text:
Video Industry News Video Company News Technology Industry News Technology Company News Novelty Industry News Novelty Company News Gay Industry News Gay Company News Legal Industry News All Company News
Released: Jul 01st, 2005
Running Time: 126
Director: Tommy Gunn
Company: Erotic City Entertainment
Distribution Companies: Metro Distributors
Cast: Candy Lee, Penny Flame, Charmane Star, Jezebelle Bond, Roxanne Hall, Trina Michaels, Destiny Deville, Austin Kincaid, Desise X, Shy Love, Lexi Marie, Brook Haven
Critical Rating: AA 1/2
Genre: All-Sex
Below average all-girl title.
With so many hot chicks, who needs a guy? Apparently not these girls, who are perhaps as voluminous as they are voluptuous. In fact, they are loud and over-reactive to the point of distraction, where one asks themselves, "Are they really that into it, or are they just being porn stars?" It doesn't take long to realize that it's the latter.
Almost as distracting are the edits and shaky videography, which, combined with the aforementioned, make this a long watch, despite a collection of very hot girls.
The DVD's behind the scenes is extensive.


text:
Family searching for missing boater, Manatee officials say
MANATEE COUNTY, Fla. - Manatee officials are asking for information leading to the whereabouts of a boater who went missing some time Sunday afternoon.
Jill Mullins arrived at her Bradenton home on 43rd Ave E when she noticed her husband Patrick Mullins was not home. His boat, a 16-foot Stump Knocker was also missing.
She contacted Patrick's brother and he went out in a boat to look for the man, and was unable to find him.
The boat was found near Egmont Key on Monday afternoon, but Patrick Mullins was not in the boat.
Anyone with information is asked to contact the Manatee County Sheriff's Office at (941) 747-3011 ext. 2260 or 2274.

subset: commoncrawl


text:
Head finished. Now onto the remaining 8 body parts.
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Weather Alerts
Run/walk at City Park to honor victims in Boston Marathon bombingsUPDATED 6:09 PM CDT Apr 22, 2013
On Monday afternoon, much of the nation halted for a moment of silence to honor the victims in last Monday's bombings at the Boston Marathon.
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text:
Video Industry News Video Company News Technology Industry News Technology Company News Novelty Industry News Novelty Company News Gay Industry News Gay Company News Legal Industry News All Company News
Released: Jun 01st, 2004
Running Time: 118
Director: Shawn Ricks
Company: Lord Perious Productions
Cast: Fabio (I), Wankus, Rod Lightning, Tony Montana, Britney Foster, Lee Stone, John West, Natasha Dolling, Fallon Sommers, Layla Jade, Amber Rain (I), Pat Myne
Critical Rating: AAA 1/2
Genre: Feature
Nice change of pace: a game show comedy with entertainment blended with sex. Recommended. Funny stuff.
Where else can you see Natasha Dolling doing the chicken dance? Or Frank Bukkwyd gaily imitating a game show host? Or Britney Foster vainly attempting to get a toe hold on Bukkwyd's penis? Or Layla Jade slowly reading a script one vowel at a time? Or see how many blows it takes each girl to drive a nail through a board?
Under the guise of a sexploitation talent contest with perverted judges, the five ladies read suggestive scripts in a purposefully humorous manner, show off their talents and stage presence, and are shown, sexually, in various categories. They all get an "A" in comedy and high marks for individual sexual positions. Only one complaint: needs more sex.


text:
Released: Sep 01st, 2000
Running Time: 76
Company: Heatwave Entertainment
Cast: Pet (I), Monica (I), Steve (II), Anett, Chris (II), Randy (II), Eddy, Kid, George (II), Gaby (I), Peti, Kimberly (I)
Critical Rating: AAA 1/2
Genre: Gay and Bi
Stock with your better bisexual tapes.
Four scenes of heated non-stop girl/boy/boy action featuring some very attractive and uninhibited performers. There's no real plot and not a real need for one. The first and second scenes occur simultaneously, and are both equally hot. However, the first scene stands out, as one of the men receives an amazing d.p. from another man and a pretty chick with a strap-on.


text:
Buzza Wuzza by Buzza Wuzza
- May 11, 2013
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Comments (11) (Please sign in to comment)
He's kind of a son of a beach?
Here is something I posted on the Doozies today:
When I was a young hippie in Berkeley Ca, I sometimes hung out at Caffe Mediterraneum on Telegraph Ave. Some old Beatniks, they still had them then, pointed out the guy behind the counter, "He invented the cappuccino," they said sagely.
I just recall seeing a swarthy young man who was famous for mixing milk with coffee, who knew?
Wiki says cappuccino was invented in 1683, but perhaps the Mediterraneum was the first to introduce it to the San Francisco Bay Area. Suffice it to say, I quit drinking cappuccinos in 1968 and always drink my coffee black, which seems to annoy the heck out of Starbucks when I try to order it.
So … A brick wall is the center of the universe? I like it.
@Plods with Beer ( did I mention beer? )
Uh, yeah!
Would you believe me if I told you I've never had a cup of coffee in my life? I like your story though.
It's the center of Rigg's universe for certain.
I agree.
Dood…. you have to rent Spaceballs (the movie) if you didn't get the Dark Helmet reference yesterday. One of Mel Brooks classics. It's a total spoof of Star Wars as only he could do it.
That's one of the few of his I haven't seen. I will, thank you.
I believe you, but I don't know why you would do that. I don't function till I've had some coffee in the morning.
That's why I never started. I don't want to have to have something like coffee or cigarettes to help me function. I have enough problems without them.
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Hello, I'm having issues getting this plugin to work properly. All I can get to display is the Search Box, but no users whatsoever. (https://pbs.twimg.com/media/BLnTj5uCQAAxldr.png)
Here is the setup:
WordPress 3.5.1
Active Directory Authentication Integration (dev 0.6)
(Originally used AD Integration by Christoph Steindorff.
Active Directory Employee Listing (dev 0.3)
It could be just that I don't have it setup correctly, maybe a miss-configuration?
I do have AD Auth. Int. working properly.
Below is a screencap of settings page, maybe I'm missing something?


text:
Video Industry News Video Company News Technology Industry News Technology Company News Novelty Industry News Novelty Company News Gay Industry News Gay Company News Legal Industry News All Company News
Released: Jan 01st, 2000
Running Time: 98
Director: Michael Stone
Company: Amazing
Distribution Companies: Metro Distributors
Cast: Charmaine, Vylet Luv, Bridgett Kerkove, Son Doobie, Summer Cummings, Skye Blue, Mark Davis, Leanni Lei, Jake Steed, Claudia Chase
Critical Rating: AAA 1/2
Genre: All-Sex
Emphasize the caliber of the sex.
Son Dobbie is a hip-hop sort who runs a radio call-in show. This is the centerpiece around which the generally hot sex scenes contained herein are marshaled, and it's a rather irritating one, but who cares. The sex, as suggested, is on target for the most part, and even the focus on the incredibly unappealing Son Doobie can thus be forgiven.
Leanni Lei and Charmaine star in the very hottest of the scenarios in a chemistry-driven threesome with Mark Davis. Each of the ladies lavish Davis' well-traveled member to incendiary effect, and the plain ol' in-and-out is also quite hot.


text:
|Publication number
|Publication type
|Application number
|Publication date
|Jun 10, 2008
|Filing date
|Oct 21, 2003
|Priority date
|May 16, 2001
|Fee status
|Also published as
|Publication number
|Oscar A. Chappel
|Original Assignee
|Perot Systems Corporation
|Export Citation
|BiBTeX, EndNote, RefMan
|Patent Citations (125), Non-Patent Citations (105), Referenced by (14), Classifications (15), Legal Events (5)
|External Links: USPTO, USPTO Assignment, Espacenet
This patent application is a continuation-in-part of, and incorporates by reference the entire disclosure of, 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 incorporates by reference the entire disclosure 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 also incorporates by reference the entire disclosure of U.S. patent application Ser. No. 10/690,258, which was filed on Oct. 21, 2003, entitled "Method of and System for Populating Knowledge Bases using Rules-Based Systems and Object-Oriented Software."
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 a knowledge base used for medical claims processing.
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 professional service providers (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 treatment procedures and services are covered by the insurance policies of 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), HCFA 1500, which is utilized by professional providers (e.g., physicians), and institutional and professional 837, the newest standard mandated by HIPAA. The claim forms traditionally were in the form of paper. However, the claim forms have evolved with technology and many are now prepared on a computer in an electronic format. While most providers utilize computers to prepare the electronic claim forms, small and rural providers continue to utilize paper claim forms.
Whether the provider utilizes paper or electronic claim forms, the various 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, the AMA, CMS, 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 different code sets containing nearly 60,000 unique codes 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 (CPT) 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 to report outpatient procedures, 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 reporting problems result in a medical procedure or service not always being easily classified with a particular code or properly reported. A provider may perform a procedure and write or dictate a treatment analysis to be submitted for insurance reimbursement. One 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 the procedure or treatment for reimbursement.
Entry of the UB-92 claim form may be a challenging task due to the complexity of information necessary and to the complexity of the medical codes and insurance information that 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 because 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, delaying payments and increasing healthcare administrative costs.
As well understood in the art, there are large numbers of providers and payers. While there are an estimated 3000(+) 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 facility 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.
At step 206, the patient is discharged by the provider 102. The provider 102 may thereupon update a patient chart for the patient with treatment and diagnosis information as understood in the art. The treatment and diagnosis information may be transposed onto the claim form 103 by determining the appropriate codes (e.g., ICD-9 codes) to enter into the correct field(s) (e.g., field 67) of the claim form 103 at step 208. Once the claim form 103 is completed and ready for submission to a payer 104, a "bill drop" or communication of the claim form 103 may be made from the provider 102 electronically or via mail at step 210. In general, the bill drop at step 210 is performed in a batch process to the clearinghouse 106 due to the fact that computer systems of the providers 102 and payers 104 do not have direct communication because the computer systems and software do not share compatible architectures.
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 communicated to and 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 specific payer systems without regard to the capabilities of the providers' systems (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 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 claim filing 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 to reject claims that do 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 filtering a translated edit via at least one rule to determine a match between a syntax of the translated edit and a syntax of the rule, executing a method call responsive to the filtering step resulting in at least one match being determined, and populating the knowledge base responsive to the executing 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 contained 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 filter a translated edit via at least one rule to determine a match between the translated edit and the rule, execute a method call responsive to the translated edit having been filtered and a determination of at least one match, and populate the knowledge base responsive to the execution of the method call.
A system for populating a knowledge base includes means for filtering a translated edit via at least one rule to determine a match between a syntax of the translated edit and a syntax of the rule, means for executing a method call responsive to the filtering having resulting in at least one match having been determined, and means for populating the knowledge base responsive to an output of the means for executing.
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 60 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,
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 is 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"
EDIT #<db-instance MEDICARE-EDIT 576961092> FOR "SLU"
EDIT #<db-instance MEDICARE-EDIT 576968652> FOR "SLU"
EDIT #<db-instance MEDICARE-EDIT 576996484> FOR "SLU"
EDIT #<db-instance MEDICARE-EDIT 576968652> FOR "SLU"
EDIT #<db-instance MEDICARE-EDIT 576972788> FOR "SLU"
EDIT #<db-instance MEDICARE-EDIT 576968652> FOR "SLU"
EDIT #<db-instance MEDICARE-EDIT 576981212> FOR "SLU"
EDIT #<db-instance MEDICARE-EDIT 576968652> FOR "SLU"
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
(EDIT #<db-instance MEDICARE-EDIT 576968652> FOR "SLU"
HAS HIGH SIMILARITY TO EDIT #<db-instance MEDICARE-EDIT
(EDIT #<db-instance MEDICARE-EDIT 576968652> FOR "SLU"
HAS HIGH SIMILARITY TO EDIT #<db-instance MEDICARE-EDIT
(EDIT #<db-instance MEDICARE-EDIT 576968652> FOR "SLU"
HAS HIGH SIMILARITY TO EDIT #<db-instance MEDICARE-EDIT
(EDIT #<db-instance MEDICARE-EDIT 576968652> FOR "SLU"
HAS HIGH SIMILARITY TO EDIT #<db-instance MEDICARE-EDIT
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
Rule EDIT-ACTION-RULE indicates (CONSOLIDATE #<db-instance
MEDICARE-EDIT 576925060> #<db-instance MEDICARE-EDIT
Rule EDIT-ACTION-RULE indicates (CONSOLIDATE #<db-instance
MEDICARE-EDIT 576925060> #<db-instance MEDICARE-EDIT
Rule EDIT-ACTION-RULE indicates (CONSOLIDATE #<db-instance
MEDICARE-EDIT 576925060> #<db-instance MEDICARE-EDIT
Rule EDIT-ACTION-RULE indicates (CONSOLIDATE #<db-instance
MEDICARE-EDIT 576925060> #<db-instance MEDICARE-EDIT
Rule EDIT-ACTION-RULE indicates (CONSOLIDATE #<db-instance
MEDICARE-EDIT 576925060> #<db-instance MEDICARE-EDIT
Rule EDIT-ACTION-RULE indicates (CONSOLIDATE #<db-instance
MEDICARE-EDIT 576925060> #<db-instance MEDICARE-EDIT
Rule EDIT-ACTION-RULE indicates (CONSOLIDATE #<db-instance
MEDICARE-EDIT 576925060> #<db-instance MEDICARE-EDIT
Rule EDIT-ACTION-RULE indicates (CONSOLIDATE #<db-instance
MEDICARE-EDIT 576925060> #<db-instance MEDICARE-EDIT
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
(35 Is 50 VALUE |42X| 11 |44X| |22X| FOR MEDICARE INPATIENT
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.
The translation grammar includes a set of patterns that may be used to express relationships that are described in the edits and also specifies circumstances under which a particular claim element needs to have a certain value (i.e., code element) when some other value (i.e., code element) is reported on a claim. For example, a particular edit might state that if a particular revenue code is reported on a claim of a particular claim type and a particular occurrence code is not reported on the claim, the claim should be rejected. The translation grammar permits the relationship between the revenue code and the occurrence code stated in the edit to be explicitly and positively stated. The relationship for the above-described example would be that the revenue code requires the occurrence code on the particular claim type. The translation grammar would therefore result in a translated edit that reflects this requirement. 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.
Following the edit translation of step 802, a file is typically produced that contains translated edits and their corresponding untranslated edits. Below is an exemplary excerpt of a translated edit file:
In the above excerpt, the payer is designated to be California Blue Cross, as evidenced by the designation CA BC. An edit VALUE CD 1 REQUIRED TO BILL PVT ROOM, which means that if a private room is to be billed to payer California Blue Cross,
Value Code is a composite code used in the healthcare industry to indicate a service and the cost of that service. In this example Value Code 01 indicates the "Most Common Semiprivate Room Rate, the second element for the value code may be a numeric value which in this case would be the daily rate in US Dollars for a semiprivate room, is translated to a plurality of translated edits, of which RC VALUE 110 REQUIRES VC VALUE 1 TOB IP is one. RC VALUE 110 REQUIRES VC VALUE 1 TOB IP means Revenue Code (RC) 0110 requires Value Code (VC) 01 on claims whose Type of Bill (TOB) indicates inpatient (IP). Although the edit above has been translated to yield a plurality of translated edits, in some cases, a one-to-one correspondence between a given edit and its translation will result.
Once the translated edits have been generated, the translated edits may be used in conjunction with knowledge-base-population rules in order to populate the knowledge base. At step 804 the translated edits are used to populate the knowledge base. At step 806, knowledge in the populated knowledge base is used to validate claims.
Each knowledge-base-population rule is given a unique name. Below is an example of a knowledge-base-population rule referred to as code-requires-other-rule-1:
Returning to
A particular knowledge-base-population rule will only match assertions for a translated edit that has the exact same pattern as the knowledge-base-population rule. In the exemplary knowledge-base-population rule code-requires-other-rule-1, only if a translated edit matches the pattern (? edit) (? name) value (? value) requires (? name-2) value (? value-2) on tob (? tob) will the variables in the conclusion of the rule be bound to the values of those variables found in the translated edit. In the example above, the match between the translated edit and the knowledge-base population rule results. Next, a method (i.e., add-required-code) is called. The method add-required-code is specialized for edit objects whose translations match the pattern of the knowledge-base-population rule code-requires-other-rule-1 and uses name-value pairs and other information instantiated by the add-required-code method. The method add-required-code performs the following functions: First, the method creates instances of the appropriate claim code objects. In the example above, the edit object creates a claim code object named Revenue Code, with a value of 0110. Next the edit object translates the bill type to ensure that all appropriate types of bill are included in the edit (e.g., type of bill IP translates to Inpatient and ultimately to type of bill 11 and 21). The edit object sends a message to the initial claim code object to add a relationship. In the current example, the relationship may be "requires VC 1 on tob 11." The claim code object creates the appropriate relationship with the Value Code object whose value is 1. Finally, the edit object directs the claim code object to insert the newly created relationships into the knowledge base.
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 inference engine that is processing the rules. 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
For example, a single edit translation has the potential to expand into multiple statements of fact that are inserted into the knowledge base. Continuing with the earlier example, the edit "RC 110 requires VC 1 on tob IP" is applicable to inpatient claims. Inpatient claims are represented by Type of Bill codes 11 and 21. In order to ensure that all appropriate types of bill are included in the knowledge base, the abbreviation "IP" is expanded to address all inpatient bill types, i.e. 11 and 21. Thus the single conclusion "add-required-code <edit> rc 110 vc 1 ip" (the result of having bound all the appropriate variables in the rule) is expanded to two calls to the "add-required-code" method as follows:
Add-required-code <edit> rc 110 vc 11; and
Add-required-code <edit> rc 110 vc 21.
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. From the above example, expansion of the edit "RC value 110 requires VC value 1 on tob IP" results in the following two expanded edits:
RC value 110 requires VC value 1 on tob 11; and
RC value 110 requires VC value 1 on tob 21.
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. Attempting to match the pattern of the original edit translation "RC value 110 requires VC value 1 on tob IP" with either of the expanded edits fails on each of the expansions because IP does not match 11 or 21. This failure indicates that the original edit has in fact been modified.
The next step is to determine how closely the expanded edits conform to the original edit. 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. This is done by creating token lists of the original edit and the expansions. In this example, the token list of the original edit is "RC value 110 requires VC value 1 on tob IP". The token lists created from the expansions are "RC value 110 requires VC value 1 on tob 11" and "RC value 110 requires VC value 1 on tob 21." Taking the intersection of the token list of the original edit and one of the expansions yields a list of tokens, e.g., "RC value 110 requires VC value 1 on tob". This intersection contains nine tokens. There were an average of ten tokens in the original lists used to create the intersection list. Dividing the length of the intersection by the average length of the lists used to create the intersection yields a 0.9 similarity. This analysis indicates that the process has changed the initial edit and that the result of that change is 90% similar to the original edit. Thus we conclude that the edit expansion has been successful and that we can insert the expansions into the knowledge base.
At step 908, instances of objects (e.g., add-required-code) that include the information bound to the matching conclusions are created. As described earlier, an edit object creates an instance of a claim object and instructs the claim object instance to create the relationship indicated by the elements of the pattern that are included in the rule conclusion. For example, the edit object creates a claim code object with the name "revenue code" and the value "0110." The revenue code is instructed to add the relationship "requires VC 1 on tob 11." The claim code object then creates an instance of the appropriate relationship and requires a relationship with the claim code whose name is "value code" and whose value is "1" on Type of Bill 11. This information is then stored in the knowledge base where it can be used by downstream claim validation systems.
At step 910, the knowledge base is populated with the information contained in the created object 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. For example, when the code-requires-other-rule-1 knowledge-base-population rule matches a translated edit, the add-required-code method is called and the bound variables following the add-allowed-code method are added, as appropriate, to an allowed codes table in the knowledge base.
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.
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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|International Classification
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|European Classification
|Oct 21, 2003
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text:
"County town of Dorset" Dorchester by Britannia2
Dorchester Travel Guide: 20 reviews and 39 photos
A small town in the south of the county is home to the counties main hospital and council headquarters - Dorchester. A good town to visit for not only shopping but also several interesting attractions including a castle keep.
The main shopping area is pedestrianised and although busy easy to negotiate. As with most Dorset towns the traffic is horrendous and for some reason the park and ride service only operates on Wednesday.
As other pages by other members show this is Casterbridge - a home to the great English writer Thomas Hardy.
The buildings in Dorchester are generally pleasing - most of the town seems quite old but there are some newer gems such as the post office.
- Pros:Pleasant English market town with good shopping
- Cons:Heavy traffic spoils the town despite a by pass
- In a nutshell:A must see town in Dorset
Reviews (7)
If you are in Dorchster and want to explore north of Dorchester there is rover ticket called a Heart of Wessex Day... more travel advice
This museum covers the whole of Dorset with county related interests. The website has details. more travel advice
The main chains all have shops here - Waitrose, WHSmith, Boots, Woolworths, Marks and Spencer, The Works, Thorntons etc.... more travel advice
There are a surprising number of arcades in this town. The whole of the shopping area is predestrianised but the arcades... more travel advice
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text:
Hawken dev diary shows off 'Front Line' map
Hawken developer Adhesive Games has published a new dev diary clip, this time showing off a map called Front Line, and discussing how the locale plays out during matches.
What do you make of the map and Hawken so far? Are you excited for the game? Let us know below.
Cheers PC Gamer.
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text:
Beef Nacho Casserole
Total Time
Prep 15 mins
Cook 20 mins
A simple meal to whip up in less than an hour that even the kids are sure to love. Layers of seasoned ground beef, corn, tortilla chips and cheese are baked under a layer of Colby cheese. Monterey Jack is also good in place of the Colby cheese.
Ingredients Nutrition
- 1 lb ground beef
- 1 1⁄2 cups chunky salsa
- 1 (10 ounce) can whole kernel corn, drained
- 3⁄4 cup creamy salad dressing (e.g. Miracle Whip)
- 1 teaspoon chili powder
- 2 cups crushed tortilla chips
- 2 cups colby cheese
- Preheat the oven to 350 degrees F.
- Place ground beef in a large skillet over medium-high heat. Cook, stirring to crumble, until evenly browned.
- Drain grease.
- Remove from the heat, and stir the salsa, corn, mayonnaise and chili powder into the beef.
- In a 2 quart casserole dish, layer the ground beef mixture, tortilla chips and cheese twice, ending with cheese on top.
- Bake for 20 minutes uncovered in.
Most Helpful
We enjoyed this easy but tasty casserole. Made as written. Thanks Cucina. Bullwinkle
I love that this uses items that I pretty much always have in the house. I added onion to the ground beef when I cooked it. I used mayonnaise and added a bit of sugar and vinegar to get that Miracle Whip tanginess. I also added some cumin for extra Mexican flavor. I recommend adding the cheese on top halway through baking bacause my cheese got very dried out. Thanx for posting this!