2022 JUN 24 (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 inventors Chicklis, Camille (Somerville, MA, US); Kern, Brian (Morristown, NJ, US), filed on September 26, 2021, was made available online on June 9, 2022.
No assignee for this patent application has been made.
News editors obtained the following quote from the background information supplied by the inventors: “For decades, the medical malpractice insurance industry has underwritten professional liability insurance policies for physicians, allied healthcare providers and medical groups/systems (collectively referred to as “providers”) by using narrow criteria. Such criterion falls into two basic categories. The first category of criteria used for medical malpractice insurance underwriting is simply biographic information, most of which can be obtained through credentialing bodies. Such credentialing information includes a provider’s specialty, which in addition to procedures and scope of practice (at times requiring further inquiry), is used to place that provider into the appropriate category and charge a corresponding “base premium.” The second category of criteria used for medical malpractice underwriting is a provider’s “claim history,” i.e., whether a provider has been involved in a lawsuit(s) and the total cost of resolving the lawsuit(s). This cost is referred to herein as “total loss.”
“Based on claim history, a healthcare provider will receive surcharges (debits) added to the base premium or discounts (credits) subtracted from the based premium. For a particular provider, the following formula is used to calculate a loss ratio:
“Total Loss/(Premium x Year in Practice)=Loss Ratio.
“For example, assume a physician pays $50,000 a year premium for ten years, and her total loss is $400,000. In this case, the loss ratio for the physician is calculated as $400,000/($50,000 x 10 years)=$400,000/$500,000=80% loss ratio. An 80% loss ratio will qualify a physician for a corresponding credit or debit. If the physician is part of a group, group credits can be applied as well. The prior art has recognized the potential benefit of machine learning applied to insurance underwriting, but lacks specificity. For example, the Ironside Group published a note on “3 Ways Machine Learning Can Enhance Insurance Underwriting”, “3 Ways Machine Learning Can Enhance Insurance Underwriting” (2019 Jul. 2), https://www.ironsidegroup.com/2019/07/02/3-ways-machine-learning-can-enhance-insurance-underwriting/indicating third-party data sets can provide a more comprehensive view of the insured, and in light of Lauryssen et al., U.S. Publ. No. 2004/0193445 A1, reference to value-based care.
“The above framework for medical malpractice insurance underwriting is devoid of any predictive analytics. By extension, the medical malpractice insurance industry is built upon reactive analytics. Despite the ever-increasing availability of new healthcare datasets, the medical malpractice insurance industry remains committed to this conventional modeling framework. As mentioned above, the conventional modeling used for medical malpractice insurance underwriting uses credentialing and claim history data almost exclusively and incorporates little to no outside data. This can be seen in medical malpractice insurance companies’ underwriting manuals, which are public documents. However, an improved underwriting process that is predictive rather than reactive can provide considerable benefits, and is therefore highly desirable. In contrast to the present invention, Ironside and Lauryssen fail to disclose specific combinations of such data which would be relevant. The present invention discloses a method and system using four specific types of data points combined into three specific data sets which are used by a computer to model a prediction of malpractice risk, and then said computer revises said model from three to 200 times so as to train said computer to evaluate provider data and an appropriate insurance premium for underwriting consideration. More specifically, combining Ironside and Lauryssen fails to teach the specifics necessary to produce the results of the present invention. The groupings disclosed by the present invention both in identification and number would not be obvious of ordinary skill in the art for the purposes of accurately predicting medical malpractice risk associated with a doctor in order to calculate an accurate premium.”
As a supplement to the background information on this patent application, NewsRx correspondents also obtained the inventors’ summary information for this patent application: “To “build” the neural network model of the present invention, it is first autonomously trained, ‘learning’ from its own error to produce a performant final model. Second, that model is used to make predictions for unseen data. In each case, similar automatic data preprocessing and post-processing occur.
“The present invention provides a method and system for automated computer-based medical malpractice insurance underwriting using value-based care data. More specifically the present invention uses a neural network structure to allow a computer to extrapolate algorithms and perform operations on data, and then continuously readjust the neural network structure based on new data to make predictions. Doing so eventually produces a model that describes how to make predictions on new data. Embodiments of the present invention trains a predictive model that learns correlations between value-based care data and medical malpractice lawsuits. The predictive model is applied to predict risk levels of being subject to medical malpractice litigation, and provider premiums for medical malpractice insurance are determined based on the predicted risk levels.
“In an embodiment of the present invention, a computer-implemented method comprises: training a machine-learning based predictive model to predict a risk of a medical malpractice claim based on training cases with known outcomes and associated training provider data sets including value-based care data and social factor data; retrieving a provider data set including value-based care data and social data for a provider; inputting the provider data set into the trained machine-learning based predictive model; predicting, using the trained machine-learning based predictive model, a risk score indicating a risk of a medical malpractice claim for the provider based on the input provider data set; and determining a premium for medical malpractice insurance for the provider based on the risk score predicted using the trained machine-learning based predictive model.
“While the entire training set doesn’t normally yield the true gradient, but it is sufficient for the predictive purposes of the present invention. The true gradient would be the expected gradient with the expectation taken over all possible examples, weighted by the data generating distribution.
“It should be noted that the present invention discloses the necessity of using at least 16 data points for training the deep neutral network and at least six sets of processing steps, three sets of training iterations, and three sets of modeling iterations. An iteration is a term used in machine learning indicating the number of times an algorithm’s parameters are updated. The present invention discloses the necessity of terminating training after 200 iterations wherein each iteration represents a model modification.
“In an embodiment, the value-based care data in the provider data set includes one or more of patient satisfaction scores, quality metrics, procedure outcome data, hospital readmission data, or utilization data.
“In an embodiment, the social factor data in the provider data set includes one or more of social factor data associated with the provider or social factor data associated with patients of the provider.
“In an embodiment, the social factor data associated with the provider includes one or more of credit score data, income data, spending data, data related to patient complaints, dated related to staff complaints, or data related to civil, criminal, or regulatory actions.
“In an embodiment, the social factor data associated with the patients of the provider includes socio-economic data associated with the patients of the provider, including one or more of income, zip code, family circumstances data, or data regarding assets of the patients.
“In an embodiment, training a machine-learning based predictive model to predict a risk of a medical malpractice claim based on training cases with known outcomes and associated training provider data sets including value-based care data and social factor data comprises: identifying positive training cases in which providers were subject to medical malpractice claims and negative training cases in which providers were not subject to medical malpractice claims; retrieving a training provider data set including value-based care data and social factor data for each of the positive training cases and for each of the negative training cases; processing and cleaning the provider data sets for the positive and negative training cases to perform imputation of missing values, reduce excessive dimensionality, and address data imbalance; and training the machine-learning based predictive model based on the training provider data sets and known outcomes of the positive training cases and negative training cases.
“In an embodiment, the method further comprises: pre-processing the provider data set to perform imputation of missing values prior to inputting the provider data set into the trained machine-learning based predictive model. Said pre-processing data for the present invention includes reviewing each data element and normalizing it and/or transforming it to have consistence with other elements in the same set of data elements. Additionally outliers are removed (eliminating the anomalies in data), or otherwise processed by transformed to have the same format as all the other data in the data set to which it is assigned. The result of such normalization and transformation is that the data appears similar across all records and fields. The normalization and transformation of the providers data set and all other data used for training the present invention includes eliminating duplicate data and confirming that only related data is stored in each data set either for training or modeling function for the present invention. Said pre-processing also includes cleaning as disclosed in paragraph [000102].
“In an embodiment, the machine-learning based predictive model is a deep neural network.
“While there is no optimal number of iterations for either the training or operation of a neural network that generalizes across all data-sets of a fixed size.
“Because of the algorithmic nature of the stochastic gradient descent method and its variants, different batch sizes can be chosen for many Deep Learning tasks, including the present invention. For example, these methods operate in a small-batch regime wherein a fraction of the training data, usually 16 to 256 data points are sampled to compute an approximation to the gradient.
“The present invention dynamically evaluates quality of fit relative to the quality and quantity of the training data set. The size of the learning rate is limited mostly by factors like how curved the relevant function plot is. A training data set includes both medical malpractice claim information, and known claim outcomes.
“When training a neural network, both the batch size and number of iterations are factor in determining the quality of the output (assuming the quality of data is similar in all cases). Thus to evaluate the best model structure among to different options (for example batch size A and number of iterations B vs. batch size C and number of iterations D), several structures are typically examined and tested. In other words, to optimally train the neural network with the same amount of training examples, the number of iterations must be determined (i.e. where batch size times the number of iterations equals the number of training examples shown to the neural network, with the same training example being potentially shown several times). This is done empirically based on the data and results.
“Please note that the higher the batch size, the more memory space one needs, and it often makes computations faster. But in terms of performance of the trained network, it makes little difference if the training information is used repeatedly (as is the case of the present invention) Similarly, the accuracy need not be 100% for the purposes of the present invention.
“The number of iterations are equal to the minimum number of iterations such that the accuracy of the model does not improve more than 2% over 2 consecutive iterations. While there is no strict minimum number of iterations, commonly many iterations are needed. As disclosed in FIG. 3A below the minimum number of iterations required by said deep neural network training process is at least three iterations. The maximum number of iterations to be allowed is 200. Upon the completion of said 200th iteration, the determined premium for medical malpractice insurance for the provider based on the risk score predicted using the trained machine-learning based predictive model will be the result of said 200th iteration.
“Said deep neural network requires at least three addition iterations in addition to the prior iterations necessitated by the training iteration. As disclosed in FIG. 3A below the minimum number of iterations required by said deep neural network training process is at least three iterations.”
There is additional summary information. Please visit full patent to read further.”
The claims supplied by the inventors are:
“1. A computer-implemented method comprising: securing at least 16 first training data points wherein said first training data points are related to medical malpractice claims; securing at least 16 second training data points wherein said second training data points are related to known payment outcomes related to said medical malpractice claims; combining said first training data points and second data points into a first data set; cleaning said first data set; securing at least 16 value-based care data points wherein said value-based care score is based hospital readmissions patient satisfaction scores, outcome data and billing/coding/staging data; and said hospital readmissions patient, said satisfaction scores, said outcome data and said billing/coding/staging are directly associated with said first training data points and said second training data points; combining said value-based care data points into a second data set; cleaning said second data set; securing at least 16 social factor data points wherein said social factor data points are related to credit score, change in income, change in personal spending habits, civil actions, criminal actions, regulatory actions, patient complaints, from medial staff and patient complaints from medical administration staff wherein said credit score, said change in income, said change in personal spending habits, said civil actions, said criminal actions, said regulatory actions, said patient complaints from medical staff and said patient complaint from medical administration staff are directly associated with first and second data points; combining said social value data points into a third data set; cleaning said third data set; training a machine-learning based predictive model to predict a risk of a medical malpractice claim by having a computer update said machine-learning based predictive model by iterative training sessions using said first data set, said second data set, and said third data set; wherein a computer will run no less than three iteration sessions of said machine-learning based predictive model, and no more than 200 iteration sessions of said machine-learning based predictive model for said training; wherein said iteration is an update to an algorithmic parameter; wherein said computer will stop running said training when said risk of a medical malpractice claim for the last two iteration sessions differs by two percent or less; retrieving a provider data set, wherein said provider data set includes provider data points, value-based care data points, and provider social data points; cleaning said provider data set wherein said cleaning normalizes said provider data set to be compatible with said first data set, said second data set, and said third data set; inputting said provider data set into said trained machine-learning based predictive model; predicting, using said trained machine-learning based predictive model, a risk score indicating said risk of a medical malpractice claim for the provider based on the input provider data set; and determining a premium for medical malpractice insurance for the provider based on said risk score predicted using said trained machine-learning based predictive model.
“2. The method of claim 1, wherein the value-based care data in the provider data set includes at least one of each of patient satisfaction scores, quality metrics, procedure outcome data, hospital readmission data, and utilization data.
“3. The method of claim 1, wherein the social factor data in the provider data set includes at least one of each of social factor data associated with the provider or social factor data associated with patients of the provider.
“4. The method of claim 3, wherein the social factor data associated with the provider includes at least one of each of credit score data, income data, spending data, data related to patient complaints, dated related to staff complaints, data related to civil, criminal, or regulatory actions; and wherein the social factor data associated with the patients of the provider includes socio-economic data associated with the patients of the provider, including one or more of income, zip code, family circumstances data, or data regarding assets of the patients.
“5. The method of claim 1, wherein training a machine-learning based predictive model to predict a risk of a medical malpractice claim based on training cases with known outcomes and associated training provider data sets including value-based care data and social factor data comprises: identifying positive training cases in which providers were subject to medical malpractice claims and negative training cases in which providers were not subject to medical malpractice claims; retrieving a training provider data set including value-based care data and social factor data for each of the positive training cases and for each of the negative training cases; processing and cleaning the provider data sets for the positive and negative training cases to perform imputation of missing values, reduce excessive dimensionality, and address data imbalance; and training the machine-learning based predictive model based on the training provider data sets and known outcomes of the positive training cases and negative training cases.
“6. The method of claim 1, further comprising: pre-processing the provider data set to perform imputation of missing values prior to inputting the provider data set into the trained machine-learning based predictive model.
“7. The method of claim 1, wherein the machine-learning based predictive model is a deep neural network.
“8. The method of claim 1, further comprising: training a second machine-learning based predictive model to predict a risk of a stop loss claim based on training cases with known outcomes and associated training provider data sets including value-based care data and social factor data; inputting a second provider data set, including value-based care data and social data for the provider, to the trained second machine-learning base predictive model; and predicting, using the trained second machine-learning based predictive model, a second risk score indicating a risk of a stop loss insurance claim for the provider based on the input second provider data set; wherein determining a premium for medical malpractice insurance for the provider based on the risk score predicted using the trained machine-learning based predictive model comprises: determining a combined premium for medical malpractice insurance and stop loss insurance for the provider based on the risk score predicted using the trained machine-learning based predictive model and the second risk score predicted using the trained second machine-learning based predictive model.
“9. A system for determining a premium for medical malpractice insurance for a provider based upon a predicted risk score using a trained machine-learning based predictive model, comprising: a processor; and a memory storing computer program instructions, which when executed by the processor cause the processor to perform operations comprising: training said machine-learning based predictive model to predict a risk of a medical malpractice claim based on training cases with known outcomes and associated training provider data sets including value-based care data and social factor data; retrieving said provider data set including value-based care data and social data for said provider; inputting said provider data set into said trained machine-learning based predictive model; predicting, using said trained machine-learning based predictive model, a risk score indicating said risk of said medical malpractice claim for said provider based on said provider data set input; and determining said premium for medical malpractice insurance for said provider based on said predicted risk score using said trained machine-learning based predictive model.
“10. The system of claim 9, wherein the value-based care data in the provider data set includes one or more of patient satisfaction scores, quality metrics, procedure outcome data, hospital readmission data, or utilization data.
“11. The system of claim 9, wherein the social factor data in the provider data set includes one or more of social factor data associated with the provider or social factor data associated with patients of the provider.
“12. The system of claim 11, wherein the social factor data associated with the provider includes one or more of credit score data, income data, spending data, data related to patient complaints, dated related to staff complaints, or data related to civil, criminal, or regulatory actions; and wherein the social factor data associated with the patients of the provider includes socio-economic data associated with the patients of the provider, including one or more of income, zip code, family circumstances data, or data regarding assets of the patients.
“13. The system of claim 9, wherein training a machine-learning based predictive model to predict a risk of a medical malpractice claim based on training cases with known outcomes and associated training provider data sets including value-based care data and social factor data comprises: identifying positive training cases in which providers were subject to medical malpractice claims and negative training cases in which providers were not subject to medical malpractice claims; retrieving a training provider data set including value-based care data and social factor data for each of the positive training cases and for each of the negative training cases; and training the machine-learning based predictive model based on the training provider data sets and known outcomes of the positive training cases and negative training cases.
“14. The system of claim 9, wherein the machine-learning based predictive model is a deep neural network.”
There are additional claims. Please visit full patent to read further.
For additional information on this patent application, see: Chicklis, Camille; Kern, Brian. Method and System for Medical Malpractice Insurance Underwriting Using Value-Based Care Data. Filed September 26, 2021 and posted June 9, 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=%2220220180446%22.PGNR.&OS=DN/20220180446&RS=DN/20220180446
(Our reports deliver fact-based news of research and discoveries from around the world.)
METLIFE INC FILES (8-K) Disclosing Submission of Matters to a Vote of Security Holders
Patent Issued for Autonomous vehicle convergence avoidance systems and methods (USPTO 11355011): United Services Automobile Association
Find out how you can submit content for publishing on our website. View Guidelines
Get breaking news, exclusive stories, and money- making insights straight into your inbox.