An excerpt from the PLUS Journal article “The Evolving Healthcare Risk and Evolving Underwriter Strategy” (December 2011) by Paul Marshall.
Actual Senior Care Long-Term Services and Supports (LTSS) exposure and care data had been difficult and expensive to obtain in the past, requiring the use of consulting firms to harvest and organize the data into usable chunks. The utilization of now publicly available data will be a key differentiator in successfully underwriting LTSS insurance and programs in the foreseeable future.
An example of this new published data that would be valuable for a LTSS healthcare liability program risk analysis is the upcoming National Survey of Residential Care Facilities (NRSCF), to be released by the National Centre for Health Statistics (NCHS) by the end of 2011. Under this umbrella, the NSRCF would consist of three products: a methods report (describing how the NSRCF was conducted) a facility data brief (containing highlights of major findings on U.S. residential care facilities), and a facility public-use data file with documentation (containing data collected about the healthcare facilities).
By April 2012, the NCHS also plans to make available to the public a resident public-use file and a data brief, reporting selected characteristics of residents of U.S. residential care facilities. The NSRCF, the first nationally representative sample survey of residential care communities, was conducted between March and November 2010. Interviewers collected information on more than 2,300 facilities and over 8,000 residents. Reports like this can be invaluable for healthcare program underwriter’s managers, giving them a real-time, all-encompassing perspective that will allow them to shape their insurance program to be right at the cutting edge of the market.
Using timely and informative LTSS data in a responsive and influential manner, when coupled with risk analytics modeling, helps to provide the insurance manager with answers to very important questions about the true exposures and gives them a much needed advantage. The reach of risk analytics is spreading through expert third-party service providers; and the advantages of sophisticated modeling tools are available to most, regardless of in-house technological expertise or available capital.
Underwriting insurance for the growing LTSS healthcare industry is becoming increasingly difficult as every day healthcare facilities tweak and evolve their operations to remain profitable under changing Medicaid / Medicare reimbursement policies and with evolving regulatory expectations. Over time, rising acuity, additional services, and diminished staffing ratios will lead to adverse incidents if not kept in check. It used to be that these fluctuations in underlying risk would go undetected, but with the utilization of modeling tools and effective risk analytics, even subtle changes in staffing, acuity, and services can be revealed. Healthcare modeling provides the insurance program underwriters with the knowledge of any change to risk drivers, thereby allowing the program leadership to make preemptive changes and to manage risk more effectively. For that reason, risk analytics are revolutionizing the processes and tools employed by insurers to more quickly and accurately market, price, and underwrite their products.
Once a change to a risk driver is detected, the underwriter can project how these changes will affect the overall portfolio of risk exposure. From that knowledge, the program manager gains deep insight into actual loss costs and can confidently adjust premiums, offer feedback regarding risk management, and continually monitor-preferably before any loss occurs. Without predictive modeling and risk analysis after an account is written, the policy is generally held in status quo with minimal consideration to any variation in underlying risk, until it’s too late and a major loss develops.
With improved risk data management, insurers can lower overall costs, charge adequate premiums, reduce claims, gain competitive advantage, and increase their market share. It all starts with underwriting the data. Every exposure must be analyzed to establish the appropriate premium in order for the program to remain viable for the long term. For that reason, experienced industry-specific underwriters who understand the specific risk are critical. Historically the theory has been vetted. Throughout many risk industries, predictive modelling strategies, when measured against traditional under-writing approaches, were found to be more accurate. Essentially, predictive modeling can help eliminate the human and emotional response that naturally occurs in the underwriting, loss control, and claim handling process.
With risk analytics, potential claim incidents can be rapidly and cost-effectively analyzed. At this time, ‘real’ risk is identified sooner, triaged appropriately, and dealt with proactively. Effective risk analytics can accelerate the acquisition of knowledge, place claims into proper context, lower claims administration costs, and help improve overall outcomes. Moreover, predictive modeling can chart the course for improved negotiations with plaintiffs, and ideally, lower overall settlements. There are many variables that go into each case that ultimately determine how it is settled. Once a case proceeds to court, the deciding factor is people in the jury box. How they will decide is extraordinarily unpredictable. With the passage of time, the cost to settle any case may increase exponentially. Risk analytics and predictive modeling provide the insurer and the defense team with rapid access to the information needed to manage incidents proactively, triage claims effectively and settle claims before that critical window of opportunity closes.
A program underwriter has to play to the strength of risk analytics in order to benefit from it, which includes being savvy and quick enough to respond. This also includes being flexible enough with the tailoring and implementation of a predictive model to match the flexibility of risk analytics as predictive modeling tools are available for any step along the continuum, including marketing analytics, underwriting, risk management, and loss mitigation.
Another advantage of predictive modeling is the ability to establish more accurate actuarial reserves. With improved accuracy in identifying overall risk, carriers can establish and responsibly change reserves as needed. Such financial efficiencies allow an organization to direct their financial resources to the most effective point. This helps make great savings as the captive program is aimed specifically at the exact areas that require focus-enabled by risk analytics.
Historically, a large portion of an insurance program’s expenses are consumed by the initial application and risk underwriting processes. Predictive “sales” modeling can assist in finding suitable accounts more efficiently than the traditional approach that requires underwriting to review and analyze 10-20 accounts before finding one that fits for the risk program’s appetite. This can be viewed as a sales divining rod-finding the suitable risk with minimum marketing or sales expense outlay.
PLUS members can read this entire article in the PLUS Journal archive.
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