Tag Archives: new york city

How to Attack the Opioid Crisis

The vastness of the opioid crisis is all around us:

  • 259 million opioid prescriptions are made every year.
  • 91 Americans die every day of opioid overdose.
  • Workplace costs of prescription opioid use are more than $25 billion, driven by lost earnings from premature death, reduced compensation or lost employment and healthcare costs.

It’s time to take action.

See also: Opioids: A Stumbling Block to WC Outcomes  

As with any large-scale, complex phenomenon, there is no silver bullet. But a framework from the Johns Hopkins Bloomberg School of Public Health suggests three areas where we should focus our efforts: preventing new cases of opioid addiction, identifying opioid-addicted individuals early and ensuring access to effective opioid addiction treatment. We believe these areas must be attacked from a variety of clinical and operational angles.

From the clinical side, the emphasis has to be largely around better clinical training and urinary drug testing (UDT). A generation of doctors has been raised based on a curriculum emphasizing the need to manage pain aggressively. Retraining physicians on best practices is needed to reinforce safe opioid prescribing patterns. Research from Utah has shown that physician education on recommended opioid prescribing practices was associated with improved prescription patterns, including 60% to 80% fewer prescriptions for long-acting opioids for acute pain. When an opioid is prescribed, the use of UDT is a cost-effective way to monitor treatment compliance and drug misuse.

To address from the operational side, we need evidence-based opioid prescription guidelines in place and systems to track opioid prescriptions and adherence to guidelines. Further, we must ensure access to effective opioid addiction treatment.

Many health organizations and state health systems are aggressively adopting pain treatment guidelines that clearly lay out when opioids should and should not be used. And the preliminary results of implementing these guidelines are promising. For example, the introduction of opioid prescribing guidelines in the Washington state workers’ compensation system was associated with a decline in opioid prescriptions, the average morphine equivalent doses prescribed and the number of opioid-related deaths.

Prescription drug monitoring programs (PDMP) allow for health systems to analyze opioid prescribing data to find potentially inappropriate prescribing behavior and illegal activity. For example, using its PDMP, New York City found that 1% of prescribers wrote 31% of the opioid prescriptions.

While prevention of initial opioid exposure is important, the treatment of opioid addiction is an important safety net when prevention fails. Pharmacotherapies including methadone, buprenorphine and naltrexone are options for routine care of opioid dependence, but they are still in the early stages of the adoption cycle.

See also: Potential Key to Tackling Opioid Issues  

The foundation to address the clinical and operational approaches to opioid epidemic is two-fold:

  1. A strong system to determine what’s acceptable through well-defined, evidence-based guidelines; and
  2. A system to use these guidelines and trigger the right actions through processes and technology.

The next article will address the nature of these two systems.

Model Errors in Disaster Planning

“All models are wrong; some are useful.” – George Box

We have spent three articles (article 1, article 2, article 3) explaining how catastrophe models provide a tool for much-needed innovation to the global insurance industry. Catastrophe models have covered for the lack of experience with many losses and let insurers properly price and underwrite risks, manage portfolios, allocate capital and design risk management strategies. Yet for all the practical benefits CAT models have infused into the industry, product innovation has stalled.

The halt in progress is a function of what models are and how they work. In fairness to those who do not put as much stock in the models as a useful tool, it is important to speak of the models’ limitations and where the next wave of innovation needs to come from.

Model Design

Models are sets of simplistic instructions that are used to explain phenomena and provide relevant insight on future events (for CAT models – estimating future catastrophic losses). We humans start using models at very early ages. No one would confuse a model airplane with a real one; however, if a parent wanted to simplify the laws of physics to explain to a child how planes fly, then a model airplane is a better tool than, say, a physics book or computer-aided design software. Conversely, if you are a college student studying engineering or aerodynamics, the reverse is true. In each case, we are attempting to use a tool – models of flight, in this instance – to explain how things work and to lend insight into what could happen based on historical data so that we can merge theory and practice into something useful. It is the constant iteration between theory and practice that allows an airplane manufacturer to build a new fighter jet, for instance. No manufacturer would foolishly build an airplane based on models no matter how scientifically advanced those models are, but those models would be incredibly useful in guiding the designers to experimental prototypes. We build models, test them, update them with new knowledge, test them again and repeat the process until we achieve desired results.

The design and use of CAT models follows this exact pattern. The first CAT models estimated loss by first calculating total industry losses and then proportionally allocating losses to insurers based on assumption of market share. That evolved into calculating loss estimates for specific locations at specific addresses. As technology advanced into the 1990s, model developers harnessed that computing power and were able to develop simulation programs to analyze more data, faster. The model vendors then added more models to cover more global peril regions. Today’s CAT models can even estimate construction type, height and building age if an insurer does not readily have that information.

As catastrophic events occur, modelers routinely compare the actual event losses with the models and measure how well or how poorly the models performed. Using actual incurred loss data helps calibrate the models and also enables modelers to better understand the areas in which improvements must be made to make them more resilient.

However, for all the effort and resources put into improving the models (model vendors spend millions of dollars each year on model research, development, improvement and quality assurance), there is still much work to be done to make them even more useful to the industry. In fact, virtually every model component has its limitations. A CAT model’s hazard module is a good example.

The hazard module takes into account the frequency and severity of potential disasters. Following the calamitous 2004 and 2005 U.S. hurricane seasons, the chief model vendors felt pressure to amend their base catalogs with something to reflect the new high-risk era we were in, that is, taking into account higher-than-average sea surface temperatures. These model changes dramatically affected reinsurance purchase decisions and account pricing. And yet, little followed. What was assumed to be the new normal of risk taking actually turned into one of the quietest periods on record.

Another example was the magnitude 9.0, 2011 Great Tōhuko Earthquake in Japan. The models had no events even close to this monster earthquake in their event catalogs. Every model clearly got it wrong, and, as a result, model vendors scrambled to fix this “error” in the model. Have the errors been corrected? Perhaps in these circumstances, but what other significant model errors exist that have yet to be corrected?

CAT model peer reviewers have also taken issue with actual event catalogs used in the modeling process to quantify catastrophic loss. For example, a problem for insurers is answering the type of question of: What is the probability of a Category 5 hurricane making landfall in New York City? Of course, no one can provide an answer with certainty. However, while no one can doubt the significance of the level of damage an event of that intensity would bring to New York City (Super Storm Sandy was not even a hurricane at landfall in 2012 and yet caused tens of billions of dollars in insured damages), the critical question for insurers is: Is this event rare enough that it can be ignored, or do we need to prepare for an event of that magnitude?

To place this into context, the Category 3, 1938 Long Island Express event would probably cause more than $50 billion in insured losses today, and that event did not even strike New York City. If a Category 5 hurricane hitting New York City was estimated to cause $100 billion in insured losses, then knowing whether this was a 1-in-10,000-year possibility or a 1-in-100-year possibility could mean the difference between solvency and insolvency for many carriers. If that type of storm was closer to a 1-in-100-year probability, then insurers have the obligation to manage their operations around this possibility; the consequences are too grave, otherwise.

Taking into account the various chances of a Category 5 directly striking New York City, what does that all mean? It means that adjustments in underwriting, pricing, accumulated capacity in that region and, of course, reinsurance design all need to be considered — or reconsidered, depending on an insurer’s present position relative to its risk appetite. Knowing the true probability is not possible at this time; we need more time and research to understand that. Unfortunately for insurers, rating agencies and regulators, we live in the present, and sole reliance on the models to provide “answers” is not enough.

Compounding this problem is that, regardless of the peril, errors exist in every model’s event catalog. These errors cannot even be avoided, and the problem escalates where our paucity of historical recordings and scientific experiments limit our industry’s ability to inch us closer and closer to greater certainty.

Earthquake models still lie beyond a comfortable reach of predictability. Some of the largest and most consequential earthquakes in U.S. history have occurred near New Madrid, MO. Scientists are still wrestling with the mechanics of that fault system. Thus, managing a portfolio of properties solely dependent on CAT model output is foolhardy at best. There is too much financial consequence from phenomena that scientists still do not understand.

Modelers also need to continuously assess property vulnerability when it comes to taking into consideration various building stock types with current building codes. Assessing this with imperfect data and across differing building codes and regulations is difficult. That is largely the reason that so-called “vulnerability curves” oftentimes are revised after spates of significant events. Understandably, each event yields additional data points for consideration, which must be taken into account in future model versions. Damage surveys following Hurricane Ike showed that the models underestimated contents vulnerability within large high-rises because of water damage caused by wind-driven rain.

As previously described, a model is a set of simplified instructions, which can be programmed to make various assumptions based on the input provided. Models, therefore, fall into the Garbage In – Garbage out complex. As insurers adapt to these new models, they often need to cajole their legacy IT systems to provide the required data to run the models. For many insurers, this is an expensive and resource-intensive process, often taking years.

Data Quality’s Importance

Currently, the quality of industry data to be used in such tools as CAT models is generally considered poor. Many insurers are inputting unchecked data into the models. For example, it is not uncommon that building construction type, occupancy, height and age, not to mention a property’s actual physical address, are unknown! For each  property whose primary and secondary risk characteristics are missing, the models must make assumptions regarding those precious missing inputs – even regarding where the property is located. This increases model uncertainty, which can lead to inaccurate assessment of an insurer’s risk exposure.

CAT modeling results are largely ineffective without quality data collection. For insurers, the key risk is that poor data quality could lead to a misunderstanding regarding what their exposure is to potential catastrophic events. This, in turn, will have an impact on portfolio management, possibly leading to unwanted exposure distribution and unexpected losses, which will affect both insurers’ and their reinsurers’ balance sheets. If model results are skewed as a result of poor data quality, this can lead to incorrect assumptions, inadequate capitalization and the failure to purchase sufficient reinsurance for insurers. Model results based on complete and accurate data ensures greater model output certainty and credibility.

The Future

Models are designed and built based on information from the past. Using them is like trying to drive a car by only looking in the rear view mirror; nonetheless, catastrophes, whether natural or man-made, are inevitable, and having a robust means to quantify them is critical to the global insurance marketplace and lifecycle.

Or is it?

Models, and CAT models in particular, provide a credible industry tool to simulate the future based on the past, but is it possible to simulate the future based on perceived trends and worst-case scenarios? Every CAT model has its imperfections, which must be taken into account, especially when employing modeling best practices. All key stakeholders in the global insurance market, from retail and wholesale brokers to reinsurance intermediaries, from insurers to reinsurers and to the capital markets and beyond, must understand the extent of those imperfections, how error-sensitive the models can be and how those imperfections must be accounted for to gain the most accurate insight into individual risks or entire risk portfolios. The difference in a few can mean a lot.

The next wave of innovation in property insurance will come from going back to insurance basics: managing risk for the customer. Despite model limitations, creative and innovative entrepreneurs will use models to bundle complex packages of risks that will be both profitable to the insurer and economical to the consumer. Consumers desiring to protect themselves from earthquake risks in California, hurricane risks in Florida and flood risks on the coast and inland will have more options. Insurers looking to deploy capital and find new avenues of growth will use CAT models to simulate millions of scenarios to custom create portfolios optimizing their capacity and create innovative product features to distinguish their products against competitors. Intermediaries will use the models to educate and craft effective risk management programs to maximize their clients’ profitability.

For all the benefit CAT models have provided the industry over the past 25 years, we are only driving the benefit down to the consumer in marginal ways. The successful property insurers of the future will be the ones who close the circle and use the models to create products that make the transfer of earthquake, hurricane and other catastrophic risks available and affordable.

In our next article, we will examine how we can use CAT models to solve some of the critical insurance problems we face.

Top 10 Mistakes to Avoid as a New Risk Manager

The transition into your first risk management job can be difficult. Whether your boss promotes you into your first risk management job or hires you from another organization, you want to excel at your new position over the long haul. In part, that means avoiding mistakes. We often learn our best lessons when we fail, but some mistakes can seriously hurt your risk management program, harm your reputation or even derail your career. Here are 10 mistakes you can avoid.

  1. Don’t rush in with all the answers. You may arrive wanting to form your own alliances and acquire your own team, but avoid making hasty decisions. Give current employees a chance to prove themselves before you transfer them or hire your own team. The same applies to vendor relationships. You can lose a great deal of knowledge about loss history and coverage negotiation if you immediately decide to switch insurance brokers. “Changing brokers can be a great way to create significant coverage gaps or an errors and omissions claim for your friend the new broker,” according to one Atlanta broker. Some vendor alliances, such as relationships with contractors and body shops, may be long-standing, especially in a small town. Rushing in and making changes can cause big ripples in a little pond.
  2. Don’t try to do everything at once. In my teens, I read a book called Ringolevio, about a kid named Emmett Groan growing up in the streets of New York City. One of his compatriots frequently warned Emmett when he was about to rush headlong into a decision, “Take it easy, greasy, you’ve got a long way to slide.” I found that advice very applicable in risk management. If you inherit a big job, you will be faced with hundreds of decisions, some big, some small. Take your time. While you may feel overwhelmed at first, chip away at the organization’s most pressing problems. Put out fires as they arise. Then schedule time for you and your advisers — your brokers, your attorneys, your actuaries and your managers – to develop sound strategies and plans.
  3. Don’t use a shotgun, use a rifle. If the organization is experiencing too many injuries, for example, don’t jump to an obvious solution like using more personal protective equipment. Talk with front-line supervisors, study historical loss data and consider several options before you throw money at a problem. Once in the door, interview employees, talk with other managers, meet with your vendors and set a few important priorities for your first six months in the job. Using a rifle approach means you’ll have to say “No” to some people. This can cause problems. When possible, explain why you’re declining to act on the problems or the specific issues others may present to you. The more transparently you operate, the less criticism you will face. Openness reduces speculation and helps avoid resentment.
  4. Don’t job hop. Most people can be very ambitious early in their careers. Yet too much ambition can hurt your career. Think long and hard before changing jobs. Bad bosses rarely outlast their employees. Deciding to change jobs because of a conflict with a supervisor is often short-sighted. The grass might seem greener on the other side, but sometimes that’s because of a septic tank (to paraphrase a famous comedian). These questions may help you avoid rash decisions.
    • Am I making the change solely to earn more money or for a more prestigious title? If so, will this change “pay for” what I will lose?
    • Am I making the change because I’m feeling unchallenged or bored? If so, what steps can I take to make my current job more challenging? For example, would becoming more active in a trade association, offering expertise to a local nonprofit or mentoring an up-and-coming risk management professional add challenge and interest?
    • How will this affect my retirement financially? Will I be changing retirement systems, or will I lose significant bonuses or vacation because of the change? Always factor those figures into the salary decision. This question becomes more important as retirement age nears.
    • How will this change affect my family and my coworkers? Our coworkers can turn even a challenging job into an appealing one. Do you really want to leave your coworkers? As for family, what ages are your children? Disrupting school-aged children can have negative, long-term consequences.
    • What are the odds I will regret this decision? Go ahead, we’re numbers people. Put a percentage to your decision, then ask yourself if you’re really ready to take that gamble.

    It takes months to settle into a new job. It’s often a year or more before we feel comfortable. Some studies show that many people who change jobs would have done much better if they had stayed put longer. Change for the sake of change frequently is not positive.

  5. Don’t entertain gossip about your predecessors. Some at your new organization may try to build an alliance with you at the expense of your predecessor. Short-circuit these conversations whenever possible. Tactfully turn the conversation to another subject or excuse yourself from the conversation. Try not to make an enemy of the person who is trying to get into your good graces.
  6. Don’t revisit your predecessor’s decisions. Especially when working with unions, you may find people lined up at your door asking you to revisit your predecessor’s judgments. Unless your predecessor’s conclusions hurt your overall program, don’t rush into undoing the decisions and the work he or she completed. You may not be operating under the same set of facts or with the same long-term vision that the former risk manager had at his or her disposal.
  7. Don’t believe your own PR. Never pretend you know more than you know, and don’t start believing your own “press.” While others may soon invite you to participate on panels and present at conferences, remain humble and teachable. It’s terribly painful to learn humility through humiliation.
  8. Don’t fail to communicate. A lack of communication is one of the most damaging mistakes a risk manager can make. A risk manager must have the ear of employees across the organization, from line supervisors to senior management. According to Don Donaldson, president of LA Group, a Texas-based risk management consulting group, “A risk manager needs to be an excellent communicator and facilitate his or her message across the entire organization. In my mind, that requires getting out of the office and pressing the flesh; seeing and being seen and listening, really listening, to determine what is going on in the organization.” Management by walking around is one strong tool in a new risk manager’s tool bag. Once people see that you’re willing to leave your office to discover what is happening, whether it’s on the shop floor or on the sewer line, they’ll more readily accept your expertise and counsel.
  9. Don’t get discouraged. “New risk managers may make the mistake of thinking that risk management is as important to others in the organization as it is to them,” according to Harriette J. Leibovitz, a senior insurance business analyst with Yodil. “It takes time, and more time for some than others, to figure out that you're more than an irritation to the folks who believe they drive all the revenue.” Over time, you will prove your value to the organization many times over. Until that day, quietly do your job and find encouragement from your risk management peers.
  10. Don’t forget to laugh. You will be privy to the peculiarities of human nature both at its finest and at its worst, so don’t forget to find the lighter side of situations when you can. A robust sense of humor will help you through the rough spots and build bonds with your coworkers.

While these are just a few tips to help you in your new role as a risk manager, your peers probably can offer many more ways to ensure success. Over my career in risk management, I have found my fellow risk management professionals to be some of the most generous people in my life, always willing to share their expertise and provide me with a helping hand. Develop and lean on your network. If this is your first job as a risk manager, you’re in for a wonderful experience. Take time along the way to enjoy the experiences, appreciate the great people you will meet and appreciate the lighter side of risk management.