1189 — A mixed-methods approach to understanding and assessing electronic health record (EHR) use at VA during major EHR transition to Cerner
Lead/Presenter: Brianne Molloy-Paolillo,
COIN - Bedford/Boston
All Authors: Molloy-Paolillo BK (Center for Healthcare Organization and Implementation Research, Bedford), Mohr, D (Center for Healthcare Organization and Implementation Research, Boston) Cutrona, S (Center for Healthcare Organization and Implementation Research, Bedford) Anderson, E (Center for Healthcare Organization and Implementation Research, Bedford) Helfrich, C (Seattle-Denver Center of Innovation) Sayre, G (Seattle-Denver Center of Innovation) Rinne, S (Center for Healthcare Organization and Implementation Research, Bedford)
Electronic health record (EHR) transitions are inherently disruptive as clinicians need to rapidly learn a new EHR system and adapt to altered clinical processes. Little is known about EHR use patterns during EHR transitions or best practices for understanding these patterns. Tracking EHR use metrics can provide feedback on implementation progress and signal key areas for intervention. The Department of Veterans Affairs (VA) is currently replacing its homegrown EHR system with a commercial Cerner EHR presenting a unique opportunity to study and examine EHR use patterns. We aimed to assess EHR usability and uptake at VAâ€™s first EHR transition site by integrating EHR use and qualitative data.
We conducted a mixed-methods analysis of EHR use data for 128 clinicians and interview data (90 interviews) from 25 staff across different service areas following Cerner go-live up to 10-months post-EHR transition at the Mann-Grandstaff VA Medical Center. Using data from the Cerner Lights On Network, we identified longitudinal changes in EHR metrics, including total time on EHR and time entering orders per patient (in minutes). We also examined differences in EHR use across specialties. To contextualize the identified patterns, we then analyzed post-EHR transition interviews with staff focusing on passages relevant to EHR usability.
Total EHR time rapidly declined from month 1 (M = 42.54) to 4 (M = 33.53) and then began to gradually stabilize between months 7 (M = 32.93) and 10 (M = 33.09). Physicians spent significantly less time in the EHR per patient (M = 33.09 vs. M = 42.54) and entering orders (M = 1.12 vs. M = 1.84) at 10-months vs. 1-month post-transition (ps < .01). Emergency, inpatient, and surgical physicians took less EHR time per patient (ps < .02) and cross-specialty, emergency, and inpatient providers spent less time entering orders (ps < .03) than primary care. Despite these improvements in EHR use metrics, most interview participants expressed concerns with the new EHRâ€™s usability and efficiency. Several participants reported existing tasks (e.g., orders, referrals) taking longer than the old system and described new task processes (e.g., how to document workload between actual visits) as confusing and labor-intensive. Some participants noted modest improvements in usability over time and commented on how shortcuts made use easier, like auto-populated text and a quicker signing process.
The triangulation of quantitative and qualitative data yielded a nuanced picture of EHR usability. While EHR use improved rapidly within the first four months following go-live and began to level off around 7 months, respondents described major persistent challenges with EHR usability and efficiency 10 months after go-live. These frustrations likely extend beyond the technology change and may reflect adaptive challenges with the EHR transition that are part of a cultural transformation.
While EHR use data is a powerful tool for understanding early patterns of EHR uptake following an EHR transition, EHR use metrics lacked vital contextual information. Interview data can be used to contextualize EHR use patterns. Combining the two methods presents opportunities to generate insights that can inform targeted training and EHR system redesign efforts, allowing health systems to improve clinician experience and enhance care delivery efficiency.