“Identifying Healthcare Insurance Payment Arbitrage Opportunities Using A Machine Learning Network” in Patent Application Approval Process (USPTO 20210279812): DeRoyal Industries Inc.

2021 SEP 27 (NewsRx) -- By a News Reporter-Staff News Editor at Health Policy and Law Daily -- A patent application by the inventor DeBusk, Brian C. (Knoxville, TN, US), filed on March 9, 2021, was made available online on September 9, 2021, according to news reporting originating from Washington, D.C., by NewsRx correspondents.

This patent application is assigned to DeRoyal Industries Inc. (Powell, Tennessee, United States).

The following quote was obtained by the news editors from the background information supplied by the inventors: “The traditional Medicare Fee-for-Service (FFS) benefit began in 1965 for U.S. citizens who are 65 years or older, or who qualify based on certain types of disability. The U.S. government serves as the insurer for these beneficiaries, and individual claims are paid to physicians and other providers as deemed necessary and beneficial. These payments are made on a claim-by-claim basis with the U.S. government assuming full cost risk for the beneficiary, minus the appropriate amount of beneficiary cost-sharing. Individuals are exposed to limited cost sharing in the form of deductibles and copayments, but these amounts can be further mitigated by supplemental insurance products. Across 60 million Medicare beneficiaries, approximately two-thirds receive their healthcare benefits through the FFS system.

“About one-third of Medicare beneficiaries are insured through private Health Maintenance Organization (HMO) or Preferred Provider Organization (PPO) plans. For these private plans, funds that are normally allocated to FFS beneficiaries’ claims are instead paid to a third party in a lump sum, with the third party assuming full insurance risk. The third party provider is allowed to manage the delivery of care to the enrollee through a variety of methods. Examples of such management include the narrowing of networks (excluding certain providers from the plan), step therapy (requiring patients try certain treatments before escalating care), and prior authorization (requiring physicians and other providers to seek approvals from the payer prior to rendering service). These private plans also structure cost-sharing payments to be collected from enrollees, but these amounts are typically less than their corresponding cost under FFS. When payments to these plans exceed their cost, the private plan produces a profit. This creates incentives to deliver care through a combination of less service use, lower-cost substitute services, and/or lower-priced services.

“The lump sum payment that the U.S. government transfers to the private plan is based on the anticipated amount of FFS spending for a beneficiary of nominal health, which private insurance companies then “bid” against. These bids are against the benchmark price-not against bids from other plan competitors. Plans with bids below the benchmark spending target receive rebates to be applied as additional benefits to the beneficiary. Plans with bids above the benchmark target must collect additional premiums from the beneficiary. In 2019, the average payment to plans for a beneficiary of average health were approximately $900/month.

“The initial payment amount is determined for an enrollee of average health, and the payment amount is adjusted on a enrollee-by-enrollee basis to account for clinical risk factors. These factors are incorporated into a risk adjustment model referred to as the Center for Medicare and Medicaid Services Hierarchical Condition Categories (CMS-HCCs). Examples of clinical conditions within this model include diabetes, mental illness, cancer, acute myocardial infarction, stroke, and congestive heart failure. Risk adjustments made through the CMS-HCC model can have a significant impact on payments made to plans. Whereas the nominal payment is around $900 per month, these adjustments result in payments that can easily vary over a wide range (i.e. $650 to $4,500 per month) based on the specific characteristics of each enrollee.

“Individual CMS-HCC coefficients are established by a linear regression across virtually the entire base of FFS beneficiaries. Because differing populations of beneficiaries have differing cost sensitivities to various clinical conditions, multiple CMS-HCC models (sets of coefficients) are deployed. For example, there is a set of CMS-HHC coefficients for beneficiaries who are new to Medicare, beneficiaries who are institutionalized, beneficiaries who have aged into Medicare and are not institutionalized with full dual-eligible benefits, and beneficiaries who receive Medicare due to disability with partial dual eligibility and are not institutionalized.

“It has been reported that the CMS-HCC models do not accurately capture the complete cost for the most clinically complex beneficiaries. When a patient has numerous complex conditions, the models tend to understate the necessary cost adjustments. Similarly, the anticipated costs of patients with few or no clinical conditions are often overestimated. Embodiments described herein correct for this underestimating and overestimating through the use of non-linear modeling.

“Although cost adjustments are made on a enrollee-by-enrollee basis, the aggregate risk is intended to be distributed across a broad population of beneficiaries. For any specific enrollee, the risk-adjusted amount is intended to represent a nominal amount of spending-not a precise spending amount for that individual. However, if it is possible to identify a set of individuals whose actual financial risk is above or below the CMS-HCC based benchmark, and if the sample group is large enough, aggregate spending for the group should be reliably higher or lower.”

In addition to the background information obtained for this patent application, NewsRx journalists also obtained the inventor’s summary information for this patent application: “The above and other needs are met by a computer-implemented method for identifying insurance risk adjustment opportunities for healthcare expenses of healthcare insurance program enrollees. In a preferred embodiment, the method includes the following steps:

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“(a) for each enrollee, determining a base risk score based on a base risk-adjusted payment model;

“(b) providing one or more inputs for each enrollee to a machine learning network, the one or more inputs including one or more of Center for Medicare and Medicaid Services Hierarchical Condition Category (CMS-HCC) values, an enrollee claims history, and enrollee historical spending amounts;

“© training the machine learning network based on the one or more inputs to predict future healthcare spending for the enrollees;

“(d) the machine learning network identifying enrollees whose predicted future healthcare spending differs from an amount determined based on the base risk score;

“(e) upon identifying an enrollee whose predicted future healthcare spending is greater than the amount determined based on the base risk score, taking one or more of the following actions:

“performing outreach to or intervention for the identified enrollee;

“disenrolling or discouraging the identified enrollee from participating in the healthcare insurance program; and

“capturing additional CMS-HCC values that may increase the payment amounts for the identified enrollee; and

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“(f) upon identifying an enrollee whose predicted future healthcare spending is less than the amount determined based on the base risk score, taking action to retain the identified enrollee.

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“In some embodiments, the step of providing one or more inputs to the machine learning network for each enrollee includes providing information related to social media activity of each enrollee.

“In some embodiments, the information related to the social media activity of each enrollee is obtained using automated software programs that collect information from social media accounts associated with the enrollees.

“In some embodiments, the information related to the social media activity of each enrollee includes one or more of metadata, text data, image data, and video data from social media accounts associated with the enrollees.

“In some embodiments, the information related to the social media activity of each enrollee is provided to the machine learning network in lieu of the enrollee claims history.

“In some embodiments, the information related to the social media activity of each enrollee is provided to the machine learning network in addition to the enrollee claims history.

“In some embodiments, step (f) includes taking action to retain the identified enrollee for additional plan years through one or more of telephone marketing, direct mailing marketing, and online marketing.”

The claims supplied by the inventors are:

“1. A computer-implemented method for identifying insurance risk adjustment opportunities for healthcare expenses of healthcare insurance program enrollees, comprising: (a) for each enrollee, determining a base risk score based on a base risk-adjusted payment model; (b) providing one or more inputs for each enrollee to a machine learning network, the one or more inputs including one or more of Center for Medicare and Medicaid Services Hierarchical Condition Category (CMS-HCC) values, an enrollee claims history, and enrollee historical spending amounts; © training the machine learning network based on the one or more inputs to predict future healthcare spending for the enrollees; (d) the machine learning network identifying enrollees whose predicted future healthcare spending differs from an amount determined based on the base risk score; (e) upon identifying an enrollee whose predicted future healthcare spending is greater than the amount determined based on the base risk score, taking one or more of the following actions: performing outreach to or intervention for the identified enrollee; disenrolling or discouraging the identified enrollee from participating in the healthcare insurance program; and capturing additional CMS-HCC values that may increase the payment amounts for the identified enrollee; and (f) upon identifying an enrollee whose predicted future healthcare spending is less than the amount determined based on the base risk score, taking action to retain the identified enrollee.

“2. The method of claim 1 wherein the step of providing one or more inputs to the machine learning network for each enrollee includes providing information related to social media activity of each enrollee.

“3. The method of claim 2 wherein the information related to the social media activity of each enrollee is obtained using automated software programs that collect information from social media accounts associated with the enrollees.

“4. The method of claim 2 wherein the information related to the social media activity of each enrollee includes one or more of metadata, text data, image data, and video data from social media accounts associated with the enrollees.

“5. The method of claim 2 wherein the information related to the social media activity of each enrollee is provided to the machine learning network in lieu of the enrollee claims history.

“6. The method of claim 2 wherein the information related to the social media activity of each enrollee is provided to the machine learning network in addition to the enrollee claims history.

“7. The method of claim 1 wherein step (f) includes taking action to retain the identified enrollee for additional plan years through one or more of telephone marketing, direct mailing marketing, and online marketing.”

URL and more information on this patent application, see: DeBusk, Brian C. Identifying Healthcare Insurance Payment Arbitrage Opportunities Using A Machine Learning Network. Filed March 9, 2021 and posted September 9, 2021. Patent URL: https://appft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PG01&p=1&u=%2Fnetahtml%2FPTO%2Fsrchnum.html&r=1&f=G&l=50&s1=%2220210279812%22.PGNR.&OS=DN/20210279812&RS=DN/20210279812

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