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Abstract

Introduction

Higher nurse turnover rates are associated with lower care quality and worse patient and nurse outcomes. Healthcare systems nationwide are implementing virtual nursing (VN), an innovative nursing care model, to reduce nurse turnover. However, there is a lack of evidence regarding the association between VNs and Electronic Health Record (EHR) documentation completeness and care services provided.

Methods

This cross-sectional study examined the association between using VN and documentation completeness and care provided. This mixed-methods study used observational and qualitative evaluation of a VN care team and bedside unit nurses at a major Southeastern hospital in the United States (US). Data collection included direct observation and semi-structured interviews. Post-observation interviews were conducted with VNs and unit nurses to obtain their perception of task completion and their perceived overall efficiency.

Results

The duration of both admission (9:45 min vs. 15:53 min) and discharge encounters (6:55 min vs. 19:07 min) was longer with VNs, indicating a more lengthy and thorough approach. The study findings show that VN was associated with increased documentation completeness for patient admissions (58.8% completion by bedside nurses, 80.2% completion by VNs) and discharges (67.8% completion by bedside nurses, 80.2% by VNs). Additionally, the interviews emphasized the time-saving benefits of VN, with interviewees acknowledging VN efficiency, communication, and collaboration.

Conclusion

This observational study contributes to the understanding of the impact of VN on care provision and documentation completeness, shedding light on the potential of VN as an effective strategy for enhancing nursing care in healthcare settings. The study findings provide valuable insights into the potential benefits of VN in improving task completeness and documentation accuracy, addressing some of the challenges posed by nursing shortages and high turnover rates.

Keywords

Virtual nursing nurse burnout nursing care nursing informatics

Introduction

Over 25% of US registered nurses may leave the profession by 2027 due to burnout, staffing deficits, and challenging work environments, which contribute to high nurse turnover rates and staffing challenges. (Suran, 2023) Higher nurse turnover rates are associated with lower care quality and worse patient and nurse outcomes. (Peng et al., 2023) US healthcare systems are exploring innovative care models to reduce nurse turnover.

Telehealth has demonstrated promise in improving nursing practice and patient experience, particularly in critical care settings. (Jenkins & White, 2001; Khairat, Pillai et al., 2020a; Winters & Winters, 2007) Improving collaboration between bedside and remote nurses in conjunction with the use of tele-ICU program technology positively impacts critical care patient outcomes. (Ruesch et al., 2012) The use of telehealth in nursing practice has shown promise for improving ICU patient care and nursing staffing models during the COVID-19 pandemic. (Gonzalez et al., 2023; Swink et al., 2023) However, there is a knowledge gap around the feasibility and acceptability of emerging telehealth models in inpatient settings such as medical and surgical units.

Review of Literature

One such model is virtual nursing (VN), which provides unit nurses with remote nursing expertise to reduce their workload. (Medicine, 2021) VN enables remote team-based care using telehealth and remote access to electronic health records (EHR). (Schuelke et al., 2019) Recent evidence underscores the growing integration of VN in inpatient care as a strategy to mitigate staffing shortages and support bedside nurses. A 2023 pilot in general care units introduced a Virtual RN (ViRN) who provided real-time clinical guidance and patient surveillance, and bedside nurses reported valuing the consistent expert support offered by ViRNs. (Roberson et al., 2023) Earlier models, such as the “virtually integrated care” (VIC) team, demonstrated that VNs could fulfill multiple roles—ranging from patient education and discharge planning to staff mentoring and quality surveillance—while improving staff, physician, and patient satisfaction. (Schuelke et al., 2019) Nevertheless, a 2024 consensus review emphasized the absence of standardized models and called for rigorous research to determine which VN strategies most effectively influence key outcomes including satisfaction, quality metrics, labor costs, and length of stay. (Boston-Fleischhauer, 2024) Collectively, these studies suggest that VN holds promise for enhancing inpatient care and workforce support, yet empirical data on optimal implementation, role standardization, and impacts on documentation and screen time remain limited.

The initial examination of VN characterized the requirements for implementing a comprehensive VN program using the Donabedian model for quality, yet there is a dearth of evidence regarding its impact on EHR documentation completeness and the range of care services provided, particularly in relation to continuous screen time for VNs. (Khairat et al., 2025) Given the potential of VN to address nurse turnover and improve patient care, it is critical to investigate its effectiveness and feasibility in inpatient settings. This study was conducted to examine the association between VN use and key outcomes such as documentation completeness and care services, providing a clear rationale for advancing the understanding of this emerging care model.

While the benefits of telehealth in nursing are well-documented in critical care, the specific application of VN in broader inpatient contexts remains a significant knowledge gap. The hypothesis is that by completing patient assessments at admission and providing patient education at discharge, VN will reduce the workload of unit nurses and improve the completeness and accuracy of documentation. However, there is a dearth of evidence regarding the implementation of this new care model, specifically the association between continuous screen time and VNs’ EHR documentation completeness and care services provided. This study examined the association between using VN and documentation completeness and care provided via a main research question: How does the implementation of VN impact the completeness and accuracy of EHR documentation and the efficiency of patient care tasks in inpatient settings? This research fills a critical gap by evaluating VN's impact on important care outcomes, offering insights into its potential to transform nursing practice. By addressing this understudied area, the study contributes to the practical implementation of VN programs in healthcare systems.

Methods

Study Design and Setting

This cross-sectional, mixed-methods study was conducted at a major Southeastern hospital among bedside nurses and VNs. Data were collected through direct observation of bedside nurses and VNs and semi-structured interviews by trained research assistants (RAs).

The VN intervention was embedded into the institutional electronic health record (EHR) to offer virtual nurses the ability to have a video call with the patient and view the patient's medical record for reviewing and documentation purposes. This study is reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (Appendix A). (von Elm et al., 2014)

Data Collection

Six graduate RAs were deployed to observe in-person patient encounters at the hospital and virtual patient encounters at the hospital's virtual nursing center (VNC) located in an administrative building adjacent to the hospital.

Before data collection, the study team visited the VNC to observe VNs informally and to obtain familiarity with the space and staff. The PI and nurse informatician provided in-depth training to the six RAs on how to conduct an observational study in a real clinical setting.

Observational Tools

Based on preliminary observations of VN and in-person encounters, two observational tools were developed for the RAs to collect data during admission assessment or patient discharge encounters. The tool included three sections (1) observed nurse demographics, (2) patient information, (3) nursing admission or discharge tasks. For nurses, the type of nurse (i.e., RN or LPN), level of education, years of nursing experience, and years using the institutional EHR system were collected. Patient information included the current location of the patient and where they are being admitted or discharged. The timeseries checklists mimicked the EHR workflow nurses use for documentation or education in the same order in which they are listed within the institutional EHR. Similar observational approaches have been employed to assess nursing workflows in the EHR. (Schachner et al., 2015) The design of this tool was informed by best practices in observational research methodology and reviewed by nursing staff to ensure accuracy. (Quinn Patton, 2013)

The admissions timeseries listed tasks related to patient's general overview, including medical and family history, allergies and medication review, general assessments, and interventions. The discharge section listed tasks related to the patient's summary of care, including discharge instructions, upcoming medical appointments, plan of care, and patient instructions. Samples of these time series checklists can be found in Appendix B.

The data collection tools were paper-based to facilitate quick data entry and error recovery in a fast-paced environment. The RAs received training on using the data collection tools to observe two types of patient encounters: patient admissions and patient discharges. The RAs observed VN encounters at the VNC first, followed by in-person encounters at the hospitals. Data collection occurred between July 13–21, 2023, for VN encounters and August 9–11, 2023, for in-person encounters.

The beginning and end of each encounter and each activity completed by the nurse during the encounter were timestamped. Tasks listed on the data collection tool without timestamps were considered uncompleted.

Upon successful data collection, the RAs converted the paper files into electronic formats. For each encounter, the number and duration of completed tasks, the duration of the encounter, and nurse demographics were summarized.

Qualitative Interviews

Post-observation interviews were conducted with VNs and unit nurses to obtain their perception of task completion and their perceived overall efficiency. The interviews were conducted in person, if time permits, after the observation sessions or virtually post-observation by the study team. Two interview guides were developed: one for bedside nursing staff who use VN and one for VNs. The interview guides were guided by the research question and underwent pre-testing during initial interviews with nursing staff. Based on feedback from these interviews and nursing leadership and staff, the interview guides were refined to improve clarity, flow, and relevance to participants’ experiences (Appendix C). While there was overlap between them, it was necessary to use two interview guides among the two groups of nurses to best represent their roles and workflow. Interviews were 30 min long and were audio-recorded and then transcribed verbatim and reviewed by the study team for accuracy. Data analysis occurred between October and November, 2023.

Data Analysis

The assessment was conducted by comparing the performance of VNs against traditional in-person nursing practices in several key areas: documentation completion rates for admissions and discharges, duration and thoroughness of admission and discharge processes, completion rates of Social Determinants of Health (SDOH) assessments and home medication reviews during admissions, and discharge education completion rates. Additionally, the experience levels of VNs in nursing and their proficiency with the electronic health record system were evaluated. Data from timeseries checklists was combined using Microsoft Excel and analyzed using Excel and SAS JMP.

Qualitative analysis was combined using a deductive and inductive approach, with the individual interview participant as the unit of analysis. (Chapman et al., 2015) Interviews were analyzed to identify themes and sentiment using Dedoose. A codebook was developed based on Consolidated Framework for Implementation Research (CFIR) constructs and the research team's initial readings of transcripts. (Damschroder et al., 2022) Two RA coders pilot-tested the initial codebook by independently coding two nurse transcripts and then comparing their results to fine-tune the codebook. Concept definitions and decision rules were revised as needed, and the enhanced codebook version will be applied to the remaining transcripts (Appendix C). Two RAs used the standardized codebook and independently coded the interview transcripts. Coding discrepancies were reconciled by discussion and consensus.

Ethics Statement

The institutional review board deemed this study not human subjects research (NHSR). Nurses and patients observed as part of the study gave verbal consent prior to observation. Nurses who participated in semi-structured interviews also gave verbal consent prior to the interview.

Results

Of 111 patient encounters observed, 81 (73%) were conducted by VN nurses, Table 1. For VN encounters, there were 33 (41%) admissions and 48 (59%) discharges compared to 8 (27%) and 22 (73%) for in-person encounters. On average, VN nurses had more years of nursing experience (26.3 years vs. 9.3 years) and more experience using the Epic^® system (10.2 years vs. 4.7 years).

Table 1.

Comparison Between in-Person and VN Patient Encounters.

	In-Person Encounters (%)	VN Encounters (%)
Total Encounters	30 (27%)	81 (73%)
Admission Encounters	8 (26.7%)	33 (40.7%)
Discharge Encounters	22 (73.3%)	48 (59.3%)
Nurse Demographics
Average years of Nursing experience (SD)	9.3 (SD)	26.3 (SD)
Average years of Epic exp.	4.7 (SD)	10.2 (SD)
Completion Time (average time, minutes)
Admission Completion	9:45 min	15:53 min
Discharge Completion	6:55 min	19:07 min
Admission Tasks
Admission Overview Section completion (out of 13)	7.75 (59.6%)	10.69 (82.2%)
Admission Assessment Section completion (out of 7)	4 (57.1%)	5.34 (76.3%)
Admission Overall completion (out of 20)	11.75 (58.8%)	16.03/20 (80.2%)
SDOH Task completion (out of 8)	3 (37.5%)	32 (96.9%)
Discharge Tasks
Discharge Instructions Overall completion average (out of 12)	8.13 (67.8%)	9.62 (80.2%)
Home Medication Review Task completion	2/8 (25%)	26/33 (78.8%)
Discharge Education Task completion	6/22 (27.2%)	38/48 (79.2%)

The duration of both admission (9:45 min vs. 15:53 min) and discharge encounters (6:55 min vs. 19:07 min) was longer with VNs.

VN nurses had higher documentation completion rates for both admissions (58.8% vs. 80.2%) and discharges (67.8% vs. 80.2%). During admissions, VN nurses completed SDOH assessments, a CMS requirement, at a significantly higher rate than in-person encounters (37.5% vs. 96.9%) (Fig. 1).

Figure 1.

Comparison of Required Task Completion Rates Between Unit Nurses (in-Person) and VNs.

VNs also showed a marked improvement in the completion of home medication reviews during admissions (25% vs. 78.8%), which is crucial for medication reconciliation. The completion rate of the discharge education task by VNs was also significantly higher (27.2% vs. 79.2%)

The response to VN documentation and efficiency has been mainly positive, with a focus on the time savings and workload burden relief for unit nurses, Table 2. Staff nurses and charge nurses appreciate the efficiency of the VN system, especially for admissions, where it saves them a significant amount of time. Additionally, VN was viewed as a valuable resource for new nurses who may find the workload overwhelming, offering them the much-needed support. Nurses also emphasized the importance of training and understanding the process, noting that the initial investment of time is well worth it for the long-term efficiency gains.

Table 2.

Feedback from Nurses on Virtual Nursing Processes, Benefits, and Drawbacks.

Theme	Quote	Sentiment Analysis
Documentation Process	“[For admissions] you're documenting their answers in the chart…Any concerns? You're putting those concerns specifically in their care planning, education…and notifying their primary RN.”“[For discharges], you're going over the after visit summary with the patient, allowing time for questions.” (Virtual Nurse)	Neutral
Documentation Process	“I can go back and look at any of [the documentation] if I need to. If I have any questions, like if I don't know if they did the [medication reconciliation], I can go in and see if they did that or not. And then [the VNs] usually put in a note as well saying RN notified after they've completed their [encounter].” (Unit Nurse)	Neutral
Documentation Process, Communication	“A very important part of our job is the information that we're gathering from those patients. We want to make sure that [the primary RN] has that information. Especially if it's for instance the patient we've just admitted tells us that they've got all their medications there at the bedside. We certainly don't want [the patient] taking [their home] medications and then [the primary RN] giving medications. This can be life threatening…[it is] super important for us to get that information to the nurse.” (Virtual Nurse)	Neutral
Nurse-to-Nurse Communication	“Our virtual nurses will send us a secure chat through [the EHR]. When they have completed the session, they will put this patient needs to be [placed on falls precautions], or they have medications that need to be stored in pharmacy. They’ll identify [patient] needs that they found on their admission screening or things that are red flags that we need to know through secure chat.” (Charge Nurse)	Neutral
Efficiency	“I think it does help us be able to multitask or be able to get more things done and then not have to spend all of the time to get the information [from the patient]. Having the high points that we need to know relayed to us, I do think it is efficient especially with admissions.” (Charge Nurse)	Positive
Efficiency	“More people will get on board with [virtual nursing] and hopefully more and more people are going to see the value in it…The [primary RNs] that are using [virtual nursing] regularly are appreciating the help that we give them. It saves them so much time.” (Virtual Nurse)	Positive
Efficiency	“Anything that can help take some of the weight off the floor nurse, [so that they are] able to do direct patient care. A lot of what nurses are doing now is charting, and if that's what virtual nurses can do, then that would be really helpful.” (Unit Nurse)	Positive
Workflow, Efficiency	“I think the biggest challenge is just the time that it takes to set up the [tablet]. And if a nurse runs into an issue where the [tablet] is not loading correctly or it's just taking longer than they wanted - or another issue that we've run into is taking too long for the virtual nurse to connect to the patient. [The floor nurse] will just say, ‘I'm not going to do this. I'll just do it myself.’ And then they just don't utilize virtual nursing.” (Charge Nurse)	Negative
Future of VN	“The only other struggle with [VN] is sometimes patients just aren't that into it. They're like, ‘Wait, what? I talked to you and now I'm talking to someone else, and then someone else is going to come in and do something?’ and it gets kind of disjointed. But yeah, I think the biggest thing was just like letting go of control enough to allow virtual nursing to trust that it was going to get done and that patients would be okay with it.” (Unit Nurse)	Negative

However, some mention that the setup process for VN can be time-consuming, which may deter nurses from fully utilizing it. Despite this, the consensus was that VN ultimately saves time when used effectively. Overall, the positive feedback outweighs the negatives, with VN being perceived as a valuable tool for improving documentation and efficiency in nursing workflows.

Discussion

This mixed-methods study examined the association of VN with documentation completeness and care provided in an inpatient setting. The study findings show that VN was associated with increased documentation completeness for patient admissions and discharges. The use of VN allowed for more thorough and accurate documentation compared to traditional, in-person nursing practices. This suggests that VN may improve documentation quality in healthcare settings.

Additionally, the potential effects of the VN model were distinguished from the influence of nurses’ experience levels. VN nurses, who generally have more years of nursing experience and greater proficiency with the EHR system, demonstrated higher documentation completeness and efficiency in patient care tasks compared to unit nurses. This distinction highlights the importance of both the VN model and the nurses’ experience levels in achieving improved outcomes. Additionally, this study revealed that VN was linked to the efficient completion of patient care tasks such as social determinants of health (SDOH) assessments and home medication reviews during patient admissions. VN facilitated the timely and comprehensive completion of these tasks, potentially fulfilling a critical Centers for Medicare & Medicaid Services (CMS) requirement to document patients’ social determinants of health, thereby improving and enhancing patient care and health equity. (Medicare & Services, 2023a, 2023b) Findings from this study support prior studies that medical reconciliation at the time of admission is a critical task that can reduce medical errors and the length of hospital stays. (Ceschi et al., 2021; Meguerditchian et al., 2013; Yamada et al., 2024)

Substantial years of clinical experience were one requirement for being selected as a VN. This guaranteed that unit nurses would receive assistance from experienced nurses with substantial experience conducting patient admission and discharge tasks. VNs who were experienced clinical nurses previously demonstrated higher documentation completeness, indicating a more thorough and uninterrupted approach This suggests that, at least in the initial stages of VN, experienced nurses may be more adequate for VN, which is consistent with previous findings that found experienced nurses are better able to adapt to virtual care environments and effectively manage patient care. (Rincon, 2023; Vaughan et al., 2024) Furthermore, the study findings illustrated that VN demonstrated comparable performance in discharge education completion rates when compared to traditional, in-person nursing practices. In particular, VNs completed individual discharge education at a higher rate than unit nurses, ensuring that patients received thorough instructions regarding their home medications and follow-up appointments, which may contribute to a reduction in hospital readmission rates. This suggests that VN may effectively support patient education efforts during the discharge process. Prior research showed that VN can enhance nurses’ competence in patient education and support, which is crucial during the discharge process. (Ko & Choi, 2024; Wang et al., 2024)

The interviews highlighted the important role of VNs in the documentation process, particularly during admissions and discharges, emphasizing the importance of accurately transferring information to primary RNs. The nursing staff acknowledged the efficiency and time-saving benefits of VN, with staff and charge nurses advocating for its use to streamline processes and support the entire nursing team. There was an emphasis on the value of VN, especially for new nurses and during busy periods such as admissions, with an overall positive outlook on its contributions to the nursing workflow and patient care.

Strengths and Limitations

This study offers valuable insights into the potential benefits of VN in enhancing task completion and documentation accuracy, while addressing the challenges posed by nursing shortages and high turnover rates. Additionally, the study emphasizes the efficiency, communication, and collaboration benefits of VN, highlighting its positive impact on nursing workflows and patient care.

This study had limitations. Data was collected at a single hospital and a VNC. However, at the time of data collection, the number of units or hospitals utilizing VN was limited, and collecting data from other sites may not have been plausible. Additionally, this study was conducted at a single hospital and a VNC, which may limit the generalizability of the study findings to other healthcare settings. The effectiveness of VN may vary based on specific healthcare settings and patient populations. Further research is needed to explore the broader impact of VN on nurse turnover rates and overall care quality in healthcare systems.

Patient severity was not accounted for to measure variations in duration and task completion based on patient conditions as this was not related to the study research question. Also, patient readmission statistics for patients who utilized VN during their hospital stay was not examined. Additional research should examine the effect of using VN on these outcomes. Finally, there is concern that the VN process of completing admission and discharge tasks leads to interruptions in continuity of patient care or care fragmentation. Future research should examine patient outcomes and patient perspectives on the integration of VN as part of standard care.

Implications to Nursing Practice

Overall, the results of this study support the hypothesis that implementing VN can reduce the workload of unit nurses. By shifting the burden of admission and discharge tasks to VNs, unit nurses will experience a lighter workload, enabling more time for direct patient care. This study demonstrates that VN enhances the completeness and accuracy of documentation, as well as the provision of specific aspects of patient care. Therefore, using VN can reduce workload and burnout among unit nurses, leading to overall improvement in nursing workforce resilience and well-being. (Khairat, Xi et al., 2020b)

Further research is needed to examine the financial implications of implementing VN, particularly with regard to return-on-investment, to inform the scalability and sustainability of this emerging care delivery model. In addition, more investigation is needed to explore the roles of certified nursing assistants in the VN model of care. Nursing assistants may assist with the preparations of VN encounters by reviewing the patient record and preparing a checklist for the VN to use based on the patient's condition. This may further streamline the efficiency of VNs by reducing preparation and miscellaneous tasks and allowing more time for direct patient interaction. In addition, engaging other nursing roles in the VN model may help mitigate nurse shortages, especially in rural hospitals.

Conclusion

This is the first-of-its-kind study to compare task completion and documentation completeness of an emerging VN healthcare delivery model by comparing VN vs. in-person patient encounters. However, it is important to acknowledge that the effectiveness of VN may vary based on specific healthcare settings and patient populations. Furthermore, future research should continue to explore the broader impact of VN on nurse turnover rates and overall care quality in healthcare systems.

Supplemental Material

sj-docx-1-son-10.1177_23779608251363667 - Supplemental material for Association of Virtual Nursing and Task Completeness: An Observational Study

Supplemental material, sj-docx-1-son-10.1177_23779608251363667 for Association of Virtual Nursing and Task Completeness: An Observational Study by Saif Khairat, Jennifer Morelli, Julia Aucoin, Barbara S. Edson, Cheryl B. Jones and Christopher M. Shea in SAGE Open Nursing

Supplemental Material

sj-doc-2-son-10.1177_23779608251363667 - Supplemental material for Association of Virtual Nursing and Task Completeness: An Observational Study

Supplemental material, sj-doc-2-son-10.1177_23779608251363667 for Association of Virtual Nursing and Task Completeness: An Observational Study by Saif Khairat, Jennifer Morelli, Julia Aucoin, Barbara S. Edson, Cheryl B. Jones and Christopher M. Shea in SAGE Open Nursing

Footnotes

Acknowledgements

The study team would like to thank the nurses at UNC Health as well as the UNC SON student RAs: Christiana Akinyemi, Kyndall Estep, Josh Saucedo, Alex Vo, and Linnea Week.

ORCID iDs

Saif Khairat

Jennifer Morelli

Ethical Approval and Informed Consent

The institutional review board of The University of North Carolina at Chapel Hill deemed this study not human subjects research (NHSR). Nurses and patients observed as part of the study gave verbal consent prior to observation. Nurses that participated in semi-structured interviews also gave verbal consent prior to interview.

Author Contributions

SK: Conceptualization, Data curation, Funding acquisition, Methodology, Project administration, Resources, Supervision, Validation, Writing – original draft

JM: Data curation, Formal analysis, Investigation, Methodology, Project administration, Supervision, Validation, Visualization, Writing – original draft

JA: Methodology, Project administration, Resources, Validation, Writing – review & editing

BSE: Conceptualization, Methodology, Resources, Writing – review & editing

CBJ: Conceptualization, Methodology, Writing – review & editing

CMS: Conceptualization, Validation, Writing – review & editing

Funding

This study was supported by the Office for the Advancement of Telehealth, Health Resources and Services Administration, DHHS (grant 6 U3GRH40003-01-01), the National Center For Advancing Translational Sciences of the National Institutes of Health (NIH) under award number RC2TR004380, and the UNC School of Nursing. The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of OAT, HRSA, NIH, or DHHS, nor does mention of department or agency names imply endorsement by the US Government.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability

The datasets presented in this article are not readily available because the datasets include protected health information (PHI) that cannot be publicly shared. Making the dataset publicly available would breach compliance with the protocol approved by our research ethics board. Requests to access the datasets may be directed to Dr. Saif Khairat at saif@unc.edu.

Supplemental material

Supplemental material for this article is available online.

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