Presentation on theme: "Comparison of Osteopathic Medical School Curricula In Teaching Clinical Reasoning Amanda Kocoloski, OMS IV; Gordon Marler, OMS IV; Nicole Wadsworth, D.O.;"— Presentation transcript:
Comparison of Osteopathic Medical School Curricula In Teaching Clinical Reasoning Amanda Kocoloski, OMS IV; Gordon Marler, OMS IV; Nicole Wadsworth, D.O.; Grace Brannan, Ph.D.; John George, Ph.D.; Melanie Davis, M.A.; Godwin Dogbey, Ph.D. AbstractData Discussion Introduction: Osteopathic medical schools utilize different styles of curriculum for preclinical education. This study compared preclinical students from 2 schools with different curricular styles and measured their performance on the web-based clinical simulation program DxR Clinician TM. Methodology: A total of 17 students from School A and 51 from School B completed the same simulated case prior to beginning clinical clerkships. Grade point average (GPA) and Medical College Admission Test (MCAT®) scores were analyzed to assess homogeneity between the groups, and then DxR Clinician TM performance was compared. Results: There was significant difference between the two groups in their mean cumulative GPA (3.60, 3.45, p=.046), composite MCAT score (24.5, 26.6, p=.012) and biological science MCAT score (8.41, 9.59, p=.003). Students from School A performed significantly fewer exams (13.1, 30.2, p=.003) and completed a lower percentage of required exams (22.2, 35.0, p=.034) than School B. However, School A did order a higher percentage of required labs (13.6, 5.00, p=.050) and provided significantly more hypotheses than School B (5.35, 3.57, p=.011). Conclusion: We noticed some significant differences between the two schools in their performance on the DxR Clinician TM case, but the groups also differed in baseline GPA and MCAT® scores. There was no significant difference between the two groups on overall, diagnostic, or clinical scores. Since the conceptual development of problem-based learning (PBL) in the 1960s, many medical schools throughout the world have implemented a form of this teaching methodology PBL emphasizes learning basic sciences within a clinical framework, fostering clinical reasoning 1 Integrated curricula that combine features of both traditional and PBL-style learning have met with some success 2 ; however, up until this point curricular influence on medical decision-making has not been studied in osteopathic medical students The DxR Clinician TM generates feedback comparable to an observed simulated patient encounter, and can be more practical and cost-effective than organizing and evaluating simulated patient encounters 3 Our hypothesis is that students in the systems-based integrated track at School A and students educated in the traditional, discipline-based curriculum at School B will differ in clinical reasoning performance on the DxR Clinician TM Methods References Introduction 1.Barrows HS. A taxonomy of problem-based learning methods. Medical Education. 1986;20(6):481-6. 2.Miller AP, Schwartz PL, Loten EG. `Systems Integration': a middle way between problem-based learning and traditional courses. Medical Teacher. 2000;22(1):51-8. 3.Turner MK, Simon SR, Facemyer KC, Newhall LM, Veach TL. Web- based learning versus standardized patients for teaching clinical diagnosis: a randomized, controlled, crossover trial. Teaching and Learning In Medicine. 2006;18(3): 208-14. Acknowledgements Baseline comparison between the two groups of students was made using grade point average and MCAT® scores at the time of medical school matriculation Participants were recruited from the integrated, systems- based curriculum of school A’s class of 2012, and the entire class of 2012 from school B All participants completed the DxR Clinician TM case prior to starting clinical clerkships. Participants from school A completed it the in the summer of 2010 and students from school B completed the case in the spring of 2010 as part of their pharmacology course Since school B uses the DxR Clinician TM in its curriculum, participants had experience using the program prior to the study. To control for this difference participants from school A were provided a 15-minute tutorial on how to use the program and worked through a practice case before completing the study case Students completed the case on their own time. They were given a time frame in which to complete the case, but no time limit for completion once the case was opened was imposed Participants had to spend a minimum of 15 minutes on the case in order for their data to be analyzed Measures of clinical and diagnostic reasoning were generated by the DxR Clinician TM and analyzed Table 1. Measures of Clinical and Diagnostic Reasoning Overall ScoreDiagnostic ScoreQuestions asked/ % of Required Labs ordered/ % of Required Clinical ScoreClinical LevelExams performed/ % of Required Number of hypotheses generated Data was collected from the DxR Clinician TM and matched with students’ GPA and MCAT® scores, and all participant identifiers were removed prior to analysis Multivariate analysis detected overall significance Tests of between-subjects effects looked for significant differences between schools on GPA, MCAT® scores, and the DxR Clinician TM measures listed in Table 1 School ASchool Bp-value GPA: Cumulative3.5983.448.046 Science3.4923.383.216 MCAT® Score: Composite24.52926.569.012 Physical7.8248.098.479 Biological8.4129.588.003 Verbal8.2948.882.172 Table 2. Entering GPA and MCAT® Scores School ASchool Bp-value Overall52.6545.66.251 Diagnostic62.7151.32.095 Clinical54.4150.11.547 Questions Asked29.1834.70.465 % of Required71.7668.30.467 Exams Performed13.0630.21.003 % of Required22.2435.04.034 Labs Ordered220.127.116.114 % of Required13.655.00.050 Hypotheses5.353.57.011 Time Spent (min.)31.3947.18.411 Table 3. DxR Clinician TM Data Multivariate tests for significance yielded a Pillai’s Trace =.206, df = 62, p =.012. Multivariate tests for significance yielded a Wilks’ Lambda =.633, df = 58, p =.003. For all their help and support, I would like to thank Dr. Grace Brannan and the CORE Research Office, as well as the Academic Affairs departments at both participating institutions. This study had several limitations. Ideally the sample would have included the entire class of 2012 from both schools; however we were only able to enroll 17 participants from School A (out of 97) and use data from 51 students (out of 105) from School B based on the minimum time requirement of 15 minutes. While we compared entering GPA and MCAT® scores to evaluate the similarity of the 2 groups prior to exposure to either curriculum, we had no baseline measurement of diagnostic reasoning or clinical decision-making ability. The groups did differ significantly on some DxR Clinician TM parameters including the number of exams performed, percentage of required exams performed, percentage of required labs ordered, and number of hypotheses generated. There was no significant difference between the groups in diagnostic or clinical scores. The DxR Clinician TM is a customizable program, but for this study the default settings were used. If this study were to be repeated, customizing the parameters of the DxR Clinician TM program, implementing measures to increase sample size and obtaining a clinical diagnostic baseline would improve external validity and applicability. Figure 1. The DxR Clinician TM program uses an algorithmic decision tree to evaluate students’ stepwise progression through the case and place each participant into a diagnostic level of competence.