Technology use and satisfaction among colleges/schools of osteopathic medical education

Abstract Context In March 2020, as the COVID-19 pandemic became a national concern, quick assessment and rapid changes needed to be made in all areas of osteopathic medical education. The Technology in Medical Education (TIME) adaptive working group was formed by the American Association of Colleges of Osteopathic Medicine (AACOM) to analyze best practices in technology applications used in the schools and colleges of osteopathic medicine (COMs). In January 2023, post-pandemic data was collected to provide comparisons of technology use over time and determine the current landscape of technology. Objectives To determine the technological uses and satisfaction at COMs nationally and offer a comprehensive list of software being used in osteopathic medical education to help inform technology decisions. Methods Survey instruments were used to gather data from 34 COMs (main campuses, branch campuses, and additional locations) during COVID-19 and 49 COMs post-COVID-19. Data was collected during COVID-19 from April 2020 to December 2020 with data analysis through April 2021. Data was collected post-COVID-19 from January 2023 to April 2023 with data analysis through March 2024. Five questions were consistent across both surveys and used for comparisons. Descriptive statistics, Fisher’s exact tests, and thematic analysis were utilized in the data analysis for both surveys. Results Of the 57 COMs surveyed during COVID-19, 34 responses were received for an overall response rate of 59.6 % (34/57). Of the 62 COMs surveyed post-COVID-19, 49 responses were received for a response rate of 79.0 % (49/62). To capture changes across time, thirty-four (34) institutions responded to the five identical questions on both surveys. While software selection was diverse across institutions, overall satisfaction remained high with 75.0 % of COMs being extremely or moderately satisfied with their software selections. Popular software packages across the two survey periods included Canvas (36.4 %, 54.9 %), Panopto (29.0 %, 27.5 %), ExamSoft (81.8 %, 84.0 %), and eValue (44.8 %, 28.6 %). Software shifts saw increased usage of Canvas (36.4–54.9 %), Yuja (3.2–11.8 %), Zoom for remote proctoring (5.2–35.3 %), and internal/custom solutions for clinical education/scheduling (3.4–10.2 %) and decreased usage of Vimeo (9.7–0.0 %), Respondus (9.0–0.0 %) and ExamMonitor (63.2–20.5 %). Comments indicated an interest in the national resources related to topics such as telehealth, public health, health system science, and health informatics. Key takeaways included the need for shared online training material in both preclinical and clinical education; development of new virtual or gaming technologies; and training faculty and staff to support technology integration. Conclusions Technology selection at medical schools is on-going but relatively consistent across time. This was especially true through the COVID-19 pandemic and with the ongoing increase in new osteopathic medical schools. This research offers a comprehensive list of software being used in osteopathic medical education including a snapshot of changes during and post-COVID-19 to help inform technology decisions across osteopathic medical education. In addition, these results are driving the planning process for AACOM initiatives into the future.