Researchers Submit Patent Application, “Machine Learning Systems and Methods for Assessing Medical Interventions for Utilization Review”, for Approval (USPTO 20220115135): Patent Application

2022 MAY 03 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- From Washington, D.C., NewsRx journalists report that a patent application by the inventor Lieberman, Daniel M. (Phoenix, AZ, US), filed on December 21, 2021, was made available online on April 14, 2022.

No assignee for this patent application has been made.

News editors obtained the following quote from the background information supplied by the inventors: “Determining whether a particular medical invention is appropriate for a given patient continues to be challenging. Such determinations are important, however, as they can have a profound impact on patient health outcomes, healthcare costs, and other individual and societal factors.

“In the context of healthcare insurance providers and other similarly situated entities, it is particularly desirable to avoid false-positives, i.e., instances in which a patient is incorrectly classified as a candidate and/or subjected to unnecessary medical interventions. Toward that end, health insurance providers often carry out a “utilization review” in which the insurer evaluates the medical necessity of a requested medical procedure for the purpose of providing preauthorization.

“Even given recent advances in medical care, insurance case management techniques, and data analysis, healthcare costs (and consequently insurance premiums) continue to rise in an unsustainable fashion. This is due in part to the difficult of determining whether a requested medical intervention is appropriate for a particular individual under the circumstances.

“Systems and methods are thus needed which overcome the limitations of the prior art. Various features and characteristics will also become apparent from the subsequent detailed description and the appended claims, taken in conjunction with the accompanying drawings and this background section.”

As a supplement to the background information on this patent application, NewsRx correspondents also obtained the inventor’s summary information for this patent application: “Various embodiments of the present invention relate to systems and methods for, inter alia: i) using machine learning techniques to determine whether a selected medical intervention is necessary; ii) utilizing heterogeneous forms of aggregated data (such as imaging, lab studies, exam findings, survey information, and the like) as inputs to a machine learning system as described herein, ii) improving insurer utilization reviews using the machine learning systems described herein; iii) using multiple pre-trained artificial neural networks to implement the machine learning systems described herein; and iv) utilizing the machine learning systems described herein to determine whether a particular health care provider or physician is appropriate given the desired medical intervention.

“Various other embodiments, aspects, and features are described in greater detail below.”

The claims supplied by the inventors are:

“1. A machine learning system for determining the appropriateness of a selected medical intervention, the system comprising: a plurality of health-related data sources, the health-related data sources providing at least one data file of a first type, and a second data file of a second type; a normalization module configured to receive the first and second data files and perform a normalization procedure on at least one of the first and second data files; and a previously trained machine learning model configured to receive the normalized data files and produce a prediction output, wherein the prediction output includes a confidence level associated with an appropriateness of the selected medical intervention.

“2. The machine learning system of claim 1, wherein the at least one machine learning model is an artificial neural network

“3. The machine learning system of claim 1, wherein the at least one machine learning model is a probabilistic neural network.

“4. The machine learning system of claim 1, wherein the at least one machine learning model is a convolutional neural network.

“5. The machine learning system of claim 1, wherein the at least one machine learning model is a decision tree.

“6. The machine learning system of claim 1, wherein the first data file is a two-dimensional image file, and the normalization procedure includes producing an input vector based on the two-dimensional image file.

“7. The machine learning system of claim 6, wherein the two-dimensional image file is selected from the group comprising an X-ray image, a cat-scan (CT) image, and a magnetic resonance image (MRI).

“8. The machine learning system of claim 1, wherein the first data file is a time-varying real value parameter, and the normalization procedure produces an input vector based on the time-varying real value parameter.

“9. The machine learning system of claim 8, wherein the time-varying real value parameter is a heart-beat audio file.

“10. The machine learning system of claim 8, wherein the time-varying real parameter is a spoken utterance.

“11. The machine learning system of claim 1, wherein the first data file is a text file, and the normalization procedure includes producing an input vector by applying natural language processing (NLP) to the text file.

“12. The machine learning system of claim 1, wherein the prediction output is further processed to determine a selected health-care provider for the selected medical intervention.

“13. The machine learning system of claim 1, wherein the data sources are selected from the group consisting of diagnostic image sources, radiological reports, lab studies, exam findings, survey results, and office notes.

“14. A method for determining the appropriateness of a selected medical intervention utilizing a machine learning system, the method comprising: receiving, from a plurality of health-related data sources, at least one data file of a first type, and a second data file of a second type; performing a normalization procedure on at least one of the first and second data files; and applying at least one previously trained machine learning model to the normalized data files to produce a prediction output; wherein the prediction output includes a confidence level associated with an appropriateness of the selected medical intervention.

“15. The method of claim 14, wherein the at least one machine learning model is an artificial neural network.

“16. The method of claim 14, wherein the at least one machine learning model is a probabilistic neural network.

“17. The method of claim 14, wherein the at least one machine learning model is a convolutional neural network.

“18. The method of claim 14, wherein the at least one machine learning model is a decision tree.

“19. The method of claim 14, wherein the first data file is a two-dimensional image file, and the normalization procedure includes producing an input vector based on the two-dimensional image file.

“20. The method of claim 19, wherein the two-dimensional image file is selected from the group comprising an X-ray image, a cat-scan (CT) image, and a magnetic resonance image (MRI).”

For additional information on this patent application, see: Lieberman, Daniel M. Machine Learning Systems and Methods for Assessing Medical Interventions for Utilization Review. Filed December 21, 2021 and posted April 14, 2022. 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=%2220220115135%22.PGNR.&OS=DN/20220115135&RS=DN/20220115135

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