Many medical schools have less than ideal processes for reporting on data in a timely manner. Data collection and Student Support initiatives often fall to different stakeholders within schools. As data (assessment & evaluation platforms, examination results, surveys, etc.) flows into the organization, results need to be disseminated across multiple stakeholders.
Depending on the methods of data collection and dissemination, schools are often left with multiple versions of the data scattered across resources. Making sense of the data is time consuming and can be prone to errors.
Medical schools must engage in continuous quality improvement initiatives to support accreditation (Element 1.1). The goal of this CQI is to enable medical schools to identify issues and course correct before a course wraps, a new class matriculates, or the next accreditation cycle begins. There is a large amount of data involved, across many different systems, and making sense of this data in order to achieve true CQI and drive real time decision making can be cumbersome.
Curriculum committees and course directors often report on data 2-6 weeks after a course closes. Any change in cohort performance trends on individual assessment items are often not analyzed until after the course wraps up. Delays in data analysis can have a negative impact on learners, ultimately leading to unsatisfactory feedback from learners.
On the flip side, Student Affairs and Student Success teams responsible for identifying and working with learners encountering academic difficulties have a lot of data to sort through. Academic difficulties can pop up from multiple areas for learners, and anxiety and stress is the most cited factor for learner remediation and performance issues. Keeping a close eye on changes in individual learner performance is important for identifying the correct success measures for supporting students.
What drives changes in learner performance might not always be obvious. A student who typically scores in the 85-90 degree range can have a drop in performance and yet not meet the thresholds for a low performance notification. Identifying changes for unexpected drops in performance is just as important as identifying low performance and failing students.
Lag times in identifying students in need of support can be costly for students and for schools. These costs can stem from test fees, human resources, rotation failure, dropout, and more. Student dropout costs can range from $200-400K for a mid-sized medical school when lost tuition, salaries, and other factors are combined. Early intervention can help mitigate these costs by identifying and supporting students before small issues become big issues.
Move to real time decision making with Analytics
One45 Analytics helps medical schools capture trends to take action on both the individual student and cohort levels. Our dashboards allow schools to easily identify low performing learners and potentially struggling students in real time. This helps support your student success teams in early intervention, saving your school and students time and financial resources.
Dashboards & reports by segment
Our segment reports allow schools to query our data warehouse to segment population groups. This enables schools to identify characteristics of learners that may struggle in a specific course, exam, etc. Knowing and understanding areas where support may be needed can help drive curriculum reform and success.
Timely access to real-time data
One45 Analytics provides daily updates to data. Our medical education dashboards enable your curriculum committee and assessment teams to easily identify the range of scores and other statistical measures in assessment items within and across courses for longitudinal analysis and interpretation in real time.
Removes data barriers
Analytics removes the barriers of access to data by automatically extracting data from your examination, curriculum, evaluation data and more. Accessing data for real time decision making across multiple teams has never been easier.
Want to learn more about how One45 Analytics can help transition your medical school from reactive to real time decision making?
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