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Verint Automates Management of Return-to-Work Health Practices

June 10, 2020 CRM News and Info

Verint on Tuesday announced new capabilities within its workforce management solution that automate compliance with CDC guidelines to reduce the risk of COVID-19 infection as employees transition back to the workplace.

New workflows automatically create a comprehensive schedule that is prescriptive, while including traditional workforce management criteria such as skill level, channels and peak hours.

Among the recommended modifications:

Implementing staggered start times;
Managing a mix of work-from-home and in-office agents;
Adhering to safe distancing scheduling recommendations for buildings’ capacities, by floor, workspaces or zones;
Instituting employee health checks, and allocating extra time for hygiene;
Providing equal opportunities for employees to rotate in and out of the office;
Ensuring employees who are not ready to return to the office are not scheduled to do so; and
Accommodating mobile scheduling requests and notifications about schedule changes.

“Effectively managing today’s workforce is crucial for improving customer experience, operational efficiency and compliance,” said Kelly Koelliker, Verint’s director of solutions marketing.

“Yet currently rising expectations of both customers and employees have made forecasting and scheduling contact center agents and customer engagement resources exponentially more challenging.” she told CRM Buyer.

Verint’s solution “leverages artificial intelligence-infused automation and mobile tools to streamline forecasting and scheduling,” Koelliker remarked, and its flexible workflows let customers “transition back as slowly or as quickly as regulations and business needs warrant.”

Getting Ahead of the Game

“The inclusion of new capabilities makes [Verint] more competitive,” observed Evelyn McMullen, research analyst at Nucleus Research.

“Some large human capital management and workforce management vendors have rolled out contact-tracing functionality, but not much else has been done to address reopening operations beyond Q&As instructing users on how to use their existing capabilities to navigate new regulations,” she told CRM Buyer.

Verint’s solution is tailored for call centers, McMullen said, so, although it’s “ahead of the market in its response to reopening measures, this could be overlooked due to the fact that it is not a primary workforce management provider and is not optimized for most industries.”

The retail industry “will absolutely need these types of automated workflows in their workforce management applications, especially as schedule planning is expected to be one of the greatest challenges upon reopening,” she pointed out.

Obeying the Rules

Ninety-three percent of more than 300 human resources executives responding to an online Challenger, Gray & Christmas survey this spring said
they would take precautions once their staff returned to work.

The respondents were from companies of various sizes with a national presence in the United State and representing a range of industries.

Rules for Work Post-Lockdown

The U.S. Centers for Disease Control and Prevention’s
guidelines include recommendations for the following:

Social distancing within the office;
Daily health checks;
Various workplace hygiene protocols, including conducting a hazard assessment of the workplace;
Improving building ventilation systems; and
Coordinating with state and local health officials to ensure compliance with local requirements.

Employers also must check the CDC website regularly for guidance updates.

The U.S. Occupational Safety and Health Administration (OSHA) also prepared
guidance for reopening workplaces.

The U.S. Equal Employment Opportunity Commission (EEOCO) has issued
00000guidelines on a variety of pandemic-related workplace issues.

State and local governments hav issued their own guidelines. Here are the
guidelines for Massachusetts, for example.

“Regulations are in flux worldwide,” observed Rob Enderle, principal analyst at the Enderle Group.

“From a global perspective, they should be — and likely have been — changing far more rapidly than they usually would, to address the expanding pandemic problem,” he told CRM Buyer.

Forty-seven percent of the respondents to the Challenger, Gray survey said they would follow the lead of their state governments with regard to reopening for business.

Another nearly 14 percent said they would heed the advice of a combination of local, state and federal leaders, as well as leading scientific experts and internal research. Of the remainder, 13 percent said they would adhere to the federal government’s guidelines, and nearly 13 percent said they would follow the advice of leading scientific experts.

Good Things Come in Small Companies

Although Verint does not crack the top 10 list of workforce management vendors, “the true advantage of a smaller player like Verint is that because they’re small, they tend to spend more time understanding and engaging with their accounts,” Enderle pointed out.

“Their power is the ability to better listen to and respond to unique customer requirements and aspirational needs,” he said. “They may never be the most feature-rich, but they should be more responsive than their larger competitors.”

The pros of Verint’s approach “include a significant amount of capability to deal with the new normal — the highly customizable nature of the offering, and how it anticipates the coming needs,” Enderle noted.

“The cons are that Verint is a relatively small company and may become spread too thinly as firms come back to market and collectively need a lot of help to adjust. They could exceed Verint’s consulting resources,” he said.

The new capabilities are for current Verint customers, whether they are using its solution on-premises or in the cloud, said Koelliker.

The new workflow “is a low-cost services package,” she noted, and a “small services engagement” will instruct customers on how to configure the solution so they can manage it for themselves.

Richard%20Adhikari Verint Automates Management of Return to Work Health Practices
Richard Adhikari has been an ECT News Network reporter since 2008. His areas of focus include cybersecurity, mobile technologies, CRM, databases, software development, mainframe and mid-range computing, and application development. He has written and edited for numerous publications, including Information Week and Computerworld. He is the author of two books on client/server technology.
Email Richard.

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CRM Buyer

Health care organizations use Nvidia’s Clara federated learning to improve mammogram analysis AI

April 16, 2020 Big Data

A group of health care organizations in the United States and Brazil have used federated learning to improve AI mammography classification models for recognizing tumors and breast tissue density assessment. An Nvidia spokesperson declined to share specific performance details, but said a combined effort led to better classification model performance than models made by any of the individual health care organizations. Impacting 2.1 million people a year, according to the World Health Organization, breast cancer is the most frequently occurring cancer in women around the world.

Federated learning is a way to create AI models from data in multiple locations without a need to store all the data in the same place. Models are trained at their source and learnings are shared with a single generalized model without the transfer of data. Apple and Google use federated learning for keyboard personalization today, but in health care, federated learning can combine anonymized data to improve machine intelligence geared toward saving human lives.

“Federated learning presents an opportunity for health care organizations worldwide to work together without compromising on the data security of patient records,” AI and health researcher Jayashree Kalpathy-Cramer said in an Nvidia blog post today sharing the news. “With this methodology, we can collectively raise the bar for AI tools in medicine.”

The work is the result of a collaboration that took place between January and March and pools resources from the American College of Radiology, Brazilian imaging center Diagnosticos da America, Partners HealthCare, The Ohio State University, and Stanford Medicine. The federated learning model was trained using more than 130,000 images from 33,000 mammography studies, Nvidia VP of health care Kimberly Powell told VentureBeat. Hospitals worked together using the Clara Federated Learning SDK.

This is the second such combined demonstration of federated learning by health care researchers using Nvidia’s Clara Medical Imaging platform. Last fall, Nvidia and Kings College London worked together on a federated learning neural network for brain tumor segmentation.

In other Clara news, Powell said Nvidia is working with collaborators to release coronavirus-related models trained with federated learning through the Clara Imaging Software in the future. Last week, Nvidia launched Clara Deploy 5.4 for multi-domain pipelines on a unified GPU infrastructure.

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Big Data – VentureBeat

Babylon Health claims its AI accurately triages patients in 85% of cases

March 31, 2020 Big Data

A team of researchers at Babylon Health, the well-funded UK-based startup that facilitates telemedical consultations between patients and health experts, claim that they’ve developed an AI system capable of matching expert clinician decisions in 85% of cases. If it holds up to scrutiny, the system could lift a burden off of the overloaded U.S. health care system, which is anticipated to face a shortfall of between 21,000 and 55,000 primary care doctors by 2023.

Triaging in this context refers to the process of uncovering enough medical evidence to determine the appropriate point of care given a patient’s presentation. Clinicians plan a sequence of questions in order to make a fast and accurate decision, inferring about the causes of a condition and updating their plan following each new piece of information.

The Babylon Health team sought an automated approach built upon reinforcement learning, an AI training paradigm that spurs software agents to complete tasks via a system of rewards. They combined this with judgments from medical experts made over a data set of patient presentations, which encapsulated roughly 597 elements of observable symptoms or risk factors.

The researchers’ AI agent — a Deep Q network — learned an optimized policy based on 1,374 expert-crafted clinical vignettes. Each vignette was associated with an average of 3.36 expert triage decisions made by separate clinicians, and the validity of each vignette was reviewed independently with two clinicians.

At each step, the agent asks for more information or makes one of four triage decisions. And at each new episode, the training environment is configured with a new clinical vignette. Then the said environment processes evidence and triage decisions on the vignette and returns a value, such that if the agent picks a triage action, it receives a final reward.

To validate the system, the researchers evaluated the model on a test set of 126 previously unseen vignettes using three target metrics: appropriateness, safety, and the average number of questions asked (between 0 and 23). During training on 1,248 vignettes, those metrics were evaluated over a sliding window of 20 vignettes, and during testing, they were evaluated over the whole test set.

The team reports that the best-performing model achieved an appropriateness score of .85 and a safety score of 0.93, and it asked an average of 13.34 (0.875). That’s on par with the human baseline (0.84 appropriateness, 0.93 safety, and all 23 questions).

“By learning when best to stop asking questions given a patient presentation, the [system] is able to produce an optimized policy which reaches the same performance as supervised methods while requiring less evidence. It improves upon clinician policies by combining information from several experts for each of the clinical presentations.” wrote the paper’s coauthors, who point out that the agent isn’t trained to ask specific questions and can be used in conjunction with any question-answering system. “This … approach can produce triage policies tailored to health care settings with specific triage needs.”

Controversy

It’s worth noting that Babylon Health, which is backed by the UK’s National Health Service (NHS), has flirted with controversy. Nearly three years ago, it tried and failed to gain a legal injunction to block publication of a report from the NHS care standards watchdog. In February, it publicly attacked a UK doctor who raised around 100 test results he considered concerning. And it recently received a reprimand from UK regulators for promoting misleading advertising.

The thoroughness of its studies has also been called into question.

The Royal College of General Practitioners, the British Medical Association, Fraser and Wong, the Royal College of Physicians issued statements questioning claims in a 2018 paper published by Babylon researchers, which asserted that its AI could diagnose common diseases as well as human physicians. “[There is no evidence] can perform better than doctors in any realistic situation, and there is a possibility that it might perform significantly worse,” wrote the coauthors of a 2018 paper published in The Lancet. “Symptom checkers bring additional challenges because of heterogeneity in their context of use and experience of patients.”

In response to the criticism, Babylon said that “[s]ome media outlets may have misinterpreted what was claimed” but that it “[stood] by [its] original science and results.” It described the 2018 test as a “preliminary piece of work” that pitted the company’s AI against a “small sample of doctors,” and it referred to the study’s conclusion: “Further studies using larger, real-world cohorts will be required to demonstrate the relative performance of these systems to human doctors.”

In this latest paper, Babylon disclosed that the chief investigator and most coinvestigators were paid employees.

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Big Data – VentureBeat

AbleTo Slashes Medical Costs by Identifying and Treating Behavioral Health Issues in Patients with Chronic Ailments

March 11, 2020 NetSuite

Posted by Caitlin Winters, Industry Marketing Lead, General Business

Ailments such as diabetes, hypertension or heart disease bring a significant toll in the form of doctor’s appointments and emergency room visits. They also bring a significant financial toll for patient and insurer alike.

But when such conditions are accompanied by depression, anxiety or any other mental health issue, things mushroom. Conversely, for a patient who suffers from both an ailment and a mental health condition — what health insurance companies refer to as “comorbidity” — effectively treating the mental health aspect can reduce medical bills by as much as 50%.

That’s precisely where AbleTo’s efforts are focused. The behavioral health company has more than ten years of experience connecting people with depression, anxiety and stress to a nationwide network of 750 social workers and behavioral coaches to help payers identify patients who are likely to suffer from depression or anxiety in addition to other serious ailments, and offer a treatment path in the form of cognitive behavioral therapy.

Wanted: Scale

Yet, in order for AbleTo to treat as many such patients as possible, it needed to scale, and in order to scale, it needed technology that could support ongoing growth. For the first several years of its existence, AbleTo relied on QuickBooks as its back-office financial system, but by 2017, it was becoming apparent that a change was in order.

“QuickBooks was great when we were a very small organization,” said Frank Hunt, senior vice president of finance. “But as the company grew considerably and the complexity of our accounting processes also increased, we needed to move onto a platform that would allow us to have multiple subsidiaries, consolidate across multiple companies, and provide better visibility into and reporting on our business.”

When Hunt joined the company in 2017, AbleTo was moving towards that goal with a contract for cloud-based ERP software. But the company hadn’t implemented anything yet, and both Hunt and Alicja Czaja, who also joined AbleTo in 2017 as accounting manager, had previous positive experiences with NetSuite. By the end of the year, the company changed direction and moved forward with deploying NetSuite.

Three months later, NetSuite was deployed (“on time and under budget,” Hunt said) and two years’ worth of transaction data had been migrated — all with help from NetSuite Professional Services. Hunt said the team supporting the deployment was always available, and that only the company’s lack of internal resources kept the deployment from proceeding even faster.

New Platform Enables Growth Spurt

After going live in the first quarter of 2018, AbleTo got quick evidence that it had made the right choice. The company grew 100% that year and another 50% in 2019, and NetSuite has enabled it to absorb that growth seamlessly.

In addition to the core financial components of NetSuite, AbleTo has tapped additional capabilities such as prepaid fixed asset modules, customized reporting, and has integrated NetSuite with a couple of other financial applications it relies on, Bill.com and Expensify. All of these moves are paying off, as evidenced by the company slashing its quarterly book-closing process from 15 days to just seven days. (It expects to have that down to five days in 2020.)

Czaja said NetSuite is a vast improvement, and she not only saves time by not having to manually enter data, but she also is enjoying the ability to create consolidated financials and easily build customized reports. She also said that AbleTo plans to start using more of NetSuite’s revenue amortization feature early this year, as well as implementing the forecasting tool later in 2020.

“I know we can use more functionality,” she said. “Eventually, when we grow and expand out the pipeline, we’ll have more people using it and we’ll use more of the accounts receivable revenue functionality.”

Focusing on What Matters

In the meantime, NetSuite is allowing AbleTo to deliver on its mission. Hunt notes that AbleTo’s approach typically reduces the amount of depression, anxiety and stress in the patients it treats by 50%. What’s more, those patients with are then 50% less likely to be admitted to a hospital or have an emergency room visit.

With NetSuite behind the business, AbleTo will be able to expand that impact to more patients and insurance companies, without worrying about its technology slowing it down.

Learn more about NetSuite’s software for healthcare and life sciences.

Posted on Tue, March 10, 2020
by NetSuite filed under

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The NetSuite Blog

The Cost of Our Health

January 15, 2020 Humor

A study published this week provides hard facts about the truly bad deal the US is getting for health care. Simply put, the US for-profit health system costs four times as much than Canada’s single-payer system. Even though the two systems provide roughly equivalent results.

Why? Because of a veritable army of administrative workers who play no direct role in providing actual medical care, and the cost of paying dividends to shareholders of insurance companies. These administration expenses add up to an annual cost of $ 817 billion, which is an average of $ 2,497 out of the pocket of every American man, woman, and child every single year.

It is bad enough that we pay four times as much for the same thing, but keep in mind that the high cost of US health care is killing people. It also hurts our economy because in the US many of these costs are shouldered by private companies. In my personal experience starting companies and working with other startups, this makes it much harder to start new companies or keep small companies running. And as we all know, small companies are the primary engine of economic growth, providing two-thirds of net new jobs and driving innovation and competitiveness.

Also note that this study did not include the time and energy patients spend dealing with health insurance companies. I primarily live in the US, but I have also lived in three countries that have single-payer systems, and the amount of extra time I have to spend dealing with health insurance companies in the US is staggering.

When people claim that a single-payer system will cost you money, they are simply lying.

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Political Irony

AI Weekly: Amazon plays the long game in health care AI

December 7, 2019 Big Data

Today concludes Amazon’s re:Invent 2019 conference in Las Vegas, where the Seattle company’s Amazon Web Services (AWS) division unveiled enhancements heading down its public cloud pipeline. Just Tuesday, Amazon announced the general availability of AWS Outposts, a fully managed service that extends AWS’ infrastructure and services to customer datacenters, co-location spaces, and on-premises facilities. And it debuted in preview Amazon Detective, which helps to analyze, investigate, and identify the root cause of potential security issues and suspicious activities. That’s not to mention AI-powered fraud detection and code review products and an expanded machine learning experimentation and development studio, as well as a dedicated instance for AI inferencing workloads.

But perhaps the most intriguing launch this week was that of Amazon Transcribe Medical, a service that’s designed to transcribe medical speech for clinical staff in primary care settings. It’s scalable across “thousands” of health care facilities to provide secure note-taking for clinical staff, with an API that integrates with voice-enabled apps and works with most microphone-equipped devices. It supports both medical dictation and conversational transcription, with conveniences like automatic and “intelligent” punctuation. And it’s covered under AWS’ HIPAA eligibility and business associate addendum (BAA), meaning any customer that enters into a BAA can use Transcribe Medical to process and store personal health information (PHI).

It’s worth noting that Amazon isn’t the only tech giant offering speech recognition products targeting health care. Microsoft this year said it would team up with Nuance to host the latter’s AI software that understands patient-clinician conversations, which integrates with medical records. Rival Philips has long offered tailor-made automatic transcription solutions for health care professionals in public hospitals and small practices. And Google, not to be outdone, is collaborating with Stanford on a “digital scribe” pilot to use voice assistants during patient checkups.

But Transcribe Medical is merely the newest in a string of products signaling Amazon’s desire to enter the AI in health care market, which is anticipated to reach $ 19.25 billion by 2026.

The bulk of Amazon’s recent efforts have piggybacked on Alexa, its voice assistant platform that’s embedded in over 100 million devices sold as of January 2019. Last week, in partnership with Giant Eagle Pharmacy, Amazon debuted a voice medication management service that allows customers to set up reminders and request refills using their prescription information. April marked the expansion of the Alexa Skills Kit (the collection of self-service APIs and tools that make it easier to build apps for Alexa) to Covered Entities and their Business Associates (subject to HIPAA), enabling the publication of voice apps that transmit and receive protected health information. And Amazon partnered with Cedars-Sinai in Los Angeles to deploy an Alexa-powered platform, Aiva, that lets patients control connected devices like TVs and request nurse or clinician assistance.

That’s only the beginning of the groundwork Amazon has laid to date. Amazon last year made three AWS offerings HIPAA eligible — Transcribe, Translate, and Comprehend — following on the heels of rival Google Cloud. It also acquired PillPack for just under $ 1 billion, an online pharmacy that lets users buy medications in prepackaged doses, and it more recently snatched up Health Navigator, a startup that develops APIs for online health services. Notably, prior to the purchase, Health Navigator invested heavily in developing natural language processing technologies to document health complaints and care recommendations, which it integrated with its customers’ online health services, including telemedical apps and medical call centers.

Arguably Amazon’s most ambitious step was the launch of Amazon Care, a pilot health care service available to its employees in and around the Seattle area. The offering — which emerged from a collaboration between J.P. Morgan and Berkshire Hathaway to explore how to reduce expenses for their combined 1.2 million employees — currently includes both virtual and in-person care, chat and remote video, and follow-up visits and prescription drug delivery to homes and offices. It’s been speculated that eventually, Amazon Care might tap data from trackers like Amazon’s Echo Buds (which reportedly spot built-in pedometers) to inform wellness recommendations and spotlight trends.

Taken together, the developments suggest Amazon views AI in health care as a frontier worth pursuing — and perhaps its next major revenue driver. It’ll likely be years before the company fully realizes its vision for the market, to be fair. But it’s making inroads in a way that even Google — which is deeply involved with health care research through Google Health and its sister companies DeepMind and Verily — hasn’t yet.

As always, if you come across a story that merits coverage, send news tips to Khari Johnson and Kyle Wiggers — and be sure to bookmark our AI Channel and subscribe to the AI Weekly newsletter.

Thanks for reading,

Kyle Wiggers

AI staff writer

P.S. Please enjoy this showcase of a study from Nvidia (“Dancing to Music”), which is being presented at NeurIPS 2019.

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Big Data – VentureBeat

Universal Health Care

August 31, 2019 Humor

Sometimes I think we fail to realize how absolutely insane our health care system truly is. This comic by Sarah Mirk imagines what it would be like if all public services in the US worked like our healthcare system.

I am perfectly happy to not insist on “Medicare for All” immediately, as long as we start to offer a “public option” where people have a choice between our current system and and a Medicare-like system. But our goal should be to move toward single-payer health insurance, the only question is how we get there.

The main objection I hear against going to a Medicare-like system with universal coverage is that it would raise our taxes. But as this article in the NY Times points out, we are looking at this the wrong way. We should think of the premiums that we (and our employers) pay for health insurance as taxes. After all, it is all just money out of our pocket.

Most Americans who have health insurance get it through their employer. I have started several companies and served as a CEO, and I can assure you that if a company didn’t have to spend the time or money providing health insurance — something that is a huge distraction and money sink from the company’s core business — then that company could easily afford to pay their employees a significantly higher salary. In fact, typically enough to more than offset the extra taxes that people would have to pay to support universal single-payer health insurance.

And there are other benefits that most people don’t even realize. For example, I have lived in three countries that have single payer systems, and in those countries insurance for your car is a small fraction of what it is in the US. Why? Because the biggest cost of car insurance is liability insurance to cover health care costs for you, your passengers, and other parties when you are involved in an accident. But if everyone’s health costs are covered by a single payer system, then there is no need for that insurance.

In addition to saving companies time and money, and saving us the premiums that are automatically deducted from our paychecks, a universal single payer system would save all the time that individual employees spend dealing with their health insurance companies and filling out paperwork. Every time I have lived in a country with public health insurance, the paperwork I had to deal with to get health care was far far less than it is in the US.

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Political Irony

NHS creates AI Lab to tackle health care challenges

August 8, 2019 Big Data

The U.K.’s National Health Service (NHS) today announced it’ll reroute £250 million ($ 302.86 million) in funds to create a new Artificial Intelligence Lab within a unit tasked with digitizing the country’s health care system, which it says will work to bring together academics, specialists, and tech companies to tackle “some of the biggest challenges in health and care.”

“We are on the cusp of a huge health tech revolution that could transform [the] patient experience by making the NHS a truly predictive, preventive and personalized health and care service,” wrote Health Secretary Matt Hancock. “It’s part of our mission to make the NHS the best it can be. The experts tell us that because of our NHS and our tech talent, the UK could be the world leader in these advances in healthcare, so I’m determined to give the NHS the chance to be the world leader in saving lives through [AI] and genomics.”

The NHS expects the AI Lab’s work will enhance cancer screenings by expediting mammograms, brain scans, eye scans, and heart monitoring, and that it’ll enable clinicians to better estimate drug, device, and surgical needs on the fly. Moreover, it says that the machine learning models it develops might help to identify which patients could be more easily treated in the community, and to identify those most at risk of post-operative complications or infections and diseases such as heart disease or dementia.

Ultimately, says the NHS England Chief Executive Simon Stevens, the goal is to upskill the NHS workforce so that they’re able to tap AI systems to automate routine tasks. Another of the AI Lab’s core missions is to inspect algorithms already in use “increase standards” of safety, making them fairer and more robust while “ensuring patient confidentiality is protected.”

“Carefully targeted AI is now ready for practical application in health services, and the investment announced today is another step in the right direction to help the NHS become a world leader in using these important technologies,” said Stevens. “In the first instance it should help personalize NHS screening and treatments for cancer, eye disease and a range of other conditions, as well as freeing up staff time, and our new NHS AI Lab will ensure the benefits of NHS data and innovation are fully harnessed for patients in this country.”

The NHS previously partnered with private parties to deploy AI-imbued services including Babylon Health, which supplies a chatbot-style app for triaging primary care, and Alphabet’s DeepMind, which recently anonymized data collected from NHS patients to develop systems that can diagnose acute kidney injury (AKI) and degenerative eye conditions. The latter collaboration proved to be somewhat controversial; the more than 1.6 million patients whose records were analyzed in the AKI-diagnosing algorithm’s creation weren’t asked for their consent, leading the U.K.’s Information Commissioner’s Office (ICO) to conclude last year that the NHS had breached U.K. law.

By bringing some of the work in-house, the NHS hopes to address those concerns.

“Today’s funding is not just about the future of care though. It will also boost the frontline by automating admin tasks and freeing up staff to care for patients,” said U.K. Prime Minister Boris Johnson. “My task is to ensure the NHS has the funding it needs to make a real difference to the lives of staff and patients. Transforming care through artificial intelligence is a perfect illustration of that.”

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Big Data – VentureBeat

How Pre-Payment Prevention Can Lower Health Care Losses

February 5, 2019 FICO

Health care payers use a variety of strategies, tools and solutions to fight fraud, waste and abuse (FWA) in their fee-for-service healthcare claims. Recently, Pre-Payment prevention of losses has been a popular topic in the payment integrity space. Why is that, and what is the best way to prevent losses to FWA prior to payment?

Here are some things to think about:

Your payment policy and your claims adjudication system are designed to prevent inappropriate payments. However, no payment policy is perfect, no adjudication system is error free, and the “bad guys” are always alert to weaknesses in your business.

The problem with reliance upon a post-payment approach to payment integrity is that once you have paid someone money that they are not entitled to, you might recover only 70% of your losses, and once you realize that you have a problem, you’ll continue to leak money until you address it.

A common approach to prevention of losses to FWA prior to payment is “Pre-Payment Provider Review.” First, you identify providers who are known problems. Then you manually review some or all of their claims prior to payment. While this is a common approach, Pre-Payment Provider Review is labor-intensive, and you typically address only the problems that you already know about.

Numerous vendors offer packages of rules-based edits and queries for identification of problematic claims prior to payment. But edits and queries focus upon known problems, and emerging patterns of FWA can easily circumvent the controls of static edits and queries, and it takes time to introduce new edits and queries in response to changes in patient and provider behavior. As with post-pay mitigation to FWA, you’ll continue to leak money while you wait to see a new edit or query implemented in production.

Sophisticated technology like explainable artificial intelligence (xAI) and machine learning are nimble, they are available in the market, and they can identify not only known FWA, but also emerging patterns of FWA.

How to Effectively Prevent Losses Prior to Payment

We recommend that you consider the following things:

We believe that the most effective approach to pre-payment prevention of losses to FWA is to risk score your claims, post-adjudication, prior to payment, in production strength software which uses xAI and machine learning and which is integrated with your host system to allow you to stop payment on inappropriate claims. The FICO insurance fraud solution delivers this capability.
Successful adoption of Pre-Payment prevention strategies requires that you have your stakeholders on board. Engage your stakeholders to maximize the results you achieve with Pre-Payment mitigation of losses to fraud, waste and abuse.
Be prepared for change, because the people who inappropriately take your money are entrepreneurial. Make sure that your Pre-Payment solution has features like explainable AI and machine learning that will allow you to keep up with the bad guys.

We welcome your comments. Stay tuned for our next blog on health care fraud, waste and abuse.

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FICO

Millennials may be the last generation to know so little about their health

February 4, 2019 Big Data

One of the problems of the Second Artificial Intelligence (AI) Winter (1987-1993) was that there was not enough data to go around. We did not yet understand the value of “Big Data,” and academia was working on models fueled by “Small Data.” However, now we have entered the era of “Bigger Data Than We Ever Imagined.” We are producing data on the order of exabytes. It is predicted that by 2020 we will use zettabytes. By 2024, yottabytes are in sight.

Hyperscalers are frantically expanding their clouds to meet the demands of the 21st century, and the whole planet will become one giant hard disk attached to, hopefully, a capable calculator. Exascalers, on the other hand, are concerned with “Fast Data”, the application of big data analytics to smaller data sets in near-real-time. They are racing beyond the petaflop to reach exaflop speed by 2021.

Both Big Data and Fast Data have already drastically reconfigured the landscape of the Financial Times Global companies by market cap.

In 2009, there were no information technology companies in the FT Global 500 top 5. However, a mere nine years later, all companies in the FT Global were information technology companies, which took over from big energy. Most importantly, all of these companies are in the process of pivoting towards AI. One of them is Tencent, the first Chinese information technology company to enter the top 5 (admittedly, this only happened for Q1. The company later lost its position after its earnings call in Q2).

However, a new data inflection point is coming, and it will be driven and influenced by the pressing need in healthcare. 2018’s FT Global top 5 has undoubtedly posed two questions: Which industries provide the most data, and which ones need the most speed? They all arrived at the same conclusion: medical and life sciences.

Why?

Because in medicine and life sciences, there are power laws at work that dwarf Moore’s Law. In the USA, health data doubles every 73 days, estimated to arrive at a hefty 2.3 zettabytes by 2020. This is a hockey stick scenario that the information technology companies cannot afford to miss. The 3 Super-A’s (Amazon, Alphabet, and Apple) all announced a ground-breaking restructuring of their divisions and far-reaching collaborations (ABC-Amazon, Berkshire, Chase) to jump on the “big and fast data” bandwagon. This could produce surprising ripples in which companies hit the top of the FT’s Global 500 list in the near future.

By 2020, some of the information technology companies won’t be information technology anymore but Life Technology companies, pointing their search and intelligence engines towards healthcare and life science. This poses a legitimate challenge to the current biopharmaceutical and healthcare companies with their dwindling intellectual property assets and lackluster efficiency of discovery. Interestingly, by Q3 of 2018, Facebook lost its spot to Berkshire Hathaway, a well-known investment company focused on reinsurance of the healthcare and life insurance industry. It is entirely possible that, by 2025, most of these companies that merge technology and healthcare could be mandated by antitrust laws to break up into information technology and life technology corporations.

However, the confluence of big and fast will also turn the tables and disrupt who owns what. Today, big and fast data lies in the hands of the privileged few, many in the top 5 or top 10 of the FT Global. However, in 2025, we will be living in a drastically different world from the one we live in today, and individuals will own their own information, in healthcare or otherwise. The first generation that will thoroughly enjoy their personal privileges of Life Data will be Generation Beta (2025 to 2039). These will be the children of Generation Z (with Z we will have exhausted the Latin alphabet and returned to the Greek).

All information, coupled with accessible calculators, will belong to its rightful owner, who will be free to do with it whatever they want: move it, sell it, share it, donate it, add to it, maintain it, remix it, delete it and create it again. New personal devices will be designed to collect, store, encrypt, and manage personal and structured medical data on an opt-in basis. As a result, users themselves will have become “the edge” and consider themselves nodes. All medical data will be sequenced by then. Hence, all medical information will be encrypted and governed by a “smart contract” – contracts as computer code — that will stipulate when and by whom it can be accessed.

New economies will be born, and medical information will become an alternative financial asset, with individuals receiving dividends on their data and due monetary benefit for participation in massive clinical trials and prospective health studies. Individuals will not be passive donors of their information but will be incentivized to be more engaged participants in research, potentially in a planetary medical data project.

Medicine will finally break out of its silos. Individuals will have the choice between “portfolio managers” who will represent the data of their clients to potential patient recruiters in return for a real-time dividend. Individuals will be kept updated ubiquitously with research on their condition. Algorithms — fast data — will continuously monitor new and upcoming trials tailored for the individual, consisting of cohorts with common diagnostics, genetics, environment, family history, and predicted clinical trajectories. The confluence of this data will finally make research understandable to individuals and elusive etiologies less obtuse and accessible.

Gen B will be born in an always-on world with quantum computing algorithms and their own AIs that harness the advances of big and fast data. Everything around them will be autonomous and smart. They will have edited genomes that will make them no longer our descendants but ancestors in their own right, ready to become the first space-faring civilization.

Walter De Brouwer is cofounder and CEO of doc.ai. He is a Belgian-born technology entrepreneur with over 27 years of experience, a Fellow of the Royal Society of Arts, and served as President of RSA Europe from 2006 to 2008. He is a member of TED, curator of TEDxBrussels, and was a distinguished lecturer at the National Science Foundation in 2013. His article, “How the People Are Taking Over the World,” was among Techonomy’s Most-Read Articles of 2014 and was cited by its editors as “perhaps the most philosophical of Techonomy’s top articles” that year. Prior to doc.ai, he was Founder and CEO of Scanadu Inc., a $ 57 million venture-backed mobile health company.

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