DEVELOPING AN EMERGENCY ROOM DIAGNOSTIC CHECK LIST USING ROUGH SETS: A CASE STUDY OF APPENDICITIS

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1 DEVELOPING AN EMERGENCY ROOM DIAGNOSTIC CHECK LIST USING ROUGH SETS: A CASE STUDY OF APPENDICITIS Abstract Steven Rubin 1, Wojtek Michalowski 2, Roman Slowinski 3 1 Department of Surgery, University of Ottawa Ottawa, Ontario, Canada 2 School of Business, Carleton University Ottawa, Ontario, Canada 3Institute of Computing Science, Technical University of Poznan Poznan, Poland This paper deals with the identification of a minimal set of tests and diagnostic procedures allowing for a diagnosis of acute appendicitis with a reasonable accuracy. In order to create such a set of tests, which we call a diagnostic check list, we are using the rough sets methodology developed for a discovery of knowledge from imprecise data. This methodology is applied to data gathered from a retrospective chart study of emergency room admissions of patients with abdominal pain at the Children s Hospital of Eastern Ontario. Empirical investigation of historical data reveals that the diagnostic process in an emergency room setting should be improved if the proposed diagnostic check list is used. 1 Introduction For many years medical diagnosis has relied on a battery of tests and diagnostic procedures to eliminate long list of possible differential diagnose. Such diagnostic process was taught to all medical personnel. Increasing number of malpractice lawsuits in the United States has further reinforced the need for even more diagnostic procedures and tests. As a rule medical students are instructed according to the precept that the more information gathered about the patient, the more secure the diagnosis, and the more appropriate the management. Present fiscal circumstances have forced medical institutions and physicians to curtail the cost of a diagnostic process. The resultant rationalisation of services requires in-depth studies to determine the most effective diagnostic procedures which will provide the physician with a sufficient level of confidence to make management decisions. There is a prevailing attitude amongst the members of medical profession that the costs of medical care can be partially controlled through rational application of tests and diagnostic procedures in the patient management process. Research in a health care management shows that the usefulness of information provided by tests and procedures diminishes as a diagnostic process approaches certainty (Johnson, 1991). Therefore different medical centres embark on different measures for evaluation and rationalisation of tests and diagnostic procedures administered in specific clinical environments. These measures either deal with the proposals for organisational changes of care providing organisations (McGillivray et al., 1993), or suggest new, usually information - based approaches to patient management (Roberts et al., 1993).

2 None of the measures proposed so far considers a discriminating quality of tests and diagnostic procedures and its impact on the final diagnosis. This paper describes the study which takes such measures into account. In this study we are looking at the tests and diagnostic procedures from the point of view of their contribution to the final diagnosis. Such an approach allows the identification of those tests and diagnostic procedures which are significant and those which, in a sense, are redundant for case management. Validity of our approach was tested using Emergency Room (ER) admittance data for the Children Hospital of Eastern Ontario (CHEO) for the period 1994/95. Data analysis was conducted using rough sets methodology (Pawlak, 1991; Slowinski, 1992) which was developed for discovering knowledge from imprecise data. The purpose of this study is to discover if it is possible, for a specific clinical environment, to develop a diagnostic check list containing a ranked set of tests and diagnostic procedures which should be requested during the management of the child with abdominal pain. The paper is organised as follows. In the next section we give essential theoretical foundations of the rough sets analysis. In section 3 we describe a retrospective chart study conducted at the CHEO. The results of this study were used to develop a check list. Section 4 presents these results and describes the check list. The paper concludes with a discussion and recommendations for future research. 2 Foundations of the Rough Sets Theory Although there are numerous papers dealing with theoretical developments in rough sets theory and their applications (see Pawlak, 1991, Slowinski, 1992; Slowinski et al., 1988 among others), here we focus only on the basic concepts and philosophy of this approach. Rough sets concern an analysis of a set of objects (observations, patients, states, etc) described by a set of multi-valued attributes (features, symptoms, tests, etc). For each object and its attribute there is a known value called descriptor. Objects, attributes, and descriptors are the three basic components of problem description comprising an information system. Such a system can also be viewed as a table with rows corresponding to objects and columns corresponding to attributes. Each row of the table contains descriptors representing information about the corresponding object. Additional information about each object concerns its membership in one of the disjoint classes, thus assuming that an object can belong to one and only one class. It is common that available information about the objects does not allow to distinguish some of them in terms of class membership. In other words, this imprecise information causes indiscernibility of the objects. Given an equivalence relation which is viewed as an indiscernibility relation and thus induces an approximation space made of equivalence classes, a rough set is characterised by lower and upper approximation of the classes of indiscernible objects. These approximations correspond, respectively, to a maximal set including objects that surely belong to a class, and a minimal set of objects that possibly belong to a class. The difference between the lower and upper approximations is a boundary set consisting of all objects that cannot be classified with certainty to a class or to its complement. Thus, it is possible to define the accuracy and the quality of approximation. The rough sets approach enables the solution of significant classification problems, namely a reduction of all redundant objects and attributes in order to obtain the minimum subset of attributes ensuring a good approximation of classes and an acceptable quality of classification. Moreover, it also allows representation of all the important relationships between the most significant attributes and particular classes in a form of a set of decision rules.

3 The rough set approach was implemented in a RoughDas computer system (Gruszecki et al., 1994) which we used for the patient data analysis described in the next section. 3 Retrospective Chart Study The study was conducted at the CHEO which is a teaching hospital serving Eastern Ontario and the Outaouais regions. It involved, over a period 1994/95, an analysis of 188 ER admissions of patients complaining about an abdominal pain. Abdominal pain continues to be a serious diagnostic dilemma. When the clinical diagnosis of appendicitis is definite, the diagnostic accuracy of the surgical specialist is usually 95% (Rubin and Martin, 1990). However, in most instances the clinical diagnosis is in doubt and the specificity of a clinical assessment is about 60% (Rubin and Martin, 1990). Often this results in a delay in specific management. Analgesics if given may further delay the diagnosis. Barium enemas or abdominal ultrasound may decrease the time from presentation to diagnosis to specific treatment. Observation in hospital or exploratory surgery are the accepted management practices of a patient with acute abdominal pain in whom a diagnosis has not been made. The initial assessment of the patients admitted to the ER with abdominal pain is usually done by an inexperienced physician or physician s assistant. These individuals are trained to take histories of the patients and to do physical examinations. The difficulty they have to cope with is related to assigning relative importance to each piece of information about the patient s condition which is being gathered. Thus, they often order multiple tests and seek assistance from the surgical specialist who has not met the patient. The tests and the communications with a specialist even further delay the diagnostic process and may decrease efficiency of case management. The process described above results in prolonged patient discomfort and anxiety. In addition it is economically inefficient. Without a definitive diagnostic tests, final management decisions depend on the most senior surgical specialist. The relative paucity of the surgical specialists and the cost of their service mitigate against having such a physician in the ER area in each hospital 24 hours a day, year round. This, together with the issues of improving efficiency of abdominal pain management prompted our research on developing a unified diagnostic check list which would facilitate diagnosis of acute appendicitis. This study attempts to establish a minimal set of tests and diagnostic procedures which is required to make a diagnosis within reasonable confidence limits and a reasonable time frame. Analysis of the charts of the ER admissions has identified the following items of information gathered from patients with an abdominal pain: patient s sex; age; hours of abdominal pain; presence of intermittent abdominal pain; vomiting; loss of appetite; diarrhoea, nausea; body temperature; pulse rate; rlq (right lower quadrant) tenderness; guarding; rebound; wbc (white cells count in blood); rectal examination; urinalysis; urbc (red cells count in urine); ultrasonography. It is important to underline that not every chart contained all 18 pieces of information, and that the difference in description between the cases was quite significant. This difference in describing the patient s condition can be attributed to the fact that the residents staffing the ER were coming from different medical schools and were exposed to different diagnostic practices. This observation further confirmed our belief that unification of charts description might definitely contribute to increased efficiency of case management. Such a description would involve identification of a minimal and most discriminant set of tests and diagnostic procedures which would contribute to the diagnosis of appendicitis. In order to create

4 such a set, we had to classify ER admissions into: (a) cases where the condition of an appendicitis was present and (b) others. This classification was conducted following pathology reports (if available), or according to the final discharge diagnosis (if patient was discharged home and did not return to the ER within next 48 hours). Such interpretation of the patients condition allowed us to create classes according to the patient s real state as opposed to the diagnosis of the attending physician. The information system (table) as required for the rough sets analysis, was created on the basis of the chart study. The table was composed of rows representing individual ER admission (object) and columns representing 18 tests and diagnostic procedures (attributes) with an additional column representing class membership (appendicitis, other). This information system was subject to an analysis using rough sets methodology. The values for each attribute and their respective ranges were coded following the specifications of an experienced paediatric surgeon. 4 Development of a Diagnostic Check List The analysis of an information system described in the previous section was conducted using the RoughDas computer system. This analysis revealed that a unified minimal set of tests and diagnostic procedures describing the condition of appendicitis does not exist. At the same time it showed that there are approximately 800 subsets (i.e. combinations of less than 18 attributes) of the tests and diagnostic procedures which can be used to differentiate between the patients possible states. In the next step of the analysis we identified the subset of the attributes (tests and diagnostic procedures) which describes in the most complete fashion the diagnosis of appendicitis. In order to achieve that we used the greedy algorithm to identify important attributes. Such an algorithm considers the attributes on a one by one basis using the criterion of the most improved classification accuracy. Application of this algorithm allowed us to identify the following subset of 7 out of 18 tests and diagnostic procedures: patient s sex; hours of abdominal pain; body temperature; pulse rate; guarding; rebound; and the wbc. This information was included into a diagnostic check list. As a follow up to the establishment of a diagnostic check list, we have conducted its validity analysis using historical chart data. This analysis involved the generation of rules which might be used to support the patient s diagnosis. These rules were generated using the minimal cover method which creates a minimal set of rules covering all possible cases. A generated set consisted of 46 rules, and the 10-fold cross validation test demonstrated that incorrect diagnosis occurs in approximately 20% of the cases when a diagnostic check list is used. Such a result presents a significant improvement in relation to the results expected from the inexperienced residents analysing the data from an ambitious case of childhood abdominal pain. Moreover, it was achieved using only a selected subset of tests and diagnostic procedures (a diagnostic check list) applied uniformly to all analysed cases. 5 Conclusions and Future Research Our study demonstrates that it is possible to establish a unified system of chart description while diagnosing acute appendicitis. The rough sets analysis provided us with a list of 7 tests and diagnostic procedures comprising a diagnostic check. If the application of the diagnostic check list suggests appendicitis, the surgeon should be called, as the chance of appendicitis is about 80%. If the surgeon s diagnosis is different, then further tests or hospitalisation for this limited

5 group of patients is indicated. However, if the application of the diagnostic check list does not suggest appendicitis, then non surgical management is appropriate. Apart from the obvious costeffective dimension such approach should help in unification of case management procedures in the ER. This factor alone contributes to increased educational benefits associated with the residency. We are planning to verify a diagnostic check list in the actual ER setting. This prolonged study will involve the use of the diagnostic check list by the ER medical staff and an analysis of the resulting case management. We believe that successful verification of the check list will lead towards development of a computerised system aiding the residents to prioritise the value of information provided by the tests and diagnostic procedures and to more definitively structure the process of reaching a final diagnosis.

6 References Gruszecki, G., R. Slowinski and J. Stefanowski RoughDas: Rough Sets Based Data Analysis System, Technical University of Poznan Technical Report, Poznan. Johnson, H Diminishing Returns on the Road to Diagnostic Certainty, Journal of American Medical Association. Vol. 265, pp McGillivray, D., R. Roberts-Brauer and S. Kramer Diagnostic Test Ordering in the Evaluation of Febrile Children, AJDC, Vol. 147, pp Pawlak, Z Rough Sets: Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishers, Dordrecht. Roberts, D., D. Bell, T. Ostryzniuk, K. Dobson, L. Oppenheimer, D. Martens, N. Honcharik, H. Cramp, E. Loewen, S. Bodnar, A. Guenther, L. Pronger, E. Roberts and Th. McEwen Eliminating Needless Testing in Intensive Care - An Information-based Team Management Approach, Critical Care Medicine. Vol. 21, pp Rubin, S. and D. Martin Ultrasonography in the Pediatric Patient with Acute Abdominal Pain, Journal of Pediatric Surgery. Vol. 25, pp Slowinski, K., R. Slowinski and J. Stefanowski Rough Sets Approach to Analysis of Data from Peritoneal Lavage in Acute Pancreatitis, Medical Informatics. Vol. 13, pp Slowinski, R. (ed) Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory, Kluwer Academic Publishers, Dordrecht. AUTHOR BIOGRAPHIES Steven Rubin is a professor in the Department of Surgery, University of Ottawa and a senior paediatric surgeon at the Children s Hospital of Eastern Ontario. Wojtek Michalowski is a professor of operations research and decision analysis in the School of Business, Carleton University. He is also director of the Decision Analysis Laboratory at Carleton University. Roman Slowinski is professor of operations research and decision sciences at the Institute of Computing Science of the Technical University of Poznan. He is also an Associate Dean of the Faculty of Electrical Engineering at this university.

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