@article{10.22454/PRiMER.2018.187050, author = {Kim, Candice and Lin, Steven and Sattler, Amelia L.}, title = {A Model Medical Student-Led Interprofessional QI Project on Lab Monitoring}, journal = {PRiMER}, volume = {2}, year = {2018}, month = {7}, doi = {10.22454/PRiMER.2018.187050}, abstract = {Introduction: Teaching quality improvement (QI) in undergraduate medical education to meet entrustable professional activities (EPA) requirements is a challenge. We describe a model where first-year medical students learn QI methods using online modules and then apply their knowledge by leading an interprofessional project in a clinical setting. This model project, set in an outpatient family medicine clinic, sought to improve patient compliance with the preventive care metric of annual serum potassium and creatinine monitoring for patients taking a diuretic, angiotensin-converting enzyme inhibitor (ACEI), or angiotensin receptor blocker (ARB) medication. Methods: A first-year medical student joined an interprofessional team of one primary care physician and three pharmacy residents. The student led the team in reviewing patient charts to identify root causes and implementing a multifaceted strategy to improve patient outreach and refill policies. The effects of these interventions were monitored weekly using an electronic health record population health tool (Epic’s Healthy Planet) over the course of 10 months. Results: At baseline, 76.7% (477/622) of patients taking a diuretic and 79.4% (752/947) taking an ACEI or ARB were compliant with monitoring, approximately 10% below the Healthcare Effectiveness Data and Information Set (HEDIS) recommendations. Within 6 months, interventions resulted in both patient cohorts meeting the HEDIS metric with sustained success for the study duration of 10 months. Conclusion: This report demonstrates that a first-year medical student can make meaningful contributions to preventive care while gaining clinically relevant QI experience. Further evaluation is needed to determine generalizability and scalability of this model.}, URL = {https://journals.stfm.org//primer/2018/kim-2017-0043/}, eprint = {https://journals.stfm.org//media/1738/kim-primer2018187050.pdf}, }