A warehouse filled with Magic Dust has been discovered in a remote area of DeWitt County. Governor calls upon school districts to remain calm………………
How do you underwrite a group health plan without historical claim data? Is there a rating manual at hand that can accurately predict future risk and rating basis for a 12 month period plus another four month tail?
In 2019 a 350 life Texas school district was unable to obtain their claim information from TRS ActiveCare, a Texas government health plan they were insured through making it impossible to experience rate the risk.
Utilizing Magic Dust plus a good dose of intuition, rates were developed based on benefits set by the district. The plan went into effect September 2019. Plan rates and benefits have since remained the same going towards their fourth year. Such is the power of Magic Dust.
How can one obtain a prescription for Magic Dust? The first step is to contact Dr. Bill for an appointment.
Navigating the Intricacies of Health Care Claims Data for Better Small Group Underwriting
MyHealthGuide Source: Peter Borans, CEO, Founder of AMS, 4/17/2023, AMS Census White Paper (full text and tables) (PDF, 10 pages)
Underwriting policies for small groups in the insurance market is a demanding task due to the intricate nature and limited availability of health care claims data. This white paper aims to enlighten executives, underwriters, insurance brokers, and TPAs on the limitations of using a census for estimating risk in small group underwriting and the challenges in converting hand written manuals into software that may perpetuate historical biases.
We present a fresh perspective on quantifying risk, which differs from the traditional methods employed for the past two decades. By comprehending the complexities of health care claims data and embracing new risk quantification concepts, insurers and underwriters can devise more accurate underwriting policies to the advantage of both the insurers and their customers.
The Drawbacks of Relying on a Census for Small Group Underwriting Risk Estimation
Utilizing a census for estimating risk in small group underwriting has been a standard practice for the past 20 years. However, this approach has its shortcomings that may lead to imprecise underwriting decisions.
One of the main limitations is that a census relies on historical demographic data, which might not accurately represent the actual health status of a specific group. While demographic data can be helpful in estimating risk, it does not consider individual health data, the inability to quickly adapt for changes to the “norm,” such as “The Covid Years,”, account for outliers, or the prevalence of chronic conditions within a group.
Challenges in Converting Manuals to Software
The process of converting hand-written manuals into software can give rise to imprecise underwriting decisions. Hand-written manuals may embed historical biases or outdated risk assessment approaches, relying on community rating, fully insured data, and little-to-no self-funded data that can result in imprecise underwriting decisions. When these manuals are transformed into software, biases and approaches may be further perpetuated, leading to more inaccuracies.
One significant disadvantage of using community rating manuals when underwriting small group risk in a self funded market is that it can cause an unequal distribution of risk among groups. Community rating systems usually establish premiums based on the overall risk of a given community or region, rather than the specific risk of a particular group.
The Shortcomings of Publicly Available Claims Databases
Publicly accessible claims databases, including CMS and third-party purchased data, can also be a potential source of false outcomes. These databases might contain inaccurate or incomplete data, they may not be updated in real-time, meaning that the data may be years old by the time it is accessed for underwriting purposes and often trending is used to fill in data gaps when information is no longer reported. Often this data may be obtained from a limited range of providers or geographic areas, leading to skewed data sets that do not accurately reflect the population being analyzed.
At AMS, our mission is to transform healthcare by offering a cutting-edge platform that simplifies complex tasks and optimizes Risk Management, Payment Accuracy, and Business Intelligence. Through the seamless integration of rich clinical and financial data, the application of advanced machine learning techniques, and our team’s extensive subject matter expertise, we’re moving beyond today’s limitations to create innovative solutions that redefine the industry and set new standards for affordability, efficiency, and intelligence. Visit mdstrat.com
Business is about solving other people’s problems. Solving the high cost of healthcare giving employers a competitive advantage is ours. Status quo convergent thinking solves problems through a very narrow lens. Divergent thinking empowers us to solve problems with a wide lens allowing us to see what others don’t. You can’t fix what you don’t see. You don’t know what you don’t know. We see opportunities others miss. We do what others don’t. Winning together is our goal.