Patent ID: 11942215
Assignee: MOTIVE MEDICAL INTELLIGENCE
Field: Medical technology (Instruments)
Classification: CPC G | IPC G

Claim 10:
11. A method for identifying and correcting a defect in a health care service, comprising:
training a deep neural network by an evidence engine, the deep neural network trained with evidentiary support including medical journals, health studies, clinical guidelines, or standards bodies, the deep neural network;
receiving, by a first input node of a secure intelligent networked engine having the deep neural network, a set of data comprising physician-directed health care service data for a previous stress test as coded and unstructured narrative text, and health care service data as the health care service is being delivered, the first input node configured to adjust for a factor lacking in claims data including undocumented comorbidities, hedging in diagnostic uncertainty, strength of clinical support, ulterior motives, defensive medicine, a presence for each factor equating to incremental statistical variability that is calculated to a sum, added to a statistical range of better practice and results in an adjusted range of better practice;
receiving, by a second input node of the secure intelligent networked engine having the deep neural network, a set of metrics associated with appropriateness of a stress test;
configuring a plurality of intermediary deep neural network nodes having a weight, bias and threshold directing an analysis by the deep neural network on the physician-directed health care service data for the stress test;
generating a first output node comprising a knowledge narrative representing a plain text description of an appropriateness measure for the stress test and a range of better practice comprising limits of the appropriateness measure, where an appropriateness measures score exceeds an upper limit in a case of overuse of a service, or is below a lower limit in a case of underuse of a service that results from operation of the deep neural network on the input elements;
generating a second output node comprising a rate of inappropriateness of the stress test, the inappropriateness having a numerator representing a number of stress tests with nuclear imaging that occurred within thirty days of an evaluation and management visit to a cardiologist and having a denominator representing stress testing that occurred within thirty days of the evaluation and management visit to the cardiologist, excluding cases with inpatients, outpatients with symptoms of acute coronary syndrome or patients who had a cardiac-related emergency department visit within a thirty day period;
generating a dynamic feedback communicatively coupling the knowledge narrative and range of better practice node and the rate of inappropriateness of the stress test for a specific health care service for continuous learning of the deep neural network;
generating a third output node comprising the appropriateness measures score for cardiovascular stress testing; and
generating a fourth output node comprising a cumulative appropriateness practice score to reflect a physician's performance across multiple measures or practice areas.