Predictive and Prescriptive Analytics for Managing High-Cost Claimants in Healthcare

A mechanism is provided in a data processing system for predictive and prescriptive analytics for managing high-cost claimants. The mechanism trains a machine learning model using the set of de-identified claims data to predict high-cost claimants using the training data. The mechanism applies transfer learning by retraining the machine learning model using a first set of customized client data to generate a client-specific machine learning model. The mechanism then applies the client-specific machine learning model to a second set of customized client data to identify a set of predicted high-cost claimants within the second set of customized client data. The mechanism generates association rules for determining recommendations for preventing the set of predicted high-cost claimants from becoming high-cost claimants and applies the association rules to the second set of customized client data to generate a set of recommendations.

BACKGROUND

The present application relates generally to an improved data processing apparatus and method and more specifically to mechanisms for predictive and prescriptive analytics for managing high-cost claimants in healthcare.

United States healthcare spending grew 4.6% to $3.8 trillion in 2019, or $11,582 per person, and accounted for 17.7% of Gross Domestic Product (GDP), according to the Centers for Medicare and Medicaid Services (CMS). The American Health Policy Institute defines a high-cost claimant (HCC) as an individual who costs $50,000 or more annually. The group's analysis of twenty-six large employers' claims data found that the average high-cost claimant costs $122,382 each year, or 29.3 times as much as the average member. Even though they represent just 1.2% of all members, high-cost claimants make up 31% of total healthcare spending for the surveyed employers. Reducing costs for HCCs requires a focus on impactable members where intervention can yield a change in future cost, utilization, and outcome.

SUMMARY

In one illustrative embodiment, a method is provided in a data processing system, for predictive and prescriptive analytics for managing high-cost claimants. The method comprises training a machine learning model using the set of de-identified claims data to predict high-cost claimants using the training data. The method further comprises applying transfer learning by retraining the machine learning model using a first set of customized client data to generate a client-specific machine learning model. The method further comprises applying the client-specific machine learning model to a second set of customized client data to identify a set of predicted high-cost claimants within the second set of customized client data. The method further comprises generating association rules for determining recommendations for preventing the set of predicted high-cost claimants from becoming high-cost claimants. The method further comprises applying the association rules to the second set of customized client data to generate a set of recommendations.

In another illustrative embodiment, a computer program product comprises a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a computing device, causes the computing device to train a machine learning model using the set of de-identified claims data to predict high-cost claimants using the training data. The computer readable program further causes the computing device to apply transfer learning by retraining the machine learning model using a first set of customized client data to generate a client-specific machine learning model. The computer readable program further causes the computing device to apply the client-specific machine learning model to a second set of customized client data to identify a set of predicted high-cost claimants within the second set of customized client data. The computer readable program further causes the computing device to generate association rules for determining recommendations for preventing the set of predicted high-cost claimants from becoming high-cost claimants. The computer readable program further causes the computing device to apply the association rules to the second set of customized client data to generate a set of recommendations.

In yet another illustrative embodiment, an apparatus comprises a processor and a memory coupled to the processor, the memory comprises instructions which, when executed by the processor, cause the processor to train a machine learning model using the set of de-identified claims data to predict high-cost claimants using the training data. The instructions further cause the processor to apply transfer learning by retraining the machine learning model using a first set of customized client data to generate a client-specific machine learning model. The instructions further cause the processor to apply the client-specific machine learning model to a second set of customized client data to identify a set of predicted high-cost claimants within the second set of customized client data. The instructions further cause the processor to generate association rules for determining recommendations for preventing the set of predicted high-cost claimants from becoming high-cost claimants. The instructions further cause the processor to apply the association rules to the second set of customized client data to generate a set of recommendations.

The illustrative embodiments provide mechanisms for not only predicting likely future high-cost claimants but also generating prescriptive recommendations for preventing members from becoming high-cost claimants.

In one example embodiment, generating the association rules comprises finding frequent common features among the set of predicted high-cost claimants; filtering the de-identified claims data for individuals having the frequent common features; and applying association rule mining on the filtered de-identified claims data to generate a set of association rules, wherein each rule in the set of association rule associates a measure with an individual who is no longer a high-cost claimant. This embodiment generates rules for creating recommendations for reducing healthcare costs by mining existing claims data.

In another example embodiment, generating the association rules further comprises generating a confidence value for each measure and ranking the measures by confidence value. In yet another example embodiment, applying the association rules to the second set of customized client data comprises outputting a predetermined number of recommendations with the highest confidence value. These embodiments provide recommendations that are most likely to result in reduction in healthcare costs.

In another example embodiment, generating the association rules comprises applying a Frequent Pattern (FP) Growth algorithm to the second set of customized client data. This embodiment uses Association Rule Learning for discovering frequent items in a transaction database without any generation of candidates.

In one example embodiment, the machine learning model comprises a bidirectional Recurrent Neural Network with attention. This embodiment allows the neural network to exhibit temporal dynamic behavior and to focus on a subset of its inputs (or features).

DETAILED DESCRIPTION

To cope with cost problems in both the private and public sectors, there is a need for better understanding of what healthcare costs lie behind high-cost claimants. According to a study published in the Journal of the American Medical Association, seven procedures account for 80% of all hospital admissions, deaths, complications, and inpatient costs from emergency room surgery. These include gallbladder removal, appendectomy, and surgery to treat ulcers. While common, these procedures are costly. National quality benchmarks and cost reduction efforts should focus these general surgery procedures. While it can be difficult for employers to address acute conditions in their employee populations, it may be helpful to know which emergency procedures may be incurred by high-cost claimants.

The illustrative embodiments provide a computer tool for predictive and prescriptive analytics using artificial intelligence and machine learning for managing high-cost claimants in healthcare. The computer tool of the illustrative embodiments provides a management strategy for HCCs using proactive identification, early intervention, and cost management that can be achieved through predictive and prescriptive analytics.

The illustrative embodiments provide a predictive and prescriptive analytics engine for identifying members who are predicted to be HCCs and to provide healthcare recommendations or early interventions to reduce healthcare cost to employers as well as insurance companies. In one embodiment, the predictive and prescriptive analytics engine applies a bidirectional recurrent neural network with attention to predict high-cost claimants. The predictive and prescriptive analytics engine infuses de-identified claims data with customized client data as input for model training. IBM® MarketScan® Research Databases provide a collection of proprietary de-identified claims data for privately and publicly insured people in the United States. The predictive and prescriptive analytics engine applies transfer learning on customized client data by retraining a common model with customized data.

The predictive and prescriptive analytics engine also performs prescriptive analytics on the combined de-identified claims data and customized client data to recommend measures to reduce costs for the identified predicted HCCs. The predictive and prescriptive analytics engine finds frequent common features among the predicted HCCs by Frequent Pattern (FP) Growth. The predictive and prescriptive analytics engine filters individuals in the historical de-identified claims data with the common features and generates association rules to determine a set of measures that lead to claimants no longer being HCCs. The predictive and prescriptive analytics engine then recommends the top measures ranked by confidence score.

FP Growth is an Association Rule Learning. FP Growth algorithm is used for discovering frequent items in a transaction database without any generation of candidates. FP Growth represents frequent item sets in frequent pattern trees which can also be called as FP-tree. In the first pass, the FP Growth algorithm counts the occurrences of items (attribute-value pairs) in the dataset of transactions and stores these counts in a “header table.” In the second pass, it builds the FP-tree structure by inserting transactions into a tree. Items in each transaction have to be sorted by descending order of their frequency in the dataset before being inserted so that the tree can be processed quickly. Items in each transaction that do not meet the minimum support requirement are discarded. If many transactions share most frequent items, the FP-tree provides high compression close to tree root. Recursive processing of this compressed version of the main dataset grows frequent item sets directly instead of generating candidate items and testing them against the entire database. Growth begins from the bottom of the header table, i.e., the item with the smallest support by finding all sorted transactions that end in that item. Call this item I. A new conditional tree is created, which is the original FP-tree projected onto I. The supports of all nodes in the projected tree are re-counted with each node getting the sum of its child counts. Nodes (and hence subtrees) that do not meet the minimum support are pruned. Recursive growth ends when no individual items conditional on I meet the minimum support threshold. The resulting paths from root to I will be frequent item sets. After this step, processing continues with the next least-supported header item of the original FP-tree. Once the recursive process has completed, all frequent item sets will have been found, and association rule creation begins.

Thus, the illustrative embodiments provide a computer tool that uses computer-specific techniques to predict HCCs from large amounts of data and to prescribe measures for ensuring the identified predicted HCCs do not become HCCs. These computer-specific techniques include artificial intelligence, machine learning, and data mining, which are techniques that cannot practically be performed in the human mind. Therefore, the illustrative embodiments configure a computer with machine learning models and association rule mining to transform the computer into a specific computer tool.

The illustrative embodiments differ from prior art solutions that ascertain risks of particular medical conditions, because the predictive and prescriptive analytics engine of the illustrative embodiments predict whether claimants will be categorized as high cost regardless of medical condition and suggests measures to reduce costs for the identified claimants. That is, the illustrative embodiments are concerned with those claimants or patients who contribute to high costs regardless of whether their medical conditions are serious, severe, or life-threatening. The predictive and prescriptive analytics engine of the illustrative embodiments can be used in conjunction with prior art healthcare cognitive systems to both improve patient health and reduce healthcare costs.

Moreover, it should be appreciated that the use of the term “engine,” if used herein with regard to describing embodiments and features of the invention, is not intended to be limiting of any particular implementation for accomplishing and/or performing the actions, steps, processes, etc., attributable to and/or performed by the engine. An engine may be, but is not limited to, software executing on computer hardware, specialized computer hardware and/or firmware, or any combination thereof that performs the specified functions including, but not limited to, any use of a general and/or specialized processor in combination with appropriate software loaded or stored in a machine readable memory and executed by the processor to thereby specifically configure the processor to perform the specific functions of the illustrative embodiments. Further, any name associated with a particular engine is, unless otherwise specified, for purposes of convenience of reference and not intended to be limiting to a specific implementation. Additionally, any functionality attributed to an engine may be equally performed by multiple engines, incorporated into and/or combined with the functionality of another engine of the same or different type, or distributed across one or more engines of various configurations.

As shown inFIG.1, one or more of the computing devices, e.g., server104, may be specifically configured to implement a predictive and prescriptive analytics engine150. The configuring of the computing device may comprise the providing of application specific hardware, firmware, or the like to facilitate the performance of the operations and generation of the outputs described herein with regard to the illustrative embodiments. The configuring of the computing device may also, or alternatively, comprise the providing of software applications stored in one or more storage devices and loaded into memory of a computing device, such as server104, for causing one or more hardware processors of the computing device to execute the software applications that configure the processors to perform the operations and generate the outputs described herein with regard to the illustrative embodiments. Moreover, any combination of application specific hardware, firmware, software applications executed on hardware, or the like, may be used without departing from the spirit and scope of the illustrative embodiments.

It should be appreciated that once the computing device is configured in one of these ways, the computing device becomes a specialized computing device specifically configured to implement the mechanisms of the illustrative embodiments and is not a general-purpose computing device. Moreover, as described hereafter, the implementation of the mechanisms of the illustrative embodiments improves the functionality of the computing device and provides a useful and concrete result that facilitates predictive and prescriptive analytics for managing high-cost claimants. A claimant is a patient or group member for which data are analyzed to predict high cost and to prescribe measures to reduce healthcare costs. That is, costs associated with patients are analyzed based on medical claims submitted by or for patients within a membership group. A claimant may be a member of a data set, a healthcare insurance group, or a group of employees. Thus, patients will also be referred to herein as claimants or members.

The predictive and prescriptive analytics engine150trains and applies one or more machine learning models for identifying members who are predicted to be high-cost claimants. The predictive and prescriptive analytics engine150also applies machine learning and association rule mining to find rules that determine a set of procedures, drugs, or rehabilitation measures that result in individual members no longer being high-cost claimants, as will be described in further detail below.

These computing devices, or data processing systems, may comprise various hardware elements which are specifically configured, either through hardware configuration, software configuration, or a combination of hardware and software configuration, to implement one or more of the systems/subsystems described herein.FIG.2is a block diagram of just one example data processing system in which aspects of the illustrative embodiments may be implemented. Data processing system200is an example of a computer, such as server104inFIG.1, in which computer usable code or instructions implementing the processes and aspects of the illustrative embodiments of the present invention may be located and/or executed so as to achieve the operation, output, and external effects of the illustrative embodiments as described herein.

As a server, data processing system200may be, for example, an IBM eServer™ System p® computer system, Power M processor-based computer system, or the like, running the Advanced Interactive Executive (AIX®) operating system or the LINUX® operating system. Data processing system200may be a symmetric multiprocessor (SMP) system including a plurality of processors in processing unit206. Alternatively, a single processor system may be employed.

A bus system, such as bus238or bus240as shown inFIG.2, may be comprised of one or more buses. Of course, the bus system may be implemented using any type of communication fabric or architecture that provides for a transfer of data between different components or devices attached to the fabric or architecture. A communication unit, such as modem222or network adapter212ofFIG.2, may include one or more devices used to transmit and receive data. A memory may be, for example, main memory208, ROM224, or a cache such as found in NB/MCH202inFIG.2.

As mentioned above, in some illustrative embodiments the mechanisms of the illustrative embodiments may be implemented as application specific hardware, firmware, or the like, application software stored in a storage device, such as HDD226and loaded into memory, such as main memory208, for executed by one or more hardware processors, such as processing unit206, or the like. As such, the computing device shown inFIG.2becomes specifically configured to implement the mechanisms of the illustrative embodiments and specifically configured to perform the operations and generate the outputs described hereafter with regard to predictive and prescriptive analytics for managing high-cost claimants.

FIG.3is a block diagram illustrating an overall flow for predictive and prescriptive analytics for managing high-cost claimants in accordance with an illustrative embodiment. The mechanisms receive claims data310, which include time series data, cost data, services, claims, known high-cost claimants (HCCs), age distribution, gender distribution, geographic distribution, service categories, diagnostics, procedures, and drugs. The cost data include total cost, in-network cost, and out-of-network cost. From claims data310, the mechanisms generate cross-client de-identified coarse data320and client-specific granular data325.

The mechanisms first trains artificial intelligence (AI) model330using cross-client de-identified coarse data320. Then, the mechanisms apply transfer learning by retraining AI model330using client-specific granular data325. AI model330then generates HCC predictions based on client-specific granular data325.

The mechanisms derive HCC population criteria at block340. Next, the mechanisms perform HCC prescriptive analytics at block350. The HCC prescriptive analytics may include applying an AI model, association rule mining, etc. The HCC prescriptive analytics generate healthcare recommendations and/or early interventions.

FIG.4depicts a block diagram illustrating mechanisms for predictive analytics for identifying predicted high-cost claimants in accordance with an illustrative embodiment. Historical data410includes cross-tenant de-identified data with secondary rights411and tenant granular protected health information (PHI) data412, which includes granular PHI data for tenants1. . . n. A tenant is a representation of an organization (e.g., a company). Secondary data is the data that has already been collected through primary sources and made readily available for researchers to use for their own research. It is a type of data that has already been collected in the past.

AI model420is trained using cross-tenant de-identified data. Then, transfer learning430is applied to retrain the model using tenant granular PHI data412to generate tenant-specific AI model440, which makes tenant-specific predictions. For example, AI model420can be used as the starting point for training tenant-specific AI model440using tenant granular PHI data412. That is, AI model420is the base model for tenant-specific AI model440.

In one embodiment, AI model420is a bidirectional recurrent neural network (Bi-RNN) with attention. A recurrent neural network (RNN) is a class of artificial neural networks where connections between nodes form a directed or undirected graph along a temporal sequence. This allows the neural network to exhibit temporal dynamic behavior. Bidirectional recurrent neural networks (Bi-RNN) connect two hidden layers of opposite directions to the same output. With this form of generative deep learning, the output layer can get information from past (backwards) and future (forward) states simultaneously. Attention configures the neural network to focus on a subset of its inputs (or features). A typical neural net is implemented as a chain of matrix multiplications and element-wise non-linearities, where elements of the input or feature vectors interact with each other only by addition. Attention mechanisms compute a mask that is used to multiply features. Using attention, the space of functions that can be well approximated by a neural network is vastly expanded.

FIG.5is a flowchart illustrating operation of a mechanism for training and applying a predictive AI model for identifying predicted high-cost claimants in accordance with an illustrative embodiment. Operation begins (block500), and the mechanism trains a machine learning (ML) model with a combination of de-identified claims data and customized client data (block501). The mechanism then performs transfer learning on the customized client data by retraining the common model (block502). The common model is the AI model trained using cross-tenant de-identified data. The mechanism applies the resulting ML model on the customized client data to predict high-cost clients (HCCs) (block503).

Next, the mechanism generates association rules (block504) and recommends top measures that make a high-cost claimants no longer high-cost, ranked by confidence score (block505). Thereafter, operation ends (block506).

FIG.6is a flowchart illustrating operation of a mechanism for performing prescriptive analytics to generate recommendations to reduce healthcare costs in accordance with an illustrative embodiment. Operation begins (block600), and the mechanism applies FP-Growth to find the frequent common features among all predicted HCCs (block601). The mechanism filters historical de-identified data for individuals with identified features (block602). Then, the mechanism applies association rule mining to find the rule: X→Y, where X is the set of measures and Y indicates that the individuals are no longer HCCs (block603).

The mechanism then applies the rules to the customized client data to generate recommendations with confidence values (block604). The mechanism ranks the recommendations by confidence value and outputs the top K recommendations (block604) with confidence scores. Thereafter, operation ends (block605).

Consider the following example of prescriptive analytics results for 10,000 patients with 1,600 predicted HCCs and common features of E1165 Type 2 diabetes mellitus with hyperglycemia (frequent features identified by FP-growth). The top recommendations are as follows:82043 Albumin; urine (e.g., microalbumin), quantitative, conf 0.238182570 Creatinine; other source, conf 0.227336415 Collection of venous blood by venipuncture, conf 0.213983036 Hemoglobin; glycosylated (AIC), conf0.206380061 Lipid panel This panel must include the following: Cholesterol, serum, total (82465) Lipoprotein, direct measurement, high density cholesterol (HDL cholesterol) (83718) Triglycerides (84478), conf 0.2049I10 Essential (primary) hypertension, conf 0.200580053 Comprehensive metabolic panel This panel must include the following: Albumin (82040) Bilirubin, total (82247) Calcium, total (82310) Carbon dioxide (bicarbonate) (82374) Chloride (82435) Creatinine (82565) Glucose (82947) Phosphatase, alkaline (84075), conf 0.193985025 Blood count; complete (CBC), automated (Hgb, Hct, RBC, WBC and platelet count) and automated differential WBC count, conf 0.1752E782 Mixed hyperlipidemia, conf 0.1625

Also consider for the 1,600 predicted HCCs and filtering for female age 18 to 65. Two of the top three recommended results were types of mammography, with high confidence scores. The rest of the results were general examinations and lab work.

Thus, the illustrative embodiments provide mechanisms for not only predicting likely future high-cost claimants but also generating prescriptive recommendations for preventing members from becoming high-cost claimants. The illustrative embodiments provide healthcare recommendations or early interventions (e.g., procedures, drugs, rehabilitation, and measures) to individuals who are predicted to be HCCs to reduce healthcare cost to employers as well as insurance companies.