personal data to be leveraged. We've reached an era in which AI can do more than out a teenage pregnancy.


Your AI May Be Ethical, But Is It Prudent?

#artificialintelligence

Do you remember a story about an irate father who marched into a Target to complain that his teenage daughter received maternity coupons, only to find out a few days later that she was pregnant? The story came from a 2012 New York times article and it signaled the arrival of predictive analytics. Despite reasonable skepticism over whether the story was real, it helped initiate an ethical debate over consumer privacy that has only intensified. Today, we live in a world with more powerful predictive capabilities and more personal data to be leveraged. We've reached an era in which AI can do more than out a teenage pregnancy.


Accurate and Interpretable Machine Learning for Transparent Pricing of Health Insurance Plans

arXiv.org Machine Learning

Health insurance companies cover half of the United States population through commercial employer-sponsored health plans and pay 1.2 trillion US dollars every year to cover medical expenses for their members. The actuary and underwriter roles at a health insurance company serve to assess which risks to take on and how to price those risks to ensure profitability of the organization. While Bayesian hierarchical models are the current standard in the industry to estimate risk, interest in machine learning as a way to improve upon these existing methods is increasing. Lumiata, a healthcare analytics company, ran a study with a large health insurance company in the United States. We evaluated the ability of machine learning models to predict the per member per month cost of employer groups in their next renewal period, especially those groups who will cost less than 95\% of what an actuarial model predicts (groups with "concession opportunities"). We developed a sequence of two models, an individual patient-level and an employer-group-level model, to predict the annual per member per month allowed amount for employer groups, based on a population of 14 million patients. Our models performed 20\% better than the insurance carrier's existing pricing model, and identified 84\% of the concession opportunities. This study demonstrates the application of a machine learning system to compute an accurate and fair price for health insurance products and analyzes how explainable machine learning models can exceed actuarial models' predictive accuracy while maintaining interpretability.