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1.发明授权公开(公告)号：US11961598B1
公开(公告)日：2024-04-16
申请号：US16913797
申请日：2020-06-26
发明人： Morgan J. Finley , Garret L. Anderson , Camille Patel , Michael Nassar , Siju Vattakunnumpurath Eugin , Daniel Owens
CPC分类号： G16H20/10 , G06F16/283 , G06N20/00 , G16H40/20
摘要： A method for predicting errors in prescription claim data is performed by a claim analysis device. The method includes extracting historical claim features from successfully processed historical claims received from the data warehouse system. The method includes extracting pending claim features from a pending claim. The method includes applying a binarization process on the extracted historical claim features to obtain a binarized training feature set. The method includes applying the binarization process on the extracted pending claim features to obtain a binarized pending feature set. The method includes calculating an aggregate distance between the binarized pending feature set and the binarized training feature set. The method includes identifying the historical claim associated with the least aggregate distance as a predictive historical claim. The method includes transmitting an alert upon determining that a billing attribute of the predictive historical claim fails to match a corresponding billing attribute of the pending claim.
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公开(公告)号：US11545248B2
公开(公告)日：2023-01-03
申请号：US17367253
申请日：2021-07-02
IPC分类号： G16H20/10 , G06F16/901 , G06F17/18 , G06N20/00
摘要： A machine learning system for training a data model to predict data states in medical orders is described. The machine learning system is configured to train a data model to predict whether a medical order requires prior authorization (“PA”) for medical orders within a medical order data set so that related systems may process incoming medical orders with PA determinations predicted by the data model. The machine learning system includes a first data warehouse system. The first prescription processing system generates a data model of historical orders and payer responses, apply a predictive machine learning model to the data model to generate a trained predictor of whether a medical order requires PA, associated with order data, apply the trained predictor to a plurality of production orders to determine PA for each of the plurality of production orders, and process the plurality of production orders with each associated PA determination.
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公开(公告)号：US20210335470A1
公开(公告)日：2021-10-28
申请号：US17367253
申请日：2021-07-02
IPC分类号： G16H20/10 , G06F16/901 , G06F17/18 , G06N20/00
摘要： A machine learning system for training a data model to predict data states in medical orders is described. The machine learning system is configured to train a data model to predict whether a medical order requires prior authorization (“PA”) for medical orders within a medical order data set so that related systems may process incoming medical orders with PA determinations predicted by the data model. The machine learning system includes a first data warehouse system. The first prescription processing system generates a data model of historical orders and payer responses, apply a predictive machine learning model to the data model to generate a trained predictor of whether a medical order requires PA, associated with order data, apply the trained predictor to a plurality of production orders to determine PA for each of the plurality of production orders, and process the plurality of production orders with each associated PA determination.
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公开(公告)号：US11056222B1
公开(公告)日：2021-07-06
申请号：US16388047
申请日：2019-04-18
IPC分类号： G16H20/10 , G06F16/901 , G06F17/18 , G06N20/00
摘要： A machine learning system for training a data model to predict data states in medical orders is described. The machine learning system is configured to train a data model to predict whether a medical order requires prior authorization (“PA”) for medical orders within a medical order data set so that related systems may process incoming medical orders with PA determinations predicted by the data model. The machine learning system includes a first data warehouse system. The first prescription processing system generates a data model of historical orders and payer responses, apply a predictive machine learning model to the data model to generate a trained predictor of whether a medical order requires PA, associated with order data, apply the trained predictor to a plurality of production orders to determine PA for each of the plurality of production orders, and process the plurality of production orders with each associated PA determination.
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