Patent Application Titled “Systems And Methods For Predicting And Improving The Healthcare Decisions Of A Patient Via Predictive Modeling” Published Online (USPTO 20220068480): Patent Application

2022 MAR 23 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- According to news reporting originating from Washington, D.C., by NewsRx journalists, a patent application by the inventor MANZI, James (Washington, DC, US), filed on August 25, 2021, was made available online on March 3, 2022.

No assignee for this patent application has been made.

Reporters obtained the following quote from the background information supplied by the inventors: “Predictive analytics is a data mining technique that attempts to predict an outcome. Predictive analytics uses predictors or known features to create predictive models that are used in obtaining an output. A predictive model reflects how different points of data interact with each other to produce an outcome. Predictive modeling is the process of using known results to create, process, and validate a model that can be used to forecast future outcomes.

“Healthcare is the maintenance or improvement of health via the prevention, diagnosis, treatment, recovery, or cure of disease, illness, injury, and other physical and mental impairments in people. The healthcare industry includes the medical providers (e.g., doctors, hospitals) who receive a fee for providing medical care to patients. The medical providers often have the most interaction with the patients and are best equipped to influence the behavior of the patients. For example, a doctor may be able to encourage a patient to come in for a medical check-up, attend a counseling program to reduce weight, or to have surgery.

“The healthcare industry also includes the health insurance companies who bear all the risk and expense for the provided medical care. Unlike the medical providers, the health insurance companies have limited interaction with the patients and much less capability to influence the patient behavior.

“For these reasons, the medical providers and health insurance companies have spent considerable resources producing software solutions having various models to predict the risks associated with insuring patients. For instance, conventional software solutions utilized by the medical providers may use algorithmic approaches to calculate a likelihood of a patient having a follow up visit subsequent to a medical procedure. While the results produced by conventional software solutions are helpful, they are also incomplete. Conventional software solutions rely on patient attributes to produce results. However, lack of data associated with other patients has caused conventional software solutions to produce ineffective and sometimes inaccurate results. Moreover, simply adding data associated with other patients to be considered by conventional software solutions is not viable solutions because of the following three reasons.

“First, the data may not be readily identifiable/ingestible by conventional software solutions. For instance, some healthcare data needs to be pre-processed and analyzed before being ingested by an algorithm. Second, healthcare data may be stored onto different platforms and managing such information on different platforms is difficult due to number, size, content, or relationships of the data associated with the customers. Third, conventional software solutions use static algorithms and produce the same result for all patients. Therefore, these software solutions are unable to produce accurate results that account for peculiar and individual patient attributes. Instead, conventional software solutions follow the same path for all patients.”

In addition to obtaining background information on this patent application, NewsRx editors also obtained the inventor’s summary information for this patent application: “For the aforementioned reasons, there is a long-felt desire in designing solutions that model both risk and impactability in order to optimize the deployment of diagnostic and engagement services. Disclosed herein are methods and system design to analyze healthcare-specific data (for a particular patient and other patients) and execute multiple artificial intelligence models to achieve meaningful results.

“In an embodiment, a method comprises receiving, by one or more processors, a request for a risk score associated with a patient of one or more medical providers; applying, by the one or more processors, scoring data associated with the patient to a risk predictive model that is trained with training data causing the risk predictive model to generate the risk score based on the scoring data, the risk score indicative of a probability of the patient to access in-person medical care at a medical provider within a temporal window that is subsequent to the one or more processors receiving the request for the risk score; responsive to determining that the risk score satisfies a criteria, applying, by the one or more processors, at least a subset of the scoring data to an impactability predictive model to generate an impactability score indicative of a probability that the patient would not access the in-person medical care at the medical provider within the temporal window responsive to receiving a notification from the medical provider; and sending, by the one or more processors, a message to a client device instructing the client device to present at least one of the risk score or the impactability score.

“In another embodiment, a system comprises a server comprising a processor and a non-transitory computer-readable medium containing instructions that when executed by the processor causes the processor to perform operations comprising: receive a request for a risk score associated with a patient of one or more medical providers; apply scoring data associated with the patient to a risk predictive model that is trained with training data causing the risk predictive model to generate the risk score based on scoring data, the risk score indicative of a probability of the patient to access in-person medical care at a medical provider within a temporal window that is subsequent to the processor receiving the request for the risk score; responsive to determining that the risk score satisfies a criteria, apply at least a subset of the scoring data to an impactability predictive model to generate an impactability score indicative of a probability that the patient would not access the in-person medical care at the medical provider within the temporal window responsive to receiving a notification from the medical provider; and send a message to a client device instructing the client device to present at least one of the risk score or the impactability score.

“These and other features, together with the organization and manner of operation thereof, will become apparent from the following detailed description when taken in conjunction with the accompanying drawings.”

The claims supplied by the inventors are:

“1. A method comprising: receiving, by one or more processors, a request for a risk score associated with a patient of one or more medical providers; applying, by the one or more processors, scoring data associated with the patient to a risk predictive model to generate the risk score indicative of a probability of the patient to access in-person medical care at a medical provider within a temporal window that is subsequent to the one or more processors receiving the request for the risk score; responsive to determining that the risk score satisfies a criteria, applying, by the one or more processors, at least a subset of the scoring data to an impactability predictive model to generate an impactability score indicative of a probability that the patient would not access the in-person medical care at the medical provider within the temporal window responsive to receiving a notification from the medical provider; and sending, by the one or more processors, a message to a client device instructing the client device to present at least one of the risk score or the impactability score.

“2. The method of claim 1, further comprising: generating, by the one or more processors, a social determinant of health score by executing a second predictive model based on a first set of publicly available data, the social determinant of health score being indicative of a health status within a geographical region associated with the patient.

“3. The method of claim 1, wherein the risk predictive model is trained with training data comprising at least one of medical data, medical image scores each indicative of a probability that a respective patient of a plurality of patients has a medical illness, social determinants of health scores each associated with a respective neighborhood, and clinician linkages each indicative of a degree of relationship between a plurality of physicians.

“4. The method of claim 3, wherein the medical image scores are generated by a second predictive model based on a plurality of medical images and a plurality of medical diagnosis labels, each medical image of the plurality of medical images are associated with a respective medical diagnosis label of the plurality of medical diagnosis labels.

“5. The method of claim 1, wherein the impactability predictive model is trained with training data comprising a plurality of identifiers associated with a plurality of patients, each identifier of the plurality of identifiers indicative of whether a respective patient of the plurality of patients accessed in-person medical care at one or more medical providers responsive to receiving the notification from the one or more medical providers.

“6. The method of claim 1, wherein the scoring data comprises at least one of medical data associated with the patient, medical image scores associated with the patient, social determinants of health scores associated with the patient, or clinician linkages associated with the patient.

“7. The method of claim 6, wherein clinical linkage indicates a degree of relationship between the one or more medical providers.

“8. The method of claim 1, wherein the presentation comprises a graph depicting the impactability score on a first axis and a number of interventions on a second axis.

“9. The method of claim 1, wherein the presentation comprises a graphical representation of an accuracy value associated with the risk predictive model or the impactability predictive model.

“10. The method of claim 1, wherein the risk predictive model is trained with a first set of training data and the impactability predictive model is trained with a second set of training data different from the first set of training data.

“11. A system comprising: a server comprising a processor and a non-transitory computer-readable medium containing instructions that when executed by the processor causes the processor to perform operations comprising: receive a request for a risk score associated with a patient of one or more medical providers; apply scoring data associated with the patient to a risk predictive model to generate the risk score indicative of a probability of the patient to access in-person medical care at a medical provider within a temporal window that is subsequent to the processor receiving the request for the risk score; responsive to determining that the risk score satisfies a criteria, apply at least a subset of the scoring data to an impactability predictive model to generate an impactability score indicative of a probability that the patient would not access the in-person medical care at the medical provider within the temporal window responsive to receiving a notification from the medical provider; and send a message to a client device instructing the client device to present at least one of the risk score or the impactability score.

“12. The system of claim 11, wherein the instructions further cause the processor to: generate a social determinant of health score by executing a second predictive model based on a first set of publicly available data, the social determinant of health score being indicative of a health status within a geographical region associated with the patient.

“13. The system of claim 11, wherein the risk predictive model is trained with training data comprising at least one of medical data, medical image scores each indicative of a probability that a respective patient of a plurality of patients has a medical illness, social determinants of health scores each associated with a respective neighborhood, and clinician linkages each indicative of a degree of relationship between a plurality of physicians.

“14. The system of claim 13, wherein the medical image scores are generated by a second predictive model based on a plurality of medical images and a plurality of medical diagnosis labels, each medical image of the plurality of medical images are associated with a respective medical diagnosis label of the plurality of medical diagnosis labels.

“15. The system of claim 11, wherein the impactability predictive model is trained with training data comprising a plurality of identifiers associated with a plurality of patients, each identifier of the plurality of identifiers indicative of whether a respective patient of the plurality of patients accessed in-person medical care at one or more medical providers responsive to receiving the notification from the one or more medical providers.

“16. The system of claim 11, wherein the scoring data comprises at least one of medical data associated with the patient, medical image scores associated with the patient, social determinants of health scores associated with the patient, or clinician linkages associated with the patient.

“17. The system of claim 16, wherein clinical linkage indicates a degree of relationship between the one or more medical providers.

“18. The system of claim 11, wherein the presentation comprises a graph depicting the impactability score on a first axis and a number of interventions on a second axis.

“19. The system of claim 11, wherein the presentation comprises a graphical representation of a accuracy value associated with the risk predictive model or the impactability predictive model.

“20. The system of claim 11, wherein the risk predictive model is trained with a first set of training data and the impactability predictive model is trained with a second set of training data different from the first set of training data.”

For more information, see this patent application: MANZI, James. Systems And Methods For Predicting And Improving The Healthcare Decisions Of A Patient Via Predictive Modeling. Filed August 25, 2021 and posted March 3, 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=%2220220068480%22.PGNR.&OS=DN/20220068480&RS=DN/20220068480

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