Assessing the Impact of Medical Scribes in an Academic Ophthalmology Setting

Purpose: To evaluate the impact of medical scribes on performance metrics in an academic ophthalmology practice across a variety of subspecialties Design: Retrospective cohort study Methods: Setting: Single center clinical practice Participants: 58,223 patient encounters from January 1, 2020 through November 10, 2023 were used. 2,269 clinic sessions were analyzed, with 686 clinic sessions without the presence of a scribe and 1,583 clinic sessions with the presence of a scribe Procedures: Metrics were compared between scribed vs unscribed encounters for each individual doctor and then further compared by subspecialty. A linear mixed-effects model was used for overall differences and differences within the cornea subspecialty. A linear regression model was used for all other subspecialties. Main Outcome Measures: Patients seen per hour, mean patient appointment time, mean patient wait time, mean chart closure time, and mean relative value units (RVUs) per hour. Results: Patients seen per hour (mean ± standard deviation) were 3.45 ± 0.23 for all scribed encounters vs 3.12 ± 0.23 for unscribed encounters (P < 0.01). There was no significant difference in patient appointment times (mean ± standard error) of 74.56 ± 4.87 minutes for scribed encounters vs 73.34 ± 4.93 minutes for unscribed encounters (P = 0.3). Patient wait time (mean ± standard deviation) was 26.94 ± 3.55 minutes for all scribed encounters vs 29.12 ± 3.57 minutes for unscribed encounters (P < 0.01). Chart closure time (mean ± standard deviation) was 105.83 ± 11.56 minutes for all scribed encounters vs 93.82 ± 11.6 minutes for unscribed encounters (P < 0.01). Epic RVUs per hour (mean ± standard deviation) were 4.48 ± 0.65 for all scribed encounters vs 3.68 ± 0.65 for unscribed encounters (P < 0.01). All reported values are standardized to a 15% proportion of new patient encounters. Conclusion: The use of scribes led to an increase in RVUs and patients seen per hour while decreasing patient wait time. While these trends varied amongst the subspecialties, all subspecialties benefited in at least one measure of efficiency in the presence of a scribe. Therefore, this study supports the utilization of scribes in an academic ophthalmology setting.

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Publication Details

Journal
Journal of Academic Ophthalmology
Published
2024-11-07
DOI
https://doi.org/10.62199/2475-4757.1008
Primary Topic
Ophthalmology and Visual Health Research
Type
article
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article

Assessing the Impact of Medical Scribes in an Academic Ophthalmology Setting

Nathan Gill, Halit Winter, Manjot K. Gill, Justin T Flood
Journal of Academic Ophthalmology
Ophthalmology and Visual Health Research
article

Assessing the Impact of Medical Scribes in an Academic Ophthalmology Setting

Nathan Gill, Halit Winter, Manjot K. Gill, Justin T Flood
article en

Abstract

Purpose: To evaluate the impact of medical scribes on performance metrics in an academic ophthalmology practice across a variety of subspecialties Design: Retrospective cohort study Methods: Setting: Single center clinical practice Participants: 58,223 patient encounters from January 1, 2020 through November 10, 2023 were used. 2,269 clinic sessions were analyzed, with 686 clinic sessions without the presence of a scribe and 1,583 clinic sessions with the presence of a scribe Procedures: Metrics were compared between scribed vs unscribed encounters for each individual doctor and then further compared by subspecialty. A linear mixed-effects model was used for overall differences and differences within the cornea subspecialty. A linear regression model was used for all other subspecialties. Main Outcome Measures: Patients seen per hour, mean patient appointment time, mean patient wait time, mean chart closure time, and mean relative value units (RVUs) per hour. Results: Patients seen per hour (mean ± standard deviation) were 3.45 ± 0.23 for all scribed encounters vs 3.12 ± 0.23 for unscribed encounters (P < 0.01). There was no significant difference in patient appointment times (mean ± standard error) of 74.56 ± 4.87 minutes for scribed encounters vs 73.34 ± 4.93 minutes for unscribed encounters (P = 0.3). Patient wait time (mean ± standard deviation) was 26.94 ± 3.55 minutes for all scribed encounters vs 29.12 ± 3.57 minutes for unscribed encounters (P < 0.01). Chart closure time (mean ± standard deviation) was 105.83 ± 11.56 minutes for all scribed encounters vs 93.82 ± 11.6 minutes for unscribed encounters (P < 0.01). Epic RVUs per hour (mean ± standard deviation) were 4.48 ± 0.65 for all scribed encounters vs 3.68 ± 0.65 for unscribed encounters (P < 0.01). All reported values are standardized to a 15% proportion of new patient encounters. Conclusion: The use of scribes led to an increase in RVUs and patients seen per hour while decreasing patient wait time. While these trends varied amongst the subspecialties, all subspecialties benefited in at least one measure of efficiency in the presence of a scribe. Therefore, this study supports the utilization of scribes in an academic ophthalmology setting.

Journal of Academic OphthalmologyVol. 16(1)
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Ophthalmology and Visual Health Research
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