Researchers Submit Patent Application, “Machine Learning Systems For Surgery Prediction And Insurer Utilization Review”, for Approval (USPTO 20190378618)

2019 DEC 31 (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), filed on June 8, 2018, was made available online on December 12, 2019.

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 patient is a surgical candidate and, in particular, which surgery should be performed, can be challenging. Such determinations are important, however, as they have a profound impact on patient health, healthcare costs, and other individual and societal factors.

“In the context of healthcare insurance providers, it is particularly desirable to avoid false-positives--i.e., instances in which a patient is incorrectly classified as a surgical candidate and/or subjected to unnecessary surgical procedures. 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 surgical procedures, 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 difficulty in determining whether a patient is a surgical candidate and, if so, which surgical procedure or procedures are a medical necessity.

“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 and patient survey results to determine whether a patient is a candidate for a particular surgical procedure; 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) using the machine learning systems described herein to improve spine surgery recommendations.

“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 surgical prediction, the system comprising: a survey module configured to generate a survey user interface and receive, from a patient interacting with the survey user interface, a set of survey results; a machine learning module configured to receive the survey results, apply at least one previously trained machine learning model to the survey results, and produce a prediction output; wherein the prediction output includes a first confidence level associated with whether the patient is a surgical candidate for a proposed surgical procedure.

“2. The system of claim 1, wherein the prediction output further includes a set of second confidence levels associated with a respective set of surgical outcomes.

“3. The machine learning system of claim 1, wherein the machine learning module is configured to further receive, and consider in producing the prediction output, at least one of: medical images, past medical history, lab reports, radiology reports.

“4. The machine learning system of claim 1, wherein the at least one previously trained machine learning model includes: a first machine learning model configured to receive a first survey input comprising a first subset of the survey results; and a second machine learning model configured to receive a second survey input comprising a first subset of the survey results;

“5. The machine learning system of claim 4, wherein the first machine learning model is a shallow artificial neural network and the second machine learning model is a probabilistic neural network.

“6. The machine learning system of claim 4, wherein the first confidence level is produced by the first machine learning model, and the set of second confidence levels associated with the respective set of surgical outcomes is produced by a combination of an intermediate output of the second machine learning model and the first confidence level.

“6. The machine learning system of claim 1, wherein the set of surgical outcomes are associated with spine surgical procedures.

“7. The machine learning system of claim 1, wherein: the survey user interface is configured to receive, in response to at least one survey question, a text input, and; the survey module is configured to convert the text input, via natural language processing, to a numerical value.

“8. A method for performing insurer utilization review comprising: receiving, at an insurer system, a preauthorization request associated with a patient and a requested treatment; receiving, from the patient, a set of survey results; and applying at least one previously trained machine learning model to the survey results to generate a prediction output, wherein the prediction output includes a first confidence level associated with whether the patient is a surgical candidate for a surgical procedure; selectably denying or approving the preauthorization request based on the requested treatment and the prediction output.

“9. The method of claim 8, wherein applying the at least one previously trained machine learning model to the survey results includes: applying a first subset of the survey results to a first machine learning model; and applying a second subset of the survey results to a second machine learning model; wherein the first machine learning model is a shallow artificial neural network and the second machine learning model is a probabilistic neural network.

“10. The method of claim 9, wherein: the prediction output further includes a set of second confidence levels associated with a respective set of surgical outcomes; the first confidence level is produced by the first machine learning model; and the set of second confidence levels associated with the respective set of surgical outcomes is produced by a combination of an intermediate output of the second machine learning model and the first confidence level.

“11. The method of claim 9, wherein the set of surgical outcomes are associated with spine surgical procedures.

“12. The method of claim 11, wherein the spine surgical procedures include laminectomy, direct visual rhizotomy, and microdiscectomy.

“13. The method of claim 8, wherein: the survey user interface is configured to receive, in response to at least one survey question, a text input, and; the survey module is configured to convert the text input, via natural language processing, to a numerical value.

“14. A method for surgical prediction, the method comprising: training at least one machine learning model based on previously performed surgical procedures; generating a survey user interface; receiving, from a patient interacting with the survey user interface, a set of survey results; applying the at least one previously trained machine learning model to the survey results to produce a prediction output that includes (i) a first confidence level associated with whether the patient is a surgical candidate; and (ii) a set of second confidence levels associated with a respective set of surgical outcomes.

“15. The method of claim 14, wherein the at least one previously trained machine learning model includes: a first machine learning model configured to receive a first survey input comprising a first subset of the survey results; and a second machine learning model configured to receive a second survey input comprising a first subset of the survey results;

“16. The machine learning system of claim 15, wherein the first machine learning model is a shallow artificial neural network and the second machine learning model is a probabilistic neural network.

“17. The method of claim 15, wherein the first confidence level is produced by the first machine learning model, and the set of second confidence levels associated with the respective set of surgical outcomes is produced by a combination of an intermediate output of the second machine learning model and the first confidence level.

“18. The method of claim 14, wherein the set of surgical outcomes are related to spine surgical procedures.

“19. The method of claim 18, wherein the spine surgical procedures include laminectomy, direct visual rhizotomy, and microdiscectomy.

“20. The method of claim 14, wherein: the survey user interface is configured to receive, in response to at least one survey question, a text input, and; the survey module is configured to convert the text input, via natural language processing, to a numerical value.”

For additional information on this patent application, see: Lieberman, Daniel M. Machine Learning Systems For Surgery Prediction And Insurer Utilization Review. Filed June 8, 2018 and posted December 12, 2019. Patent URL: http://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=%2220190378618%22.PGNR.&OS=DN/20190378618&RS=DN/20190378618

(Our reports deliver fact-based news of research and discoveries from around the world.)