Abstract:Most of the data mining projects generate information (summarized in the form of graphs and charts) for business executives and decision makers; however it leaves to the choice of decision makers either to use it or disregard it. The manual use of the extracted knowledge limits the effectiveness of data mining technology considerably. This chapter proposes an architecture, in which data mining module is utilized to provide continuous supply of knowledge to a rule based expert system. Proposed approach solves t… Show more
“…Daily claim rejection graph of December 2014, that is, after implementation of the rule‐based system (Source: Abdullah, )…”
Section: Resultsmentioning
confidence: 99%
“…The architecture of the RBES as published by Abdullah () is shown in Figure . The dotted box, containing four components, namely, Rule‐Based Engine, Rule Learning Module (comprising of a warehouse and production rule‐mining module), KB, and Knowledge Editor, constitutes the RBES, which is being evaluated in this research paper.…”
Section: The Medical Billing‐related Es: a Brief Outlinementioning
confidence: 99%
“…Processing time of correct claim comprise of 2 to 3 days of billing time plus 5 to 12 days (approx.) of insurance processing time (Source: Abdullah, )…”
Section: Evaluation Of the Rbes For Medical Billingmentioning
confidence: 99%
“…), which results in rejection, correction time of 2 to 3 days, and insurance processing time of 5 to 12 days (approx.) again (Source: Abdullah, )…”
Section: Evaluation Of the Rbes For Medical Billingmentioning
confidence: 99%
“…Claim processing time (days) of faulty claim with rule‐based system (RBS) does not include 5 to 12 days of insurance first processing time as error is identified, and claim is blocked by the RBS on same day (Source: Abdullah, )…”
Section: Evaluation Of the Rbes For Medical Billingmentioning
Most of the research in the area of performance evaluation of rule‐based expert systems (RBESs) is focused on verification and validation issues. Many researchers discuss usability, usefulness, portability, and response time for the evolution of RBES. The final goal of all such studies is to construct a system with optimal, accurate knowledge base. Arguably, a system with best knowledge base is actually worthless if it is never utilized in the real world. We have proposed “benefit” as a measure of evaluation and suggested some guidelines for performance evaluation of RBESs. The proposed measure has been demonstrated for performance evaluation of an RBES applied in the medical billing domain. Results showed that the system has saved hundreds of working hours during the evaluation period of 3 months. Moreover, other associated measures have also been considered. Associated measures in the medical billing domain are “claim rejection rate”—reduced by 54%—and “claim aging,” which has decreased from 34 to 28 days due to the RBES. Guidelines proposed by this research can be applied for the evaluation of expert systems implemented in other application domains, including in the first place business decision support systems.
“…Daily claim rejection graph of December 2014, that is, after implementation of the rule‐based system (Source: Abdullah, )…”
Section: Resultsmentioning
confidence: 99%
“…The architecture of the RBES as published by Abdullah () is shown in Figure . The dotted box, containing four components, namely, Rule‐Based Engine, Rule Learning Module (comprising of a warehouse and production rule‐mining module), KB, and Knowledge Editor, constitutes the RBES, which is being evaluated in this research paper.…”
Section: The Medical Billing‐related Es: a Brief Outlinementioning
confidence: 99%
“…Processing time of correct claim comprise of 2 to 3 days of billing time plus 5 to 12 days (approx.) of insurance processing time (Source: Abdullah, )…”
Section: Evaluation Of the Rbes For Medical Billingmentioning
confidence: 99%
“…), which results in rejection, correction time of 2 to 3 days, and insurance processing time of 5 to 12 days (approx.) again (Source: Abdullah, )…”
Section: Evaluation Of the Rbes For Medical Billingmentioning
confidence: 99%
“…Claim processing time (days) of faulty claim with rule‐based system (RBS) does not include 5 to 12 days of insurance first processing time as error is identified, and claim is blocked by the RBS on same day (Source: Abdullah, )…”
Section: Evaluation Of the Rbes For Medical Billingmentioning
Most of the research in the area of performance evaluation of rule‐based expert systems (RBESs) is focused on verification and validation issues. Many researchers discuss usability, usefulness, portability, and response time for the evolution of RBES. The final goal of all such studies is to construct a system with optimal, accurate knowledge base. Arguably, a system with best knowledge base is actually worthless if it is never utilized in the real world. We have proposed “benefit” as a measure of evaluation and suggested some guidelines for performance evaluation of RBESs. The proposed measure has been demonstrated for performance evaluation of an RBES applied in the medical billing domain. Results showed that the system has saved hundreds of working hours during the evaluation period of 3 months. Moreover, other associated measures have also been considered. Associated measures in the medical billing domain are “claim rejection rate”—reduced by 54%—and “claim aging,” which has decreased from 34 to 28 days due to the RBES. Guidelines proposed by this research can be applied for the evaluation of expert systems implemented in other application domains, including in the first place business decision support systems.
Automated medical claims processing and billing is a popular application domain of information technology. Managing medical related data is a tedious job for healthcare professionals, which distracts them from their main job of healthcare. The technology used in data management has a sound impact on the quality of healthcare data. Most of Information Technology (IT) organizations use conventional software development technology for the implementation of healthcare systems. The objective of this experimental study is to devise a mechanism for use of rule-based expert systems in medical related edits and compare it with the conventional software development technology. A sample of 100 medical edits is selected as a dataset to be tested for implementation using both technologies. Besides empirical analysis, paired t-test is also used to validate the statistical significance of the difference between the two techniques. The conventional software development technology took 254.5 working hours, while rule-based technology took 81 hours to process these edits. Rule-based technology outperformed the conventional systems by increasing the confidence value to 95% and reliability measure to 0.462 (which is < 0.5) which is three times more efficient than conventional software development technology.
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