With limited healthcare resources, clinics and physicians face productivity challenges. Currently, minimal workflow standardization exists. As electronic health record systems (EHR) offer more capabilities to track times for which clinic stakeholders – physicians, patient flow coordinators, nurses, technicians, trainees, and other staff – interact with the system, such secondary data can reflect clinic activities. Despite the large data repository, little is known about its accuracy and applications. Therefore, this study aimed to understand the accuracy of time-motion metrics within EHR data and demonstrate preliminary trends in the clinic workflow.

Methods This study was conducted at the Ambulatory Cardiac Clinics at Peter Munk Cardiac Center, University Health Network in Toronto, Canada. Two trained observers collected a convenient sample of over 60 hours of observational data using a time-motion approach during 7 clinic days. Automated data captured by this clinic’s EHR, EPIC, of all in-person outpatient visits during regular hours between January to December 2023 were used. Time-related and descriptive variables with >10% missingness were excluded. The acceptable error for accurate timestamp and interval variables was ±5 minutes and ±5% of the total interval time, respectively, for ≥ 90% of EPIC data compared to observational data. Using acceptable variables as metrics, EPIC data was analyzed using summary statistics and multivariable regressions to capture preliminary clinic workflow trends.

Results Across 9 cardiologists, 158 observational data records were compared with EPIC data. Of 49 time- and date-related variables, only 6 had less than 10% missingness (appointment date, appointment weekday, appointment time, appointment length, patient check-in time, and department-recorded check-in time), all of which were accurate. 15 descriptive variables also had less than 10% missingness. During the study period, 51,887 records from EPIC during weekdays 7 am - 5 pm were analyzed. The median patient appointment lasted 20 minutes interquartile range 15, 60. Furthermore, morning and afternoon clinic transitioned from 12 – 1pm and the 30-minute appointment load peaked during the middle of each morning and afternoon clinic.

Conclusion Secondary data automatically collected as part of the EPIC system is accurate and can be used to measure, manage, and improve clinic efficiency. However, further standardization of when stakeholders engage with the EHR is necessary to reduce missingness and increase the number of variables as clinic efficiency metrics. Additionally, more variables allow for regionalization, physician workload, and patient satisfaction to be determined for clinic function and management in addition to appointment-related metrics.