| CPC G16H 40/20 (2018.01) [A61B 5/7267 (2013.01); G06N 20/00 (2019.01); G06Q 10/10 (2013.01); G06Q 40/08 (2013.01); G16H 10/60 (2018.01); G16H 15/00 (2018.01); G16H 40/67 (2018.01); G16H 50/20 (2018.01); G16H 50/70 (2018.01); G16H 70/20 (2018.01); G16H 70/60 (2018.01)] | 19 Claims |
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1. A system for improving accuracy of medical coding, the system comprising:
a patient monitoring device configured to communicate with at least one treatment device configured to administer a medical treatment to the patient;
a non-transitory memory configured to store a plurality of machine learning classifiers comprising at least a first machine learning classifier and a second machine learning classifier,
wherein the first machine learning classifier of the plurality of machine learning classifiers is configured to identify a service code associated with a threshold probability of reimbursement by a payor,
wherein the first machine learning classifier is trained using historical reimbursement data associated with the payor,
wherein the second machine learning classifier of the plurality of machine learning classifiers is configured to determine a likelihood that a diagnosis code corresponding to the service code is accurate,
wherein the second machine learning classifier is trained using diagnostic data associated with rejected and accepted medical service codes, and
wherein the first machine learning classifier is configured to transform the historical reimbursement data associated with the payor and information associated with the patient into the threshold probability of reimbursement by the payor; and
one or more hardware processors in communication with the non-transitory memory, the one or more hardware processors configured to:
receive identification information corresponding to the patient from a clinician device;
associate the patient monitoring device with the patient based on the identification information;
receive patient data from the patient monitoring device, wherein the patient data is representative of at least one or more symptoms of the patient and a diagnosis, and wherein the one or more symptoms and diagnosis are input by a clinician;
automatically store at least the patient data in an electronic medical record system based at least in part on the identification information;
determine insurance information of the patient;
analyze, using the second machine learning classifier, the patient data to determine a likelihood of accuracy for the diagnosis code, wherein the diagnosis code is input by a clinician;
determine, using the second machine learning classifier, that the diagnosis code is not accurate based on a determination that the diagnosis code input is not associated with the diagnosis input;
determine, using the second machine learning classifier, a plurality of candidate diagnosis codes based at least in part on the symptoms of the patient, wherein each candidate diagnosis code is associated with a disease that comprises said symptoms,
for each candidate diagnosis code, using the second machine learning classifier, analyze the patient data to determine a likelihood of accuracy for the candidate diagnosis code;
suggest, using the second machine learning classifier, at least some of the candidate diagnosis codes based on a determination that one or more candidate diagnosis codes are associated with the diagnosis input;
receive a selection of an updated diagnosis code from the clinician device, wherein the updated diagnosis code is selected from the suggested candidate diagnosis codes;
analyze, using the first machine learning classifier, the patient data, the updated diagnosis code, and the insurance information to determine at least one service code associated with a threshold probability of reimbursement by the payor and a confidence score associated with the at least one service code; and
output the at least one service code, based on the confidence score, to the clinician device, wherein the service code is a basis for the clinician in deciding a treatment plan for the patient.
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