AI for health care is all the rage. Who wouldn't be excited about applications that could help detect cancer, diagnose COVID-19 or even dementia well before they are otherwise noticeable, or predict diabetes before its onset? Machine and deep learning have already been shown to make these outcomes possible. Possible, that is, in the research lab. In health care, there is often a long lag between research findings and implementation at the bedside.

"As developers and healthcare providers increasingly grapple with issues of liability and malpractice, machine learning will transition from a …

Did you miss a session from the Future of Work Summit? In health care, the process of underwriting and claims analysis can be both labor-intensive and error-prone. Claim adjusters and underwriters are often required to read and carefully parse hundreds of documents per case. Each year, the insurance market invests an estimated more than $3 billion in work hours devoted solely to collating and summarizing medical records. A 2006 U.S. National Institutes of Health study identified several major challenges in researching medical records, including assessing the quality of data and combining data from companies with dissimilar coding systems.

In 2017 we set out to sort the substance from the sensation in our first hype vs. impact article, as shown in the infographic below. In 2022 we revisit our predictions and set out our stall for the future. Thankfully the majority of our 2017 predictions have come to fruition with AI, big data, IoT, usage-based and telematics insurance all continuing to have an impact. As predicted, other areas have not been so lucky and have, therefore been dropped from our 2022 assessment. Self-driving vehicles: Autonomous vehicles saw huge hype in 2017 but with predictably little impact since, given the complexity of getting to mass adoption.

With the continuous advent of technology usage, numerous innovations are being developed and used to improve society's quality of life. Artificial intelligence is one of these innovations, and now it is being applied in health insurance, a critical and incredibly important component of today's healthcare industry. Artificial intelligence has numerous impacts in the health insurance industry to date, and these impacts may be separated into two types - the benefits it has brought, and the drawbacks it inevitably has. Read on to know more. Doing manual verification of claims is incredibly tedious work, with average, mid-sized insurers receiving about 700,000 claims from hospitals every year. With a pandemic still in our midst, the number of claims will inevitably boom.

Some improvements may seem mundane, such as automating claims processing or using chatbots (virtual agents) to handle interactions with policyholders. Yet the financial opportunities for health insurers are far from humdrum. A 2018 Accenture analysis estimated insurers can save $7 billion in 18 months by using AI to automate core administrative functions -- the equivalent of $1.5 million in operating income for every 100 full-time employees. Then there are the future possibilities of AI, such as orchestrating a seamless patient experience -- from selecting specialists within an insurer's network to prescribing of drugs upon discharge. At Blue Health Intelligence (BHI), which provides clinical data expertise for Blues plans around the United States, programmers attacked the problem of high-cost claimants, defined as members having annual costs surpassing $250,000.

Deep learning (DL) tools like convolutional neural networks which contain millions of simulated neurons structured in layers enable artificial intelligence (AI) to imitate the learning, reasoning, perception and problem solving of the human brain. This technological change is revolutionizing every dimension of the insurance industry. While insurers, suppliers, insurance brokers and consumers are getting better at implementing DL and AI tools for the improvement of reasoning and productivity, cost minimization and the enhancement of the customer experience, this technological shift will be even faster. In this blog post, we are going to share with you four aspects insurers need to focus on and how they can get prepared so as to get the most out of this tech-driven transformation. The number of connected consumer devices will drastically rise in the near future.

Google announced a 10-year digital technology partnership with the The Mayo Clinic on Sept. 10, 2019. In this photo, Mayo Clinic medical center stands in Rochester, Minnesota, U.S., on Wednesday, Jan. 30, 2013. The Mayo Clinic and Google are partnering to accelerate the delivery of healthcare "through digital technologies." Mayo said its selection of "Google Cloud" is designed to be "the cornerstone of its digital transformation" as part of a 10-year partnership with Google, a unit of Alphabet. Financial terms of the deal announced Tuesday weren't disclosed but Google will be making a sizable investment in the relationship by opening a new office in Rochester, where Mayo is based, so its engineers can work closely with Mayo researchers, doctors and data scientists.

In partnership with local health insurer, CDPHP, researchers from the Institute for Data Exploration and Applications (IDEA) at Rensselaer Polytechnic Institute are using artificial intelligence to improve patient health by developing a better understanding of high needs patients and identifying aspects of care that lead to better outcomes. "It's not enough to just figure out who are the highest needs patients, you really need to know why and what approaches can help them," said Kristin Bennett, a Rensselaer math professor and associate director of IDEA. "Our approach develops explainable models that help us understand who these high needs patients are, why some people in this group do well, and some do not." The project builds on the "cadre" modeling technique developed by Bennett. As opposed to deep learning, in which a computer identifies a pattern but the path to its decision is not clear, cadre models bring another level of understanding into the equation.

But by 2017, that price tag had ballooned to $3.5 trillion flowing to and from insurers, Medicare and Medicaid via patient premiums and claims payouts to healthcare providers and drug companies. All told, keeping the U.S. healthcare system spinning took six billion insurance-related transactions (an increase of 1.2 billion transactions from 2016), according to the nonprofit Council of Affordable Quality Healthcare. Could artificial intelligence (AI) technologies help control the industry's rising costs and tsunami of paperwork? Insurers could save up to $7 billion over 18 months using AI-driven technologies by streamlining administrative processes, according to a recent Accenture study. By automating routine business tasks alone, the study projects that health insurers could save $15 million per 100 full-time employees.