May, Jerrold

Spangler, William

Strum, David

Vargas, Luis

English

AIS Electronic Library (AISeL)

Association for Information SystemsAIS Electronic Library (AISeL)AMCIS 2001 Proceedings Americas Conference on Information Systems(AMCIS)December 2001Using Data Mining Techniques to CharacterizeDiagnostic and Procedural PatternsWilliam SpanglerDuquesne UniversityJerrold MayUniversity of PittsburghDavid StrumQueenLuis VargasUniversity of PittsburghFollow this and additional works at: http://aisel.aisnet.org/amcis2001This material is brought to you by the Americas Conference on Information Systems (AMCIS) at AIS Electronic Library (AISeL). It has been acceptedfor inclusion in AMCIS 2001 Proceedings by an authorized administrator of AIS Electronic Library (AISeL). For more information, please contactelibrary@aisnet.org.Recommended CitationSpangler, William; May, Jerrold; Strum, David; and Vargas, Luis, "Using Data Mining Techniques to Characterize Diagnostic andProcedural Patterns" (2001). AMCIS 2001 Proceedings. 73.http://aisel.aisnet.org/amcis2001/73376 2001  Seventh Americas Conference on Information SystemsUSING DATA MINING TECHNIQUES TO CHARACTERIZEDIAGNOSTIC AND PROCEDURAL PATTERNSWilliam E. SpanglerDuquesne Universityspangler@duq.eduJerrold H. MayUniversity of Pittsburghjerrymay@pitt.eduDavid P. StrumQueen’s Universitystrumd@post.queensu.caLuis G. VargasUniversity of Pittsburghvargas@katz.pitt.eduAbstractThis research explores the use of data mining techniques to identify diagnostic and procedure code usagepatterns in two American hospitals.  To determine code utilization, we borrow measures of firm concentrationfrom industrial economics.  We then use those measures (1) to ascertain the extent to which physicians utilizethe available codes in classifying patients, and (2) to discover the nature of the associations between diagnosticand procedure codes.  By characterizing associations between diagnostic and procedure codes in more generalterms as relationships between problems and solutions, we seek eventually to extend the use of these techniquesto other domains.Introduction In the medical domain, there are two generally accepted hierarchical numerically coded knowledge bases.  One is used fordiagnoses and the other for surgical and medical procedures.  This research explores the use of data mining techniques to identifydiagnostic and procedure code usage patterns in two American hospitals.  To determine code utilization, we borrow measures ofindustry concentration from industrial economics.  We then use those measures (1) to ascertain the extent to which physiciansutilize the available codes in classifying patients, and (2) to discover the nature of the associations between diagnostic andprocedure codes.  By characterizing associations between diagnostic and procedure codes in more general terms as relationshipsbetween the descriptions of problems and descriptions of the solutions to those problems, we seek eventually to extend the useof these techniques to other domains.This paper focuses on the domain-specific issues inherent in discovered code patterns, particularly  the incidence of diagnosticcodes, and the type and number of procedural codes associated with each.  Understanding the circumstances under whichdiagnostic and procedural codes are used can assist various types of decision makers in the medical domain.   For example, modelsof code usage can assist hospital managers in scheduling patients (by allowing predictions of medical procedures from initialdiagnoses), and can assist insurance and other types of auditors (by conversely allowing predictions of expected diagnoses fromobserved procedures). While specific code patterns are primarily of interest to the medical community, the notion ofdiagnostic/procedural code relationships potentially is applicable in a more general sense to any domain—and data set—that canbe described in terms of relationships between problems (diagnoses) and solutions (procedures). Impacts on Code Usage PatternsA number of factors potentially affect the relative incidence and types of medical codes used by physicians in hospitals, includingthe type of hospital (e.g., primary, secondary, tertiary care hospitals, teaching hospitals, and trauma  centers), the nature of theparticular specialty (such as surgery), rules and regulations prescribing certain types and standards of care, as well as the culturaland regulatory environment of various nations.  Stausberg et al, for example, studied diagnostic and procedural code usage in aSpangler et al./Characterizing Diagnostic and Procedural Patterns2001  Seventh Americas Conference on Information Systems 377∑=niip12German teaching hospital, and discovered that a small number of diagnostic and procedure codes tended to predominate acrosscases (i.e., a small number of the codes tended to account for a majority of the observed cases) (Stausberg, et al., 2001).  This ledStausberg et al to argue in particular for the replacement of the ICD coding scheme for the classification of morbidity.  Ourresearch seeks, in part, to determine whether the same usage patterns are evident in American hospitals, and regardless of theoutcome, to discover what other factors might be involved in code utilization.  The Stausberg et al findings might be generalizableacross nations and hospitals, but they also might be attributable to German cultural and regulatory practices, the type of hospitaland specialties practiced, and so on.Research QuestionsThe specific research questions under investigation in this research are as follows.• Which factors determine or impact code usage patterns?  As noted, potential factors include culture, hospital type, specialtytype, and regulatory environment. They also include potential interactions between and  among factors.  For example, anydifferences between our study and the Stausberg et al study (i.e., a cross-national difference) hypothetically could beexplained by the differences in hospital types, specialties practiced, individual physicians, and/or regulations.• How do those factors impact the various aspects of code usage patterns?  Code usage patterns include a) the types of codesused, b) the relative incidence or frequency of particular codes, and c) the specific relationships between diagnostic andprocedure codes (i.e., the types and relative frequencies of procedure codes associated with each diagnostic code)Research Method and Data SetThe empirical data were collected from two independent hospitals.  The first (‘PCH’) is a primary/secondary care hospital andtrauma center, while the second (‘TCH’) is a tertiary care and teaching hospital.  The data include 59864 separate cases (patientsurgeries), each containing 23 attributes detailing the diagnoses, procedures, patient demographic information, and informationabout the individual surgeon and anesthesiologist.  The relative incidence of diagnostic and procedure codes, and the relationshipsbetween them, is characterized by the frequency distribution patterns of procedures, diagnoses, and procedures within diagnosticcategories.  In order to compare the various distribution patterns across potentially hundreds of different code categories, we canquantify the patterns in a single number called a concentration index (CI).  A concentration index is a concept borrowed from thefield of industrial economics, which attempts to characterize the concentration (and dominance) of various firms within aparticular industry (Shepherd, 1997). Because we are attempting to characterize code usage concentration, as well as theconcentration of procedure codes within particular diagnostic categories, the use of concentration indices in the ostensiblyunrelated field of data mining (and medicine) seems appropriate.  The degree of concentration can be measured using various methods.  For the initial analysis of the data, we chose the Hirschman-Herfindahl index (HHI), which serves as an example of these types of methods.  The HH method calculates an  index from thefollowing formula:Where:n = number of firmsPi is the percentage share of the ith  firm (* 100)  -- (i = 1 … n).The output of the formula is an HHI number between 0 and 10,000.  Specifically, larger numbers indicate more highlyconcentrated industries (where one or a few firms dominate), while smaller numbers indicate more diverse industries (where themarket is shared somewhat equally by a number of firms).  In the domain of diagnostic and procedure codes, the industry conceptis replaced by a particular medical context (e.g., hospital or specialty) and the firms are replaced by the codes themselves.  In thisregard, an HHI indicates the extent to which diagnoses and procedures are represented in a few, or perhaps across many,individual codes.  For example, a large HHI number (generally over 1000) would indicate that a relatively small number ofdiagnostic codes accounts for a disproportionately large number of all diagnoses.  An HHI also can be used to measure theconcentration of procedure codes within individual diagnostic codes.  In this case, each diagnostic code (industry) will haveassociated procedure codes (firms), and the codes will be variously concentrated within individual diagnoses, as calculated bythe HHI.Data Management and Decision Support378 2001  Seventh Americas Conference on Information SystemsPreliminary Results and DiscussionPreliminary indications of code usage are somewhat limited at this point, and are presented here mainly as an illustration of thetypes of usage patterns that might be ascertained over the course of this research.  Results are described in two forms: (1) thepercentage of procedure and diagnostic codes used (out of the total available), by hospital; and (2) the HHI concentration forprocedure and diagnostic codes, again by hospital.  In the first case, the data indicate that utilization of procedure codes in bothhospitals is close to 50%, with the utilization for hospital TCH (52.7%) being somewhat higher than hospital PCH (43%).   Theaverage for both hospitals is 48.3%.  By contrast, diagnostic code utilization is much lower, with TCH using only 20% of alldiagnostic codes and PCH using 18.9%.  The primary initial conclusion is that code utilization varies based on the type of code,with physicians using a much larger percentage of available procedure codes than diagnostic codes.For the concentration index (HHI), the results also vary depending on the type of code.  The overall concentration of procedurecodes, as measured by the HHI, is considered low, with the average HHI for the two hospitals averaging 493.  The HHI did notvary significantly between the two hospitals (PCH = 494; TCH = 492).  The average HHI for the diagnostic codes was somewhathigher (565), but varied considerably between the two hospitals (PCH = 355; TCH = 741).  The difference between diagnosticand procedure codes is attributable to the relatively higher (although still low) concentration for hospital TCH.  Here, the initialconclusion is that, although not all of the codes are used (as indicated above), the concentration of the codes actually used is low(for both procedure and diagnostic codes).  That is, code usage is reasonably well distributed, and is not dominated by a smallgroup of codes.Future WorkFinishing this research entails continuing the analysis of code usage patterns described above, which will include: • applying the utilization and concentration analyses to the more specific hospital and specialty descriptors—for example, todetermine whether there are differences between generic categories of diagnostic and procedure codes, or between variousspecialties• extending the analysis to determine the relationships between diagnostic and procedure codes• comparing the results to other studies of code usage, including those in other nations, in an attempt to understand what typesof differences might exist, and whyIn each case, this includes the continued use of HHI in characterizing code usage, which in turn makes this research potentiallygeneralizable outside of the medical domain.  A concise measurement of problem and solution characterizations (i.e., codes)should provide insight into the generic question of how decision makers approach the task of formulating problems andconstructing plans for their solution.ReferencesShepherd, W.G. The Economics of Industrial Organization , Upper Saddle River, NJ:  Prentice Hall, 1997.Stausberg, J., Lang, H., Obertacke, U. and Rauhut, F.  “Classifications in Routine Use:  Lessons from ICD-9 and ICPM inSurgical Practice,” Journal of the American Medical Informatics Association  (8:1), 2001, pp. 92-100.

Using Data Mining Techniques to Characterize Diagnostic and Procedural Patterns

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Using Data Mining Techniques to Characterize Diagnostic and Procedural Patterns

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