1. Using Text Mining and Natural Language Processing for Health Care Claims Processing Cihan ÜNAL 135478

2. Why to use NLP in Text Mining? Text mining is concerned with the detection of patterns in Natural Language Texts. However, Data Mining is concerned with the detection of patterns in databases. The entities and relationships that act as indicators of recoverable claims are mined from; Management notes, Call centre logs, Patient records. 2

3. Information Processing Application IP application benefits from having access to both Structured information (as found in databases) Unstructured information (traditionally found in document) Linguistic analysis of the text is done by IP application. Document categorization, Textual information domination, Text reference entities and relationships. 3

4. Natural Language Processing NLP deals with the automatic processing and analysis of unstructured textual information. relies on statistical techniques, rule-based techniques. 4

5. Natural Language Processing(cont.) Statistical human language processing systems require collections of training material. Such as relationships and dependencies. Rule based techniques require knowledge. Such as online dictionaries, linguistic theories or any classification system. 5

6. Content Intelligence System CSI is able to leverage existing knowledge sources and provide the capability for users to customize the knowledge base with concepts. Statistical tagger, Rule based partial parser, External resources such as Wordnet. 6

7. Concept Specification Language CSL is used to specify rich linguistic patterns that incorporate as fundemental the notion of recursion of patterns and various linguistic predicates. CSL and concept matching are embodied in the CIS to analyse the structure of words, phrases, sentences. 7

8. Concept Specification Language (cont.) The stages of analysis; Abbreviation expansion, Spelling correction, Tagging, Partial parsing. 8 Then, the specific information is extracted. Then, the specific information is extracted.

9. Concept Specification Language (cont.) CSL allows the definition of key concepts the specification of the interrelationship among concepts in the form of multiple operators OR, NOT, Precedes, Immediately Precedes, Is Related or Causes. the advance categories for concepts. Concept can be a word, general/specific term or have synonyms. 9

10. Concept Specification Language (cont.) concept AccidentsAndTrauma ( %Trauma | %AccidentalFall | %Accident-Sports | %Accident-Involving-Children | %Accident-Auto ) Figure 1. A High Level Concept. 10

11. Concept Specification Language (cont.) AccidentsAndTrauma Trauma AccidentalFall Accident-Sports...... Fall-From-Different-Level... Figure 2. A Taxonomy. 11

12. Concept Specification Language (cont.) concept FallFromDifferentLevel( Related( %SlippedOrFell, (off | from | to | feet)) | (%SlippedOrFell & down & /NOUN ) Figure 3. A Low Level Concept. 12

13. Indicators In Documents There might be a large amount of information that is relevant to the claim. Case: A patient is treated in the emergency room for a broken arm which requires a initial examination, an x-ray and the application of a cast. Associated with each of these, there will be charges, textual comments and so on. 13

14. Indicators In Documents(cont.) Types of indicators are listed as Commercial Coordination of Benefits Medicare Coordination of Benefits No-fault Recovery Subrogation Recovery Workers Compensation 14

15. Indicators In Documents(cont.) 15 Figure 4. Indicators Found in Customer Service Notes

16. Indicators In Documents(cont.) Precision is calculated on a random selection of 100 matches. The average precision for the Commercial and Medical Coordination of Benefits is 99%. Multiple Plan Child Coverage is 84%. 16

17. Indicators In Documents(cont.) Recall is determined through a test procedure where a human evaluates the documents that are matched with the indicators. The average recall for the Coordination of Benefits is 85%. Multiple Plan Child Coverage is 81%. 17

18. Indicators In Documents(cont.) Then, these indicators are used to determine which claims require further human investigation. Also, some claims can be combined together with additional information to form an actual case. 18

19. Creating Concepts The text-based concept creation algorithm contains eight steps: Input of text fragments Fragments split into words Selection of relevant words Optional operations on relevant words Concept matching Removal of Concept matches Building of Concept chains Chains are written as CSL Consept 19

20. Creating Concepts(cont.) The Rule Base contains domain independent concept definitions, along with rules that transform general concepts taht matched the text fragments into concepts of the resulting concept. As an example of a rule: Subj_Passive_Verb_Obj Subj_Verb_Obj 20

21. Creating Concepts(cont.) 21 Figure 5. CSL from Text

22. Evaluating Indicators Well established human procedures Human knowledge can be encoded. Use it as a starting point for scoring and ranking. Structured information (dollar value, diagnosis code) Unstructured information (diagnoses and treatments extracted from call logs) 22

23. Evaluating Indicators(cont.) Conflicts between structured and unstructured: Structured data field contains “not a work related injury”. A call log(unstructured data) is a “work place injury”. The claim requires human investigation. 23

24. Evaluating Indicators(cont.) By generalizing over the different types of accidents, a case can be created. 24

25. Evaluating Indicators(cont.) 25 Figure 6. Indicators in Scoring

26. Evaluating Indicators(cont.) 26 Figure 7. Scored Claims

27. References [1] Fred Popowich, School of Computing Science, Simon Fraser University, SIGKDD Explorations, pp. 59-66, Vol.7, Issue 1, Using Text Mining and Natural Language Processing for Health Care Claims Processing [2] Richard Wolniewicz, 3M Health Information Systems, www.3Mhis.com, Auto- coding and Natural Language Processing 27