Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology 1 Using Text Mining and Natural Language Processing for Health Care Claims Processing Advisor : Dr. Hsu Presenter : Wen-Hsiang Hu Authors : Vancouver; Burnaby SIGKDD Explorations, 2005, Pages:59 -66

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 2 Outline Motivation Objective Introduction Content Intelligence System Concept Specification Language Indicators in Documents Creating concepts Evaluating indicators Conclusions Future Research Personal Opinion

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 3 Automated Medical Claims Auditors Figure 1 illustrates how the output of a natural language processing system, which performs detailed linguistic analysis using domain specific information in the form of Concept Taxonomies, is then used by a mining system to produce output which is then subjected to human analysis.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 4 Motivation traditional techniques, statistical techniques, do not produce deeper analysis

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 5 Objective an application for processing health care claims to determine whether claims involve potential fraud or abuse to determine whether claims should be paid by or in conjunction with other insurers or organizations.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 6 Content Intelligence System The Axonwave Content Intelligence System (CIS) contains core natural language processing systems that perform both rule-based and statistic- based NLP. The CIS is able to leverage existing knowledge sources

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 7 Concept Specification Language CSL and concept matching are embodied in the CIS, which analyzes the structure of words, phrases and sentences The first stage of analysis consists of abbreviation expansion and spelling correction, which is then followed by tagging and then partial parsing.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 8 Concept Specification Language - Creating concepts How CSL can be created using Natural Language Processing (NLP) techniques. 1.Input of text fragments. 2.Fragments split into words. 3.Selection of relevant words. 4.Optional operations on relevant words. 5.Concept matching. 6.Removal of Concept matches. 7.Building of Concept chains 8.Chains written as CSL Concept.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 9 Concept Specification Language (cont.) Note that this concept contains individual words, (specifically “off”, “from”, “to” and “feet”), which will match text that is linguistically linked (in this case, “Related”) to a word or phrase that matches the SlippedOrFell concept. This concept will match phrases such as “fell 15 ft” or “fell down a flight of stairs” and will annotate the text with the tag FallFromDifferentLevel. Once the text is tagged, the tags like these can then be used as indicators for the subsequent mining phase.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 10 Concept Specification Language- Indicators in Documents They are indicators which suggest that a claim falls into one of the following categories. Commercial Coordination of Benefits Medicare Coordination of Benefits No-fault Recovery Subrogation Recovery Workers Compensation Based on the rules that are used by claims examiners, we were able to construct a taxonomy of indicators that play a role in determining likelihood of one of these categories.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 11 Concept Specification Language (cont.) CSL is used to specify rich linguistic patterns (Fig 3.) Each of the subconcepts will have its own definition, resulting in a rich hierarchical taxonomy of concepts (Fig 4.)

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 12 Indicators in Documents (cont.) many matches average precision is 99% ; average recall is 85%

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 13 Indicators in Documents (cont.) The next step of the process is to use these indicators to determine which claims require further human investigation

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 14 Evaluating indicators For each of the high level indicators, rules are defined with initial weights specified by human experts.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 15 Evaluating indicators (cont.) In Figure 9, where the score (1<= Scr <=100) is the value calculated from the different indicators. Consider the case where a patient has one claim for an injury resulting from different types of accidents all happening at malls. By generalizing over the different types of accidents, the data may call for further investigation

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 16 Conclusions We have seen techniques to semi-automatically create the Concept Specification Language used in knowledge models. These techniques are applicable to health care claims auditing systems

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 17 Future Research With this data, it is possible to automatically change the weights associated with the different indicators, or even introduce new indicators into the equation. When more indicator-enhanced claim data becomes available, it will be possible to apply additional data-mining techniques to detect previously unknown patterns.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 18 Advantage integrate text mining, natural language processing, and concept specification language Drawback Does not assign the weights to different indicators automatically Application … Personal Opinion