The influence of a Sprint optimization and training intervention on time spent in the electronic health record (EHR)

Amber Sieja; Melanie D Whittington; Vanessa Paul Patterson; Katie Markley; Heather Holmstrom; Stephen Rotholz; Christine Gonzalez; Michael Scott Carpenter; Chen-Tan Lin

doi:10.1093/jamiaopen/ooab073

The influence of a Sprint optimization and training intervention on time spent in the electronic health record (EHR)

JAMIA Open. 2021 Aug 23;4(3):ooab073. doi: 10.1093/jamiaopen/ooab073. eCollection 2021 Jul.

Authors

Amber Sieja¹, Melanie D Whittington², Vanessa Paul Patterson², Katie Markley³, Heather Holmstrom⁴, Stephen Rotholz⁵, Christine Gonzalez⁶, Michael Scott Carpenter⁶, Chen-Tan Lin¹

Affiliations

¹ Department of Medicine, University of Colorado School of Medicine, Aurora, Colorado, USA.
² Navigation Lab, Data Science to Patient Value, University of Colorado School of Medicine, Aurora, Colorado, USA.
³ University of Colorado Health Medical Group and University of Colorado Health, Aurora, Colorado, USA.
⁴ Department of Family Medicine, University of Colorado School of Medicine, Aurora, Colorado, USA.
⁵ Department of Obstetrics and Gynecology, University of Colorado School of Medicine, Aurora, Colorado, USA.
⁶ University of Colorado Health, Aurora, Colorado, USA.

Abstract

Objective: We report the influence of Sprint electronic health record (EHR) training and optimization on clinician time spent in the EHR.

Materials and methods: We studied the Sprint process in one academic internal medicine practice with 26 providers. Program offerings included individualized training sessions, and the ability to clean up, fix, or build new EHR tools during the 2-week intervention. EHR usage log data were available for 24 clinicians, and the average clinical full-time equivalent was 0.44. We used a quasi-experimental study design with an interrupted time series specification, with 8 months of pre- and 12 months of post-intervention data to evaluate clinician time spent in the EHR.

Results: We discovered a greater than 6 h per day reduction in clinician time spent in the EHR at the clinic level. At the individual clinician level, we demonstrated a time savings of 20 min per clinician per day among those who attended at least 2 training sessions.

Discussion: We can promote EHR time savings for clinicians who engage in robust EHR training and optimization programs. To date, programs have shown a positive correlation between participation and subjective EHR satisfaction, efficiency, or time saved. The impact of EHR training and optimization on objective time savings remains elusive. By measuring time in the EHR, this study contributes to an ongoing conversation about the resources and programs needed to decrease clinician EHR time.

Conclusions: We have demonstrated that Sprint is associated with time savings for clinicians for up to 6 months. We suggest that an investment in EHR optimization and training can pay dividends in clinician time saved.

Keywords: burnout; electronic health record; electronic medical record; optimization; training.