Improving Nurse Documentation Time via an Electronic Health Record Documentation Efficiency Tool

Abstract

Background

Nursing documentation burden is a growing point of concern in the United States health care system. Documentation in the electronic health record (EHR) is a contributor to perceptions of burden. Efficiency tools like flowsheet macros are one development intended to ease the burden of documentation.

Objective

This study aimed to evaluate whether flowsheet macros, a documentation efficiency tool in the EHR that consolidates documentation into a single click, reduces the time spent on documentation activities and the EHR overall.

Methods

Nurses in the health system were encouraged to create and utilize flowsheet macros for their documentation. Flowsheet documentation and time in system data for nurses' first and last shifts in the evaluation period were extracted from the EHR. Linear regression with control variables was utilized to understand if the utilization of flowsheet macros for documentation reduced the time spent in flowsheets or the EHR.

Results

The results of linear regression showed a significant, negative relationship between flowsheet macros use and time in flowsheets (adjusted odds ratio [AOR] = −0.291, 95% confidence interval [CI] = −0.342 to −0.240, p  < 0.001). Flowsheet macros use and time in system also had a significant, negative relationship (AOR = −0.269, CI = −0.390 to −0.147, p  ≤ 0.001). Subgroups for department specialties showed time savings in flowsheet activities for medical surgical, critical care, and obstetrics units, however, a significant relationship was not found in emergency and rehabilitation units.

Conclusion

Utilization of flowsheet macros was associated with a decrease in the amount of time a nurse spends in both flowsheets and the EHR. Adoption and timesavings varied by the department setting, suggesting flowsheet macros may not be applicable to all patient types or conditions. Future research should investigate if the time savings from this tool yield benefits in perceptions of nurse documentation burden.

Background and Significance

Nurse burnout presents risks to organizations as it has been associated with worsening safety and quality of care. 1 Additionally, survey data have shown that nurse burnout has been associated with increased nurse turnover. 2 Burnout has been reported as a primary reason for nurses having left the profession or considering leaving the profession. 3 This risk of rising turnover comes at a time when the National Center for Health Workforce Analysis is estimating a shortage of 78,610 full-time equivalent registered nurses (RNs) in 2025. 4 This creates a pressing challenge for health systems to address the multifaceted issues affecting nurse burnout and resignation.

Adoption of electronic health records (EHRs) reached 97% of U.S. hospitals by 2014, a 977.8% increase from the 9.0% recorded in 2008. 5 The EHR is credited with improvements in patient safety, a reduction in medical errors, and overall supporting better patient outcomes. 6 Despite these gains in patient safety, recent research has highlighted that EHR technology may be associated with increased clinician burnout. 7 However, user interface design may influence this burnout, as nurses report that better usability and design may lower the odds of burnout. 8 The American Nursing Informatics Association cited EHR usability as a contributor to excessive burden due to insufficient human factors engineering. 9 Content of the documentation may also influence perceptions of burden. In a scoping review of literature related to documentation burden, the authors posit there is a distinction to be made between documentation burden, which includes necessary documentation tasks, and excess burden, which are unnecessary tasks that could result from tasks not aligned with care delivery. 10

Flowsheet documentation in the EHR accounts for 35.3% of nurses' time during a shift. 11 Prior research showed nurses must document between 631 and 875 unique data points during a single shift. 12 Because of the association between EHR use and nurse burnout, 7 improving usability of flowsheet documentation in the EHR presents an opportunity for both efficiency gains and potential to avoid the undesirable effects of documentation burden. One such feature is “flowsheet macros,” which allows for the creation of templated responses to flowsheets. This feature is conceptually similar to note documentation tools targeted towards providers, which allows them to more quickly complete notes. Nurses are the primary adopters of flowsheet macros, especially to facilitate documentation of “normal” patient assessments. 13 Flowsheet macros simplify the documentation workflow by autopopulating responses in a single click, which is a previously explored documentation burden reduction effort. 14 Our EHR vendor introduced flowsheet macros in 2023, allowing users to answer multiple rows of data with a single clickable action ( Fig. 1 ).

Fig. 1.

Example of flowsheet documentation completed via flowsheet macros. ©2025 Epic Systems Corporation.

Click reduction is a common measure for tracking system improvements and is a desirable improvement reported by nurses. 15 How click reductions translate to time saved is difficult to measure. Advancements in auditing data of EHR use have allowed for time tracking by user and activity. 16 One quality improvement project investigating EHR redesign leveraged a similar EHR vendor-provided dataset to measure pre-/post-EHR activity time following flowsheet redesign. 17 However, no project that we are aware of, to date, has measured changes in EHR time across an entire health system following the implementation of a documentation efficiency tool. Leveraging the availability of activity data and time spent in the EHR, this project sought to evaluate whether utilization of flowsheet macros is associated with a reduction in time spent in the EHR.

Methods

Implementation

This academic health system (AHS) consists of four acute care hospitals, an orthopedic hospital, and over 300 ambulatory locations. All four acute care hospitals are American Nurses Credentialing Center (ANCC) Magnet-designated. Flowsheets are used for documentation across various settings and patient care roles. Flowsheet macros were introduced for personal creation and use on any flowsheet for all health system users with flowsheet access at the AHS. The feature was made available on December 10, 2023. Central system templated flowsheet macros were neither available, nor were users able to share macros with each other. Users were able to create flowsheet macros for any flowsheet row they had available to document (excluding flowsheet rows that resulted in creating a score, which were used for documenting infusions, or stored images, which are excluded by the vendor) and could select any response value they preferred. Features related to the flowsheet macros were published via an optional e-learning in the organization's learning management system. Additionally, flowsheet macros were presented at nursing forums, and unit rounds were conducted by the nursing informatics team to support staff creation, adoption, and utilization. The nursing informatics team also provided just-in-time training as requested by clinicians, and virtual workshops were offered by the nursing informatics and training team. Lastly, flowsheet macros were featured to each campus' nurse leaders and frontline nursing forums to create additional awareness.

Project Design

This is a retrospective evaluation of a performance improvement project, investigating frequency of flowsheet macros use and impacts on time spent in the EHR across the AHS. EHR data from Epic System's (Verona, WI) was queried. All user shift data for EHR users whose shift began between December 10, 2023, and June 30, 2024, were exported from SlicerDicer (Caboodle database). Shifts where the user was listed as an RN and the shift was recorded as a 12-hour shift and began between 6:00 a.m. and 8:59 a.m. were included. Evening and night shifts were excluded due to the typically lower volume of nursing activity occurring during the night shift. 18 All other shifts were excluded from the analysis. The minutes the user was “active” in flowsheets and “active” in the EHR during that shift were also extracted. Active time is when the user is either scrolling, clicking, or moving their mouse on the desktop or mobile application. Time in flowsheets specifically is the amount of time in that unique or related activity.

Flowsheet documentation history was queried from Epic's Caboodle database. Flowsheet documentation completed between December 10, 2023, and June 30, 2024, by RNs, where the documentation was recorded during the timeframe that the user was “signed in” to the EHR as a “Primary Registered Nurse” was included. This served as an indicator that the nurse was serving in a direct patient care function at the time flowsheet documentation was completed. Flowsheet documentation data were then matched to the shift data based on the user's login name and date of documentation and shift.

For analysis, the user's first shift was selected as the first shift in the project period where the user completed at least 50 flowsheet rows (the minimum number of rows to document at least one shift assessment on one patient), and none were completed via flowsheet macros. The user's last shift was selected as the latest shift in the project period where the user completed at least 50 flowsheet rows. If the user's login department for the first and last shifts were not the same, the user was excluded from the project, as this signaled the nurse may float to different units within the organization. Different units have different documentation requirements based on the patient population.

The overall unique number of flowsheet values recorded during the shift was calculated, and the percentage completed via flowsheet macros. The Caboodle database has an indicator on each recording if it was completed utilizing the flowsheet macros tool. The time in flowsheets and overall system time in both the first and last shifts were calculated. The change in time between the first and last shift, the number of days between the first and last shift, and the change in unique recordings were also calculated.

Descriptive statistics include daily percentage of flowsheet documentation completed via flowsheet macros, the mean change in overall documentation completed, minutes in flowsheets, and minutes in the EHR. Linear regression was used to determine if increased use of flowsheet macros led to a decrease in time spent in flowsheets or the EHR overall.

After nurses were selected and matched between the two data sources, all unique identifiers were removed for analysis, and datasets were labeled using subject numbers. All de-identified data were prepared and analyzed in IBM SPSS Statistics (version 28.0.1.1). Because the quality improvement project dataset was limited and project subjects could not be identified, it was not considered human subjects research and did not necessitate review by the AHS Institutional Review Board (IRB).

Results

Overall, 2,626 nurses were included in the project. Sixty-two nurses from behavioral health units were excluded due to a lack of flowsheet macros adoption. Mean documentation completed via flowsheet macros during the user's last shift in the project period was 6.21%, with flowsheet macros completed between 0.00% of documentation and 75.73% of documentation. The mean change in flowsheets during the last shift was −2.60 minutes, and mean change in the EHR overall was +5.16 minutes. Rehabilitation and medical surgical units had the highest mean amount of documentation completed via flowsheet macros in the last shift (8.2%). Rehabilitation units had the greatest reduction in time in flowsheets (−6.26 minutes) and the greatest reduction in time in the EHR (−5.12 minutes). Mean flowsheet macros use, changes in volume of flowsheet row documentation, change in flowsheet minutes, and change in system minutes are shown in Table 1 .

Table 1. Characteristics of flowsheet macros use and time in flowsheets and system, by unit type.

Unit type	n (%)	Number of users any macros use (%)	Mean macros use (%)	Mean first shift rows	Mean last shift rows (difference)	Mean first shift flowsheet minutes	Mean last shift flowsheet minutes (difference)	Mean first shift system minutes	Mean last shift system minutes (difference)
Medical Surgical	1,160 (44.2)	307 (26.5)	8.2	429.0	471.9 (+42.9)	46.6	45.3 (−1.3)	154.9	166.3 (+11.4)
Critical Care	714 (27.2)	198 (27.7)	4.7	717.8	690.6 (−27.2)	62.7	47.1 (−5.6)	130.5	128.2 (−2.3)
Obstetrics	292 (11.1)	78 (26.7)	6.9	568.2	518.9 (−49.3)	51.5	46.3 (−5.2)	117.8	118.2 (+0.5)
Emergency	387 (14.7)	29 (7.5)	2.2	303.1	323.6 (+20.5)	14.9	16.6 (+1.7)	157.1	162.8 (+5.7)
Rehabilitation	73 (2.8)	23 (31.5)	8.2	462.1	444.0 (−18.1)	49.1	42.8 (−6.3)	160.4	155.3 (−5.1)

The results of linear regression showed that after controlling for the user's first shift's time in flowsheets, the difference in total flowsheet rows documented between the first and last shift, their unit type, and the hospital location where they worked showed a significant, negative relationship between flowsheet macros use and time in flowsheets (adjusted odds ratio [AOR] = −0.291, 95% confidence interval [CI] = −0.342 to −0.240, p  < 0.001), meaning each percentage point increase in flowsheet macros use reduced time in flowsheets by 17.5 seconds. Flowsheet macros use and time in system also had a significant, negative relationship (AOR = −0.269, CI = −0.390 to −0.147, p  < 0.001). Linear regression results are presented in Table 2 . Variations were seen across unit types, where some specialties saw increased or decreased likelihood of changes in flowsheet and system minutes when compared with medical surgical units. Variability by hospitals was also significant.

Table 2. Results of multivariable linear regression predicting change in flowsheet minutes and system minutes.

	Flowsheet minutes			System minutes
	Adjusted odds ratio a	95% Confidence interval	p -value	Adjusted odds ratio a	95% Confidence interval	p -value
Last shift, percentage of macros used	−0.291	−0.342 to −0.240	<0.001	−0.269	−0.390 to −0.147	<0.001
First shift flowsheet minutes	−0.436	−0.465 to −0.407	<0.001	−0.403	−0.473 to −0.334	<0.001
Change in total rows documented	0.039	0.037 to 0.041	<0.001	0.067	0.062 to 0.072	<0.001
Department Type (compared to Medical Surgical)
Critical Care	3.796	2.046 to 5.546	<0.001	−4.677	−8.858 to −0.496	0.028
Obstetrics	1.729	−0.542 to 3.999	0.136	−2.966	−8.390 to 2.684583	0.284
Emergency	−12.202	−14.451 to −9.953	<0.001	−19.163	−24.535 to −13.791	<0.001
Rehabilitation	−0.371	−4.882 to 4.141	0.872	−9.839	−20.617 to 0.939	0.074
Hospital (compared to Hospital 1)
Hospital 2	−4.296	−8.586 to −0.006	0.050	−7.884	−18.133 to 2.365	0.132
Hospital 3	−2.068	−3.945 to −0.191	0.031	−3.191	−7.674 to 1.293	0.163
Hospital 4	−3.966	−5.693 to −2.240	<0.001	−7.716	−11.840 to −3.591	<0.001
Hospital 5	1.774	0.837 to 4.386	0.183	−0.264	−6.503 to 5.975	0.934

^aAdjusted odds ratio controls for the user's first shift time in flowsheets, difference in total flowsheet rows documented between the first and last shift, the unit type, and the hospital location.

At the unit type level, medical surgical, critical care, and obstetrics units had a significant, negative relationship between flowsheet macros use and change in flowsheet minutes. Medical surgical and critical care units also had a significant, negative relationship between macros use and system time ( Table 3 ).

Table 3. Results of multivariable linear regression predicting change in flowsheet minutes and system minutes, by unit type.

	Change in flowsheet minutes			System minutes
Unit Type	Adjusted odds ratio	95% Confidence interval	p -value	Adjusted odds ratio	95% Confidence interval	p -value
Medical Surgical	−0.335	−0.398 to −0.272	<0.001	−0.328	−0.486 to −0.171	<0.001
Critical Care	−0.387	−0.522 to −0.251	<0.001	−0.455	−0.730 to −0.179	0.001
Obstetrics	−0.251	−0.391 to −0.111	<0.001	−0.167	−0.488 to 0.154	0.305
Emergency	−0.021	−0.211 to 0.170	0.833	−0.125	−0.663 to 0.412	0.647
Rehabilitation	−0.223	−0.533 to 0.087	0.155	−0.229	−0.886 to 0.429	0.490

The change in flowsheet minutes between the first and last shift in the project period was highly variable. However, increased macros use during the last shift saw the change in minutes more reliably fall below 0, indicating a decrease in time spent in flowsheets during the last shift. This is especially true beginning at 40.0% of documentation filed via flowsheet macros, which includes 116 users. Of these 116 users, 107 had a decrease in flowsheet minutes per row documented between their first and last shift in the project period. Fig. 2 shows the mean change in flowsheet minutes from the first to last shift, distributed by the percentage of documentation completed via flowsheet macros in the last shift. As the percentage of documentation completed via macros increases along the x-axis, mean change in time in flowsheets more consistently falls close to or below 0 minutes.

Fig. 2.

Mean change in minutes in flowsheets by percentage of documentation completed via macros during the nurse's last shift in the project period.

Discussion

Overall, 24.2% of nurses in the project analysis adopted flowsheet macros during the last shift in the project period, which reflects a growing interest in the tool. But, the flowsheet macros tool still requires improvements to satisfy the previously reported desire for better EHR human factors design, 19 as the majority of nurses have not yet adopted this new technology. Improvements could be made from an organizational perspective by reducing barriers to use and releasing pretemplated flowsheet macros. Alternatively, the EHR may consider improvements by allowing for documentation via flowsheet macros from a home screen within the chart or on a summarized multipatient view outside the patient chart, reducing the navigation required to complete the documentation. Rehabilitation and medical surgical units were the highest adopters of flowsheet macros in the project. This could be due to the type of documentation required among these patient populations or the frequency of required assessments. Further, because our study was based on user-created flowsheet macros, it is also possible that managers or nurses in these units promoted adoption to other nurses within their units, outside of the training provided. Only one user adopted flowsheet macros on behavioral health units, potentially due to the importance of psychosocial assessments on these units.

Across all units, flowsheet macros significantly reduced the time spent in both flowsheets and the EHR after controlling for confounding variables. This aligns with other research findings that found increasing EHR usability corresponds with a decrease in time spent completing documentation. 20 Interestingly, the user's time in flowsheet during the first shift had a greater odds ratio than flowsheet macros use on time in flowsheets and time in system, suggesting individual behavior in the EHR outweighs the effect of the efficiency tool. Perhaps unsurprisingly, the more flowsheet rows that were documented, the greater the time in flowsheets and system, which corresponds with the idea that the more documentation a user completes, the more time a user must take to enter the documentation. But, because the odds ratio is not as large as that of the flowsheet macros use variable, this may be mitigated with a documentation efficiency tool.

Compared to medical surgical units, critical care units spent significantly more time in flowsheets and less time in system. This may be explained by the greater percentage of system time spent in flowsheets by critical care units (36.7%) compared to medical surgical units (27.2%) during the project period's last shift. This would also help to explain that when subgrouping the population by individual unit types, critical care units had the greatest time savings from usage of flowsheet macros. This could be due to the more frequent and lengthier assessments required in the ICU compared to patients on other units, as reflected in the mean rows documented per shift being greater than the mean rows documented on other units. Further, critical care units saved even more time in the EHR overall. This could be a result of reducing the time needed to file a single, lengthy assessment. If a user is disrupted during an assessment, they may toggle back and forth between other screens on the EHR, increasing their flowsheet and system time. However, it is possible that if users can quickly complete the assessment and then address the interruption, it could lead to less toggling between screens. Medical surgical units had significant reductions in time in flowsheets and EHR, but did not experience the difference between flowsheet time and EHR time like critical care units, perhaps due to having shorter assessments. Obstetrics units had significant flowsheet time savings with increasing flowsheet macros use, but no significant effect on system time. This might suggest that efficiency gains realized from flowsheet macros use were diverted to other activities within the EHR.

Developing recommendations to address the growing documentation burden crisis is gaining in importance, with organizations like the American Medical Informatics Association (AMIA) carefully crafting calls to actions for various stakeholders, including EHR vendors. 21 In a separate report, AMIA highlights three overarching themes influencing the burnout crisis, with corresponding recommendations. In the absence of changing regulatory requirements or right-sizing documentation (two of the themes introduced), improvement to efficiency is the alternative pathway forward. 22 Our results show that flowsheet macros may be a way to improve efficiency within the limitations of current documentation requirements. Identifying methods to improve EHR efficiency for nursing and reduce burden is important to limiting the effects of clinician burnout. 23 Organizations must identify appropriate use-cases for flowsheet macros and implement and train correspondingly.

Limitations

This quality improvement project looks at the change in time in the EHR and system usage of flowsheet macros at two distinct time points. It is possible that there was additional variation in both flowsheet macros use and time spent in the EHR between the first and last shift during the project period. Additionally, although training occurred in advance, nurses may have spent time during their first shift building the new feature, inflating their time during the first shift. Future studies could assess means or medians across multiple shifts, or account for patient assignments during shifts to more accurately record time spent per patient. Further, it is possible that future research could employ repeated measures to follow nurse data over multiple shifts. The results do not account for potential variation in patient condition or acuity, which would likely influence documentation requirements. Additionally, the study period begins in December when respiratory infections typically begin to increase and ends in June as they wane, 24 potentially confounding results. If time allows, future research could compare time points from similar seasons.

This project looked at one documentation efficiency tool within one AHS, it is not clear if similar results would be found on other documentation efficiency tools nor at other health systems and generalizability should be carefully interpreted. Documentation requirements and preferences vary across hospitals, organizations, and individual users; thus, not all documentation for a patient can be pretemplated. Accuracy, completeness, and quality of flowsheet documentation vary by unit and are subject to each patient's condition, which can fluctuate during their hospital stay, thus, it was not assessed in this project. Future research should investigate whether quality or accuracy is impacted by documentation efficiency tools. This study only looked at user-created flowsheet macros as system-level macros were not available, thus, nurses using flowsheet macros had to be motivated to create macros and could be biased as more efficient users, though they were not aware that their time in system was to be later evaluated. Lastly, this project reviewed nursing use of flowsheet macros. Flowsheet documentation is often available to other clinical stakeholders, who may not see the same yields in time savings. While reducing time can have positive effects on nurse burnout, 7 it remains unclear if the time savings yielded in this project had any impact on the perception of nurse documentation burden or burnout. Qualitative research could augment these findings to further understand how saving time in the EHR corresponds with these perceptions.

Conclusion

Increasing utilization of flowsheet macros can be associated with a decrease in the amount of time a nurse spends in both flowsheets and the EHR. Adoption and time savings varied by the department setting, suggesting flowsheet macros are not necessarily suitable for all patient conditions or environments. Nonetheless, the development of such efficiency tools by the EHR vendor and popular adoption at this organization is encouraging. Future research should investigate the impact of flowsheet macros on nursing documentation quality as well as nurse perception of improvement in documentation burden.

Clinical Relevance Statement

Nursing documentation burden is an increasingly recognized issue within the nursing practice. Balancing documentation requirements and user workflow is essential to capturing the patient data necessary for optimal patient care, while reducing the strain on nurses is important. Flowsheet macros are one efficiency tool within the EHR that can allow for nurses to document necessary patient data while reducing the time spent completing this documentation.

Multiple-Choice Questions

1. When seeking to reduce nurse documentation time, which of the following actions would be reasonable?

   1. Eliminate documentation requirements

   2. Utilize a documentation efficiency tool

   3. Encourage copying/pasting data

   4. Reduce documentation requirements

   Correct Answer : The correct answer is option b. This paper finds that documentation efficiency tools can lead to a reduction in documentation time. Past research also finds that enhancements to user interface design can make documentation easier. This paper does not explore changing documentation requirements or simply copy/paste of data.

2. Flowsheet macros adoption was not equal across all units, which type of unit had the greatest adoption?

   1. Rehabilitation

   2. Critical Care

   3. Medical Surgical

   4. Obstetrics

   Correct Answer : The correct answer is option a. Emergency department nurses had the lowest rate of flowsheet macros adoption. The other unit types all had adoption by at least 20% of the nurses in the project.

Funding Statement

Funding None.