text:
|Publication number
|Publication type
|Application number
|Publication date
|26 Oct 2010
|Filing date
|21 Oct 2003
|Priority date
|16 May 2001
|Fee status
|Publication number
|Oscar A. Chappel
|Original Assignee
|Perot Systems Corporation
|Export Citation
|BiBTeX, EndNote, RefMan
|Patent Citations (203), Non-Patent Citations (145), Referenced by (22), Classifications (15), Legal Events (4)
|External Links: USPTO, USPTO Assignment, Espacenet
This patent application is a continuation-in-part of, and incorporates by reference the entire disclosure of, co-pending U.S. patent application Ser. No. 10/336,104, which was filed on Jan. 3, 2003. U.S. patent application Ser. No. 10/336,104 is a continuation-in-part of U.S. patent application Ser. No. 09/859,320, which was filed on May 16, 2001 now U.S. Pat. No. 7,236,940. This patent application incorporates by reference the entire disclosure of U.S. patent application Ser. No. 09/859,320. This patent application also incorporates by reference the entire disclosure of a U.S. patent application entitled "Method of and System for Rules-Based Population of a Knowledge Base Used for Medical Claims Processing," filed on the same date as this patent application Ser. No. 10/690,257.
1. Technical Field of the Invention
The present invention is generally related to medical claims processing, and more specifically, but not by way of limitation, to a method of and system for rules-based population of knowledge bases using object-oriented software.
2. Description of Related Art
The healthcare industry has become very large from the standpoint of financial transactions. Healthcare providers ("providers"), such as hospitals, physicians, and ancillary services (e.g., laboratories, pharmacies), have expanded treatment and services as medicine has become more diverse in treating people for many more ailments than in the past. One reason for the expanded treatment and services includes advancements in research and development of technology to aid physicians in diagnosing and treating patients.
Accordingly, the healthcare insurance industry has grown to assist patients in paying for healthcare expenses. In providing for payment of services, healthcare insurance companies and other payment organizations (e.g., Medicare, Medicaid, etc.) ("payers") have established medical services and procedures to be covered for the treatment of patients. The providers and other organizations (e.g., industry standards groups and government regulators) have developed a variety of protocols to submit payment requests or medical insurance claims ("claims") to the payers for payment or co-payment of treatment and services rendered by the providers.
The protocols that have been developed by the payers and other organizations were developed in an effort to form standards by which payers recognize treatment procedures and services performed. The protocols enable the payers to more easily determine if the treatment procedures and services are covered by the insurance policies of the patients. As the industry developed, a number of different protocols developed in the way of claim forms, including UB-92 (formerly UB-82), which is utilized by institutional providers (e.g., hospitals), and HCFA 1500, which is utilized by professional providers (e.g., physicians). The claim forms traditionally were in the form of paper. However, the claim forms have evolved with technology and are now able to be filled out on a computer in an electronic format. While most providers utilize computers to fill out the electronic claim forms, small and rural providers continue to utilize paper claim forms.
Whether the provider utilizes paper or electronic claim forms, codes that identify medical diagnosis and treatment that have been generated by healthcare industry standards groups (e.g., National Uniform Billing Committee (NUBC), State Uniform Billing Committee (SUBC), government regulators, payers, etc.), are used in filling out claim forms for submission to payers. By utilizing standardized codes, providers and payers may communicate in a uniform manner. There are approximately 20 code sets today that have been developed for providers to utilize based on the specific field of medicine, service, treatment, etc., that is provided to the patient. For example, the International Classification of Disease 9th revision codes, generally known as ICD-9 codes, are utilized to describe diagnoses and the treatment of medical conditions afflicting various body parts (e.g., head, arms, legs, etc.). Other types of codes include Common Procedure Terminology (CPT4) codes, which are used for physician codes; Diagnosis Related Group (DRG), which are used for in-patient procedures; and Healthcare Procedure Coding Systems (HCPCS), which are used for drugs, durable medical equipment and outpatient services. As understood in the art, the codes generally are updated annually and new types of codes are created as medical procedures and specialties are formed.
While the code sets have been established to enable the healthcare and insurance industries to use common codes, there are many reasons why a medical procedure or service may not always be easily classified with a particular code. A provider may perform a procedure and write or dictate a treatment analysis to be submitted for insurance reimbursement. A claim coder (i.e., individual who interprets the treatment analysis and assigns the proper code into the claim form) may interpret the treatment analysis differently from a different claim coder. And, based on the correctness of, and compliance to, claim submission rules of the claim codes submitted, the payer may or may not approve of the procedure or treatment for reimbursement.
Entry of the UB-92 and HCFA 1500 claim forms may be a challenging task due to the complexity of information necessary and because both the medical codes and insurance information must be determined and entered. While one may become an expert at entry of the claim form, because each payer has different rules for authorizing payment based on the information submitted on the claim form and each provider has different methods or procedures for determining the information to be entered into the claim form, the claim submission and reimbursement process often becomes a financial burden for both the provider and payer.
As well understood in the art, there are large numbers of providers and payers. While there are an estimated 300(+) major providers and payers, there are several thousands of physicians, all of whom submit claim forms to the thousands of payers. Because patients of a single provider may have insurance with many tens or hundreds of payers, the providers are overburdened and practically incapable of maintaining knowledge as to the rules and requirements, addressees, contacts, etc., for each payer. One quickly understands the magnitude of the coordination of communications needed between the providers and payers.
To assist both the provider and payer with the coordination of claim submission, an industry of clearinghouses has developed.
The clearinghouses 106 perform, at least in part, distribution duties similar to a postal distribution center for the providers of the claim forms 103 in either paper or electronic formats. The clearinghouses 106 perform, at least in part, communication of status (e.g., acceptance, rejection, etc.) of the submitted claims from the payers 104 to the providers 102. The process by which the claims are accepted or rejected by the payers 104 is generally known as the adjudication process.
Some of the reasons for the computer systems of the providers 102 and payers 104 not having compatible architectures include: (1) the healthcare industry having traditionally performed paper processing transactions, (2) the computer and software systems of the providers 102 and payers 104 never having been developed to communicate with one another, (3) the codes developed for the providers 102 not necessarily having been adopted by the payers 104, (4) the clearinghouses 106 having been established to manage and process claims, thereby not having an incentive to adopt a direct, real-time payment system between the providers and payers, (5) the payers having limited incentive to expedite payment as delay in payment increases interest revenue for the payers, (6) the number of people, organizations and government entities defining codes adding layers of complexity to the process, and (7) technology not having been fully adopted by the healthcare industry. For example, there are very few direct connections between trading partners (i.e., specific provider 102 and payer 104).
Software developers and information technology companies that the providers 102 and payers 104 have utilized to develop systems and software to manage the claims processing have generally been devoted to either the provider 102 or payer 104, so that the concerns of the other side essentially have been unincorporated in the development process. In other words, the business model for the systems have focused on either the payer 104 or provider 102 side in terms of collecting revenue. On the provider side, the systems are established to conform to the needs of the general population of payers 104 (e.g., to form submission compliance with as many payers 104 as possible), which typically causes the systems to be less compatible with any specific payer 104. On the payer side, the systems are established to conform to the needs of the general population of providers 102 (e.g., to receive form submission from as many providers 102 as possible), which typically causes the systems to be less compatible with any specific provider 102.
While the incompatibility of the systems of the providers 102 and payers 104, and lack of desire and motivation of the clearinghouses 106 and payers 104 have held back progress in improving the technology for the healthcare industry to more efficiently and effectively process claims, the major problems that the industry has to overcome include, but are not limited to, the (i) dynamic environment of rapidly changing codes, (ii) conflicting reporting requirements, and (iii) contradictory payment guidance. These problems and turmoil have resulted in a complete industry being created to focus on interpreting the changes in codes and reporting guidance and creating software programs to evaluate the contents of the claim forms 103 and assess the validity of the claim forms 103 before being sent to the payer 104 for adjudication and settlement. This industry, which includes clearinghouses 106, receives change notifications and error reports in many different forms. In many cases, an originator of the change announces how the change should be handled by payers and fiscal intermediaries. These change handling instructions are referred to as "edits" as understood in the healthcare industry or various other industries.
Continuing with
The term "edits" historically was used to describe the process of correcting information in a data file. While the edits still refer to correcting information, the term "edits" in the healthcare industry for insurance claims provides for a directive to correct information that is incorrect or does not comply with business rules established by a payer. In other words, the edits may be considered statements of situations that cause an error to occur to hinder payment or processing of final adjudication of a particular insurance claim. The business rules of payer 104, which may be established arbitrarily or based on the policies of the payer 104, for example, may be established and modified on an annual basis or more frequently. For example, one business rule may be as simple as requiring the last name to be entered with all capital letters. Another business rule may indicate that a certain procedure is to be denied reimbursement if a certain diagnosis not requiring the procedure to be performed is reached. Yet another business rule may require a certain identifier in a field if an intern assists in a medical procedure. And, if any of these business rules are violated, an edit is generated and applied to the insurance claim form 103 to notify the provider 102 that a correction is needed per the instructions of the edit.
An example of an edit includes the following: "Move UPIN from 82AA to 82BA," where UPIN is an abbreviation for universal provider identification number and AA and BA are field identifiers in the form locator referring to an attending physician on a UB-92 claim form 103. As understood in the art, the word "move" alternatively may be written as "copy," "change," include," or other synonym in an edit. The choice of words for an edit is arbitrary as there are no particular standard terms to be used for edits for the individuals who generate the edits. And, because of the multitude of different, yet related terms, the edits associated with the business rules may mean the same thing or be substantially semantically similar and unnecessarily increase the overall number of edits to which the providers must adhere.
The end goal for the providers 102 is to expedite final adjudication of medical claims by minimizing rejection of the medical claims by the payer 104 due to errors entered on the claim forms 103. To minimize the errors entered on the claims form 103, an understanding of the edits is desirable as the edits offer a roadmap for mistakes that may be made in view of the business rules and codes utilized to adequately and correctly complete the claim forms 103 for claim validation. Claim validation occurs when at least the following items are satisfied: (i) the claim form 103 is complete in the eyes of the payer 104, (ii) the data are properly formatted in the proper location on the claim form 103, and (iii) the data accurately reflects medical services provided and meets service constraints, which are generally embodied in the edits. However, because of the large volume of edits and frequency of edit modifications, creation of a complete understanding and knowledge base of the tens of thousands of edits is substantially impossible for an individual attempting to design a system to expedite medical claims processing.
After the process of applying edits to a claim form at step 212, if there are no edits applied to the claim form 103 because no errors were detected, then the claim form 103 is communicated to the payer 104 in a format that the payer 104 requires. Otherwise, if errors were detected on the submitted claim form 103, then the claim form 103 and associated edits 215 are communicated back to the provider 102 for correction to the claim form 103.
At step 216, the payer 104 receives the claim form 103. A receipt of receiving the claim form 103 may be communicated back to the provider 102 via the clearinghouse 106 for notification purposes. At step 218, the payer 104 adjudicates on approving the claim for payment purposes. The adjudication is based on rules or policies that the payer 104 may have for the health insurance plan of the patient. Generally, the edits include enough of the policies so that the claims are approved by the payer 104, but is not always the case.
At step 220, a status including the results of the adjudication process of step 218 may be communicated via the clearinghouse 106 back to the provider 102. If the claim was rejected, the provider 102 may be allowed to cure the defect. Additionally and/or alternatively, the provider 102 or patient may appeal the rejection at this stage without having to resubmit another or amended claim form 103. If the claim was approved, then payment 223 of the claim may be resubmitted to the provider 102, either directly or indirectly. At step 224, the provider 102 receives the payment 223 and applies it to collections at step 226. At this point, the claim is considered closed as payment by the payer 104 has been tendered.
A method of populating a knowledge base includes creating at least one claim element based on information related to at least one of a claim form and an updated specification, creating at least one rule from said information related to at least one of a claim form and an updated specification, applying said at least one rule to said at least one claim element to form knowledge, and populating the knowledge base with said knowledge acquired from said applying step.
An article of manufacture for populating a knowledge base used in validating medical claims includes at least one computer readable medium and processor instructions included on the at least one computer readable medium. The processor instructions are configured to be readable from the at least one computer readable medium by at least one processor and thereby cause the at least one processor to operate as to create at least one claim element based on information related to at least one of a claim form and an updated specification, create at least one rule from said information related to at least one of a claim form and an updated specification, apply said at least one rule to said at least one claim element to form knowledge, and populate the knowledge base with said knowledge acquired from said applying step.
A system for populating a knowledge base includes means for creating at least one claim element based on information related to at least one of a claim form and an updated specification, means for creating at least one rule from said information related to at least one of a claim form and an updated specification, means for applying said at least one rule to said at least one claim element to form knowledge, and means for populating the knowledge base with said knowledge acquired from said means for applying.
A more complete understanding of the principles of the present invention may be obtained by reference to the following Detailed Description when taken in conjunction with the accompanying Drawings wherein:
Medical insurance claims processing has become a significantly complex, expensive, and time consuming part of the medical industry due to the vast number of treatment codes, rules, and edits (i.e., claim form change instructions written in English language form) that have been formed over the years by governmental entities, medical industry standards committees, medical providers 102, and payers 104. The frequency of changes of the treatment codes continues to cause additional edits to be generated to resolve claim form entries that do not conform to the rules or errors that the payers 104 reject as being non-conforming or impermissible due to the rules of the payer 104 for the medical procedure or treatment to be reimbursed. In submitting medical insurance claims, the providers 102 generally send the claim forms 103 in batch electronically or via mail to payers 104 via a clearinghouse 106 (See
In improving the efficiency of applying the edits to the claim forms 103, the total number of edits are consolidated by performing a comparison between the words of each of the edits to determine the degree of similarity of the words and/or semantics of the edits being compared. In one embodiment, a token list composed of each word of an edit string is generated for each edit being compared. A comparison may be made between the token lists and a percentage or discrete value may be formed based on the number of matched words divided by the average number of words in the edits being compared to indicate the degree of semantic similarity between the two edits. A scale may utilize the discrete value and a symbolic description may be generated based on the discrete value to indicate the degree of similarity between the two edits. In one embodiment, the scale is a Likert scale, whereby ranges of values (e.g., between 0.0 and 1.00 percent) are utilized to categorize the degree of similarity with the symbolic description. The symbolic description may include text, such as "significant similarity," "high similarity," "moderate similarity," "marginal similarity," and "insignificant similarity." Alternatively, grades (e.g., "A," "B," "C," etc.) or values (e.g., 100, 95, 90, etc.) may be generated as the symbolic description.
A sorted list of edits may be generated based on the discrete value and/or symbolic description. In one embodiment, the sorted list may be sorted in descending order from most similar semantically to least similar. The edits that are most similar may be consolidated by using a rule that uses the symbolic description in consolidating or recommending consolidation of the edits. In consolidating the edits, a union of the token lists of the edits being consolidated may be formed to a single edit that describes the edits determined to have a "significant similarity," for example. In one embodiment, a predetermined threshold may be utilized. For example, edits having a similarity within ten percent may be considered significantly similar and be automatically consolidated. Alternatively and/or additionally, edits considered to have a "high similarity" and/or "moderate similarity" may be recommended to be manually inspected for the edit consolidation process. Edits that are determined to have "marginal similarity" and/or "insignificant similarity" may be treated as edits that are not combinable with other edits. For example, edits that are determined to be above a predetermined threshold of similarity, such as 69 percent, may be formed as separate edits. By consolidating the edits, a manageable number of edits may be produced to be applied to rejected claim forms 103 being submitted by the providers 102 for reimbursement by the payers 104. By applying a reduced number of edits to the rejected claim form 103, the efficiency of processing of the claim forms may be improved to minimize or eliminate the need to submit claim forms 103 as a batch process. Depending upon how the similarity is measured, the number of edits may be reduced from thousands to hundreds.
Other objects may be utilized to describe or model the entities associated with medical claims processing. For example, objects "InstitutionalProvider" 312 and "Payer" 314 may be utilized to describe particularities of different providers 102 and payers 104. In one embodiment, an instance of the payer object may include rules that a payer 104 has for adjudicating submitted claims and edits associated therewith based on the rules of the payer 104. Accordingly, by having objects that model the different entities (e.g., provider 102 and payer 104) with the claims filing process 200, more specific information for consolidating the edits may be generated. Additionally and/or alternatively, the entity objects 312 and 314 may be applied to a distributed network (see, for example,
In another embodiment, the provider server 402 may submit the claim form 103 directly to the payer server 404 via the network 408. The network 408 may be the Internet, satellite network, wide area network (WAN), telecommunications network, or other communication system.
The provider server 402 may include a processor 410 coupled to a memory 412, input/output (I/O) unit 414, and storage unit 416. The processor 410 may operate software 418 for entry of patient information into the claim form 103. The software 418 may be object oriented such that the information is applied to elements of an object to be communicated over the network 408 for processing via the I/O unit 414. The memory 412 may store the software 418 prior and/or during execution. The storage unit 416 may store a database 420 that maintains the claim forms 103 in an object oriented or other format. The I/O unit 414 of the provider server 402 may communicate the claim form 103 using data packets 422 or other communication protocol as understood in the art.
The clearinghouse server 406 may operate to receive the claim form 103 via the network 408. In one embodiment, the clearinghouse server 406 receives claim forms 103 in a batch process. However, the principles of the present invention may provide for receiving claim forms 103 on an individual basis. The clearinghouse server 406 generally includes the same hardware as the provider server 402, including a processor 424 coupled to a memory 426, I/O unit 428, and storage unit 430. The processor 424 may execute software 432 that receives the information of the claim form 103. If the claim form 103 is represented as an object, then the software 432 may process the claim utilizing objects to verify the information of the claim form 103. In processing the claim, claim verification rules in the form of objects that specify how content is to be submitted on the claim form 103 in order to be adjudicated by one or more payers 104 may be utilized.
In one embodiment, the clearinghouse server 406 may apply the claim verification rules to the submitted claim form 103 and, in the case of an error (i.e., information entered into the claim form 103 not complying with the claim verification rules), apply or associate one or more edits to the claim form 103. The edit(s) applied to the claim form 103 may be based on edits specific to the payer 104 that the claim form 103 is being submitted or based on a reduced set of edits according to the principles of the present invention. The results of the processing of the claim form 103 may be stored in a database 434 with or without the claim form 103 in an object oriented or other format. The I/O unit 428 may communicate the processed claim form 103 and any associated edits or other information to the payer server 404 via the network 408 in data packets 436.
The payer server 404 may include generally the same hardware as the provider server 402, including a processor 438 coupled to a memory 440, I/O unit 442, and storage unit 444. The processor 438 may execute software 446 to process the claim forms 103 received for adjudication by the payer 104. The I/O unit 442 may receive the claim form 103 via data packets 436 and communicate the claim form 103 to the processor 438. The software 446 may utilize objects to verify the information of the claim form 103 and associated edit(s), if any, to adjudicate the validity of the claim for reimbursement to the provider 102 or patient. In accordance with the principles of the present invention, a consolidated set of edits may be utilized to reduce processing time. Alternatively, a reduced set of edits specific to the payer 104 may be utilized. A database 448 may be utilized to store (i) the submitted claim form 103, (ii) results of the verification, and (iii) results of the adjudication process in object oriented or other format. Accordingly, a rejected claim may be communicated to the provider server 402 via the clearinghouse server 406 or directly to the provider server 402 by the I/O unit 442. It should be understood that other or additional servers may be utilized in accordance with the principles of the present invention to generate and/or apply the consolidated set of edits to the submitted claim forms 103.
Almost all expert systems also have an explanation system 508, which allows the system 500 to explain its reasoning to the user 502. The inference engine 510 may be utilized to generate knowledge of the information provided thereto as understood in the art. The explanation system 508 and the inference engine 510 may be coupled to case specific data 514 containing specific information related to the problem being solved (e.g., reduction in the number of edits). The knowledge base editor 512 helps the expert or knowledge engineer to easily update and check a knowledge base 516 that is provided or formed. The knowledge base 516 may be made up of a collection of facts and rules that constitute the information model usable by the system 500.
In operation, the user 502 interacts with the system 500 via a user interface 506 that may be graphical (e.g., graphical user interface (GUI)), natural language or any other style of interaction. The inference engine 510 may be used to reason with both the expert knowledge (extracted from an expert in the field) and data specific to the particular problem being solved. The inference engine 510 may allow the user 502 to test rule conditions and add new information for the system 500 to use as it is executing. The expert knowledge is typically in the form of a set of IF-THEN rules. The case specific data 514 includes both data provided by the user 502 and partial conclusions (along with certainty measures) based on this data. In a simple forward chaining rule-based system, the case specific data are the elements in working memory.
One feature of expert systems is the way domain specific knowledge is separated from more general purpose reasoning and representation techniques. The general purpose reasoning may be referred to as the expert system shell 504. Given a new kind of problem to solve (e.g., reduction in edits), an expert system shell 504 that provides the right sort of support for that problem may be composed, so that expert knowledge may be provided to solve the problem. As understood in the art, commercially available expert systems are available to reduce development time.
The system 500 may be executed on one of the servers of the distributed network 400. Alternatively, the system 500 may be executed on an independent computing system (not shown). The processes of
At step 606, words of at least two edits are compared to determine similarity and/or semantic similarity. The comparison may be performed as understood in the art. One embodiment may include forming tokens for each word of an edit and comparing the tokens. The comparison may include comparing a single edit to each other edit of the edit list. The similarity may be a degree of similarity based on the intersection of the words being compared. Alternatively, the similarity may be based on the words and synonyms of those words. Still yet, words being substantially semantically similar (e.g., copy and move) may be used for the comparison. The degree of similarity may be a discrete value indicative of a ratio or percentage formed by the intersection of words as determined by the comparison divided by the total number of words of the two edits being compared. A scale may be utilized in the comparison process to establish the degree of similarity and, optionally, a symbolic description may be applied to the edits for later usage in consolidating similar edits. In one embodiment, a Likert scale as shown in TABLE 1 may be utilized in forming the symbolic description. The edits may be sorted by similarity to assist in the consolidation process.
Likert Scale
Range (percent)
Symbolic Description
Significant Similarity
High Similarity
Moderate Similarity
Marginal Similarity
Insignificant Similarity
At step 608, a new or single edit may be formed based on the similarity between the at least two edits. The single edit may include at least a portion of each edit being consolidated into the single edit. In other words, the single consolidated edit may be the union of two or more edits being combined to form the single edit. The consolidated edit may be written to the database 434. The process ends at step 610.
Two instances of the edit object 302 may be formed as Edit1 object instance 704 and Edit2 object instance 706 (i.e., the edit objects 704 and 706 are instantiated). In one embodiment, to form the edit objects, edits are inserted into a hash table keyed to the edit strings and the two edit object instances 704 and 706 may be generated with edit strings from the hash table keys. The edit consolidation process 600 starts at step 608, where the user commands the Edit1 object instance 704 to build a list of words from the edit string associated with the object. In one embodiment, the list of words are tokens containing each word of the edit string. At step 710, the user commands the Edit2 object instance 706 to build a list of words from the edit string associated with the object. The command may instruct some or all of the edit objects that are to be compared to the Edit1 object instance 704.
EXHIBIT 1 provides an exemplary instance of an edit object 302 of
EXHIBIT 1. Instantiated Medicare Edit
#<db-instance MEDICARE-EDIT 576968652> is a MEDICARE-EDIT
At step 712, the user 502 commands the instantiated Edit1 object 704 to compare the edit to other edits (i.e., compare the edit strings via the tokens or token list to tokens of other edits). The Edit1 object 704 compares the associated tokens with tokens of the Edit2 object 706 at step 714. In making the comparison, the software (e.g., software 432) may utilize an inference engine or an instance of a pattern matcher class to assist in rule processing, where rules and assertions may be maintained in assertion and rule attributes of the edit objects 704 and 706. During edit comparison, each edit or edit object 704 and 706 assesses the similarity of the associated tokens to the tokens of the other edit being compared. In one embodiment, the Likert scale is used in converting the ratio or percentage of similarity to a symbolic representation of the similarity (See, for example, TABLE 1).
Each edit involved in a comparison with other edits creates facts that indicate the symbolic similarity to other edits. EXHIBIT 2 provides examples of results of comparisons of edits. As shown, each of the edits have ratios above 0.81 (81 percent) and below 0.90 (90 percent), thereby indicating that the edits all have "high similarity" to the edit that is performing the comparison.
EXHIBIT 2. Edit Comparison Ratios
(#<db-instance MEDICARE-EDIT 576977460> 0.8571428571428571)
(#<db-instance MEDICARE-EDIT 576981212> 0.8235294117647058)
(#<db-instance MEDICARE-EDIT 576972788> 0.8125)
(#<db-instance MEDICARE-EDIT 576961092> 0.8235294117647058)
(#<db-instance MEDICARE-EDIT 576996484> 0.8823529411764706)
At step 716, the Edit1 object 704 initiates the process of creating edit facts. EXHIBIT 3 provides examples of similarity facts for the edits that are created based on results of comparisons of different edits. The facts may be in the form of "<Edit 1> has (degree of similarity) similarity to <Edit 2>." Where (Degree of similarity) is an arbitrarily assigned value similar to those found in a Likert Scale. For example a similarity between 0.9 and 1.0 may be considered HIGH, a similarity between 0.8 and 0.89 may be considered MODERATE, a similarity between 0.6 and 0.79 may be considered MARGINAL, and a similarity between 0.0 and 0.59 may be considered MINIMAL. As indicated, the edit object performing the comparison is MEDICARE-EDIT 576968652. The MEDICARE-EDIT objects being compared are 576961092, 576996484, 576972788, 576981212, and 576977460. "SLU" is the code used to represent the provider, or originator of the edit 104.
EXHIBIT 3. Examples of Similarity Facts Based on Comparison
EDIT #<db-instance MEDICARE-EDIT 576968652> FOR "SLU" HAS HIGH SIMILARITY TO
EDIT #<db-instance MEDICARE-EDIT 576961092> FOR "SLU"
EDIT #<db-instance MEDICARE-EDIT 576968652> FOR "SLU" HAS HIGH SIMILARITY TO
EDIT #<db-instance MEDICARE-EDIT 576996484> FOR "SLU"
EDIT #<db-instance MEDICARE-EDIT 576968652> FOR "SLU" HAS HIGH SIMILARITY TO
EDIT #<db-instance MEDICARE-EDIT 576972788> FOR "SLU"
EDIT #<db-instance MEDICARE-EDIT 576968652> FOR "SLU" HAS HIGH SIMILARITY TO
EDIT #<db-instance MEDICARE-EDIT 576981212> FOR "SLU"
EDIT #<db-instance MEDICARE-EDIT 576968652> FOR "SLU" HAS HIGH SIMILARITY TO
EDIT #<db-instance MEDICARE-EDIT 576977460> FOR "SLU"
The facts may be stored in assertion attributes of an inference engine object. In addition, the inference engine adds assertions provided in EXHIBIT 4. The assertions provide actions for handling different degrees of similarity. For example, edits with "high similarity" are to be automatically consolidated and edits with "marginal similarity" are to be manually reviewed.
EXHIBIT 4. Assertions for Handling Edits
Consolidate edits with high similarity.
Consolidate edits with significant similarity.
Manually-review edits with marginal similarity.
Separately-implement edits with slight similarity.
Separately-implement edits with no similarity.
At step 718, the Edit1 object 704 initiates populating similar edits into the Edit1 object 704. In populating the similar edits into the Edit1 object 704, the inference engine loads a single rule of the form shown in EXHIBIT 5. The rule determines how edits are to be processed. The edit class has methods defined for each of the actions.
EXHIBIT 5. Rule
If <edit-1> has <level> similarity to <edit-2> and <action> edits with
<level> similarity then <action> <edit-1> <edit-2>
By utilizing the rule, the instantiated edit object of EXHIBIT 1 has a "EDIT-FACTS" attribute updated to include the edit strings of the similar edits as shown in EXHIBIT 6.
EXHIBIT 6. Updated Edit Object with EDIT-FACTS
#<db-instance MEDICARE-EDIT 576968652> is a MEDICARE-EDIT
((EDIT #<db-instance MEDICARE-EDIT 576968652> FOR "SLU" HAS HIGH SIMILARITY
TO EDIT #<db-instance MEDICARE-EDIT 576961092> FOR "SLU")
(EDIT #<db-instance MEDICARE-EDIT 576968652> FOR "SLU" HAS HIGH SIMILARITY TO
EDIT #<db-instance MEDICARE-EDIT 576996484> FOR "SLU")
(EDIT #<db-instance MEDICARE-EDIT 576968652> FOR "SLU" HAS HIGH SIMILARITY TO
EDIT #<db-instance MEDICARE-EDIT 576972788> FOR "SLU")
(EDIT #<db-instance MEDICARE-EDIT 576968652> FOR "SLU" HAS HIGH SIMILARITY TO
EDIT #<db-instance MEDICARE-EDIT 576981212> FOR "SLU")
(EDIT #<db-instance MEDICARE-EDIT 576968652> FOR "SLU" HAS HIGH SIMILARITY TO
EDIT #<db-instance MEDICARE-EDIT 576977460> FOR "SLU"))
At step 720, a consolidate method may sort edits in descending order of similarity and consolidate the token lists of the initial edit and the most similar edit at step 722. It may then iterate over the remaining similar edits identifying the different tokens that are to be accounted for in the single new edit being created. As shown in EXHIBIT 7, exemplary rules may be generated based on the rule (EXHIBIT 5) and assertions (EXHIBIT 4)
EXHIBIT 7. Rules Generated by Rule and Assertions
Rule EDIT-ACTION-RULE indicates (CONSOLIDATE #<db-instance MEDICARE-EDIT
576925060> #<db-instance MEDICARE-EDIT 576937628>).
Rule EDIT-ACTION-RULE indicates (CONSOLIDATE #<db-instance MEDICARE-EDIT
576925060> #<db-instance MEDICARE-EDIT 576925580>).
Rule EDIT-ACTION-RULE indicates (CONSOLIDATE #<db-instance MEDICARE-EDIT
576925060> #<db-instance MEDICARE-EDIT 576951716>).
Rule EDIT-ACTION-RULE indicates (CONSOLIDATE #<db-instance MEDICARE-EDIT
576925060> #<db-instance MEDICARE-EDIT 576932428>).
Rule EDIT-ACTION-RULE indicates (CONSOLIDATE #<db-instance MEDICARE-EDIT
576925060> #<db-instance MEDICARE-EDIT 576962876>).
Rule EDIT-ACTION-RULE indicates (CONSOLIDATE #<db-instance MEDICARE-EDIT
576925060> #<db-instance MEDICARE-EDIT 576934508>).
Rule EDIT-ACTION-RULE indicates (CONSOLIDATE #<db-instance MEDICARE-EDIT
576925060> #<db-instance MEDICARE-EDIT 576936356>).
Rule EDIT-ACTION-RULE indicates (CONSOLIDATE #<db-instance MEDICARE-EDIT
576925060> #<db-instance MEDICARE-EDIT 576951116>).
Rule EDIT-ACTION-RULE indicates (CONSOLIDATE #<db-instance MEDICARE-EDIT
576925060> #<db-instance MEDICARE-EDIT 576972268>).
Based on the rules and consolidation assertions, a union forming a single edit using the token lists (i.e., words of the edit strings) produces a single edit as shown in EXHIBIT 8. The single edit in this case is constructed from five edits, thereby reducing the processing of the edits by a factor of 80 percent. A recommendation of the single, consolidated edit may be determined by the Edit1 object instance 704 at step 724 and recommended to the user 502 at step 726.
EXHIBIT 8. Single Edit Formed from Similar Edits
The single edit may be utilized in the claims process by any of the entities, including provider 102, clearinghouse 106, or payer 104 to increase efficiency in processing the claims. By increasing the efficiency of processing the claims, the provider 102 may receive payment on the claims in a shorter period of time, thereby increasing the revenue stream to the provider 102.
In order to validate claims for the providers to receive payment, a claim element structure is created. A claim element provides a fundamental framework within which claim validation and adjudication policies may be specified. Each claim element includes a rules system component that allows the claim element to execute rules as well as share the knowledge attained by the rules with other claim elements.
Many types of claim elements may be formed, such as, for example, a reference claim element and a reported claim element. The reference claim element provides specific knowledge needed to enable claim validation. For example, a reference claim element may be instantiated with information from the claim forms and updates, such as, for example, Specification 837. The reported claim element represents instances of claim elements that are reported on a specific claim. The reported claim element creates a reference to the corresponding reference claim element in order to validate the reported claim element. The reported claim element may include a string that represents a particular portion of information on a claim form (e.g., a string of alphabetic characters for a patient name or a string of numeric characters for a patient birth date).
Attributes are given to the claim elements based on the information obtained from the specifications and updates. One claim attribute, "TITLE," lists the title given to the claim element (e.g., "Birth date"). Another attribute may be "ABBREVIATION," which includes a list of possible abbreviations utilized to refer to the claim element (e.g., "BD"). In the reference claim elements, a "VALID-VALUES" attribute is also assigned. The "VALID-VALUES" attribute includes a list of the possible values a claim element may comprise, when such information is provided in the specification, e.g., a valid date, a condition code, or a type of bill. The reported claim elements include a "VALUE" attribute that stores the actual value from a reported claim form (e.g., an actual reported birth date, such as Apr. 6, 1965).
At step 802, the information obtained from the claim element specifications is translated into edits in order to form knowledge to populate the knowledge base. For instance, one fact (edit) that may be deduced from Specification 837 is that a valid Medicare claim requires an entry in field 4 on the UB-92 form. Another exemplary fact (edit) for a valid Medicare claim is that field 4 on the UB-92 form has the title of "Type of Bill." In order to be able to more easily work with the edits, a translation grammar is used in the edit-translation 802. The translation of these facts yields the following translated claim element edits:
Medicare REQUIRES Field 4 ON UB-92
Field 4 OF UB-92 HAS TITLE Type of Bill.
The translation grammar includes identification of particular claim-element locations that must be populated under certain circumstances. The translation grammar also allows for abbreviations to be applied to the name of a code.
More specifically, the translation grammar facilitates the understanding of medical claims processing by creating a specific vocabulary and grammar suitable for use by computers and rule-based systems. The vocabulary is composed of a combination of words that are unique to the healthcare industry (e.g., condition code, occurrence code, form locator, patient, provider, etc.). In addition to the healthcare unique vocabulary, standard English language words are also used to communicate in business and informal daily interactions. The English language words are generally adjectives, nouns, verbs, and prepositions. Some examples of English language words used in the translation grammar are as follows:
Nouns: revenue code, service line, claim element, type of bill;
Verbs: requires, allows, excludes;
Prepositions: on, for, in, from, to;
Adjectives: value, range.
The nouns of the translation grammar are used to describe items of interest in the healthcare claim processing domain. The verbs describe relationships between the nouns in the domain. The translation grammar prepositions constrain the relationships described by the verbs and the adjectives are used to enhance the descriptions of the nouns.
The translation grammar describes legal patterns of usage for the vocabulary set forth above. The translation grammar is an adaptation of American English grammar patterns for declarative sentences. Each translation sentence is composed of a noun phrase, verb phrase and optional prepositional phrases. For example, the translation sentence "Revenue Code value 0420 requires Occurrence Code value 11 on TOB 14X includes the noun phrase "Revenue Code value 0420," the verb phrase "requires Occurrence Code value 11," and the prepositional phrase "on TOB 14X."
In addition, the translation grammar provides a mechanism that allows a computer to identify inconsistencies and assist a human operator in reducing such inconsistencies. For instance, the exemplary untranslated sentence "Reject Medicare outpatient if revenue code 42X exists and not occurrence code 11, 29, 35" is translated to three separate translations as follows:
Revenue Code value 42X requires Occurrence Code value 11 on TOB outpatient for payer Medicare.
Revenue Code value 42X requires Occurrence Code value 29 on TOB outpatient for payer Medicare.
Revenue Code value 42X requires Occurrence Code value 35 on TOB outpatient for payer Medicare.
A second exemplary untranslated sentence "Reject revenue code 42X exists and not value code 50" is translated into "Revenue Code value 42X requires Value Code value 50." The first exemplary untranslated sentence yields three translated sentences with each sentence including a noun phrase, a verb phrase, and two prepositional phrases. However, the second exemplary untranslated sentence yields one translated sentence with one noun phrase and one verb phrase. As shown by these examples, the first exemplary translated sentences are more precise and more restrictive than the second translated sentence. The translation grammar aids a human operator in identifying and correcting these inconsistencies as noted above.
A manual translation using the translation grammar is typically performed, although an automated translation may also be performed. The translation is performed on both the consolidated edits as well as individual edits that have required manual analysis. The translation grammar is used to translate the edits into translated claim element edits that may more readily be filtered by a set of knowledge-base-population rules. Once the translated edits have been generated, the translated claim element edits may be used in conjunction with the knowledge-base-population rules in order to populate the knowledge base. Following the edit translation of step 802, a file is typically produced that contains translated edits and their corresponding untranslated edits.
At step 804 the translated edits are used to populate the knowledge base. The attributes and/or values of the claim elements are assigned at steps 802 and 804 as appropriate. Rules are enacted by the claim elements to form knowledge that may be entered into the knowledge base. At step 806, knowledge in the populated knowledge base is used to validate claims. To validate claims, information from reported claim elements is checked against information from reference claim elements. For example, if the "VALUE" from a reported claim element is listed as one of the possible values in the "VALID-VALUES" attribute of the corresponding reference claim element, then the claim may be validated.
The knowledge created by the rules and utilized by the claim elements may be form-neutral or form-specific. For instance, the valid values for the patient name field, patient address field, birth date, etc. may be the same for both the UB-92 form and the HCFA 1500 form. The knowledge in the knowledge base may also map to various different fields on each claim form. In this way, updates and edits to the claim elements may apply to one or more claim element. An edit to the "VALID-VALUES" attribute of a patient name claim element for a UB-92 form also applies to the "VALID-VALUES" attribute of a patient name claim element for a HCFA 1500 form. The claim elements act as intelligent agents to update or edit other claim elements that may depend on or refer to the specific edited claim element (e.g., patient name claim element for a UB-92 form).
Each knowledge-base-population rule is given a unique name. Below is an example of a knowledge-base-population rule referred to as:
For example, when applying the above knowledge-base population rule to the claim element edits "Medicare REQUIRES Field 4 ON UB-92" and "Field 4 OF UB-92 HAS TITLE Type of Bill," a conclusion, or knowledge, is developed based on the implied "then" statement. In this example, the variables (? payer), (? element), (? title), and (? form id) have the values of "Medicare," "Field 4," "Type of Bill," and "UB-92" respectively. The knowledge obtained by the implied "then" statement is that "Medicare requires Type of Bill on UB-92."
Returning to
When the pattern of a given knowledge-base-population rule is matched by a translated edit, the bound variables listed in the knowledge-base-population rule are instantiated by the method for the knowledge-base-population rule. When the variables are instantiated, the applicable conclusion of the knowledge-base-population rule is added to a set of edit-rule conclusions. The conclusions of all of the knowledge-base-population rules in which a pattern match has been found are executed via the corresponding methods. Applicable method calls cause SQL queries to be made that cause population of the knowledge base to occur.
Returning to
The expansion of claim element edit translations focuses on the payers who require an entry in a particular claim element on a particular claim form. For example, a single edit can be constructed to indicate that a list of payers require a specific claim element (e.g., claim element SoA on the UB-92 form FL-20 is required by payer Medicare, BC, CHAMPUS, and Commercial). This exemplary claim element edit translation would be expanded to create a single statement of a fact for each payer as follows:
Claim element SoA on UB-92 FL-20 is required by payer Medicare;
Claim element SoA on UB-92 FL-20 is required by payer BC;
Claim element SoA on UB-92 FL-20 is required by payer CHAMPUS; and
Claim element SoA on UB-92 FL-20 is required by payer Commercial.
Once the edit has bee expanded, the Payer-requires-claim-element-rule can determine that a required claim element should be added for the specified payers.
A standard list of abbreviations is used to provide for expansion of abbreviated information that has been instantiated in a variable. For example, the Payer-requires-claim-element-rule would instantiate the variable (? Title) to SoA. The abbreviation SoA would expand into the claim element title Source of Admission. In a similar manner, the variable (? Payer) would be instantiated to BC which expands to Blue Cross.
At step 907, a translated-edit quality check is performed. The quality check of step 907 includes a check to determine that the variable bindings have been correctly bound and expanded. As described above, embodiments of the invention permit edits to be compared to one another via the contents of their edit strings and determining their level of similarity. In order to verify that the variable bindings have been appropriately bound and expanded the variable bindings, pattern matching may be employed.
The expanded translations are first iterated through using pattern matching with the corresponding translated edit prior to expansion. If the pattern matcher fails on every instance of the expanded translated edit, it has been verified that at least one modification has been made to each of the translated edits relative to the corresponding unexpanded translated edit.
Next, the similarity of the expanded translated edits is compared to the variable bindings of the original structure of the unexpanded translated edit so that it can be determined whether a predetermined level of structural similarity exists between the expanded translated edits and the unexpanded translated edit. If both of the sub-steps of step 907 yield sufficient results, the translation and expansion of the edits are deemed to have been correctly performed.
At step 908, instances of claim elements (e.g., claim element titled "Birth date") that include the information bound to the matching conclusions are created. For example, one conclusion for a Birth date claim element may be that only numeric characters are valid values. Another conclusion may be that only a specific format of the birth date may be allowed (e.g., two-digit month/two-digit day/four-digit year). Conclusions may also be the element number (e.g., the place on the form (UB-92 or HCFA 1500) that the claim element may be found), a description of the claim element, or whether the claim element may be multi-valued. All of these conclusions may be bound to the claim element as attributes to create instances of the claim elements. At step 910, the knowledge base is populated with the information contained in the claim element instances. The knowledge-base-population rules are used to identify where the knowledge from each of the translated edits needs to be placed in the knowledge base. The called methods add information from the translated edit into the knowledge base in the appropriate place.
One of the advantages of embodiments of the present invention is that it is not necessary to rewrite the knowledge-base-population rules when edit changes occur. Principles of the present invention permit the knowledge base to be readily updated in the event that changes are made with respect to how a given claim form must be filled out, wherein an update is considered to be a type of knowledge-base population. The translation grammar is generalized so that a translated edit may be created for a given change and the grammar may be used to identify where the translation needs to be placed in the knowledge base. Furthermore, the knowledge may be form neutral, allowing updates and edits to map over to each claim form, thereby decreasing the number of necessary edits and allowing the information from one form layout (i.e., UB-92) to be readily transformed to another form layout (i.e., HCFA 1500).
The previous description is of a preferred embodiment for implementing the invention, and the scope of the invention should not necessarily be limited by this description. The scope of the present invention is instead defined by the following claims.
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|U.S. Classification
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|International Classification
|Cooperative Classification
|European Classification
|21 Oct 2003
Owner name: PEROT SYSTEMS CORPORATION, TEXAS
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:CHAPPEL, OSCAR A.;REEL/FRAME:014625/0020
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|2 Jan 2014
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text: DETROIT - The Michigan Tech women's basketball team had its 11-game winning streak come to an end on Thursday as No. 21-ranked Wayne State handed the Huskies a 74-63 loss at the Matthaei Center. Tech made only 3-for-19 from 3-point territory and went scoreless for a six-minute stretch in the second half, falling to 12-3 overall and 10-1 in the GLIAC.

subset: commoncrawl