Abstract:
Methods and systems for identifying and resolving payment integrity issues are provided. Analytic techniques that may include a query component, an artificial intelligence engine, and a text mining component are integrated into a payment integrity system. One or more of the analysis components provide information from a source transaction system to an audit module. The audit module utilizes the outputs from the analysis components to resolve whether or not a claim payment was overpaid or underpaid. The audit module may subsequently instruct one of the analysis components to further analyze information from the source transaction system in accordance with previously provided information from another analysis component. The audit module may also subsequently instruct the recovery and adjustments module to act upon the result of the audit. The payment integrity system may also receive an indication of a provider claim dispute and resolve the dispute. The payment integrity system may receive a notification of an unsolicited reimbursement (e.g., refund or repayment) from a provider, adjust the associated claim, access data for corresponding claims having a similar characteristic, and audit the corresponding claims. The recovery and adjustments module utilizes the outputs from the audit module to complete the process of recovering overpayments or adjusting claims for underpayment.

Description:
FIELD OF THE INVENTION  
       [0001]     This invention relates generally to payment integrity systems that support a payment process with service providers and clients. More particularly, the invention provides methods and systems for identifying and resolving payment integrity issues.  
       BACKGROUND OF THE INVENTION  
       [0002]     Healthcare claims payors are susceptible to fraud, abuse and errors due to the complex rules, regulations, and calculations applied in the payment process and data integrity issues that are common in payor systems. Credible estimates of the overall amount of losses in health care payments processes range from 5% to 15% of the total reimbursements. Because the amount of reimbursement is greater than a trillion dollars per year in the United States, the losses translate to many billions of dollars per year.  
         [0003]     Current healthcare claims payor processes for ensuring payment integrity are often manually intensive and ineffective. Typically, an auditing specialist investigates data records related to a claim, resolves the claim, and issues a payment or requests a refund from the associated party. Significant overpayments and underpayments are not identified due to low use of sophisticated analytical techniques. The payor processes currently in place are not optimized for efficiency, which inhibits the ability of claims payors to dedicate sufficient headcount to manage payment integrity given budget constraints. An array of niche vendors serve the healthcare claims payor market supporting limited services to help manage the problem but do not provide integrated and sophisticated solutions to the root causes of payment integrity problems. Consequently, the above-mentioned inefficiencies result in additional costs in administering an insurance program. Ultimately, individuals paying insurance premiums must bear the costs. Moreover, current approaches are typically focused on post-payment recovery as opposed to pre-payment prevention. Post-payment recoveries are on average only recouped 75% of the time, and are administratively costly to process when compared to pre-payment prevention.  
         [0004]     Therefore, there exists a need in the art for systems and methods that assist healthcare claims payors in identifying and resolving payment integrity issues.  
       BRIEF SUMMARY OF THE INVENTION  
       [0005]     The present invention provides methods and systems for identifying and resolving payment integrity issues. Among other advantages, the disclosed methods and systems enhance the efficiency of a payment integrity system for insurance claims that include health care services.  
         [0006]     With one aspect of the invention, a plurality of analytic techniques is integrated in a payment integrity system. Various underlying analytic methods are used in the components and modules including, but not limited to: machine learning technologies such as text mining, data mining, neural networks or natural language processing; optimization technologies such as genetic algorithms and swarm algorithms; and modeling, simulation and forecasting technologies like agent-based modeling, and discrete event simulation. With an embodiment of the invention, a discovery module includes a query component, an artificial intelligence engine, and a machine learning component. Embodiments of the invention may support different types of machine learning components, including a text mining component, a data mining component, a neural network, or a natural language processor. One or more of the analysis components provide information from a source transaction system to an audit module. The audit module utilizes the outputs from the analysis components to resolve a potentially inappropriate payment. With a variation of the embodiment, the audit module instructs one of the analysis components to further analyze information from the source transaction system in accordance with previously provided information from another analysis component.  
         [0007]     With another aspect of the invention, an audit module determines an error in an insurance claim. The audit module instructs an artificial intelligence engine, which uses the appropriate and relevant underlying analytic method, to retrieve data records for other claims that may have a similar error. The audit module audits the retrieved data records. In an embodiment of the invention, the audit module determines a list of known members who have other insurance coverage and instructs an artificial intelligence engine to provide data records of other members who may have other insurance coverage, applying appropriate analytic methods such as the artificial intelligence capabilities of pattern recognition or machine learning techniques such as text mining.  
         [0008]     With another aspect of the invention, the audit module of a payment integrity system informs a primary transaction system of an error type so that the primary transaction system may take corrective action for subsequent transactions.  
         [0009]     With another aspect of the invention, an audit module of a payment integrity system determines a productivity measure and/or a quality measurement for an auditor.  
         [0010]     With another aspect of the invention, the recovery and adjustments module processes the results of the audit module to request and process repayments in the case of overpayments or to process adjustments and further payment in the case of underpayments.  
         [0011]     With another aspect of the invention, a recovery and adjustments module of a payment integrity system determines a productivity measure and/or a quality measurement for a recovery and adjustments processor.  
         [0012]     With another aspect of the invention, a results tracking module reports and tracks outstanding payments and reports completed payments and recoveries.  
         [0013]     With another aspect of the invention, a payment integrity system receives an indication of a provider claim dispute and automatically resolves the dispute if audit rules are applicable and notifies an auditing specialist if the rules are not applicable. The audit module instructs a discovery module to access data for corresponding claims having a similar characteristic for further auditing in the audit module.  
         [0014]     With another aspect of the invention, a recovery and adjustment module of a payment integrity system receives a notification of an unsolicited repayment from a provider, applies the repayment to the associated claim, and instructs a discovery module to access data for additional claims having a similar characteristic. An audit module audits the additional claims. 
     
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0015]     The present invention is illustrated by way of example and not limited in the accompanying figures in which like reference numerals indicate similar elements and in which:  
         [0016]      FIG. 1  shows an architecture of a computer system used in an advanced payment integrity system in accordance with an embodiment of the invention.  
         [0017]      FIG. 2  shows an architecture of an advanced payment integrity system in accordance with an embodiment of the invention.  
         [0018]      FIG. 3  illustrates interaction between an audit module and analysis components in accordance with an embodiment of the invention.  
         [0019]      FIG. 4  shows a flow diagram for auditing claims in accordance with an embodiment of the invention.  
         [0020]      FIG. 5  shows an architecture of a claim transaction system in accordance with an embodiment of the invention.  
         [0021]      FIG. 6  shows an architecture of an advanced payment integrity system in accordance with an embodiment of the invention.  
         [0022]      FIG. 7  shows an architecture with an audit module in accordance with an embodiment of the invention.  
         [0023]      FIG. 8  shows an architecture with a recovery and adjustment module in accordance with an embodiment of the invention. 
     
    
     DETAILED DESCRIPTION OF THE INVENTION  
       [0024]     In the description of the invention, the term “claim” is clarified to denote a demand for payment which is due one, e.g., an insurance claim.  
         [0025]     Elements of the present invention may be implemented with computer systems, such as the system  100  shown in  FIG. 1 . Computer  100  includes a central processor  110 , a system memory  112  and a system bus  114  that couples various system components including the system memory  112  to the central processor unit  110 . System bus  114  may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. The structure of system memory  112  is well known to those skilled in the art and may include a basic input/output system (BIOS) stored in a read only memory (ROM) and one or more program modules such as operating systems, application programs and program data stored in random access memory (RAM).  
         [0026]     Computer  100  may also include a variety of interface units and drives for reading and writing data. In particular, computer  100  includes a hard disk interface  116  and a removable memory interface  120  respectively coupling a hard disk drive  118  and a removable memory drive  122  to system bus  114 . Examples of removable memory drives include magnetic disk drives and optical disk drives. The drives and their associated computer-readable media, such as a floppy disk  124  provide nonvolatile storage of computer readable instructions, data structures, program modules and other data for computer  100 . A single hard disk drive  118  and a single removable memory drive  122  are shown for illustration purposes only and with the understanding that computer  100  may include several of such drives. Furthermore, computer  100  may include drives for interfacing with other types of computer readable media.  
         [0027]     A user can interact with computer  100  with a variety of input devices.  FIG. 1  shows a serial port interface  126  coupling a keyboard  128  and a pointing device  130  to system bus  114 . Pointing device  128  may be implemented with a mouse, track ball, pen device, or similar device. Of course one or more other input devices (not shown) such as a joystick, game pad, satellite dish, scanner, touch sensitive screen or the like may be connected to computer  100 .  
         [0028]     Computer  100  may include additional interfaces for connecting devices to system bus  114 .  FIG. 1  shows a universal serial bus (USB) interface  132  coupling a video or digital camera  134  to system bus  114 . An IEEE 1394 interface  136  may be used to couple additional devices to computer  100 . Furthermore, interface  136  may configured to operate with particular manufacture interfaces such as FireWire developed by Apple Computer and i.Link developed by Sony. Input devices may also be coupled to system bus  114  through a parallel port, a game port, a PCI board or any other interface used to couple and input device to a computer.  
         [0029]     Computer  100  also includes a video adapter  140  coupling a display device  142  to system bus  114 . Display device  142  may include a cathode ray tube (CRT), liquid crystal display (LCD), field emission display (FED), plasma display or any other device that produces an image that is viewable by the user. Additional output devices, such as a printing device (not shown), may be connected to computer  100 .  
         [0030]     Sound can be recorded and reproduced with a microphone  144  and a speaker  166 . A sound card  148  may be used to couple microphone  144  and speaker  146  to system bus  114 . One skilled in the art will appreciate that the device connections shown in  FIG. 1  are for illustration purposes only and that several of the peripheral devices could be coupled to system bus  114  via alternative interfaces. For example, video camera  134  could be connected to IEEE 1394 interface  136  and pointing device  130  could be connected to USB interface  132 .  
         [0031]     Computer  100  can operate in a networked environment using logical connections to one or more remote computers or other devices, such as a server, a router, a network personal computer, a peer device or other common network node, a wireless telephone or wireless personal digital assistant. Computer  100  includes a network interface  150  that couples system bus  114  to a local area network (LAN)  152 . Networking environments are commonplace in offices, enterprise-wide computer networks and home computer systems.  
         [0032]     A wide area network (WAN)  154 , such as the Internet, can also be accessed by computer  100 .  FIG. 1  shows a modem unit  156  connected to serial port interface  126  and to WAN  154 . Modem unit  156  may be located within or external to computer  100  and may be any type of conventional modem such as a cable modem or a satellite modem. LAN  152  may also be used to connect to WAN  154 .  FIG. 1  shows a router  158  that may connect LAN  152  to WAN  154  in a conventional manner.  
         [0033]     It will be appreciated that the network connections shown are exemplary and other ways of establishing a communications link between the computers can be used. The existence of any of various well-known protocols, such as TCP/IP, Frame Relay, Ethernet, FTP, HTTP and the like, is presumed, and computer  100  can be operated in a client-server configuration to permit a user to retrieve web pages from a web-based server. Furthermore, any of various conventional web browsers can be used to display and manipulate data on web pages.  
         [0034]     The operation of computer  100  can be controlled by a variety of different program modules. Examples of program modules are routines, programs, objects, components, data structures, etc., that perform particular tasks or implement particular abstract data types. The present invention may also be practiced with other computer system configurations, including hand-held devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, network PCS, minicomputers, mainframe computers, personal digital assistants and the like. Furthermore, the invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.  
         [0035]      FIG. 2  shows an architecture of advanced payment integrity system  200  in accordance with an embodiment of the invention. Advanced payment integrity system  200  may support transactions for medical insurance claims, royalty payments, life insurance claims, property causality claims, and so forth. For example, advanced payment integrity system  200  may support administrative insurance operations for health insurance, home owners insurance, third party liability, and workers compensation. Payment integrity system  200  comprises source transaction system  201 , extraction module  203 , discovery module  205 , audit module  215 , recovery and adjustments module  217 , and results tracking module  219 . Source transaction system  201  stores data records, text information, and other data in at least one data structure. Extraction module extracts, translates, and routes data from source transaction system  201  to discovery module  205 . Advanced payment integrity system  200  performs end to end processing from discovery (corresponding to discovery module  205 ) through audit (corresponding to audit module  215 ) through recovery or adjustment (corresponding to recovery and adjustments module  217 ) to results tracking (corresponding to results tracking module  219 ). Advanced payment integrity system  200  may identify both unintentional errors as well as potential fraud and abuse. Advanced payment integrity system  200  may proactively process claim payments in order to identify payment errors. Advanced payment integrity system  200  may reactively resolve inbound disputes and voluntary refunds and then identify other claim payments that may have a similar error. Also, advanced payment integrity system  200  drives efficiency, automation, and higher performance by associating claim payments into large blocks of claim payments. Advanced payment integrity system  200  supports cost containment, auditing, dispute management, and recovery processes that may be automated as well as manual and that are consistently executed.  
         [0036]     Discovery module  205  supports one or more of the analytic components. In the embodiment shown in  FIG. 1 , discovery module  205  comprises query/rules component  207 , artificial intelligence (AI) engine  209 , and text mining component  211 . The embodiment may also support other analytical components  213 . For example, other analytical components may include a payee identification component that verifies the identification of the payee through registered characteristics, e.g., with fingerprint and iris images using biometric technology. Other analytical components include machine learning technologies such as text mining, data mining, neural networks or natural language processing; optimization technologies such as genetic algorithms and swarm algorithms; and modeling, simulation and forecasting technologies like agent-based modeling, and discrete event simulation. Other analytical components may be incorporated with some or all of components  207 ,  209 , and  211 . For example, artificial intelligence engine  209  may utilize machine learning technologies, optimization technologies, and/or modeling, simulation, and forecasting technologies.  
         [0037]     Machine learning technologies include text mining, data mining, neural networks and natural language processing. These technologies can all be used to let our system more quickly understand unstructured data, identify and delineate interesting segmentations, groupings and patterns in the data and help to further human understanding and interpretation of large sets of data. Machine learning technologies rely not only on advanced statistical methods like linear and non-linear regression techniques, but also upon non-statistical methods like decision trees and neural networks.  
         [0038]     Optimization technologies include genetic algorithms, evolutionary computing and swarm algorithms/optimizations. Optimization technologies may be used to let our system determine what claims might yield the best results, or potentially determine how to assign claims to a claim auditor. Most optimization technologies mimic the way large populations solve problems over a long period of time.  
         [0039]     Modeling, simulation and forecasting technologies include agent-based modeling, discrete event simulation and finite element analysis. These technologies may be used to help identify new patters for overpayment (or fraud), or expected volumes of a certain type of overpayment scenario to assist in targeting. Agent-based models attempt to simulate various complex phenomena by the use of virtual agents that represent the different components of a business system. Each agent is programmed with simple rules including parameters of the model that represent the situation of interest. Each model may execute for thousands of iterations to find the best prediction.  
         [0040]     Query/rules component  207  performs a series of queries of source transaction system  201  based upon a specific selection criteria. For example, an auditing specialist may run a query in which paid claims are retrieved where the dates of service are after the termination date of insurance coverage if the auditing specialist is interested in determining overpayments caused by retroactive termination of coverage. In addition, artificial intelligence engine  209  utilizes a variety of analytic technologies and techniques (but does not specify specific conditions) to search for data records to determine data records that appear to be anomalous. Using machine learning techniques, text mining component  211  searches for unstructured data that matches a specified text string in text data or is sufficiently similar to warrant review as potential error payments.  
         [0041]     Audit module  215  utilizes data provided by discovery module  205  to audit a claim payment. A claim payment may be initiated by a service provider or by a member who receives healthcare service. The embodiment supports both pre-payment audits as well as post-payment audits to enhance first time accuracy and continuous improvement. Additionally, audit module  215  supports the identification and resolution of both overpayments and underpayments. Audit module  215  organizes and manages the workflow of payments that may be reviewed through a manual audit by an auditing specialist. Moreover, audit module  215  may automatically audit a claim payment or partially audit a claim payment if established audit rules are applicable to the claim payment. Audit module  215  provides a workflow solution (using work management techniques) to create an efficient process for conducting necessary reviews of potential error payments. Audit module  215  supports current standard claim payments quality audits as well as cost containment or dispute driven audits.  
         [0042]     In the embodiment, query/rules component  207  may also support a random auditing process. Query/rules component  207  randomly selects a record from an accessed set of records from source transaction system  201 . Audit module  215  consequently audits the corresponding claim.  
         [0043]     Advanced payment integrity system  200  may combine the results of a plurality of analytical components in order to support an audit process by audit module  215 . Advanced payment integrity system  200  may combine multiple methods of advanced analytical techniques to enhance potential error claim payment detection with workflow and rules engine-optimized processing capabilities to audit and adjust claim payments that are determined to contain errors. Audit module  215  combines concurrently retrieved data from a plurality of analysis components. For example, audit module  215  may correlate an output from query/rules component  207  with an output from artificial intelligence engine  209 . As another example, audit module  215  may utilize concurrently accessed data from query/rules component  207  and text mining component  211 . Moreover, audit module  215  may utilize data that are sequentially accessed from a plurality of analysis components so that the output from one analysis component can be used to perform a deeper analysis with another analysis component. For example, audit module may utilize data accessed by query/rules component in which data records are accessed from source transaction system  201  to create a list of all members who are known to have duplicate or additional coverage, e.g., other primary coverage. Audit module  215  may subsequently instruct artificial intelligence engine  209  to use pattern recognition abilities to identify other members who have a higher likelihood than the average member of having other primary coverage. Consequently, advanced payment integrity system  200  may uncover more potentially erroneous payments to scrutinize and higher probability sets of data records to audit than would otherwise be the case if discovery module  205  were limited to using a single analytical technique or to performing analytical techniques without any interaction.  
         [0044]     Recovery and adjustments module  217  is designed to track and process recoveries or additional payments. Recovery and adjustments module  217  may be integrated with the audit module to create efficiency in the process of resolving the results of audits. Results tracking module  219  provides reporting and tracking of the total payment integrity process.  
         [0045]      FIG. 3  illustrates architecture  300  providing interaction between audit module  307  and analysis components  303  and  305  in accordance with an embodiment of the invention. First analysis component  303  retrieves record  351  and second analysis component  305  retrieves data  353  from source transaction system  301 . (In the embodiment, data  353  may be a data record or unstructured text data.) First analysis component  303  provides first result  355  and second analysis component  305  provides second result  357  to audit module  307 . As previously discussed, audit module  307  may concurrently obtain first result  355  and second result  357 . Audit module  307  may also obtain first result  355  and subsequently obtain second result  357  by subsequently requesting further analysis (by providing data criteria through interface  359 ) from second analysis component  305 . The embodiment supports even deeper analysis by further invoking other analysis components (not shown) with additional data criteria that are derived from analysis components  303  and  305 , in which analysis components may be “chained together”.  
         [0046]     The embodiment of the invention also supports iteratively processing data from source transaction system  301  by first analysis component  303  and second analysis component  305 . For example, audit module  307  may obtain first result  355  from first analysis component  303  by retrieving record  351 . Audit module  307  subsequently instructs second analysis component  305  to provide second result  357  by retrieving data  353 . Audit module  307  may further instruct first analysis component  303  to provide a third result (not shown) by retrieving another record (not shown). Audit module  307  may use the third result to audit a claim payment. Moreover, audit module  307  may iteratively continue the process (between first analysis component  303  and second analysis component  305 ) further instructing second analysis component  305  and so forth in order to converge on an optimized and maximized set of payments to review.  
         [0047]      FIG. 4  shows flow diagram  400  for auditing claims by audit module  215  (as shown in  FIG. 2 ) in accordance with an embodiment of the invention. In step  401 , audit module  215  receives a request to audit a claim payment. For example, audit module  215  may receive an e-mail request from a service provider or a processed data record that includes information regarding the disputed claim payment. (While process  400  may interact with an auditing specialist, process  400  may be fully or partially automated by audit module  215 .) In step  403 , audit module determines the discrepancy (error) associated with the claim payment. If audit module determines that there is no error, process  400  is terminated in step  405 . Otherwise, in step  407  audit module  215  requests that discovery module  205  (e.g., artificial intelligence engine  209 ) retrieve records that are associated with other records that may have a similar error. Also, audit module  215  may request query/rule component  207 , or text mining component  211 , or other analytic methods previously referenced to retrieve other records. In step  409  audit module  215  analyzes the retrieved data records. In step  411 , audit module  215  identifies claim payments that are associated with errors similar to the error identified in step  403 . In step  413 , audit module  215  audits the identified claim payments for other discrepancies. (Audit module  215  may fully or partially automate this task if established rules can be used or may interact with an auditing specialist.) Consequently, audit module  215  may provide an output to recovery and adjustments module  217  to adjust or recover payments (either underpayment or overpayment). In such a case, audit module  215  outputs data for identified claim payments to recovery and adjustment module  217  in step  414 . Process  400  is terminated in step  415 .  
         [0048]      FIG. 5  shows an architecture of claim transaction system  500  in accordance with an embodiment of the invention. Claim transaction system  500  includes primary claims system  503  and advanced payment integrity system  505 , which are interfaced to source transaction system  501 . Primary claims system  503  functions as the main mechanism for processing claim payments, corresponding to payment requests  551  and transaction payments  553 . Primary claims system  503  creates data (e.g., data records) that document claim payments and that are stored in source transaction system  501 . (In an embodiment of the invention, source transaction system  501  may be implemented as a database layer of primary claims system  503 .) As discussed previously, advanced payment integrity system  505  processes discrepancy input  555  relating to a claim payment that has been previously processed by primary claims system  553 . Discrepancy input  555  may pertain to an overpayment or underpayment and may be generated by the payment integrity system either before or after payment. If advanced payment integrity system  505  uncovers a systematic error, advanced payment integrity system  505  may inform primary claims system  503  through corrective information interface  559  so that corrective action (which may comprise automatic feedback) may be taken for future transactions of claim payments. In response to the resolution of a claim discrepancy, advanced payment integrity system  505  generates a response, e.g., a letter requesting payment (corresponding to overpayment) or a check compensating a service provider (corresponding to underpayment). In the embodiment, response  557 , which may be generated by recovery and adjustments module  217  as shown in  FIG. 2 , may be a letter requesting a repayment for an overpayment, a letter and check informing a member or a provider of a payment correction for an underpayment, an indicator to write off overpayment, or a request to a collection agency.  
         [0049]      FIG. 6  shows an architecture of advanced payment integrity system  600  in accordance with an embodiment of the invention. Advanced payment integrity system  600  is similar to advanced payment integrity system  200  that is shown in  FIG. 2 . Advanced payment integrity system  600  comprises extraction module  603  that accesses data from source transaction system  601 , discovery module  605 , audit module  615 , recovery and adjustments module  617 , and results tracking module  619 . Moreover, audit module  615  supports logging, reviewing, and processing provider disputes  621 . Audit module  615  uses the outcome and instructs discovery module  605  to identify additional claims that may have a similar error. (As with discovery module  205  shown in  FIG. 2 , discovery module  605  includes query/rules component  607 , artificial intelligence engine  609 , text mining component  611 , and another analytic component  613 .) The additional claims may be further audited (either automatically by audit module  615  or by audit module  615  interacting with an auditing specialist) and corrected. Recovery and adjustments module  617  then processes any further action to make recoveries or adjustments on these additional claims.  
         [0050]     Also, recovery and adjustment module  617  supports unsolicited refunds and returned payments  623 . Recovery and adjustments module  617  may use the outcome of an unsolicited refund or returned payment to instruct discovery module  605 . Discovery module  605  subsequently identifies additional claims that may have a similar error. The identified claims are further audited by audit module  615  and corrective action is taken as needed. For example, as with discovery module  205  shown in  FIG. 2 , discovery module  605  includes query/rules component  607 , artificial intelligence engine  609 , text mining component  611 , and another analytic component  613 . The additional claims may be further audited (either automatically by audit module  615  or by audit module  615  interacting with an auditing specialist) and corrected. Recovery and adjustments module  617  then processes any further action to make recoveries or adjustments on these additional claims.  
         [0051]      FIG. 7  shows architecture  700  with audit module  215  in accordance with an embodiment of the invention. As shown in  FIG. 2 , audit module  215  interfaces with extraction module  203  in order to access data in source transaction system  201  and interfaces with recovery and adjustments module  217 . In the embodiment, an auditing supervisor (not shown) may instruct extraction module  203  to access data records from source transaction system  201  in order to import data for corresponding claim payments to audit module  215 . Audit module  215  includes data validation component  701 , audit process component  703 , workstation interface  705 , and QA process component  707 . Data validation component  701  validates data retrieved from source transaction system  201 . For example, data validation component  701  may verify that retrieved data records are for an active member or a registered service provider. Audit process component  703  may automatically audit a claim payment if the claim payment fits a set of established audit rules and or may otherwise interact with an auditing specialist (associated with workstation  709 ) through interface  705 . Audit process component  703  may utilize QA (quality assurance) component  707  to interact with the auditing specialist and perform accuracy reviews of the results of audits. Quality assurance component  707  may ask the auditing specialist questions or may answer questions from the auditing specialist to provide quality assurance during the interaction. Audit process component  703  may include an expert system that utilizes rules (that may be based on the collective wisdom of expert auditing specialists). Audit module  215  may also determine a productivity measurement and/or quality measurement of an auditing specialist. The auditing specialist may enter an assigned work queue upon login. The auditing specialist is able to access all of the claims assigned to the auditing specialist. Also, audit module  215  may directly instruct the primary payment system or recovery and adjustments module  217  to recover or adjust the claims that audit module  215  discovers are erroneous and make additional payment or deduct overpayments from future claim payments.  
         [0052]      FIG. 8  shows architecture  800  with recovery and adjustment module  217  in accordance with an embodiment of the invention. Recovery and adjustment module  217  receives recovery and adjustment input  851  typically from an audit module (not shown) and provides adjustment output  853  and reports  855 . Also, recovery and adjustment module  217  interfaces with specialist&#39;s workstation  801  and supervisor&#39;s workstation  803 . In the embodiment, a supervisor (corresponding to supervisor&#39;s workstation  803 ) may assign recovery or adjustment requests to a specific specialist (corresponding to specialist&#39;s workstation  801 ) based on the specialist&#39;s current workload. Supervisors are able to import recovery or adjustment requests from outside sources (e.g., creating an Excel form for external units to complete and e-mail directly to the supervisor.) Also, the supervisor may assign return checks and refund checks to a specialist through a batching process. The supervisor may also unassign checks and recoveries to provide greater control over the workload of each specialist.  
         [0053]     The specialist (corresponding to workstation  801 ) may enter the assigned work queue upon login. From workstation  801 , the specialist is able to access all of the outstanding letters and checks assigned to the specialist. Upon entering a first letter queue, the specialist accesses all of the adjustments or recoveries assigned to the specialist that need to be either applied to balances, written off, sent to a vendor (e.g., a collection agency), or require the generation of an overpayment request or adjustment letter. Overpayment request letters are typically generated with a template using standard text based on the recovery or adjustment reason. The letter may also be customized to add additional facts and information.  
         [0054]     The specialist may easily search for the original overpayment request. If there is a remaining balance after posting a check, the specialist may generate another overpayment request letter. If a service provider returns too much money, the specialist may return (or credit) the excess amount to the service provider.  
         [0055]     Recovery and adjustment module  217  also supports an error queue (not shown) so that specialists may pend recoveries for the supervisor or external departments to review. Also, recovery and adjustment module  217  may generate a variety of reports  855  to ensure that accurate amounts for recovery and adjustments are reported to management.  
         [0056]     Recovery and adjustment module  217 , as previously discussed with  FIG. 5 , may directly instruct the primary payment system to recover or adjust the claims that recovery and adjustment module  217  processes and make additional payment or deduct overpayments from future claim payments.  
         [0057]     As can be appreciated by one skilled in the art, a computer system with an associated computer-readable medium containing instructions for controlling the computer system may be utilized to implement the exemplary embodiments that are disclosed herein. The computer system may include at least one computer such as a microprocessor, a cluster of microprocessors, a mainframe, and networked workstations.  
         [0058]     While the invention has been described with respect to specific examples including presently preferred modes of carrying out the invention, those skilled in the art will appreciate that there are numerous variations and permutations of the above described systems and techniques that fall within the spirit and scope of the invention as set forth in the appended claims.