Many changes are taking place in medical school curriculums to reflect shifts in the practice of medicine and evolving student learning habits. With such fundamental changes underway, it’s now more important than ever for students to utilize a team approach to healthcare and take advantage of advances in medical technologies. Decision support tools in the form of automated medical algorithms can help students in clinical training.
Starting in the first year of medical school, group assignments are often required to promote teamwork, collaborative efforts and learning, and overall enhancement of medical education. I remember one of my first group projects in medical school, dealing with ACL injury, diagnosis, and treatment options. Working with my team on our assigned portion, we learned about the proper diagnosis and management of this common sports related injury. With an annual incidence of more than 200,000 cases in the United States alone, ACL injury is a major cause of morbidity in athletes. Today, students and clinicians, can utilize a medical algorithm as part of their clinical decision support to enhance their education on ACL injury, starting with identifying which athletes are at high risk for injury to the anterior cruciate ligament. Additional research with medical student groups may bring them to an older, but still referenced algorithm on the Follow-up Evaluation of Athletes After Knee Ligament Surgery.
Starting with the 3rd year of medical school, spending nearly every day in the hospital or clinic, students often attempt to feverishly look up diagnoses, acronyms, and definitions to keep up with the attending while discussing patients on the team. One common complaint encountered during emergency room rotations is acute chest pain. With a push from many medical societies to limit the use of radiation in the emergency department, students can take a history, perform a physical exam, and then utilize the Revised Geneva Score to determine the probability of a pulmonary embolism while creating their differential diagnosis. This, combined with other laboratory data can help determine who should receive further workup for a pulmonary embolism, improving patient care and possibly reducing costs in the emergency department
While studying for exams can be monotonous and arduous, using algorithms to help improve study habits are another great resource. Students working with a study group can quiz each other by using the Glasgow coma scale (GCS) to come up with a vignette and then see if their classmate is able to correctly identify the GCS. (Medical students: many questions come up during medical school exams and board examinations where you need to correctly calculate the GCS in a clinical vignette.)
Depending on your year in medical school, it may seem that the beginning of residency is far away, but let me tell you from personal experience, time goes by so quickly. After your journey through medical school, full of long nights, late night eats, and barrels of coffee, you will have the foundation to become a great physician. Always remember, a great physician may not necessarily always know the correct answer “off the top of their head”, but will know the resource or app to find the correct information to best care for their patient.