Session Time: 6:00pm-7:00pm
Presentation Time: 6:00pm-7:00pm
Location: Hall D1
Methods: The CME intervention comprised two cases presented in a virtual patient simulation (VPS) platform, allowing learners to choose from numerous lab tests, diagnoses, and treatments matching the scope and depth of actual practice. Learner clinical decisions were analyzed using an artificial intelligence decision engine; tailored clinical guidance (CG) was provided based on current evidence and expert recommendation in response to each learner decision. Learner decisions were collected post-CG and compared with each user's baseline (pre-CG) data using a 2-tailed paired t-test to determine P values. Data is reflective of learners who participated in the assessment from 8/12/15 to 3/31/16.
Results: Significant improvements were observed from pre-CG to post-CG performance:
Case 1 (n=92 nephrologists, n=11 transplant surgeons):
[middot] More nephrologists ordered donor-specific antibody (DSA) and glycated hemoglobin A1C (HbA1C) tests (67% vs 86%, P<0.001)
[middot] Higher number of transplant surgeons ordered tacrolimus trough levels (73% vs 100%, P<0.021)
[middot] More nephrologists (9% vs 45%, P<0.0001) and transplant surgeons (9% vs 45%, P<0.018) selected DSA rebound and injury suppressors
[middot] More nephrologists ordered follow-up monitoring for diabetes (28% vs 60%, P<0.0001), CNI inhibitor levels (51% vs 75%, P<0.0001) and patient education on post-transplant management and counseling (55% vs 74%, P<0.004)
Case 2 (n=87 nephrologists, n=13 transplant surgeons):
[middot] Nephrologists ordering the majority of tests required to evaluate the patient increased: CBC with differential (89% vs 99%, P<0.002), eGFR (74% vs 89%, P<0.005), fasting lipid panel (78% vs 89%, P<0.032), tacrolimus trough level (87% vs 95%, P<0.028), and urinalysis (82% vs 92%, P<0.021),
[middot] Transplant surgeons ordering a fasting lipid panel increased (77% vs 100%, P<0.024)
[middot] Both Nephrologists and transplant surgeons demonstrated significant improvements in ordering interventions for improving adherence to therapy (P<0.01).
Conclusion: This study demonstrated the success of online, simulation-based education that immerses and engages transplant specialists for an authentic and practical learning experience that can improve evidence-based clinical decisions in the management of kidney transplant recipients.
CITATION INFORMATION: Gitzinger S, Jackson E, Blevins D. Improving Clinical Decisions for Kidney Transplant Recipients Through Online Medical Simulation. Am J Transplant. 2017;17 (suppl 3).
To cite this abstract in AMA style:Gitzinger S, Jackson E, Blevins D. Improving Clinical Decisions for Kidney Transplant Recipients Through Online Medical Simulation. [abstract]. Am J Transplant. 2017; 17 (suppl 3). https://atcmeetingabstracts.com/abstract/improving-clinical-decisions-for-kidney-transplant-recipients-through-online-medical-simulation/. Accessed November 29, 2020.
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