The Face of Medicine: How Data is Changing Medical Education
Written By: Ashley M. Scott (Guest Writer)
As we evolve into a data-driven society, cultural and technological improvements are impacting how data supports more objective decision-making in public health initiatives. In the same way, higher education administrators are incorporating data analytics to drive change and support improvements in medical curriculums.
What is Data Analytics?
Data Analytics is the process of examining raw data sets, uncovering patterns, and extracting valuable insights to formulate conclusions. Traditional data management tools include Excel spreadsheets and manual record-keeping managed by a person or team. According to a recent article, “healthcare organizations have seen an explosive health data growth rate of 878 percent since 2016, according to statistics compiled by Dell EMC” (Donovan, 2016). With the increase of data, analysts are looking to integrate machine learning algorithms, automation, and other capabilities to protect and meet the data demand.
How Does Data Enhance Medical Education?
Today, medical schools are using their data to better understand their students’ needs, evaluate their strategies, boost academic performance, and improve their bottom line.
Data is commonly categorized in two ways: quantitative and qualitative. Quantitative refers to the quantity or amount of a specific item; it is a numerical value. Qualitative refers to quality traits that do not include numerical values such as color, shape, or texture. In academia, datasets can include information such as instructors’ names, student GPA scores, class sizes, and more. When used strategically, data reports can shed light on trends over time in specific areas including,
1. Evaluating instructor performance
In 2012, the Measures of Effective Teaching (MET) Project released by the Bill and Melinda Gates Foundation showed that student surveys served as a valid method of measuring teaching effectiveness (Mercer-Golden, 2016). Shortly after, more medical schools began distributing evaluation surveys to students at the end of their classes and hospital clinical rotations.
The survey can often take up to 5 minutes to complete and consist of open-ended and multiple choice questions. The survey composition is critical to enhancing educational outcomes because students’ feedback provides insight into their learning experience. Several biases can arise if the survey questions are written misleadingly or confusingly, which can lead to inaccurate and skewed data results due to poor analyses.
Ultimately, institutions can identify if their instructors are motivating students to perform at their best and hold them accountable to accomplish specific learning objectives. In return, this analysis can provide direct insight to what students value in their experience, predict their preparedness for hospital rotations, and explore ways to enhance academic performance in the classroom.
2. Improving and developing class curriculums
In recent years, innovations in education introduced learning styles such as small group sessions, peer review activities, hybrid learning, and timed learning challenges. Such modifications are considered successful when data reports indicate that the majority of students prefer this technique or perform better in courses when they apply their knowledge and skills outside the classroom.
3. Keeping track of student attendance and pass rates
At-risk students are students who fail more than one course and are therefore at a higher risk of being put on academic probation. Data collection of student learning metrics helps identify these students, deliver academic support services, and reduce their chances of struggling in their coursework.
4. Alternative Testing Options
Most online assessments are supported by a cloud-based platform or type of machine learning service. This alternative option is helpful to grade assignments and exams faster and more accurately than a human. It may require some input from a human being, but the results will have higher validity and reliability. In addition, this helps to tag specific questions to course learning outcomes, track students’ performance, and identify at-risk students.
5. Adaptive Learning
Adaptive learning modifies teaching methods by analyzing student performance in real time. This is executed with machine learning techniques that adjust to each student’s learning styles from the traditional learning method. New upgrades include pretest tools that identify a learner’s current position and return personalized suggestions to strengthen comprehension skills.
How Data is Bridging Medical Education and Patient Satisfaction