Managing Claims Across Silos
The long-minimized and largely untapped synergy between casualty claims and benefit programs may offer opportunities for both industries. Some argue that these worlds are just too different and distinct to bring together, whether through simple alignment or partial to full integration.
Managers are often more comfortable in their own functional areas and sometimes crossing over can stretch expertise and focus. Fundamentally, however, claims are claims though subject to the unique rules of processing and resolution, many of which are dictated by third parties as well as statutes and regulations.
There’s been a shift in thinking and a growing interest in a more collaborative, aligned and even fully integrated services approach — one which takes many forms, but at its core incorporates a more collaborative and combined strategy from date of incident through claim closure. The targeted goals for this approach are:
- Ensuring an appropriate employee experience throughout the life of the claim
- Targeting and delivering outcome optimization
- Minimizing the cost of risk associated with the reasons employees are under medical care and/or unable to contribute productively to their employer’s mission
On its face, the value of collaboration seems obvious. From both an employee benefits and risk management perspective, providing care for the individual is of the utmost importance. One of the main objectives is ensuring the right outcomes, which includes leveraging the basic skill sets of investigation, verification, documentation and equitable resolution that are common between these two realms.
The nuances and distinctions that exist between them are not insignificant, but the key goals are the same — caring for people under medically-related distress (regardless of source), minimizing disruptions to workforce productivity, and closing claims efficiently and effectively with fairness to all parties and their respective goals and objectives.
Although these objectives have varying levels of importance in each field, they are fundamental to process effectiveness in both. This is not to say that there aren’t peculiar and unique aspects of each that require certain expertise and skills to achieve more specific end goals.
However, while blending skill requirements among a common group of claims professionals can be challenging, it is not rocket science. Defining and filling positions to enable successful claims handling in both worlds is imminently doable. The biggest hurdle may in fact be the necessary extent of collaboration among and between these typically distinct functional areas and their leaders in order to secure the best outcomes for injured employees.
Many employers are already effectively managing employee injury and disease exposures. There are discernable trends emerging toward fewer silos, and more performance-oriented measurements that are focused on short- and long-term strategies. Those companies taking a more collaborative approach can benefit from key elements such as:
- Compassionate care that puts employee interests first
- Integrated reporting and measurement across departments
- Robust analytics that result in prescriptive actions with impact
- Innovative tools targeted to specific process opportunity areas
- A more holistic focus on the care of affected employees
- The over-arching goal of a healthy, productive workforce
So whether or not you have direct responsibility for both functional areas, I urge you to lead the charge that would leverage this opportunity for the benefit of your organization.
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.