CareRise on the Rise
The Affordable Care Act is creating a new landscape of health care risks. Risk & Insurance® talked to Tim Goux, the founder and CEO of CareRise, to get his take on some of the challenges post-acute health care facilities are facing and his motivation for forming his company.
Tim, we know you have a background in health care and that your family has owned and operated post-acute care facilities for more than 40 years. What was your motivation for starting CareRise?
I literally grew up in the business. My father purchased his first skilled nursing facility in 1968, the year I was born. In the fall of 1999, I got a call from my insurance broker telling me that the General Liability premium for our 200-bed facility was going to more than triple. My heart felt like it went down to my stomach. The down payment for the entire year was equal to the previous year’s premium. It didn’t take me long to figure out that due to judgments in Florida and Texas on fall cases and wound care cases the insurance industry’s response was to triple and quintuple the premium.
What was one of your first steps?
In April of 2000 I wrote the business plan for CareRise. Through a family friend, I was able to get it to a semi-retired executive at Lloyd’s of London. He said ‘Tim this makes complete sense I want to come over to the states to see how you plan to implement this.’
That was followed by my own visit to Lloyd’s. I went to London at the age of 33 with a laptop and a notebook. After a week in the Lloyd’s tower talking to syndicate after syndicate I realized that what I was suggesting to them didn’t exist.
So that was back in 2000. It’s quite a stretch between then and now, when many of us are hearing about your company for the first time. Can you help us understand why all this took so long?
I kept CareRise quiet for years. I had my other companies and I didn’t need the capital. I purposefully did not advertise. I didn’t want to go national, at least not at the beginning. It wasn’t until 2008 when we got notification that we were getting our first patent that I started reaching out to carriers to try and take our program national.
So how did that work? Who did you first contact?
When I learned that we were going to get the patent I wrote to the four largest players in this space. I didn’t know them. I sent a letter to Lexington and three other companies. The day after they got my letter, Lexington called me. Within weeks I was in Boston for meetings with them and they subsequently vetted us for seven to eight months. We then partnered with them and now CareRise is in 20 states and 135 cities.
What’s at the core of what you’re doing? What industry need are you fulfilling?
A number of things really. The Affordable Care Act is pushing patient days from the hospitals to the post-acute care facilities. The other piece is that administrative turnover in post-acute care facilities is quite high, as much as 50 percent annually on average. We’re providing registered nurses on the ground, working with our proprietary software, to provide consistent, effective risk management in these facilities. You have to have independent risk management that can add some long-term stability to the health care delivery system. CareRise is that independent intermediary, if you will.
So what can we expect from you in coming months, or for the year ahead? What’s your growth arc look like?
You’re going to think I’m crazy but moving forward with a new program that we are going to announce in a matter of weeks, it wouldn’t surprise me if we experienced 100 percent growth in the next year.
Listen to Your Customers
The correlation between patient satisfaction and medical malpractice risk has been extensively studied and is well understood and accepted in the health care environment.
Only a small number of patients with a valid legal claim ever file a medical malpractice suit. Conversely, a small percentage of physicians attract a disproportionate share of the suits that are filed, regardless of the validity of the complaint.
Many studies have shown that the volume and nature of unsolicited patient complaints about a physician are a strong predictor of that physician’s likelihood of being sued.
The nexus between customer complaints and risk goes beyond the health care industry.
Knowing this, many health care organizations, including my own, use patient satisfaction and patient complaint data as a risk management tool.
My organization contracts with a third party to analyze our patient complaint data; allowing us to identify, with a high degree of reliability, physicians at high risk of being sued for medical malpractice. If a high-risk physician is identified through this process, we attempt to mitigate that risk through a series of peer-to-peer interventions ranging from an awareness discussion, counseling, or potentially, a formal corrective action plan.
Through awareness, encouragement and coaching, physicians typically self-correct the behaviors that are causing friction with their patients and reduce their probability of being sued, thus lowering the cost of risk for the entire organization.
The nexus between customer complaints and risk goes beyond the health care industry, however.
In 2012, Toyota settled a class-action lawsuit alleging accelerator pedal defects for $1.1 billion. Customers had complained that the accelerator pedals were getting stuck and causing dangerous acceleration; eventually the resulting accidents led to tragedy and the inevitable lawsuits.
Following the downturn in the real estate market in 2009, Wells Fargo suspended or reduced customer home equity lines of credit in what they believed was commensurate with the reduced market values of the mortgaged properties.
Customers complained that their homes had not been properly reappraised and that the bank had not provided adequate notice of the credit line reductions, and eventually filed a class-action lawsuit that was settled in 2012.
There are many other examples too numerous to cite.
Risk managers in all industries should consider using customer satisfaction and customer complaint data as a proactive risk mitigation tool.
While we are all familiar with those chronically dissatisfied customers who will never be pleased despite our best efforts, trends or patterns in the nature of customer complaints and feedback can be a harbinger of bigger problems on the horizon.
Early identification and mitigation of these issues can potentially lessen the impact to the organization if they are adequately addressed.
As a risk manager, are you listening to what your customers are telling you about your organizational risk? If not, perhaps you should.
6 Truths about Predictive Analytics
Predictive data analytics is coming out of the shadows to change the course of claims management.
But along with the real benefits of this new technology comes a lot of hype and misinformation.
A new approach, ACE 4D, provides the tools and expertise to capture, analyze and leverage both structured and unstructured claims data. The former is what the industry is used to – the traditional line-item views of claims as they progress. The latter, comprises the vital information that does not fit neatly into the rows and columns of a traditional spreadsheet or database, such as claim adjuster notes.
ACE’s recently published whitepaper, “ACE 4D: Power of Predictive Analytics” provides an in-depth perspective on how to leverage predictive analytics to improve claims outcomes.
Below are 6 key insights that are highlighted in the paper:
1) Why is predictive analytics important to claims management?
Because it finds relationships in data that achieve a more complete picture of a claim, guiding better decisions around its management.
The typical workers’ compensation claim involves an enormous volume of disparate data that accumulates as the claim progresses. Making sense of it all for decision-making purposes can be extremely challenging, given the sheer complexity of the data that includes incident descriptions, doctor visits, medications, personal information, medical records, etc.
Predictive analytics alters this paradigm, offering the means to distill and assess all the aforementioned claims information. Such analytical tools can, for instance, identify previously unrecognized potential claims severity and the relevant contributing factors. Having this information in hand early in the claims process, a claims professional can take deliberate actions to more effectively manage the claim and potentially reduce or mitigate the claim exposures.
2) Unstructured data is vital
The industry has long relied on structured data to make business decisions. But, unstructured data like claim adjuster notes can be an equally important source of claims intelligence. The difficulty in the past has been the preparation and analysis of this fast-growing source of information.
Often buried within a claim adjuster’s notes are nuggets of information that can guide better treatment of the claimant or suggest actions that might lower associated claim costs. Adjusters routinely compile these notes from the initial investigation of the claim through subsequent medical reports, legal notifications, and conversations with the employer and claimant. This unstructured data, for example, may indicate that a claimant continually comments about a high level of pain.
With ACE 4D, the model determines the relationship between the number of times the word appears and the likely severity of the claim. Similarly, the notes may disclose a claimant’s diabetic condition (or other health-related issue), unknown at the time of the claim filing but voluntarily disclosed by the claimant in conversation with the adjuster. These insights are vital to evolving management strategies and improving a claim’s outcome.
3) Insights come from careful analysis
Predictive analytics will help identify claim characteristics that drive exposure. These characteristics coupled with claims handling experience create the opportunity to change the course of a claim.
To test the efficacy of the actions implemented, a before-after impact assessment serves as a measurement tool. Otherwise, how else can program stakeholders be sure that the actions that were taken actually achieved the desired effects?
Say certain claim management interventions are proposed to reduce the duration of a particular claim. One way to test this hypothesis is to go back in time and evaluate the interventions against previous claim experience. In other words, how does the intervention group of claims compare to the claims that would have been intervened on in the past had the model been in place?
An analogy to this past-present analysis is the insight that a pharmaceutical trial captures through the use of a placebo and an actual drug, but instead of the two approaches running at the same time, the placebo group is based on historical experience.
4) Making data actionable
Information is everything in business. But, unless it is given to applicable decision-makers on a timely basis for purposeful actions, information becomes stale and of little utility. Even worse, it may direct bad decisions.
For claims data to have value as actionable information, it must be accessible to prompt dialogue among those involved in the claims process. Although a model may capture reams of structured and unstructured data, these intricate data sets must be distilled into a comprehensible collection of usable information.
To simplify client understanding, ACE 4D produces a model score illustrating the relative severity of a claim, a percentage chance of a claim breaching a certain financial threshold or retention level depending on the model and program. The tool then documents the top factors feeding into these scores.
5) Balancing action with metrics
The capacity to mine, process, and analyze both structured and unstructured data together enhances the predictability of a model. But, there is risk in not carefully weighing the value and import of each type of data. Overdependence on text, for instance, or undervaluing such structured information as the type of injury or the claimant’s age, can result in inferior deductions.
A major modeling pitfall is measurement as an afterthought. Frequently this is caused by a rush to implement the model, which results in a failure to record relevant data concerning the actions that were taken over time to affect outcomes.
For modeling to be effective, actions must be translated into metrics and then monitored to ensure their consistent application. Prior to implementing the model, insurers need to establish clear processes and metrics as part of planning. Otherwise, they are flying blind, hoping their deliberate actions achieve the desired outcomes.
6) The bottom line
While the science of data analytics continues to improve, predictive modeling is not a replacement for experience. Seasoned claims professionals and risk managers will always be relied upon to evaluate the mathematical conclusions produced by the models, and base their actions on this guidance and their seasoned knowledge.
The reason is – like people – predictive models cannot know everything. There will always be nuances, subtle shifts in direction, or data that has not been captured in the model requiring careful consideration and judgment. People must take the science of predictive data analytics and apply their intellect and imagination to make more informed decisions.
Please download the whitepaper, “ACE 4D: Power of Predictive Analytics” to learn more about how predictive analytics can help you reduce costs and increase efficiencies.
This article was produced by the R&I Brand Studio, a unit of the advertising department of Risk & Insurance, in collaboration with ACE Group. The editorial staff of Risk & Insurance had no role in its preparation.