Once an injured worker has low back pain, chances are pretty good he’ll have a recurrence. In fact, new research shows that after an acute episode of LBP, one-third of people will have another episode within a year; and the odds of a recurrence triples after two episodes. Those statistics are somewhat surprising, given that recurring LBP can be avoided. Gustavo C. Machado and his colleagues at the University of Sydney’s School of Public Health tracked more than 1,000 LBP patients in Australia from 2011-12. Their results were published by the American Physical Therapy Association. “We know if you do exercise and receive advice you reduce the risk of having a recurrence,” said Machado, lead author of the study. “Some research shows if you do exercise, you reduce your risk of a back pain episode by 35%; and if you do exercise and get some sort of education or information, that risk is reduced by 45%. So it’s a big reduction in risk. “ With LBP one of the most prevalent causes of workers’ compensation claims, the industry could prevent unnecessary pain among workers and save significant dollars for payers by better educating workers on LBP. LBP An estimated one-third of workers’ compensation claims involve LBP, with direct costs of more than $14 billion annually. Some 1 in 4 workers with LBP remain out of work for up to six months, increasing the chances of permanent disability. Workers of any age can experience LBP, though it typically affects those over the age of 40. The reasons for LBP can vary with age. “With older workers, you see degenerative changes,” said Daniel Sanchez, a physical therapist and VP of Operations for OnSite-Physio, a company that provides PT to injured workers on site. “For younger workers, the LBP tends to be muscular soft tissue in nature. But both degenerative and soft tissue LBP can be recurring.” See also: The State of Workers’ Compensation   Acute episodes of LBP last for no more than six weeks, while chronic LBP continues for at least three months. While injured workers with acute LBP should avoid vigorous activity, that does not mean they should be bedridden. “The first thing initially is to rest, but don’t over rest,” Sanchez explained. “Depending on the severity, maybe a day or two of not overdoing it, but not being laid out in bed all day. Just not doing heavy exercise; no movements that aggravate the pain.” The simple act of reaching down to pick up something can aggravate the pain. For the first couple of days, injured workers should “take it easy, allowing the body to heal,” Sanchez said. Machado concurred. “For those patients, the best thing is to remain active. So do not stay in bed, just keep on the move. Try to keep up with routine activities, such as work,” he said. “It’s also very important to receive advice and education about pain, about recurrences.” The majority of LBP cases resolve within a few weeks. For those that linger, a different regimen is needed. “A quick fix for LBP does not exist,” Machado said. “The latest research shows taking pills doesn’t help much. Exercise and education seem to be the key for treating back pain, reducing back pain and preventing recurrences. The problem is people are not engaged in exercise.” Exercises There are a variety of exercises touted as best for addressing back pain: pilates, yoga and strengthening among others. They are equally effective. “The more research that comes out, the more it’s clear there is no one exercise that is better,” Machado said. “The best is the one you like to do, they one you’re going to engage in, the one you’re going to stick to.” A combination of exercises that improve stability, flexibility and strength will increase mobility and range of motion, thereby reducing pain. When muscles are tight and weak, the joints cannot move properly, Sanchez said.

• Strengthening exercises. The most effective of these, according to Sanchez, are those that target the muscles in the front and back of the spine; the deeper, transverse abdominis that support the spine. Planks are among the most effective and can help the intrinsic, tiny muscles that attach to each vertebrae. These provide postural support which is very important.
• Flexibility exercises. These, especially to the lower extremities, are extremely important. The lumbopelvic hip complex includes muscles that attach from the lower extremities, such as the hip rotators, hamstrings and hip flexors. Exercises that target these areas can relieve tight hips, which helps relieve chronic LBP.
• Posture. Practicing sound posture, good body mechanics and lifting habits are also helpful, Sanchez said. Having a neutral spine is the goal.

Future of LBP The research on LBP should include more robust studies looking at recurrence, Machado said. In the meantime, he and his colleagues are analyzing other aspects of LBP. “The main one we are looking at is over diagnosis and treatment, because a main issue is that people get lots of X-rays and imaging and that’s usually unnecessary,” Machado said. “Lots of people also get opioids. We know that’s a big problem, especially in the U.S. It is not helpful. There are few benefits and really serious risks for side effects.” Machado has a trial study starting soon in Sydney. Along with others, he is also looking into technology; specifically, smart phone apps that claim to help back pain. See also: 25 Axioms Of Medical Care In The Workers Compensation System   “We found over 69 apps to download. They’re making big promises but have not been tested for effectiveness,” he said. “We are planning to do another study in a few months on a specific app that could be promising… This one recommends a 10-week exercise program, mainly strengthening. The problem is there is no research testing this app as to whether it’s effective in reducing pain.” For now, Machado hopes the latest study will provide guidance for providers treating patients with LBP. “We didn’t know how common recurrence was; it shows one-third after recovery will have another episode,” he said. “That’s something a clinician can use to educate a patient when they come, to say ‘look, a third of people have a recurrence, so engage in exercise.’ They can use this to educate them now.” For more information visit https://www.apta.org or http://www.onsite-physio.com.

Major websites all over the world use a system called CAPTCHA to verify that someone is indeed a human and not a bot when entering data or signing into an account. CAPTCHA stands for “Completely Automated Public Turing test to tell Computers and Humans Apart.” The squiggly letters and numbers, often posted against photographs or textured backgrounds, have been a good way to foil hackers. These are annoying but effective. The days of CAPTCHA as a viable line of defense may, however, be numbered. Researchers at Vicarious, a Californian artificial intelligence firm funded by Amazon founder Jeffrey P. Bezos and Facebook’s Mark Zuckerberg, have just published a paper documenting how they were able to defeat CAPTCHA using new artificial-intelligence techniques. Whereas today’s most advanced AI (artificial intelligence) technologies use neural networks that require massive amounts of data to learn from (sometimes millions of examples), the researchers said their system needed just five training steps to crack Google’s reCAPTCHA technology. With this, they achieved a 67% success rate per character — reasonably close to the human accuracy rate of 87%.  In answering PayPal and Yahoo CAPTCHAs, the system achieved an accuracy rate of greater than 50%. See also: The Insurer of the Future – Part 3   The CAPTCHA breakthrough came hard on the heels of another major milestone from Google’s DeepMind team, the people who built the world’s best Go-playing system. DeepMind built a new AI system called AlphaGo Zero that taught itself to play the game at a world-beating level with minimal training data, mainly using trial and error — in a fashion similar to how humans learn. Both playing Go and deciphering CAPTCHAs are still clear examples of what we call narrow AI, which is different than Artificial General Intelligence (AGI) —  the stuff of science fiction. Remember R2-D2 of “Star Wars,” Ava from “Ex Machina” and Samantha from “Her?” They could do many things and learned everything they needed on their own. The narrow AI technologies are systems that can only perform one specific type of task. For example, if you asked AlphaGo Zero to learn to play Monopoly, it could not, even though that is a far less sophisticated game than Go; if you asked the CAPTCHA cracker to learn to understand a spoken phrase, it would not even know where to start. To date, though, even narrow AI has been difficult to build and perfect. To perform very elementary tasks such as determining whether an image is of a cat or a dog, the system requires the development of a model that details exactly what is being analyzed and massive amounts of data with labeled examples of both. The examples are used to train the AI systems, which are modeled on the neural networks in the brain, in which the connections between layers of neurons are adjusted based on what is observed. To put it simply, you tell an AI system exactly what to learn, and the more data you give it, the more accurate it becomes. The methods that Vicarious and Google used were different; they allowed the systems to learn on their own, albeit in a narrow field. By making their own assumptions about what the training model should be and trying different permutations until they got the right results, they were able to teach themselves how to read the letters in a CAPTCHA or to play a game. This blurs the line between narrow AI and AGI and has broader implications — in robotics and in virtually any other field in which machine learning in complex environments may be relevant. See also: Seriously? Artificial Intelligence?   Beyond visual recognition, the Vicarious breakthrough and AlphaGo Zero success are encouraging scientists to think about how AIs can learn to do things from scratch. And this brings us one step closer to coexisting with classes of AIs and robots that can learn to perform new tasks that are slight variants on their previous tasks — and ultimately the AGI of science fiction. So R2-D2 may be here surprisingly sooner than we expected.

This is the fourth in a series. You can find the first three parts here, here and here. The Insurer of the Future’s use of blockchain will depend on whether it is a new entrant or a traditional player. For new entrants, blockchain will be at the core of both their business model and their operating model. The insurer will use blockchain to:

• Underpin a series of smart-contract-enabled parametric insurance products (if event X happens, and "oracle" Y confirms that, then pre-agreed sum of money Z is paid out automatically); and
• Maintain secure policy records significantly more cheaply than its legacy competitors.

If the Insurer of the Future was a traditional player, it’s more likely to be using blockchain as a "bolt on," supporting new products that wouldn’t otherwise be cost-effective. The insurer might, for example, use blockchain ledgers to support micro-insurance policies. An example could be insuring jewelry just for the time its owner plans to wear it this evening. Or providing top-up insurance to participants in the gig economy, lasting just for the length of each gig. See also: Blockchain: Basis for Tomorrow   But whether the Insurer of the Future is a new entrant or an existing insurer, blockchain will be just one of a number of new tools at its disposal. This is one area in which new technology will be incremental to the industry rather than truly disruptive. Unless you think otherwise?

Disruptive Innovation First things first, let me set some context. A question I get asked a lot by insurers and industry folk is, “why should we be interested in what Lemonade are doing?” It’s a great question and exactly what they should be asking. (I also point out that they need to be really interested in what ZhongAn is doing, as well). To massively over-simplify and paraphrase Clayton Christensen, Lemonade has brought simplicity, convenience and affordability to a marketplace where the existing offering is complicated, expensive and inaccessible. This is why the incumbent insurers need to take note when Lemonade pays a claim in three seconds. Otherwise, they could end up like DEC. Once the market leaders in minicomputers, DEC dismissed the rise of PCs, only to watch helplessly as IBM and Apple ate their lunch with personal computers. Or Kodak, the inventor of digital photography. The company was too wedded to an outdated business model that relied on people printing their photos. That was until it was too late, and Kodak went from being the world’s fourth largest brand to bankrupt in less than two decades! Now, it might have taken about 15 years for the demise of Kodak and about 10 for DEC to wake up and smell the coffee. The point being that disruptive innovations don’t take hold overnight; they need time to gain traction and build momentum. But in this digital age, this speed of change is increasing. This is the key characteristic in the World Economic Forum’s definition of the 4^th Industrial Revolution. It took Google just five years to hit a $1 billion in revenues. And Amazon only four! Just think about this for a second. A decade ago, we didn’t have the iPhone, the iPad, Kindle, Uber, AirBnB, Android, Spotify, Instagram, WhatsApp, 4G. Could you imagine life without these now? Could you conceive that insurance is going to change and for the better? You trust me, and I will trust you There is another reason why incumbent insurers should be watching Lemonade very closely. It has addressed the fundamental issue with insurance and customer perception, which is trust, behavior and the conflict of interest. There’s a ton of research and data that shows customers don’t trust insurers. And for good reason. Insurers make the product complicated by using fancy jargon that Joe and Josephine Bloggs can't understand. Insurers get paid up front and then create hurdles and barriers when the customer rightfully asks the insurer to do what they’ve already paid them to do. And worse, the customer has to prove they are not a liar to the insurer’s satisfaction before a penny is paid out. “Insurance fraud has become a self-fulfilling prophecy for incumbent insurers,” Daniel said. “They don’t trust customers to be fair and honest. This drives their behavior toward customers. And guess what, customers respond accordingly. Which justifies the insurer’s behavior in the first place. It’s a vicious circle that neither side can break.” See also: Lemonade’s New Push: Zero Everything   Lemonade’s virtuous circle This conflict of interest doesn’t exist in the Lemonade business model. By operating as a tech platform that is also an insurance carrier, Lemonade has separated cost of operations from the pool of risk capital. It has also raised the bar when it comes to total cost of operations at 20% GWP. Lemonade don’t profit from non-payment of a claim (in the way an incumbent insurer does). The company starts by trusting customers to make honest claims. Which is why Lemonade pays out straight away, with around a third of claim payouts fully automated. No human intervention at all. Lemonade accepts that there are a few bad apples but works on the premise that most of us are fundamentally decent people. It is usually at this point that the diehards and old laggards of the insurance industry start throwing fraud and loss data at me. Citing decades of data that proves Lemonade will eventually crash and burn under the weight of inflated and illegal claims. My response is always the same “hands up everyone who is a bad person.” Of course, no hands go up because the vast majority of us are decent, respectful, honest people. Which is why Lemonade has now had six, yes ,SIX, customers who have handed claims payouts back. Just think about this for a moment. A customer makes a claim (in seconds), gets paid (immediately), finds the situation has changed (later), realizes he got paid too much (oops!), then gives the payment back (you kidding me?). Could the customer’s behavior be directly related to Lemonade’s behavior? Yes, certainly! You only have to look at customer behavior at Grameen Bank in Bangladesh to see that trust can be relied  upon. Here, unsecured personal loans are repaid on time without the need for credit scores and debt collection agencies. You don't have to take my word for it, either. Hot out of the oven is this video of Lemonade customers in New York. So, what’s the story, one year on? Lemonade has been true to its word on the subject of transparency. Throughout the year, the company has published its numbers, warts and all, for everyone to see. Building and maintaining trust is fundamental to Lemonade’s business model, and this starts with being open and honest. Daniel has shared with me the latest numbers, and they are very impressive. I won’t repeat them here, because I know the team will be posting them all shortly in the latest Transparency Chronicles. They’re proud of the numbers, and rightly so. See also: Lemonade: World’s First Live Policy   All I will say is that Daniel and the team have steered a considered and thoughtful course in their first year. They could have chased the numbers, as many first year startups would do, only to regret the quality of business they end up with. But Lemonade's team has stuck to their knitting, have impressive growth numbers, a quality customer base completely aligned to the brand and are now licensed in 18 states (with more to follow). “Our job has only just started,” Daniel said. “Over the next year, we will continue to make insurance easier and better for our customers. One area we’ve started to look at now is the underlying insurance language and the products that form the heart of all insurance.” Are you surprised? You shouldn’t be! Lemonade is a highly professional startup and will no doubt become the definitive case study for exactly how “it” should be done. But has this surprised Daniel? “There are two things that have surprised us this year,” Daniel told me. “First, the extent of the warm reception we’ve received across the industry and from customers. We hoped customers would like us, but we never took for it granted. “After all, you can’t beta test a new insurance company. The MVP (minimally viable product) approach simply doesn’t apply to insurance. It’s regulated and has to be the real deal from the get-go, right first time. So, for us, having customers put their faith in Lemonade from Day One has been very satisfying. “The second is that our faith in humanity and behavioral economics has been affirmed. There will always be people who want to game the system, but on the whole, all our expectations about customer behavior have been exceeded. “Who would have thought we would have six customers who gave their claim payouts back. That is very gratifying and also humbling for us. And gives us encouragement to continue doing what we are doing.” Lemonade is live; insurance will never be the same again! For me, I’m convinced. Historians will look back to Sept. 21, 2016, the day that Lemonade opened for business, as a watershed for the insurance industry. Which means, of course, that the key question now is, who among the incumbent insurers will provide the Kodak moment? The one who simply missed that the world had changed until it was too late.

We know that early intervention is critical to prevent delayed recoveries for injured workers. One of the challenges has been to identify those at higher risk of poor outcomes. Fortunately, we have the tools to determine which patients are more likely to develop chronic pain and languish in a disability mindset. The process is fairly simple and backed by strong, research-based evidence. With increased awareness among payers, providers and other industry stakeholders, we can prevent creeping catastrophic claims, help injured workers regain function quicker and significantly reduce workers’ compensation costs. Reasons for Getting Stuck Psychosocial risk factors used to be little more than a buzz term among workers’ compensation professionals. While those of us who’ve worked extensively with chronic pain patients understood that psychological issues can easily derail a workers’ compensation claim, the research that proves this to be true has become widespread only in recent years. In fact, some of the most recent research says that psychological factors can be more of a predictor of poor outcomes than the underlying medical conditions. We now know for certain that the biomedical model of disease does not hold true for everyone, and the biopsychosocial model of illness must be considered. Where the first is based on the idea that a physical ailment can be cured through medical solutions, the second acknowledges that some people have an underlying psychobiological dysfunction that has clinically significant distress or disability. They are the injured workers who can greatly benefit from early identification and intervention. Inadequate coping skills and a lack of knowledge of what is causing their pain can drive delayed recoveries and overuse of treatments and medications. Chronic pain is the final common pathway of this delayed recovery. See also: A Biopsychosocial Approach to Recovery   Research validated through meta analyses, prospective studies and control group studies shows that injured workers with delayed recoveries typically have:

• Catastrophic thinking
• A history of anxiety or depression
• Anger and perceived injustice about their plight
• An external locus of control
• Minimal resilience

They may also have fear avoidance, meaning they engage in little to no physical activity out of fear they will injure themselves more and experience increased pain. There are myriad reasons why some people have these issues. The cause could be childhood and life experiences, their relationship and interactions with their environments, issues in the workplace or home or other reasons altogether. It’s important that we identify injured workers with these issues as soon as possible after their injuries. Pain Screening Questionnaires One of the most effective ways to pinpoint injured workers with psychological issues is through specially designed, self-administered questionnaires. The one we use to identify patients at risk of developing chronic pain and disability is the Pain Screening Questionnaire (PSQ). The PSQ was developed by a Swedish professor of clinical psychology and is used in many countries. It has been shown through studies to accurately predict time loss, medical spending and function — but not pain. The PSQ takes about five minutes to complete and consists of 21 questions that focus on the injured worker’s:

• Pain attitudes, beliefs and perceptions
• Catastrophizing
• Perception of work
• Mood/affect
• Behavioral response to pain
• Activities of daily living

The injured worker is asked to rate on a scale of 1 to 10 things such as, "How would you rate the pain you have had during the past week?"; "In your view, how large is the risk that your current pain may become permanent?"; and "An increase in pain is an indication that I should stop what I’m doing until the pain decreases." Depending on the score, the injured worker is categorized as low risk, moderate risk, high risk, or very high risk. Those on the lower end of the scale are most appropriately managed through take-home educational materials on chronic pain. Moderate-risk injured workers are good prospects for a self-managed workbook style intervention. High- and very-high-risk injured workers should be referred for additional assessment and an intervention program, such as cognitive behavioral therapy (CBT). See also: Impact on Mental Health in Work Comp   In a program of early identification and intervention, Albertson’s Safeway found 12% of injured workers scored high. Those affected were referred to CBT. After an average of just six CBT sessions, a large percentage of them were able to return to work. Because of the results, primary treating physicians who work with Albertson’s injured workers have been referring them to the program earlier in the claims process. Conclusion It is estimated that 10% of workers’ compensation claims consume at least 80% of medical and indemnity resources. The vast majority of these are injured workers with delayed recoveries due to psychosocial risk factors. With solid science backing up the successful identification and interventions of these employees, we can prevent needless disability and substantially reduce workers’ compensation costs.

Why should you care about machine learning (ML)? ML essentially refers to the phenomenon of computers and other devices that appear to be intelligent because they can learn from patterns in the environment to achieve a specified goal or predict an outcome. These patterns could come from a variety of sources, such as images, voice, free text or even structured data. The machine’s ability to anticipate or interpret the information given can make it seem almost human-like. For example, in the case of workers’ compensation claims, you can apply machine learning to feed the machine claim notes, and it will tell you which job class the claim is for. What makes this possible is ML’s ability to look at combinations of features in data and learn from them to make a variety of associations. Imagine a machine looking at claimant age, diagnosis codes and Part, Nature and Cause (PNC) codes in tandem to instantly determine the likelihood of litigation. Machines now have this ability, thanks to the increasing variety and volume of available data points. They can generate specific algorithms to decode a seemingly infinite number of patterns to make massive amounts of information truly useful. Why Does This Matter to Workers’ Compensation? Teams are constantly trying to get in front of their claims so that they can be proactive rather than reactive. To do that, they need a variety of signals sent to them in real time to figure out the best strategy for a claim. ML helps provide these signals much faster than ever before, and there are four key reasons why. 1. Machine learning can handle many, many combinations in the data … fast. Think of all the data you have to sort through to assess a claim. ML could be applied to increase speed and accuracy as well as to simplify the entire process and unlock invaluable predictive insights. Case in point: To get an accurate prediction of the projected cost of a claim, you might need to juggle as many as 45 different data features, including PNC codes, claimant information and diagnosis information. If you assume 10 values for each feature, which is a conservative estimate (International Classification of Diseases (ICD) codes have as many as 80,000-plus potential values), the number of possible combinations can reach a mind-boggling 10^45. ML algorithms, however, can navigate these combinations and associate patterns with specific data characteristics in minutes. Not only that, they can also determine which set of features contributed most to the outcomes. Talk about the ability to improve claim teams’ efficiency! See also: Machine Learning – Art or Science?   The ability for ML to sift through all these combinations of features and use all their interactions is absolutely game-changing. 2. It can handle “holes” in the data. Aside from quickly sorting through multiple data points and turning them into something meaningful and applicable, ML helps address another common problem: the presence of gaps in some of the fields in the claim or bill data, especially in the early stages of a claim. Techniques like data augmentation, where models are trained on various versions of a claim that expose different levels of gaps, can help ML models tolerate data “holes.” While it’s always important to get the best data one can for a claim, it’s equally important for models to be able to operate with incomplete information. ML makes it possible to move forward despite imperfect or incomplete claims. 3. It can handle changing data. Workers’ compensation claims are also constantly evolving, sometimes dramatically. What started as a neck injury can evolve into a spinal injury. In an ideal world, a claims team would be notified as soon as a major shift happened in one of their claims instead of waiting until the traditional 30-day, 60-day and 90-day check-in points. ML helps here, as well. Because ML models can handle gaps in the data, they can also instantly navigate the changing nature of claims, alerting claims teams immediately of changes. Not only can ML tackle changes in claim data itself, it can also handle changes in the overall operations of a claims team. A robust retraining schedule helps the ML models stay current so that they are perpetually unearthing new patterns to better assist when claims operations or macro-effects, such as new regulations, occur. 4. It can work with more natural forms of data, such as free text, voice and images. While this is all excellent news for claims teams interested in harnessing the power of ML to drive better care, I’ve saved perhaps the best benefit for last. One of the biggest advantages of ML is its ability to handle not only structured datasets but unstructured as well. What does this mean? ML can detect new patterns out of things like natural text and images. This has never been possible before. A simple application of this is in the use of ICD codes. Claims that have a combination of ICD-9 and ICD-10, for example, can be really clunky to deal with because the mapping between them is a little complicated. But with ML and natural language processing (NLP) techniques, we can use the ICD descriptions instead of the pure codes to unearth relevant themes and topics. Those become features for the models, and no more mapping is required. This is an area within ML that is evolving rapidly. We can see it all around us with devices like Amazon’s Echo and Apple’s Siri. In all likelihood, ML will generate a hotbed of activity in workers’ compensation predictive analytics, as well. Imagine a scenario where a claims examiner could get suggestions for doctors to recommend to the injured worker, all while they are typing in their claim notes. See also: Machine Learning: a New Force  Despite All Its Power, ML Is Just a Tool No matter how awesome and transformative ML seems, it’s not a magic bullet. At its core, it’s a useful new set of tools meant to empower claims examiners to do what they do best — be a coach whose main goal is getting the injured workers through their recovery. ML can redefine the role of the examiner as a coach and a problem-solver while removing large parts of their “rote” work. This enables examiners to spend more time talking to injured workers. Tack on the fact that ML can improve the efficiency of the entire claims process — from the ability to assess and process more claims faster and more accurately to getting employees the right care throughout their journey so that they can return to work and resume their lives — and ML has the capacity to provide data-driven insights that will help employees feel better faster, all while reducing costs overall. Deploying a combination of tools like ML with empathetic examiners who have strong problem-solving skills will elevate the entire workers’ compensation experience — for claims teams, employees and the companies they represent. It has the capacity to ultimately drive better care. While ML can’t do it all, it can modernize and fundamentally transform workers’ compensation. This article was first published in Claims Journal.

Hurricanes Harvey, Irma and Maria laid bare fundamental inadequacies of the current flood insurance program in the U.S. Too few homeowners had flood insurance in place. Federal Emergency Management Agency (FEMA) flood maps were inadequate to encompass actual flood risks and, even more importantly, outreach programs by FEMA and the National Flood Insurance Program (NFIP) were inadequate to properly communicate risks to the market. See also: Time to Mandate Flood Insurance?   The current hurricane season revealed an astounding lack of resiliency in the U.S. on many levels: (1) lack of insurance coverages; (2) inadequate mapping of flood risks; (3) failure to properly educate homeowners about their actual flood risks; and, (4) gross under-investment in resilient infrastructure by all levels of U.S. governments. As Congress prepares to slash budgets and cut taxes, the fact that Hurricane Irma easily topped City of Miami’s sea walls with only Category 1 strength winds and brought a four-foot river of water down Brickell Avenue, the center of Miami’s Financial District, shows how much work needs to be done to achieve resiliency in just one U.S. city. In its rush to reduce taxes, Congress is ignoring the U.S. infrastructure deficit, which is estimated to be in the multiple trillions of dollars by the American Society of Civil Engineers. The slide deck that I prepared for the Future of Flood Summit discusses new, cost-effective tools for flood risk modeling, flood risk report production and flood risk communication. A central theme is: "Can the insurance industry do a better job of helping insureds and societies cope with the increasing risks they are facing, as our climate changes and sea levels rise?” You can find the presentation here.

What a difference two words make. Last week, President Trump declared the opioid epidemic a “national public health emergency.” The declaration will speed up how quickly specialized personnel can be hired, expand access to treatment for some addicts and make some HIV/AIDS programs more flexible. But many people wish he’d left out the words “public health.” That’s because a “national emergency” would have freed up money, and lots of it. The Public Health Emergency Fund at Health and Human Services currently contains only $57,000. And the president did not ask Congress to refill it. But we shouldn’t entertain the idea that the federal government, or any other entity, is going to “fix” the opioid epidemic, just as you can’t pin blame for the crisis on a single entity. The epidemic is all-encompassing, far-flung and complex, and it unfolded over two decades and millions of bad decisions. See also: 6 Shocking Facts on Opioid Abuse   Pharmaceutical manufacturers are partly to blame because they marketed opioids as safe when taken as prescribed. Doctors and medical institutions compounded the problem because they didn’t adequately question and research these false claims. Drug distributors shipped massive amounts of drugs to places that obviously didn’t need them, and pharmacists looked the other way when filling prescriptions that were clearly too large. The Drug Enforcement Administration allowed manufacturers to make more and more opioids, even as overdose death rates skyrocketed. And many patients and drug users didn’t take responsibility for their own health. There’s no one person or organization responsible for the crisis, and there’s no easy fix, no magic bullet. I was disturbed by the recent reports that the Trump administration was “scrambling” to formulate an opioid plan. This epidemic didn’t have simple causes, and the response to it should not be rushed out. Meaningful change will require a response that recognizes millions of addictions have been created that aren’t going anywhere. Each of the parties that took part in creating of this epidemic must be a part of the solution. For instance, doctors and medical schools need to develop drastically different prescribing protocols to avoid creating addictions. Their far-more-challenging task will be to develop ways to deal with all of the patients who have been prescribed high doses of opioids for many years and are understandably terrified that they will be taken off their meds, even though the drugs are probably sapping their lives of vitality. How do you treat those patients so they don’t turn to street drugs? The federal government does have one big stick in its arsenal that hasn’t been used, which is the fact that the DEA is in charge of setting manufacturing quotas for all controlled substances. The DEA could use this power to force drugmakers to better track where their opioids are ending up. This hasn’t happened, and in fact, the DEA permitted hike after hike in manufacturing quotas, finally cutting the rates only in the last two years. See also: Opioids: Invading the Workplace   In the end, I think the gathering tsunami of lawsuits against the drug companies may prove to be more effective than the federal government’s response. The eventual settlements could dwarf the $206 billion in Big Tobacco settlements from 1998. We need to make sure that any settlement provides lots of money for research and treatment. But neither the federal government nor plaintiffs' lawyers are going to “solve” this epidemic. Addictions, once created, don’t die easily. The opioid crisis is going to be a part of life in the U.S. for a long time.

As 21st-century consumers, we are fully aware of the trends that shape and change the way we interact with the world. Although there are stark differences in the way, say, a millennial interacts with the world from that of a baby boomer, the themes are the same. Digitalization, business model disruption, mobile technology and process automation are some of the trends we have all become conditioned to. Advances in artificial intelligence, sensor technology, robotics and genomics are some of the forces that are likely to shape our future environment. Perhaps due to regulatory hurdles, perhaps due to complexity in the sales and new business processing transaction or perhaps due to the long-term nature of the policyholder-insurer relationship, the life insurance industry has remained somewhat insulated from these forces of change. Whereas other industries have been disrupted head-on by startups, much of the insurtech traction has been gained by new entrants modernizing aspects of the value chain, rather than overhauling the product or business model, as a whole. Recently, venture funding has begun to find its way to the types of companies that can now threaten life insurance as we know it. Lemonade, Policy Genius, Fabric and Ladder are a few examples of companies attempting to reshape the entire industry. Common among these startups is the fact that none are anchored down by legacy systems or held to strict ROI hurdles on new investments. They have the freedom and flexibility to redesign the policyholder-insurer relationship to fit the needs of the policyholder, rather than the needs of a distribution channel or administrative system. Even if large insurers want to be the change catalysts, internal efforts to disrupt, or cannibalize, entrenched business models are not likely to pass through the risk management controls that these insurers have in place. So, what is the large, entrenched incumbent to do? The controls that traditionally inhibit radical innovation from occurring in an organization are strong, but they can be circumvented. The cheapest and quickest way to reimagine life insurance from within the walls of a behemoth, is to do just that: reimagine. There is no risk, nor hard cost, to imagination, and the simple exercise of “Design Thinking applied to Life Insurance Risk” can uncover the likely traits that the inevitable disruptor is likely to possess. Here, we’ll take you through a simplified example of how to conduct such an exercise by examining the framework (Design Thinking) and the customer need (Life Insurance Risk), then layering in relevant Consumer Trends to make a prediction of what The Future of life insurance might be. Design Thinking Innovation has grown to become a highly technical discipline, which has spawned rigorous post-graduate programs. The underlying mechanisms of innovation, however, are simple and unchanging, despite countless volumes published on the “proper” or “best” way to innovate. The emergent discipline of Design Thinking does an excellent job of keeping tried-and-tested innovation mechanisms at its core, without over-emphasis on process or creativity. Design Thinking maintains the principles that smart solution design is human-centric and ambiguous. Restated, it says that by understanding changes in the consumer’s point of view, we can employ limitless creativity to reimagine any product. Innovation should begin with empathy for the consumer (and in the case of life insurance, the distributor), then seek to match expertly-designed solutions to the problems or needs that the consumers are facing. See also: This Is Not Your Father’s Life Insurance   A great starting point for our thought experiment is to empathize with the end consumer. Market research companies will charge tens of thousands of dollars to help you do this, but it may be just as effective to simply remove oneself from the paradigm that an insurance professional lives in, and to imagine oneself as the 21st-century consumer that you are. Then, answer a set of hypothetical questions to uncover their paradigm. How do they see the world? What are their expectations when purchasing products or interacting with companies? How are these expectations changing? How do they view life insurance? How do they manage the risks associated with death? Life Insurance Risk In the empathy exercise described above, the line of questioning you would follow to get a rich understanding of the consumer’s point of view is likely to lead you to an exploration of how consumers think about, plan for, and deal with death. It’s somewhat obvious that the average consumer’s relationship to risks associated with death has changed substantially over the past few generations. It wasn’t long ago that there was a very real risk of a factory worker, as primary wage earner, not coming home at the end of the day, having suffered a fatal accident at the workplace. Additionally, many chronic and critical illnesses that were once almost certainly fatal have become manageable with modern advances in medicine. This has led to a substantial improvement in life expectancy, which, for the consumer, means that the incidence of death risk has become very low in their lives. Further, with the reduction in the incidence of death in early life, the impact of death on families has changed. Consumers do not live as much with the acute fear of the death of a wage earner as they do with the fear of accumulating insurmountable medical expenses associated with accidents or chronic conditions. Coinciding with that is a notable shift in the complexity associated with dealing with accidents or chronic conditions. Family members caring for loved ones are often left to negotiate through treatment options, financial obligations, and legal matters all on their own. The empathy exercise shows that the risks associated with death, despite how an insurance professional may be conditioned to behave, are not wholly financial. While we all know this to be true, it is easy to for the insurer to lose this perspective, and thus lack empathy for its policyholders. What’s particularly interesting, especially in the case of term life insurance providers, is that the insurers have a vested interest in helping these families extend the lives of their policyholders, but their products and processes do not reflect this. Traditional controls such as reducing liability risk exposure have probably prevented insurers from becoming the consumer advocate. Shouldn’t the life insurer play the role of the advocate for the consumer in negotiating these complex processes associated with life insurance risk? It is these types of insights that deep consumer empathy can yield. Consumer Trends Having explored the consumers point of view, it’s important next to understand the context that dictates consumer behavior, or the way they interact with the world. In relation to life insurance risk, consumers have experienced many, many years of continuous reinforcement that an insurance contract is a paper one, brokered by an employer or agent. This may have served to maintain the insurance industry status quo, but insurers must be aware that the gravestones are plentiful of companies who believed that their consumers would continue to behave the same way, despite changes in their environment. Let’s define Consumer Trends as the patterns which impact the way that consumers interact with the world. These can be technologies, lifestyles, popular influences, scientific advances, and marketplace dynamics, among other things, which all define the environment in which consumers live, and those they are moving to. For each we identify, we should ask critical questions about the implications of such a trend to the life insurance business model. While it would be exhausting to list all the trends impacting life insurance consumer context, it’s worth considering some notable ones:

• Longevity - As explored above, advances in medicine and genomics are improving longevity to levels previously thought impossible. Can improvements in longevity be factored into product design?
• Crowdsourcing - Social media and associated technologies now allow consumers to use “the crowd” to appeal for support such as funding for medical procedures or funerals. To what extent is this emerging trend offsetting the need for a fixed financial benefit from life insurance?
• Sensor Technology - mobile apps, wearables, and the “Internet of Things” (IOT) are allowing consumers to gain immediate feedback on their health and wellbeing. This may lead to situations where consumers know more about their health than their doctors, and subsequently, life insurance underwriters. What is the implication of sensor technology on product design?

It may be worthwhile to bring in experts with varying perspectives into this step, such as experts in new technology, or to simply observe consumers in their natural environment and note how their habits might be affected by trends. The Future Once the above steps are complete, the final step is to assemble the consumer insights and trend analyses into a hypothesis, or set of hypotheses, for how the solution of life insurance risk management might change. We will leave it up to the reader’s creativity to imagine the ways in which particular traits of the business model are likely to change. Given the pace of change in the world today, conducting this type of design exercise regularly, or with a varying mix of perspectives, will yield new and different insights each time. What we can learn from this exercise is that there certainly is opportunity for insurers to rethink the value proposition of a life insurance contract. The data assembled above points to a need for insurers to reimagine the fundamental relationship between the policyholder and the company. See also: What’s Next for Life Insurance Industry?   What we can’t know is the exact nature of the eventual disruptor, or if the disruptor gains any sort of first-mover advantage. Large insurance companies, however, can make a reasonable prediction as to what the disruptor might look like, and can begin to prepare themselves for the eventualities. The boldest insurance companies may attempt to become the disruptor, while the slightly less bold may seek to acquire companies that align with their predictions. More cautious companies can still participate in the change, maybe by investing in startups through venture funds, or partnering with accelerators or universities. Of course, many will decide to keep their heads in the sand and ignore all the signs. They may cite risk aversion as a reason to not invest in innovation or disruption, but will ignore the question “What is the risk of not innovating?”

“Blockchain will transform the world.... Distributed ledger technology represents the biggest change to the business world since the adoption of double-entry accounting centuries ago.... Every industry will experience upheaval as blockchain becomes the foundation of the new business environment.”

This type of hyperbole is common today when discussing blockchain technology. The key questions for business leaders are about how much is hype and how much is reality. Insurance executives are asking, “What’s the real story?”

SMA recently asked insurance executives for their views on blockchain to determine the level of awareness in the industry and their expectations about business use and value. The title of a new SMA Research Brief provides the main storyline, Blockchain in Insurance: Awareness Grows, Activity Still Limited. Compared with just one year ago, awareness has increased significantly. This is not surprising because blockchain articles, videos and speeches are a dime a dozen these days. Many insurers have been to workshops, singled out individuals to become subject matter experts and even joined consortiums related to blockchain. Education and understanding are growing steadily across the industry.

See also: Blockchain: Basis for Tomorrow  

A handful of well-publicized projects leveraging blockchain have been undertaken, and some insurers have experimented with the technology, but the vast majority of insurers are in the watch-and-wait mode. Even so, insurers are beginning to see the potential. In addition to the digital currencies such as Bitcoin that first raised the visibility of blockchain, insurers expect the tech to be an important enabler for microinsurance, peer-to-peer insurance, asset tracking and authentication, smart contracts and the exchange of sensitive information and documents. P&C commercial lines insurers see the most potential, given the more complex nature of their products and their ecosystem of partners.

Blockchain may well become foundational to the business world and pervasive in insurance, but it is likely to evolve gradually over the next decade. Ultimately, the technology may become invisible, just a natural part of the digital infrastructure that runs the world, much like key emerging technologies of a prior age. Once-highly-touted technologies such as TCP/IP and HTTP are rarely discussed in business circles today – they are just there behind the scenes as pervasive enablers of the internet and the digital world. A similar trajectory may await blockchain.

So, the real story? Yes, blockchain is vitally important, and it is a technology with the potential to transform the way the world conducts business. But, as its usage evolves and expands across insurance and other industries over the next couple of decades, it will become less hyped and more of a standard building block.