Patent Issued for Use determination risk coverage datastructure for on-demand and increased efficiency coverage detection and rebalancing apparatuses, methods and systems (USPTO 11790454): Bind Benefits Inc.

2023 NOV 02 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- Bind Benefits Inc. (Minneapolis, Minnesota, United States) has been issued patent number 11790454, according to news reporting originating out of Alexandria, Virginia, by NewsRx editors.

The patent’s inventors are Chiv, Henning (Castro Valley, CA, US), Chock, Matthew (Apple Valley, MN, US), Eiden, Glen (Forest Lake, MN, US), Fast, Trevor (San Francisco, CA, US), Hastings, Charley (Lindstrom, MN, US), Haupt, Jason (Zimmerman, MN, US), Miller, Anthony (Minneapolis, MN, US), Wagoner, Shawn (Edina, MN, US), Wiandt, Matthew (Edina, MN, US).

This patent was filed on October 21, 2019 and was published online on October 17, 2023.

From the background information supplied by the inventors, news correspondents obtained the following quote: “Insurance companies offer products such as home and life insurance to cover risks against property and injury. Actuaries at the insurance companies analyze various risks in setting the costs of such products. Computer software such as Milliman’s Arius and Triangles on Demand products are used by insurance actuaries to assess various risks.

“Generally, the leading number of each citation number within the drawings indicates the figure in which that citation number is introduced and/or detailed. As such, a detailed discussion of citation number 101 would be found and/or introduced in FIG. 1. Citation number 201 is introduced in FIG. 2, etc. Any citations and/or reference numbers are not necessarily sequences but rather just example orders that may be rearranged and other orders are contemplated. Citation number suffixes may indicate that an earlier introduced item has been re-referenced in the context of a later figure and may indicate the same item, evolved/modified version of the earlier introduced item, etc., e.g., server 199 of FIG. 1 may be a similar server 299 of FIG. 2 in the same and/or new context.”

Supplementing the background information on this patent, NewsRx reporters also obtained the inventors’ summary information for this patent: “The Use Determination Risk Coverage Datastructure for On-Demand and Increased Efficiency Coverage Detection and Rebalancing Apparatuses, Methods and Systems (hereinafter “UDRCD”) transforms coverage enrollment request, event signal, ACGG request, search request inputs, via UDRCD components (e.g., ACM, EF, UF, ACGG, ARD, SP, AP, etc. components), into coverage enrollment response, add-in recommendation, ACGG response, search response outputs. The UDRCD components, in various embodiments, implement advantageous features as set forth below. It is to be understood that the word “recommendation” as used throughout this document is used to refer to recommendation of add-ins from a set of atomized add-in options, and is not used to refer to medical recommendations or to assisting members to diagnose their health issues.

“Introduction

“The UDRCD provides unconventional features (e.g., a self-evolving atomized coverage graph data structure that includes a set of clinical condition objects that include treatment paths data, a set of treatment objects, and a set of provider objects that specify how likely a provider is to utilize each of the available treatment paths) that were never before available in information technology analytics and processing for risk coverage.

“In one embodiment, the UDRCD includes Condition-based coverage: Atomization of coverage that is on-demand and based on procedure or condition rather than annual coverage based on broad service categories. In one implementation, the UDRCD includes on-demand coverage atomization to reduce entry costs for members to gain coverage to truly insurable events (unpredictable, highest dollars claims); allows members to personalize coverage to their needs; presents treatment and/or provider pathway at the time of need providing relevant choices at relevant times. Underwriting is done today by volume in service categories. This is misaligned with individual disease progression and epidemiology. Underwriting on-demand coverage with atomization utilizes different data science tools and methodology. In one implementation, the UDRCD includes a novel combination of atomized design (also includes choice of provider), on demand coverage at time of need, and structure of the plan. In one implementation, on-demand insurance may include: atomization of plan coverage; attaching insurance coverage to the atomic components; ability to make continuous insurance decisions to make relevant choices at a relevant time, instead of once per year. In typical annual health insurance none of this atomization exists because coverage choices are made long before health care needs are known.

“In another embodiment, the UDRCD includes condition-based coverage: identification of care and coverage need; offer for coverage to patient/member; payment for the coverage. In one implementation, the UDRCD includes processes to identify who should get coverage, what coverage they should get, how they should get it, how it should be priced, and how they should pay for it. In one implementation, the UDRCD includes an Early Listening System (ELS) that determines the event that has occurred and where to direct that event to determine coverage. The ELS is able to understand events from electronic health information (e.g., HL7) data such as encounter data and orders, health care Electronic Data Interchange (EDI) X12 transactions, consumer app and website actions such as search, and other acquired or licensed third party consumer data. In one implementation, the ELS informs a recommendation engine which presents condition-specific coverage information. In another implementation, the ELS and the recommendation engine may be implemented in a component and collectively referred to as ELS.”

The claims supplied by the inventors are:

“1. An atomized coverage graph generating apparatus, comprising: a memory; a component collection in the memory, including: an atomized coverage graph generating component; a processor disposed in communication with the memory, and configured to issue a plurality of processing instructions from the component collection stored in the memory, wherein the processor issues instructions from the atomized coverage graph generating component, stored in the memory, to: obtain, via at least one processor, an atomized coverage graph generating request; determine, via at least one processor, a set of clinical conditions associated with the atomized coverage graph generating request, wherein the set of clinical conditions is obtained from a treatment characteristics pathway, wherein the treatment characteristics pathway includes a clinical outcome pathway; determine, via at least one processor, for each clinical condition in the set of clinical conditions, a set of treatments associated with the respective clinical condition; determine, via at least one processor, for each clinical condition in the set of clinical conditions, treatment paths data that specifies a set of treatment paths associated with the respective clinical condition, wherein each treatment path comprises an ordered subset of treatments from the set of treatments associated with the respective clinical condition; determine, via at least one processor, for each treatment, a set of providers associated with the respective treatment; determine, via at least one processor, practice patterns data that specifies, for each clinical condition treated by each provider, how likely the respective provider is to utilize each of the treatment paths in the set of treatment paths associated with the respective clinical condition; and generate, via at least one processor, an atomized coverage graph data structure that includes a set of clinical condition objects corresponding to the set of clinical conditions, a set of treatment objects corresponding to the determined treatments, and a set of provider objects corresponding to the determined providers, wherein each clinical condition object includes treatment paths data associated with the respective clinical condition, wherein each treatment object is linked to an associated condition object, wherein each provider object is linked to an associated treatment object, and wherein each provider object includes practice patterns data associated with the respective provider, and wherein at least some of the objects in the atomized coverage graph data structure are dynamic nodes configured to be generated dynamically using associated engageable code; issue, via at least one processor, issues instructions from the atomized coverage graph generating component, stored in the memory, to: determine that frequency of utilization of a dynamic node exceeds a specified threshold; and convert the dynamic node in the atomized coverage graph data structure to a static node.

“2. The apparatus of claim 1, wherein the set of clinical conditions associated with the atomized coverage graph generating request is determined by grouping diagnosis codes for related ailments into clinical conditions.

“3. The apparatus of claim 1, wherein a set of treatments associated with a clinical condition is determined by analyzing historical claims data associated with the clinical condition.

“4. The apparatus of claim 1, wherein treatment paths data is filtered to include only high frequency treatment paths.

“5. The apparatus of claim 4, wherein a high frequency treatment path is a treatment path utilized by at least a minimum percentage of members.

“6. The apparatus of claim 4, wherein a high frequency treatment path is one of a plurality of high frequency treatment paths that in aggregate are utilized by at least a minimum percentage of members.

“7. The apparatus of claim 1, wherein treatment paths data further specifies a ranking for each treatment path.

“8. The apparatus of claim 1, wherein treatment paths data further specifies key pathway nodes for each treatment path.

“9. The apparatus of claim 1, wherein practice patterns data of a provider object further specifies the corresponding provider’s treatment cost associated with each linked treatment object.

“10. The apparatus of claim 1, wherein the engageable code 1s a SQL query.

“11. The apparatus of claim 1, wherein the frequency of utilization is measured based on one of: a counter of utilization, a percentage of utilization.

“12. The apparatus of claim 1, further, comprising: the processor issues instructions from the atomized coverage graph generating component, stored in the memory, to: determine that an importance quotient of a subset of objects in the atomized coverage graph data structure exceeds a specified threshold; and split off the subset of objects into new nodes in the atomized coverage graph data structure.

“13. The apparatus of claim 12, wherein the importance quotient is determined using one of: a frequency of utilization measure, a machine learning structure.

“14. A processor-readable atomized coverage graph generating non-transient physical medium storing processor-executable components, the components, comprising: a component collection stored in the medium, including: an atomized coverage graph generating component; wherein the atomized coverage graph generating component, stored in the medium, includes processor-issuable instructions to: obtain, via at least one processor, an atomized coverage graph generating request; determine, via at least one processor, a set of clinical conditions associated with the atomized coverage graph generating request, wherein the set of clinical conditions is obtained from a treatment characteristics pathway, wherein the treatment characteristics pathway includes a clinical outcome pathway; determine, via at least one processor, for each clinical condition in the set of clinical conditions, a set of treatments associated with the respective clinical condition; determine, via at least one processor, for each clinical condition in the set of clinical conditions, treatment paths data that specifies a set of treatment paths associated with the respective clinical condition, wherein each treatment path comprises an ordered subset of treatments from the set of treatments associated with the respective clinical condition; determine, via at least one processor, for each treatment, a set of providers associated with the respective treatment; determine, via at least one processor, practice patterns data that specifies, for each clinical condition treated by each provider, how likely the respective provider is to utilize each of the treatment paths in the set of treatment paths associated with the respective clinical condition; and generate, via at least one processor, an atomized coverage graph data structure that includes a set of clinical condition objects corresponding to the set of clinical conditions, a set of treatment objects corresponding to the determined treatments, and a set of provider objects corresponding to the determined providers, wherein each clinical condition object includes treatment paths data associated with the respective clinical condition, wherein each treatment object is linked to an associated condition object, wherein each provider object is linked to an associated treatment object, and wherein each provider object includes practice patterns data associated with the respective provider, and wherein at least some of the objects in the atomized coverage graph data structure are dynamic nodes configured to be generated dynamically using associated engageable code; issue, via at least one processor, issues instructions from the atomized coverage graph generating component, stored in the memory, to: determine that frequency of utilization of a dynamic node exceeds a specified threshold; and convert the dynamic node in the atomized coverage graph data structure to a static node.”

There are additional claims. Please visit full patent to read further.

For the URL and additional information on this patent, see: Chiv, Henning. Use determination risk coverage datastructure for on-demand and increased efficiency coverage detection and rebalancing apparatuses, methods and systems. U.S. Patent Number 11790454, filed October 21, 2019, and published online on October 17, 2023. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(11790454)&db=USPAT&type=ids

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