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appointment, anything not done during the visit must be completed after clinic, on administrative time, or on the weekend — documentation time often referred to as “ pajama time. ” Further studies looking at the proportion of this 7.4 min per appointment recorded during the visit and what proportion occurs during “ pajama time ” will be important to understanding the EHR impact on physician well-being and should be the subject of future research. With validation of new PEP measures in January 2020, which re fl ect workload, time outside of scheduled patients, and “ pajama time, ” studies to better answer this question will be possible in the near future. While no comparative data exists for otolaryngology speci fi cally regarding esti mates of EHR burden per patient, Overhage and McCallie in 2020, summarized over 100 million encounters, with 155,000 physicians across 417 Health Systems. 13 They noted an average time per encounter of 16.2 min, more than double the 7.4 min reported in this manuscript. 13 While the study by Overhage and McCallie was much larger, it is important to note that it was performed in a different nationally available EHR, different user activity log protocol, and across all medical specialties, making direct comparison of results challenging. A fi nal consideration when interpreting the PEP data values is the fact that only the attending and APP times are tracked. In their current state, the PEP data capture does not include contributions from APPs seeing the attendings ’ patients, and additional contributions from personnel such as residents, scribes, or other ancillary staff. Although direct evidence of this ancillary contribu tion is not available in the PEP data, the data and Table II may provide some insights. APPs have longer PEP data measures across the board while averaging fewer appointments per day and maintaining similar char acter length of documentation. One explanation may be that attendings see more patients in clinic or that APPs are less ef fi cient. Anecdotally this is not consistent with the authors ’ experiences, and an alternative explanation may be that attendings may be more likely to have help. These trainees, nurses, medical assistants, and APPs con tribute to the overall visit work and likely decrease their attendings ’ overall EHR documentation burden. Notwithstanding the concerns about accuracy of the PEP data values, the measurements are precise and reproducible which makes them attractive markers to evaluate the change over time to measure the effect of an intervention. Traditionally these PEP values have been leveraged by information system and technology (IS&T) departments and presented at the EHR vendor ’ s user group meetings. With more and more physicians being granted access to the PEP data, a few publications sought to utilize the PEP data across varied medical special ties. 11,14 Speci fi cally, in ptolaryngology, one of the partici pating institutions is using the PEP data to evaluate the effect of a scribe pilot program on EHR documentation burden (study on-going, results pending). The variability between institutions and between individuals may provide targets for local process improve ments. For instance, otolaryngologists at institution 1 may want to direct their IS&T or physician customization teams to work on order writing tools such as order sets

and preference list updates, in order to bring their otolar yngology departments closer to the national means. Insti tutions 1 and 2 may want to review what otolaryngologists are using in the “ other ” activities in the EHR, which include analytics, reporting, and custom features. There may be a simple explanation such as integrated image review, etc., but the increased time in “ other ” may be something extraneous that can be streamlined to improve ef fi ciency. On a single institutional basis, individual out liers for a speci fi c activity could be approached with topical FAQs and “ quickwin ” tutorials to help improve their EHR ef fi ciency. For example, a provider who spends twice the mean time in notes could be contacted via email with a short document or how-to video with the top 10 tips to cus tomizing and improving your documentation ef fi ciency. While the PEP data is validated by the EHR vendor, there are limitations to this multi-institutional study. One major limitation was reviewed in the discusses around the vendor-reported daily EHR activity values. These not only underestimate the true time interacting with the EHR per ambulatory clinic day for a typical otolaryngologist, but they cannot distinguish between time in clinic and outside of regularly scheduled clinic hours. Extrapolation of per appointment values likely provides a more accurate and scalable picture of EHR burden for otolaryngologists as detailed above. Conversations with the EHR vendors ’ phy sician well-being departments are in process to improve accuracy of metrics, and the vendors have already vali dated new robust PEP metrics to assess workload includ ing “ pajama time. ” Small private practices are underrepresented within the customer base of the EHR vendor and are also underrepresented in this sample. As discussed, the PEP data accounts only for scheduled pro vider time and care must be taken when generalizing data to total EHR burden. Confounding variables such as scribe or transcription service contributions to documentation were not captured and could not be adjusted for. Future iterations of the PEP may broaden collection of data for inpatient services, as well as the collection of the “ time in chart ” for trainee, APP, and other ancillary services; this may provide better clarity of these confounding variables in future studies. In addition, future studies looking specif ically at how trainee clinic coverage and scribe utilization affect PEP data variability may elucidate their overall role in the documentation burden. CONCLUSION On average, otolaryngologists spend 70 min daily and 7.4 min per patient interacting with the EHR. While 70 min per day is the reported per day value, extrapolating the likely more accurate per appointment value of 7.4 min yields an estimate of 2.4 hours (146 min) interacting with the EHR per ambulatory clinic day. Further, variability and trends between and among institutions may provide targets and metrics for quality improvement initiatives.

BIBLIOGRAPHY 1. Golub JS, Johns MM 3rd, Weiss PS, Ramesh AK, Ossoff RH. Burnout in academic faculty of otolaryngology-head and neck surgery. Laryngoscope 2008;118:1951 – 1956.

Laryngoscope 131: May 2021

Giliberto et al.: National Time Spent in EHR

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