DOI: 10.5061/dryad.xksn02vt5
title: Does autotext usage decrease documentation time among resident physicians? A retrospective analysis of EHR usage data

description: Objective Usage of autotext or “dotphrases” is ubiquitous among provider
 workflows in electronic health records (EHRs). Yet little is known about
 the impact of these tools in inpatient settings and among resident
 physicians. We aimed to evaluate the association between autotext usage
 and documentation time among resident physicians in an academic medical
 center using the Cerner® EHR. Dataset Description The association between
 auto text executions and documentation time per patient seen for 705
 resident physicians rotating at a large academic medical center from July
 2021 to June 2023 was analyzed via linear regression after controlling for
 specialty, post-graduate year (PGY), provider gender, and patient volume.
 NOTE: The dataset in this study cannot be shared publicly due to the risk
 of identifying subjects who constitute a vulnerable population and may be
 known personally to members of the research community (physicians in
 training). Inclusion of details of gender, department, and year in
 training, which pose a risk of allowing subjects to be identified, is
 integral to the analysis of the data, so the data cannot be published in a
 meaningful form without these details. Accordingly, a short sample dataset
 that retains the data structure but with randomly generated values that
 mimic the actual data is provided instead.  The dataset used in
 this study was prepared from raw data downloaded from the Cerner Lightson
 and Cerner Advance toolkits and aggregated at the level of an individual
 resident physician over an academic year. As the study covers two academic
 years, 2021-2022 and 2022-2023, there are two entries for some resident
 physicians who were at the institution during both academic
 years.  The dataset includes a randomly generated anonymous
 identifier for each provider, as well as demographics on department,
 gender, and PGY, and data on autotext usage, patient volume, documentation
 time per patient seen, and total EHR time per patient seen. Results There
 was no significant overall association between autotext executions per
 patient seen and documentation time per patient seen in specialties using
 Dynamic Documentation as their primary workflow (β=-0.1 min per autotext
 execution per patient seen, 95% CI -0.6 to 0.5 min, p=0.79).
 However, there was increased documentation time among residents with no
 autotext usage compared to residents who used autotext, and this effect
 was mediated by the use of personalized autotexts. Specialty, PGY, gender,
 and patient volume were significant determinants of documentation time.
 Discussion Efforts to decrease documentation time among resident
 physicians should encourage autotext adoption but should not be focused on
 the promotion of autotext usage alone. Further research should address the
 questions of identifying other determinants of documentation time,
 autotext design standards, and how autotext usage affects measures of note
 quality. Conclusion Autotext adoption decreases documentation time among
 resident physicians, but among those who adopt autotext, higher levels of
 usage show no benefit.
description: # Does autotext usage decrease documentation time among resident
 physicians? A retrospective analysis of EHR usage Data Dataset DOI:
 [10.5061/dryad.xksn02vt5](10.5061/dryad.xksn02vt5) ## Description of the
 data and file structure Short synthetic sample intended to mimic data used
 in retrospective study of autotext usage and documentation time among
 resident physicians. *A synthetic dataset is presented because the actual
 dataset used in this study could not be sufficiently anonymized to share
 publicly due to the nature of the data (see Abstract.)* The dataset
 originally used in this study was prepared from raw data downloaded from
 the Cerner LightsOn and Cerner Advance toolkits and aggregated at the
 level of an individual resident physician over the course of an academic
 year. As the study covers two academic years, 2021-2022 and 2022-2023,
 there are two entries for some resident physicians who were at the
 institution during both academic years.  The dataset includes a randomly
 generated anonymous identifier for each provider, as well as demographics
 on department, gender, and PGY, and data on autotext usage, patient
 volume, documentation time per patient seen, and total EHR time per
 patient seen. *The data presented in this repository is designed to mimic
 the structure of this data accordingly.* ### Files and variables ####
 File: curated\_mimic\_autotext\_data.csv **Description:**  ##### Variables
 * anon_id: Anonymous identifier for each resident physician.  For
 instance, rows 4 and 5 have the same anon_id because they represent the
 same individual, an internal medicine resident, across two academic years,
 2021-2022 (ac_year =1, when this individual was a PGY-1) and
 2022-2023 (ac_year =2, when this individual was a PGY-2.) * department:
 Name of department in which each resident physician is training.  * dept:
 Numerical index of department for use in regressions.  * PGYyear:
 Post-graduate year of individual resident in each row.  * ngender:
 numerical proxy for gender (0 = male gender, 1 = female gender.) *
 DocTime: Documentation time per patient seen in minutes averaged over the
 academic year.  * AutoTxt: Autotext executions per patient seen averaged
 over the academic year. * ac_year: academic year (1 = 2021-2022, 2 =
 2022-2023.)  * sum_patientsseen: Total patients seen over the course of
 the academic year under consideration. * n: Random variable representing
 assignment (1 = yes, 2 = no) for row to be analyzed for the purposes of
 analyses such as Kruskal-Wallis tests in which resident with data in
 multiple years had to have one year of data dropped * drop: variable equal
 to 1 when in department of Emergency Medicine or Anesthesia, 0 otherwise,
 as residents in these departments were dropped from analyses as described
 in the manuscript.  * d_private: Dummy variable equal to 1 when there was
 any personal autotext use for the resident physician in the academic year
 being analyzed in this row, 0 otherwise.  * d_public: Dummy variable equal
 to 1 when there was any system autotext use for the resident physician in
 the academic year being analyzed in this row, 0 otherwise.  *
 d_pri_public: Dummy variable equal to 1 when there was any autotext use
 whatsoever for the resident physician in the academic year being analyzed
 in this row, 0 otherwise. * nonadopt: Dummy variable equal to 1 for
 autotext nonadopters, 0 otherwise (1 - d_pri_public.) * ActualTime: Total
 EHR time per patient seen in minutes averaged over the academic year.  ##
 Code/software All analyses conducted using STATA 17.0 (College Station,
 Texas, United States.) Attached file "autotext residents_analysis for
 dryad.do" is a STATA .do file giving code that replicates the crucial
 analyses reported in tables and paragraphs in the main texts when used
 with the full dataset.  ## Access information Other publicly accessible
 locations of the data: * none Data was derived from the following sources:
 * Cerner LightsOn and Cerner Advance toolkits, licensed to Kaleida Health,
 Buffalo, NY.