Abstract:
A system performing machine learning to predict and identify claims that have a high likelihood of migrating across a predetermined risk threshold and to generate intervention strategies to mitigate the likelihood of migration. The processing system includes a computer server, database engine, computer programming instructions, network connectivity, associated claims, payment, medical, pharmacy and other relevant data, a plurality of statistical and machine learning algorithms and a method for electronically displaying and attaching the results to a business process. The system will use all available data to analyze the medical treatment pattern of a claimant and based on automated findings make recommendations as to appropriate interventions to positively impact claims costs.

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
       [0001]    This application is a continuation of co-pending U.S. patent application Ser. No. 14/464,288, filed Aug. 20, 2014, the entire disclosure of which is incorporated herein by reference for all purposes. 
     
    
     BACKGROUND 
       [0002]    Aspects of this invention relate to a computerized assessment and recommended intervention for high risk workers&#39; compensation claims and, more specifically, to applying machine learning technologies to data and delivering results via electronic means over a computer network in an effort to identify and mitigate costs associated with high risk workers&#39; compensation claims. 
         [0003]    The American workers&#39; compensation system is a no-fault scheme that has been implemented by each of the fifty states. While each state&#39;s law may vary from that of another state, all states share the same basic concepts: (1) if an employee is injured at work, the employer must provide defined benefits (medical, indemnity (lost wages) and disability) regardless of fault and (2) the employer is immune from a tort lawsuit by the employee on account of the employee&#39;s work injury. Generally, most states allow employers to insure their obligations to provide workers&#39; compensation benefits through, for example, a primary insurance policy that provides first dollar coverage, a large deductible policy, a state managed fund, or a self-insurance program. Employers that are allowed to self-insure their workers&#39; compensation liabilities are generally required to purchase some type of excess insurance that provides coverage for claims that exceed a self-insured retention (SIR). 
         [0004]    As medical and pharmaceutical technology has advanced, so have the expenses associated with these treatments. In addition, medical cost inflation has also aggressively trended upward over the last several decades. It is well known that medical expenses per capita in the US far exceed other industrialized nations. Not unexpectedly, in the last decade, the primary expense associated with workers&#39; compensation claims has dramatically shifted from lost wages to medical and pharmaceutical related expenses. Currently, medical and pharmacy expense make up, on average, 60% of the total costs in workers&#39; compensation claims. Understanding these trends coupled with predicting and early intervention on high risk claims is paramount to tackling the problem of medical cost inflation and over utilization in workers&#39; compensation claims. 
         [0005]    Given the shift to medical related claim&#39;s expenses and the medical cost trends in the United States, workers&#39; compensation insurers are acutely aware of the need to better manage claim&#39;s related medical costs. On complex claims, human adjusters must plow through voluminous medical records to understand the medical cost drivers on a claim and formulate a plan to mitigate claims costs. An automated approach to such review would drastically reduce time to identify and intervene on problem claims. 
         [0006]    Workers&#39; compensation claims can be classified into two broad categories—Medical Only and Indemnity claims. Medical only claims incur limited medical costs, no lost wage costs, and then close. Indemnity claims involve injuries that cause the employee to be out of work for a period of time. While most injured employees return to work, some do not. Those who do not return to work receive workers&#39; compensation benefits for life or for a substantial period of time. Some injuries are catastrophic and are known to be high risk at claim outset. This class of claim would include injuries such as death, some amputations, serious burns, brain injuries and paralysis. These claims are assigned to only experienced adjusters and nurse case managers. 
         [0007]    Although some claims are readily identifiable from the outset as high risk, a large percentage of high cost claims can be labeled as migratory claims. A migratory claim appears to be much like a normal risk claim and then medical conditions gradually worsen over time. For example, initially, a low back strain may result in lost time, limited medical treatment, and dispensing of pharmaceuticals. However, the normal claim can migrate to high risk if the claimant continues to experience pain and opts for a costly surgical intervention. In a typical migratory large loss claim, the claimant will have multiple surgeries over an extended period of time and likely end up on a cocktail of high-powered addictive drugs. Without a different medical treatment protocol, this pattern will repeat over and over incurring hundreds of thousands even millions of dollars in costs. 
         [0008]    The workers&#39; compensation insurance industry and, in particular, self-insured employers and their excess carriers, have been slow to adopt automation. While automation has occurred, most of it has centered on workflow for handling claims and sending alerts when red flags appear on a claim. Further, conventional approaches lack the ability to effectively predict migratory claims. At most, current implementations merely include rudimentary models based on the summation of red flags to generate a single risk score. An automated approach for the accurate and early identification of such claims and for suggesting intervention techniques would greatly improve existing manual processes to identify migratory claims. 
       SUMMARY 
       [0009]    Briefly, aspects of the present invention permit quickly and accurately predicting claim outcomes by applying statistical and/or machine learning techniques not only for scoring claims at their inception but also for migratory claims. Moreover, aspects of the invention provide fully integrated scoring engines to automatically generate predictions, store predictions, validate ongoing model performance, allow for automated model retraining, and electronically deliver the results. Advantageously, aspects of the invention facilitate targeted interventions based on the predictions to mitigate the risk of migratory claims. 
         [0010]    One aspect of the present invention comprises a system for an automated claim risk factor identification and mitigation system. 
         [0011]    In another aspect, software instructions are stored on one or more tangible, non-transitory computer-readable media and are executable by a processor. 
         [0012]    In yet another aspect, a processor executable method is provided. 
         [0013]    This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter. 
         [0014]    Other features will be in part apparent and in part pointed out hereinafter. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0015]      FIG. 1  is a diagram depicting a system of identifying claim risk factors and suggesting intervention strategies for mitigating potential claim losses, according to one embodiment of the invention. 
           [0016]      FIG. 2  is an exemplary flowchart depicting the data intake component in further detail according to one embodiment of the invention. 
           [0017]      FIG. 3  is an exemplary flowchart depicting the processed data component wherein various forms of data are stored in a repository according to one embodiment of the invention. 
           [0018]      FIG. 4  is an exemplary flowchart depicting a generalization of the scoring engine component according to one embodiment of the invention. 
           [0019]      FIG. 5  is an exemplary flowchart depicting a detailed view of the scoring engine component according to one embodiment of the invention. 
           [0020]      FIG. 6  is an exemplary flowchart depicting a model training/retraining process used by the scoring engine component according to one embodiment of the invention. 
           [0021]      FIG. 7  is an exemplary flowchart depicting a model scoring layer in greater detail according to one embodiment of the invention. 
           [0022]      FIG. 8  is an exemplary flowchart depicting a report engine according to one embodiment of the invention. 
           [0023]      FIG. 9  is an exemplary screenshot depicting contents of the prediction report according to one embodiment of the invention. 
           [0024]      FIG. 10  is an exemplary screenshot depicting contents of the prediction report in further detail according to one embodiment of the invention. 
       
    
    
       [0025]    Corresponding reference characters indicate corresponding parts throughout the drawings. 
       DETAILED DESCRIPTION 
       [0026]      FIG. 1  is a diagram depicting a general overview of one embodiment of an automated claim risk factor identification and mitigation system. Aspects of the invention combine statistical and machine learning techniques along with related data to predict high risk workers&#39; compensation claims and ultimately make suggestions as to how to mitigate ongoing claim risk. In an embodiment, the system is configured with two models—one predicting claims that could exceed $50,000 in total cost and another model that predicts whether a claim is likely to exceed the self-insured retention or deductible. It is to be understood that an unlimited number of predictive models are within the scope of the invention. Given the breadth of data, the system is capable of handling predictions on a wide range of dependent variables. The system accommodates such flexibility. The platform contains all processes necessary to automatically train, test and validate all models independently at any interval of time. This includes the ability to automatically train, test, and validate a model at a frequency that is responsive to unexpected changes to model performance metrics data  581 . 
         [0027]    The models in this embodiment predict a binary indicator—High Risk/Normal Risk with an indication of fit. The prediction data is stored along with all input data every the time model scoring is run. This embodiment accommodates up to “N” models, wherein the models are configured to run both independently (in serial or parallel execution) and in tandem (in serial execution) depending on needs. The model output is stored for all executions of each model and further summarized for presentation via a network connected online reporting tool. The online tool reports claim level prediction output including a severity ranking in a number of claim related risk factors. Elevated indications in certain risk factors are tied to specific interventions. For example, a high risk indication in a Pharmacological Risk Factor generates an intervention of Seeking a Pharmacy Benefit Manager. It is this automated intervention strategy coupled with the identification of high risk claims (and particularly high risk claim factors) using multiple predictive models that provides improved predictions and mitigation of high risk claims in accordance with aspects of the invention. 
         [0028]    Referring further to  FIG. 1 , the automated claim risk factor identification and mitigation system comprises four components that will be described in greater detail below. The components refer to software objects in one embodiment, or processes invoked by the software objects in alternative embodiments. A data intake component  100  comprises a process that retrieves and/or accepts workers&#39; compensation claims data, payment data, medical data, pharmacy data, and data from other sources that are eventually applied to a model to be used in the system. In alternative embodiments, the sources include but are not limited to U.S. census data, Social Security disability data, state regulatory issues, medical coding data, pharmacy databases, chronic condition and co-morbidity data, etc. A processed data component  110  refers to a database used by the system to house relevant data and to construct model-ready data for scoring. 
         [0029]    In  FIG. 1 , a scoring engine component  120  is preferably an automated scoring engine operating in accordance with aspects of the invention. As will be described in greater detail below, the scoring engine  120  also contains an automated variable creation component, which is a process that creates variables by statistical techniques or other binning techniques, for use by the process model. Further, the scoring engine  120  contains one or more predictive models. In one alternative embodiment, two models are housed. A first model is based on a neural network, and predicts whether a claim is likely to exceed $50,000 in total paid. The other is based on a logistic regression model and determines whether a claim is likely to exceed a self-insured retention or deductible. Additionally, scoring engine  120  stores incoming scoring data, an unlimited number of predictive models, execution logs, model validation data, and current or historical scored claims. And a prediction database and report component, i.e., a report engine  130  houses several components that serve to supplement the output from scoring engine  120 , and to present the output to a user. 
         [0030]      FIG. 2  depicts a detailed view of the data intake component  100  in accordance with one embodiment of the invention. The data intake component  100  comprises a data intake process that is made up of two processes. A first process combines an extract, transform and load (ETL) process to bring raw data files into a SQL database engine. After loading to the database, a second process validates the data using check routines to ensure quality and completeness. Claims or policy data  210 , claim payment data  220 , and medical and/or prescription billing data  230  is matched so that proper connections are established between the different data types. Alternative embodiments provide for various categories of data quality checks, including but not limited to: a) ensuring the claim and medical data belong to a known policy serviced by the provider, b) ensuring certain data fields are NOT NULL or correctly populated, c) eliminating duplicates, and/or d) ensuring proper connections between claim and medical bill data. In an alternative embodiment, various forms of other data  240  are similarly matched so that proper connections are established between the different data types. This ensures that each data type is connected to the proper claim. 
         [0031]      FIG. 3  depicts a detailed view of the processed data component  110  in accordance with one embodiment of the invention. After the data is validated by data intake component  100 , the validated data is then loaded to a claim data repository  350  for analysis and model scoring. The claim data repository  350  is, for example, a SQL database comprised of all the claim, payment and medical data that has met the requisite quality checks. In addition, this database houses certain cross-reference tables necessary for analysis and model scoring. These additional tables include, for example, medical coding cross-references, pharmacy data, jurisdictional risk data, etc. Notably, the claim data repository  350  contains an evidence based medical treatment crosswalk for comparison to medical bill data. This SQL database serves as the core repository of claims data utilized by the models. The loaded and validated data in claim data repository  350  comprises the claims or policy data  210 , claim payment data  220 , and medical and/or prescription billing data  230  in one embodiment of the invention. In an alternative embodiment, the loaded and validated data comprises other data  240 . 
         [0032]    In alternative embodiments, the cross-reference tables made available by claim data repository  350  are standard to the industry; examples include the NCCI Part of Body, Nature of Injury, and Cause of Injury cross-reference tables. In other alternative embodiments, the cross-reference tables are widely available, but not known to be used widely in the industry. For example, the HCUP comorbidity and chronic condition databases (available at: http://www.hcup-us.ahrq.gov/tools_software.jsp) is implemented in the database in one alternative embodiment. As a further example, a U.S. census database is implemented to impute socio-demographic details about each claimant. 
         [0033]      FIG. 4  depicts a more detailed view of scoring engine component  120  in accordance with an embodiment of the invention. The scoring engine  120  receives necessary relevant data  410  from the claim data repository  350 . A model  420  serves to score and classify the necessary relevant data  410 . The model  420  depicted here indicates, for example, classification is implemented using a machine learning or “neural network” model. All open claims are processed and stored for archival and analysis, represented by historical scored open claim data  574 , and scored open claim data  572  is pulled by the prediction database  820  of  FIG. 8 . Claims identified as standard risk  430  are not processed further, while claims identified as high risk  440  are passed along to the report engine  130  for further processing. 
         [0034]      FIG. 5  depicts a further detailed description of several components related to the scoring engine component  120 . As shown, the scoring engine  120  includes a model data layer  510 , an automated variable creation component  520 , supporting model data structures  530 , and a model scoring layer  540 . According to aspects of the invention, the automated variable creation component  520  comprises a process that creates variables by statistical techniques, or other binning techniques, for use by the process model or process models implemented by the scoring engine  120 . Variable selection and construction is a key component of building successful predictive models. Certain variables contain information that can be binned based on importance with respect to the dependent variable. Assessing the importance of a given variable is particularly important where the cardinality for a particular SQL field is large. For example, in one embodiment, one ICD9 code, indicates more importance (“risk”) than another ICD9 code in a given model predicting total claim cost. In alternative embodiments, the same ICD9 code, when applied in different models, has completely differently levels of risk. Therefore, it is imperative that this relative importance is tuned for a specific model. 
         [0035]    In an embodiment, the automated variable creation process  520  automatically takes an individual ICD9 code and generates an importance score with respect to a dependent variable given to the process. However, this process is not limited to scoring only the ICD9 code variables. Thus, other variables are also scored in this manner in alternative embodiments. The scores generated by this process become part of the variables utilized in the predictive models. Preferably, standard data mining techniques are utilized to produce this score such as binning. In alternative embodiments, “riskiness” related to a particular variable, for example an ICD9 code variable, is derived from data that serves as the foundation of automated variable creation process  520 . In additional alternative embodiments, a process automatically generates a score for a variable that is passed to the model used by scoring engine  120 . In still other alternative embodiments, various tables also exist within the scoring engine component  120  are invoked by the model data layer  510  as supporting model data structures  530  comprising reference resources such as claim exclusion tables, NCCI cross-reference tables, ICD9 cross-reference tables, target variable manipulation tables, and other tables. 
         [0036]    Still referring to  FIG. 5 , the scoring engine  120  can be broadly defined as a collection of processes and data used to generate, store, and validate predictions. This process extracts data (i.e., the relevant data  410 ) from claim data repository  350  for transformation into a model scoring record. A “model scoring record” represents all relevant predictive variables summarized at the claim level with a corresponding dependent variable (variable for prediction) when such record is used for training, testing and validation. A model scoring record will not have a dependent variable when presented to the predictive model for scoring. Each model will have its own set of input variables used for prediction. A model record is constructed utilizing the source data from claim data repository  350  and the application of model specific data that was created by the automated variable creation process  520  and/or other processes  530 . Once built, the model scoring record is stored for subsequent scoring by the predictive model. The scoring process is run, whereby the model scoring records are scored utilizing the respective model. As part of the same process, the model also scores updated diagnostic data (“validation data”) to benchmark model performance. 
         [0037]    Each model has its own model database that contains data and electronic program code for transforming the raw data into model scoring records. In addition, each model database will contain the logic and data necessary to automatically train, test and if necessary validate the model based on feedback as indicated by model retraining process  550  and model feedback data  560 . 
         [0038]    In an embodiment, all of the resulting model scoring records and associated scoring data are written out to SQL database tables in a model output layer  570  for storage and analysis. 
         [0039]    With respect to the automated variable creation process  520  as used in the model scoring engine  120 , alternative embodiments provide for a number of transformations to be performed to data that results in an enhanced prediction regarding the riskiness of a given claim. In one alternative embodiment, for example, ICD9 code data is transformed in various ways. First, for example, groups of ICD9 codes are arranged into high risk workers&#39; compensation injury classifications—Back and Thoracic, Knee, Shoulder, Burns, Reflex Sympathetic Dystrophy Syndrome, Pain, Diabetes, etc. However, this classification process groups ICD9 codes into certain injury types and not all ICD9 codes in a given classification are created equal with respect to riskiness. In this manner, the medical data from claims is leveraged to better understand the riskiness of a given ICD9 within a certain classification. In alternative embodiments, various other forms of data are transformed in various ways to render an enhanced prediction regarding the riskiness of a given claim. 
         [0040]    As mentioned when describing the scoring engine component  120  in  FIG. 1 , in an embodiment, the model scoring engine collectively refers to the input data, the model training/testing/validation processes, the resulting predictive models and the generated output data. In an embodiment, the general component parts of scoring engine  120  are: the model data layer  510 , which contains the data and transformations used to prepare for model scoring; the model retraining process  550 , which is an automated mechanism to retrain a model with new or updated data; the model scoring layer  540 , which provides an application of a specific model to its related model data. In addition, scoring engine  120  includes a model output layer  570  for the data captured as a result of processing in the model scoring layer  540 . 
         [0041]    Referring further to the model data layer  510 , to enable automated scoring, the data presented to the respective predictive model must be properly transformed. For each model, a model scoring record is defined. The scoring engine  120  consumes data from the claim data repository  350  for non-closed claims and writes open claim input data  513  which is used to construct model scoring records for the non-closed claims. In addition, training, testing and validation model scoring records are also created, supplemented in an alternative embodiment by validation set input data  516 . Some models employ automated random or other sampling techniques to balance the training, testing, and validation model scoring records according to aspects of the invention. Preferably, every time the scoring engine  120  is executed, the model data layer  510  for that model is refreshed with the most current claim information. 
         [0042]    Still referring to  FIG. 5 , the model retraining process  550  will now be described in greater detail. The model scoring engine  120  contains the ability to individually train/retrain each model. Every predictive model performing classification or regression must be initially trained with model scoring data that has the dependent variable present. The scoring engine  120  is utilized to train each predictive model prior to its first scoring execution. Moreover, scoring engine  120  is capable of automatically retraining and testing each model as part of the scoring process. This ability forms a core of a true machine learning process. In this manner, the scoring engine  120  can dynamically adapt its predictive models as new data, representing changing circumstances, enters the system. 
         [0043]    In the model scoring layer  540 , trained models and the associated training and testing data are stored as objects inside a component of the database engine. These objects are created as a result of the implementation platform. It is not necessary that the models be stored as database objects. Each model can be referenced as function call whereby the function is passed a model scoring record and the function returns the original model scoring record plus a prediction and an indication of certainty about that prediction. In operation, model scoring layer  540  scores open claims as well as validation model scoring records. The scoring of validation data is performed in an effort to understand model performance over time. 
         [0044]    Referring now to model output layer  570 , the model scoring layer  540  preferably hands its data to model output layer  570  for storage in database tables. This includes scored open claims  572 , scored validation data  576 , and metadata generated by the execution of scoring engine  120  (e.g., date/time stamps, step identifiers, errors. etc.). Both the scored claims and the validation claims have current and historical tables. In an embodiment, the scored open claims are stored as historical scored open claim data  574  and the scored validation claims with history are stored as scored validation data  579 . In an embodiment, model output layer  570  includes a model execution log  583  as illustrated in  FIG. 5 . 
         [0045]    In an embodiment, scoring engine  120  retains the prediction along with the model scoring record for each of the open claims and validation data. In an embodiment, model output layer  570  also catalogs a confusion matrix for assessing the effectiveness of the learning. The confusion matrix is based on the last execution of the model using the validation data. The validation set input data  516  is used when cataloging the confusion matrix. In another embodiment, the confusion matrix is captured historically. 
         [0046]    As described above, scoring engine  120  is configured to implement various predictive models. In an embodiment, scoring engine  120  uses one or more predictive models, alone or in combination. A detailed summary of each exemplary model is provided in Appendix A and Appendix B, respectively. Appendix A sets forth a first model, MdlITRIAGEINT001, which identifies claims that are more likely to exceed the self-insured retention or deductible. Appendix B sets forth a second model, MdlITRIAGEEXT001, which identifies claims that are likely to exceed a cap, such as $50,000 in total cost. It should be apparent that the predictive models are used, in part, to identify claims at different points in a claim&#39;s lifecycle. For example, mdlTriageINT001 is looking for claims that have the potential to breach the self-insured retention. The model mdITRIAGEEXT001, on the other hand, is trained to identify a claim that is likely to exceed $50,000 in total expenses. An appearance on a prediction report generated by the report engine  130  of  FIG. 1  is an indication that a given claim is classified as elevated risk. Thus, inclusion on a report produced by the report engine  130  alerts a claim&#39;s analyst to review the treatment pattern for a given claim and develop an appropriate action plan based on the automated recommendations. 
         [0047]      FIG. 6  depicts the model training/retraining process  550  used by scoring engine  120  in accordance with an embodiment of the invention. The SQL Server Integration Services training package, which is a part of the SQL Server 2008 R2 Database Platform, provides a suitable implementation of model training/retraining process  550 . The R2 Database Platform provides higher level tools that reduce the need for programming, and it is important to note that the services provided by this platform may be implemented in other languages and platforms with equal effectiveness. In this embodiment, the entire predictive model automation process is constructed utilizing a combination of the SQL Server Database Engine, SQL Server Integration Services Packages and models and predictive model objects stored in SQL Server Analysis Services. 
         [0048]    As shown in  FIG. 6 , beginning at  610 , a list of claims used to train the model is rebuilt. Step  620  indicates that the list was successfully rebuilt. In a preprocessing step, the list of claims used in a TEST set to evaluate the performance of the model is cleared from the mining structure during a truncate test table step  630  and indicated at  640  when successfully completed. The mining structure and mining models are reprocessed during an analysis services processing step  650 . This step includes loading the new claims from the rebuilt list of claims in the build model step  610  into the mining structure. A random 30% of the data from these new claims are held out from the previously mentioned TEST set. Also, retraining of the model occurs during the analysis services processing step  650  based on the remaining 70% of new claims from the build model step  610 . The TEST set now stored in the mining structure from the analysis services processes step  650 , if successful at  660 , is written to a database table during a data flow task step  670  and indicated at  680  as successful. 
         [0049]      FIG. 7  depicts an embodiment of the model scoring layer  540  in greater detail, as implemented in the previously mentioned SQL Server Integration Services training package. As depicted, a store run time step  710 , indicated at  720  when successful, controls an archive opens step  730 , indicated at  740  when successful. As shown, from the archive opens step  730 , operations proceed to a score open claims step  750  to track and archive the status of open claims. Success of the score open claims step  750  is indicated at  760 . 
         [0050]    The store run time step  710  further controls a score validation claims step  770 , indicated at  780  when successful. Model performance is checked over time in the score validation claims step  770  as well as a store validation metrics step  785 , indicated at  790  when successful. To verify that a given model is performing as expected, certain test cases, termed a “validation set,” are pulled out of the general population of claims data and scored by the models. The validation set changes over time to include new claims that have recently closed or met some other criteria. The output of the model is checked against reality by tracking the costs associated with a given claim. Since the claim&#39;s outcome is known, scoring the associated model record will lead to either an accurate or inaccurate prediction. Data is pulled from the claim data repository  350  depicted in  FIG. 3 . The validation set input data  516  depicted in  FIG. 5  creates test cases. A confusion matrix is created from scored validation data  576  and then used to generate model performance metrics data  581 . This data indicates whether the model is performing as expected. 
         [0051]      FIG. 8  depicts the report engine  130  in further detail. Incoming claim files  810  that have been designated high risk by the scoring engine  120  are stored in a prediction database  820  and are used by a prediction report  830 . An intervention database  825  maintains model interventions associated with different risks. The scoring engine  120  generates a great deal of data about an individual claim. Not only is a current prediction available, but historical predictions are also available. This is a more complete picture of an individual claim&#39;s risk than a single point estimate. In addition, the many variables that are found in the model scoring record are not always of value for deciphering what is driving claim riskiness. Organizing the model data and the entire prediction history paints a more compelling picture of claim risk factors. Thus, the data from the model output layer  570  is transformed into the prediction report  830 . After the process is run, scored open claim data  572  is available for use by the prediction database  820 . The data is then pulled from the prediction database  820  and is used by the report  830 . The report engine  130  preferably generates a prediction report  830  that displays, for example, model variables categorized into 1 of 8 “risk factor categories” included as illustrated in Table 1: 
         [0000]    
       
         
               
               
             
           
               
                 TABLE 1 
               
               
                   
               
             
             
               
                 Claimant 
                 Attributes particular to the injured worker. The severity 
               
               
                 Personal 
                 score would consider factors such as the age and gender 
               
               
                 Risk Factors 
                 of the claimant as well as the claimant&#39;s tenure with 
               
               
                   
                 the employer. 
               
               
                 Claim 
                 Attributes that are related to the type of injury suffered 
               
               
                 Risk Severity 
                 by the claimant. The severity score would consider the 
               
               
                   
                 seriousness of the work related injury - broken arm, 
               
               
                   
                 strained back, burned hand, head injury, etc. 
               
               
                 Non-Pharma- 
                 Attributes found in the medical data that are not related 
               
               
                 cological 
                 to pharmacy. The severity score will consider the diag- 
               
               
                 Treatment 
                 noses that appear on medical bills as well as the medi- 
               
               
                 Risk Factors 
                 cal services begin performed. 
               
               
                 Pharma- 
                 Attributes found in the medical data that involve 
               
               
                 cological 
                 prescription drugs. The severity score will consider 
               
               
                 Treatment 
                 the types of drugs that are prescribed to the claimant. 
               
               
                 Risk Factors 
               
               
                 Claimant 
                 Attributes associated with the medical bills that are 
               
               
                 Biological 
                 related to coexisting medical conditions. Coexisting 
               
               
                 Risk Factors 
                 conditions are medical issues that are not necessarily 
               
               
                   
                 related to the compensable workers compensation injury, 
               
               
                   
                 but nevertheless appear in the medical bill data. The 
               
               
                   
                 severity score would consider issues such as diabetes, 
               
               
                   
                 hypertension and other similar conditions. 
               
               
                 Physician 
                 Not available at this time. 
               
               
                 Outcome 
               
               
                 (when Avail- 
               
               
                 able) 
               
               
                 Claimant 
                 Attributes related to the claimant&#39;s lifestyle choices 
               
               
                 Psycho-Social 
                 and psychological related risk factors. The severity 
               
               
                 Risk Factors 
                 score would consider things like tobacco use, alcohol 
               
               
                   
                 use, substance abuse, depression, post-traumatic 
               
               
                   
                 stress disorder, etc. 
               
               
                 Lost Time 
                 Attributes related to the lost time expenses and 
               
               
                 Risk Factors 
                 estimates. The severity score considers the indemnity 
               
               
                   
                 expenses on the claim as well as how the diagnosis codes 
               
               
                   
                 (from the medical data) impact the expected lost time. 
               
               
                 Regulatory/ 
                 Attributes related to the legal environment associated 
               
               
                 Legal 
                 with the jurisdiction governing the claim. The severity 
               
               
                 Risk Factors 
                 score considers the jurisdictional related factors such 
               
               
                   
                 as the ability to settle medical/indemnity and who can 
               
               
                   
                 direct care on the claim. 
               
               
                   
               
             
          
         
       
     
         [0052]    As part of the transformation into each of these “risk factor categories,” in some instances, additional explanatory predictive models are utilized to grade risk in the respective category. In risk categories without a predictive model, the risk score is based on crosswalk data that identifies risk. For example, the state risk is based on a third party tool that assesses workers&#39; compensation risk by jurisdiction. A further transformation takes the current point estimate prediction and factors in past prediction changes to give an indication of the prediction trend for each claim—Increasing, Decreasing or Flat. The trend indication allows for varying degrees of change (depending on the current prediction score relative to the previous score change) before alert of an increasing or decreasing trend is presented. This more accurately predicts whether reports a change in trend is material. Representative selections of code that perform various portions of this transformation, such as the initial selecting of data used to render the evaluation of comorbidity factors, are detailed below: 
         [0000]    
       
         
           
             
               
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                           + 
                           
                             CormorbidityFlag 
                              
                             
                                 
                             
                              
                             
                               2 
                               * 
                             
                              
                             
                               ( 
                               
                                 - 
                                 1.50739 
                               
                               ) 
                             
                           
                           + 
                           
                             CormorbidityFlag 
                              
                             
                                 
                             
                              
                             
                               3 
                               * 
                             
                              
                             
                               ( 
                               
                                 - 
                                 1.83119 
                               
                               ) 
                             
                           
                           + 
                           
                             CormorbidityFlag 
                              
                             
                                 
                             
                              
                             
                               4 
                               * 
                             
                              
                             
                               ( 
                               
                                 - 
                                 0.71518 
                               
                               ) 
                             
                           
                         
                         ) 
                       
                     
                     ) 
                   
                 
               
               &gt; 
               
                 0.30 
                  
                 
                     
                 
                  
                 then 
                  
                 
                     
                 
                  
                 3 
                  
                 
                     
                 
                  
                 
                   
 
                 
                  
                 When 
                  
                 
                     
                 
                  
                 
                   1 
                   / 
                   
                     ( 
                     
                       1 
                       + 
                       
                         exp 
                          
                         
                           ( 
                           
                             4.39616 
                             + 
                             
                               CormorbidityFlag 
                                
                               
                                   
                               
                                
                               
                                 1 
                                 * 
                               
                                
                               
                                 ( 
                                 
                                   - 
                                   2.46296 
                                 
                                 ) 
                               
                             
                             + 
                             
                               CormorbidityFlag 
                                
                               
                                   
                               
                                
                               
                                 2 
                                 * 
                               
                                
                               
                                 ( 
                                 
                                   - 
                                   1.50739 
                                 
                                 ) 
                               
                             
                             + 
                             
                               CormorbidityFlag 
                                
                               
                                   
                               
                                
                               
                                 3 
                                 * 
                               
                                
                               
                                 ( 
                                 
                                   - 
                                   1.83119 
                                 
                                 ) 
                               
                             
                             + 
                             
                               CormorbidityFlag 
                                
                               
                                   
                               
                                
                               
                                 4 
                                 * 
                               
                                
                               
                                 ( 
                                 
                                   - 
                                   0.71518 
                                 
                                 ) 
                               
                             
                           
                           ) 
                         
                       
                     
                     ) 
                   
                 
               
               &gt; 
               
                 0.15 
                  
                 
                     
                 
                  
                 then 
                  
                 
                     
                 
                  
                 2 
                  
                 
                     
                 
                  
                 else 
                  
                 
                     
                 
                  
                 1 
                  
                 
                     
                 
                  
                 end 
               
             
              
             
                 
             
           
         
       
     
         [0053]    The resulting data from these additional transformations are the foundation of the prediction report  830 . 
         [0054]    Referring further to  FIG. 8 , the prediction report  830  generated by the report engine  130  serves as the primary user interface into the prediction and intervention suggestion system. The report contains claim identification data in addition to the risk factor categories and prediction trend indication. In an embodiment, an indication of the $50,000 prediction MdlITRIAGEEXT001 appears on this report. In an alternative embodiment, a prediction and trend indication is given for the MdlITRIAGEINT001 model (likely to exceed retention model). 
         [0055]    Most, if not all, model variable categories have interventions stored in the Intervention database  825 . In some instances, an identified risk factor linked to a specific intervention as indicated at  840  can be determined based upon prediction report  830 . For example, claimant personal risk factors cannot be mitigated by intervention. Likewise, regulatory and/or legal risk factors are based on the law of the jurisdiction governing the claim and, thus, cannot be changed. However, most other risk factors have specific suggested interventions. The specific suggested interventions are cataloged in intervention database  825 . The design is flexible so as to support interventions for both generic and specific risk factors. There is no limit to the number of interventions that can be configured. Included below in Table 2 is an exemplary list of interventions tied to specific risk factors; a more complete description of particularly relevant interventions is described below. The interventions are based on the expert medical opinion of a medical doctor but apply generically based on the risk factor category: 
         [0000]    
       
         
               
               
               
               
               
               
               
             
               
               
               
               
               
               
               
             
           
               
                   
                 TABLE 2 
               
               
                   
                   
               
               
                   
                 Non- 
                   
                   
                   
                   
                   
               
               
                   
                 Pharmacological 
                 Pharmacological 
                 Claimant 
                 Claimant 
               
               
                   
                 Treatment 
                 Treatment 
                 Biological 
                 Psycho-Social 
                 Lost Time 
               
               
                   
                 Risk 
                 Risk 
                 Risk 
                 Risk 
                 Risk 
                 Other 
               
               
                   
                 Factors 
                 Factors 
                 Factors 
                 Factors 
                 Factors 
                 Considerations 
               
               
                   
                   
               
             
             
               
                   
               
             
          
           
               
                 Interventions to 
                 Utilization 
                 PBM use, 
                 IME, 
                 Cognitive 
                 Return to 
                 Functional 
               
               
                 consider 
                 Review: 
                 including 
                 Second 
                 Behavioral 
                 Work 
                 Restoration 
               
               
                   
                 using State- 
                 formulary 
                 Opinion or 
                 Therapy 
                 Program 
                 Program 
               
               
                   
                 Specific Practice 
                 management, Drug 
                 Chart Review 
                 (CBT) 
                   
                 Evaluation 
               
               
                   
                 Guidelines/ 
                 Indication 
                 with 
                 Evaluation 
               
               
                   
                 ODG/ACO 
                 Review, 
                 Telephonic 
               
               
                   
                 EM 
                 Independent 
                 Peer 
               
               
                   
                 Practice 
                 Pharmacist 
                 Intervention 
               
               
                   
                 Guidelines/ 
                 Evaluation +/− 
               
               
                   
                 Medical 
                 Telephonic 
               
               
                   
                 Society 
                 Peer 
               
               
                   
                 Practice 
                 Intervention 
               
               
                   
                 Guidelines/ 
               
               
                   
                 Cochrane/ 
               
               
                   
                 Others 
               
               
                   
                 IME, 
               
               
                   
                 Second 
               
               
                   
                 Opinion or 
               
               
                   
                 Chart 
               
               
                   
                 Review 
               
               
                   
                 with 
               
               
                   
                 Telephonic 
               
               
                   
                 Peer 
               
               
                   
                 Intervention 
               
               
                 Rationale 
                 Is the 
                 Are the 
                 Is the 
                 Could there 
                 Are there 
                 High scores 
               
               
                   
                 appropriate 
                 appropriate and 
                 appropriate 
                 be a 
                 any 
                 in 2 or 
               
               
                   
                 and best 
                 most cost-effective 
                 and best 
                 psychological 
                 possible 
                 more Risk 
               
               
                   
                 treatment 
                 drugs being used? 
                 treatment 
                 disorder 
                 accommodations 
                 Categories 
               
               
                   
                 being 
                 Are the 
                 being 
                 or risk 
                 that would allow the 
                 may point 
               
               
                   
                 rendered? 
                 prescribed 
                 rendered? 
                 factor? 
                 IW to 
                 to the need for an 
               
               
                   
                   
                 drugs 
                   
                 Could there be 
                 return to 
                 intensive, 
               
               
                   
                   
                 related to 
                   
                 substance 
                 work? 
                 multidisciplinary 
               
               
                   
                   
                 the claim? 
                   
                 abuse? 
                 Are there other 
                 intervention, where 
               
               
                   
                   
                 Are there 
                   
                   
                 avenues for 
                 physical 
               
               
                   
                   
                 adverse 
                   
                   
                 return to 
                 and 
               
               
                   
                   
                 events due 
                   
                   
                 work, 
                 psychosocial 
               
               
                   
                   
                 to the 
                   
                   
                 including 
                 concerns 
               
               
                   
                   
                 drugs? 
                   
                   
                 volunteer 
                 are 
               
               
                   
                   
                   
                   
                   
                 work and 
                 addressed, 
               
               
                   
                   
                   
                   
                   
                 specialized 
                 with a view 
               
               
                   
                   
                   
                   
                   
                 RTW 
                 to restoring 
               
               
                   
                   
                   
                   
                   
                 programs? 
                 function, 
               
               
                   
                   
                   
                   
                   
                   
                 decreasing 
               
               
                   
                   
                   
                   
                   
                   
                 pain and 
               
               
                   
                   
                   
                   
                   
                   
                 reducing Rx 
               
               
                   
                   
                   
                   
                   
                   
                 costs 
               
               
                   
               
             
          
         
       
     
         [0056]    Utilization Review (UR)—Is allowed in many jurisdictions; other jurisdictions do not address the use of UR but do not prohibit it. A consideration should be given to do formal or informal utilization review through a reputable URO in order to assess whether or not a particular course of action is supported by evidence based medical guidelines, such as those published by: States, WLDI (ODG), ACOEM, Medical Societies or independent groups such as Cochrane. 
         [0057]    Independent Medical Evaluation (IME), Second Opinion or Chart Review with Telephonic Peer Intervention allows one to assess adequacy of diagnosis and treatment plan and suggest alternative management. 
         [0058]    Pharmacy Benefit Managers (PBM) have many available tools to ensure appropriate and cost-effective use of prescription medications, in addition to any savings they accomplish by reducing pharmacy bills. In alternative embodiments, some of these strategies include formulary management, drug indication reviews (DIR; helps identify the appropriateness of a medication to the compensable diagnosis), independent pharmacy evaluations and more intensive programs to assess and manage prescription patterns (including telephonic peer review consultations). Modern Medical, Inc. has an excellent Opioid Defense Manager that identifies opioid overuse prospectively and intervenes at the level of the prescriber and injured worker. 
         [0059]    Cognitive Behavioral Therapy (CBT) can help to address psychosocial risk factors that delay recovery and increase the cost of a claim. COPE, a national CBT provider group understands workers&#39; compensation and does not use psychiatric diagnostic or billing codes. They can evaluate the patient and recommend, if appropriate, a limited intervention to help injured workers recover more quickly. 
         [0060]    Functional Restoration Programs (FRP) are multi-disciplinary, intensive interventions that address both psychosocial factors (fear, disability mindset, catastrophic thinking, stress, anxiety) and medical factors (deconditioning, pain and opioid abuse). Their intervention is intensive (30-40 hours weekly for 2-6 weeks, depending on severity and age of the claim) and has as its aim the recovery of function, return to work, reduction in pain and elimination or decrease in the use of opioids and other medications. An evaluation lasts one to two days in one embodiment, but varies in duration in other embodiments, and can identify patients that are most likely to succeed. 
         [0061]    Still referring to  FIG. 8 , in an embodiment the prediction report  830  is delivered via the Internet, shown at  850 , or other suitable data communications network to web report users  860  and web service consumers  870 . Not all consumers  860  of this prediction and intervention process will want to receive reporting via online. In one embodiment, consumers  860  will integrate the output into existing business systems. In other embodiments, integration is not necessary or desired. Therefore, a secure web service, for example, provides prediction report data generated by the report engine  130  to such web service consumers  870 . The web service requires individualized authentication and will take a claim number or the like as input and return the contents of the corresponding prediction report  830  as an output generated by the report engine  130 . 
         [0062]      FIGS. 9 and 10  are exemplary screenshots, for illustrative purposes only, depicting contents of the prediction report according to one alternative embodiment. Risk factor categories are displayed including, for example, Lost Time Risk Factors  910  and Regulatory/Legal Risk Factors  920 . A Risk Score  930  can be thought of as a certainty measure that further explains the High Risk/Standard Risk determination made by scoring engine  120 . The closer this probability is to 1 the more certain the model is about the high risk prediction. The closer the probability is to 0 the more certain of a standard risk claim—with 0.5 being an exemplary transition point between high risk and standard risk. The score from the model is displayed in  FIG. 9  as a probability score*100 and is in the column titled “Risk Score.” 
         [0063]    Although described in connection with an exemplary computing system environment, embodiments of the aspects of the invention are operational with numerous other general purpose or special purpose computing system environments or configurations. The computing system environment is not intended to suggest any limitation as to the scope of use or functionality of any aspect of the invention. Moreover, the computing system environment should not be interpreted as having any dependency or requirement relating to any one or combination of components illustrated in the exemplary operating environment. Examples of well-known computing systems, environments, and/or configurations that are suitable for use with aspects of the invention in various embodiments include, but are not limited to, personal computers, server computers, hand-held or laptop devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, mobile telephones, network PCs, minicomputers, mainframe computers, distributed computing environments (such as cloud-based computing) that include any of the above systems or devices, and the like. 
         [0064]    Embodiments of the aspects of the invention are described in the general context of data and/or processor-executable instructions in various embodiments, such as program modules, stored one or more tangible, non-transitory storage media and executed by one or more processors or other devices. Generally, program modules include, but are not limited to, routines, programs, objects, components, and data structures that perform particular tasks or implement particular abstract data types. In various embodiments, aspects of the invention are also practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In various embodiments of a distributed computing environment, program modules are located in both local and remote storage media including memory storage devices. 
         [0065]    In alternative embodiments, processors, computers and/or servers execute the processor-executable instructions (e.g., software, firmware, and/or hardware) such as those illustrated herein to implement aspects of the invention. 
         [0066]    Alternative embodiments of the aspects of the invention are implemented with processor-executable instructions. The processor-executable instructions are organized into one or more processor-executable components or modules on a tangible processor readable storage medium in various embodiments. Aspects of the invention are implemented with any number and organization of such components or modules in various embodiments. For example, aspects of the invention are not limited to the specific processor-executable instructions or the specific components or modules illustrated in the figures and described herein. Other alternative embodiments of the aspects of the invention include different processor-executable instructions or components having more or less functionality than illustrated and described herein. 
         [0067]    The order of execution or performance of the operations in embodiments of the aspects of the invention illustrated and described herein is not essential, unless otherwise specified. That is, in alternative embodiments, the operations are performed in any order, unless otherwise specified, and embodiments of the aspects of the invention include additional or fewer operations than those disclosed herein. For example, it is contemplated that executing or performing a particular operation before, contemporaneously with, or after another operation is within the scope of aspects of the invention. 
         [0068]    When introducing elements of aspects of the invention or the embodiments thereof, the articles “a,” “an,” “the,” and “said” are intended to mean that there are one or more of the elements. The terms “comprising,” “including,” and “having” are intended to be inclusive and mean that alternative embodiments include additional elements other than the listed elements. 
         [0069]    In view of the above, it will be seen that several advantages of the aspects of the invention are achieved and other advantageous results attained. 
         [0070]    Not all of the depicted components illustrated or described are required in alternative embodiments. In addition, alternative implementations and embodiments include additional components. Variations in the arrangement and type of the components are capable of being made in alternative embodiments without departing from the spirit or scope of the claims as set forth herein. Alternative embodiments provide that additional, different or fewer components are capable of being provided and components combined. Further, alternative embodiments provide for a component implemented alternatively or in addition by several components. 
         [0071]    The above description illustrates the aspects of the invention by way of example and not by way of limitation. This description enables one skilled in the art to make and use the aspects of the invention, and describes several embodiments, adaptations, variations, alternatives and uses of the aspects of the invention, including what is presently believed to be the best mode of carrying out the aspects of the invention. Additionally, it is to be understood that the aspects of the invention is not limited in its application to the details of construction and the arrangement of components set forth in the following description or illustrated in the drawings. The aspects of the invention are capable of other embodiments and of being practiced or carried out in various ways. Also, it will be understood that the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting. 
         [0072]    Having described aspects of the invention in detail, it will be apparent that modifications and variations are possible without departing from the scope of aspects of the invention as defined in the appended claims. It is contemplated that various changes could be made in the above constructions, products, and process without departing from the scope of aspects of the invention. In the preceding specification, various preferred embodiments have been described with reference to the accompanying drawings. It will, however, be evident that alternative embodiments provide for various modifications and changes to be made thereto, and additional embodiments implemented, without departing from the broader scope of the aspects of the invention as set forth in the claims that follow. The specification and drawings are accordingly to be regarded in an illustrative rather than restrictive sense.