Toward Alleviating Clinician Documentation Burden: A Scoping Review of Burden Reduction Efforts

Abstract

Background Studies have shown that documentation burden experienced by clinicians may lead to less direct patient care, increased errors, and job dissatisfaction. Implementing effective strategies within health care systems to mitigate documentation burden can result in improved clinician satisfaction and more time spent with patients. However, there is a gap in the literature regarding evidence-based interventions to reduce documentation burden.

Objectives The objective of this review was to identify and comprehensively summarize the state of the science related to documentation burden reduction efforts.

Methods Following Joanna Briggs Institute Manual for Evidence Synthesis and Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines, we conducted a comprehensive search of multiple databases, including PubMed, Medline, Embase, CINAHL Complete, Scopus, and Web of Science. Additionally, we searched gray literature and used Google Scholar to ensure a thorough review. Two reviewers independently screened titles and abstracts, followed by full-text review, with a third reviewer resolving any discrepancies. Data extraction was performed and a table of evidence was created.

Results A total of 34 articles were included in the review, published between 2016 and 2022, with a majority focusing on the United States. The efforts described can be categorized into medical scribes, workflow improvements, educational interventions, user-driven approaches, technology-based solutions, combination approaches, and other strategies. The outcomes of these efforts often resulted in improvements in documentation time, workflow efficiency, provider satisfaction, and patient interactions.

Conclusion This scoping review provides a comprehensive summary of health system documentation burden reduction efforts. The positive outcomes reported in the literature emphasize the potential effectiveness of these efforts. However, more research is needed to identify universally applicable best practices, and considerations should be given to the transfer of burden among members of the health care team, quality of education, clinician involvement, and evaluation methods.

Keywords

electronic health record - documentation - documentation burden

Background and Significance

Recently, there has been increased attention on the worsening extent of documentation burden experienced by health care providers around the globe. In May 2022, U.S. Surgeon General Dr. Vivek Murthy released an advisory on health care worker burnout and endorsed the ambitious goal of the 25 × 5 Initiative to reduce documentation burden to 25% of its current state by 2025.[1] The electronic health record (EHR) remains; however, essential to clinical care workflows as well as billing, compliance/regulatory, and safety requirements. In this scoping review, we seek to identify and comprehensively summarize literature on efforts to specifically reduce documentation burden experienced by the health care workforce.

Clinical documentation has become increasingly burdensome due to compliance, billing regulations, variations in workflows and policies, EHR capabilities, and other factors.[2] [3] For the purpose of this work, we define clinical documentation as the work of comprehensive record keeping related to the provision of clinical care. Documentation burden, as conceptualized in the literature, often refers to demands related to clinical charting, information retrieval, and administrative responsibilities imposed on health care professionals by requirements beyond direct patient care.[2] [3] [4] This includes the creation and maintenance of patient records, complying with regulatory standards, and meeting billing requirements. Documentation burden is defined in the 25 × 5 Summary Report as “the stress imposed by excessive work required to generate clinical records of health care-related interactions.”[4] Indeed, studies show that documentation consumes clinicians' time, leading to less face-to-face time with patients, increased errors, and unintended negative outcomes.[5] [6] [7] [8] Increasing clinical documentation burden leads to job dissatisfaction, stress, and clinician burnout.[8] Physicians, nurses, midwives, physician assistants, nurse practitioners, and other health professionals across various specialties and care settings have indicated that documentation burden impacts care delivery and clinician wellness.

Contributors to documentation burden are multifaceted and range from policy-related mandates, such as prior authorization, to organizational culture and human factors.[2] [9] Additionally, many EHR systems are characterized by poor usability and interoperability that, coupled with the ever-expanding volume of data stored within the EHR, contribute to challenges in not only documenting but also searching for and accessing information efficiently.[2] [9] In addition, today's clinical notes often contain duplicative information, which increases time, cognitive effort, and burden to clinicians when seeking information from a patient's record.[10] [11]

The burden of clinical documentation associated with the use of EHR systems has received attention from various national and global stakeholders.[1] [12] Professional societies representing both physicians and nurses have recognized documentation burden as a problem and steered efforts to address this concern. For example, the American Nursing Informatics Association has developed a framework outlining six domains of burden to provide structure for documentation burden reduction efforts.[13] Additionally, the American Medical Association (AMA) has recognized the role of documentation burden in contributing to physician burnout. The AMA's “Getting Rid of Stupid Stuff” (GROSS) initiative provides guidance and strategies to physicians and organizations to reduce the burden of EMR documentation with the goal to reduce provider burnout broadly.[14] In early 2021, organizations across the United States including Columbia University, Vanderbilt University Medical Center, the American Medical Informatics Association (AMIA), and the National Library of Medicine collaborated to establish the AMIA 25 × 5 Symposium and the resulting AMIA 25 × 5 Task Force, with the goal to reduce the documentation burden that clinicians experience to 25% of its current state within 5 years.[2] Due to the impact of clinician satisfaction on health system performance, the Institute for Healthcare Improvement's Triple Aim expanded into the Quadruple Aim focusing on improving patient experiences, enhancing population health, reducing costs, and improving clinician satisfaction.[15] To mitigate clinician burden, the Office of the National Coordinator for Health Information Technology has established three goals: reduce the time and effort to record information in EHRs, reduce the time and effort required to meet regulatory requirements, and improve the functionality and ease of use of EHRs.[16]

While documentation burden has been recognized extensively and many initiatives are underway to address the issue, there is a gap in the literature about pragmatic efforts to address documentation burden reduction and their effectiveness. We pose the question: what are health care stakeholders (health systems, health care leaders, clinicians, and others) doing to reduce documentation burden? This review aims to identify and synthesize real-world efforts undertaken by health systems to reduce documentation burden for their providers and staff.

Methods

We conducted our review in alignment with recommendations from the JBI Manual for Evidence Synthesis [17] and following PRISMA-ScR guidelines to promote transparency and reproducibility of this work.[18] The following databases were used: PubMed, Medline (Ovid), Embase, CINAHL Complete, Scopus, and Web of Science. We conducted key word searches using the following: “documentation redesign,” “'workflow' AND 'documentation burden,'” “'documentation burden' AND 'reduction,'” “'documentation burden reduction' AND 'electronical health record,'” and “reducing documentation burden.” Additionally, used Google Scholar to identify articles that were not captured by our search of the databases above. Inclusion criteria consisted of articles published in 2013 onward, as 2013 was the year that documentation burden associated with EHRs, rather than paper-based documentation burden, started to appear in the literature. We reviewed the reference lists of included articles for additional studies that met inclusion criteria. We included only articles and abstracts that were published in English. We excluded articles that did not specifically report about documentation burden reduction efforts. Of note, we used “effort” broadly to capture any initiative, intervention, strategy, proposal, or similar, as described by the author(s), that was intended to reduce documentation burden. For example, we excluded articles that only described the problem but did not describe efforts or solutions intended to impact change to documentation practices or reduce burden. However, in alignment with the goals of a scoping review to map emerging and existing literature related to this topic,[19] we included articles that reported on documentation burden reduction efforts even if outcomes were not reported.

Two reviewers, E.A.S. and S.A., initially independently screened article titles and abstracts. Then, E.A.S. and S.A. independently reviewed full texts of the articles. Any discrepancy over a decision about article inclusion was resolved by consensus and included a third reviewer (M.A.A.). Rayyan.ai[20] was used to facilitate screening and selection of included studies, as well as to detect and remove duplications. Data were extracted from all selected articles, including: (1) title, (2) year, (3) author(s), (4) journal, (5) geographic location, (6) target population (e.g., Doctor of Medicine [MD], registered nurse), (7) setting (e.g., academic medical center, outpatient primary care), (8) purpose, (9) documentation burden reduction effort, (10) measure(s), (11) governance (the people, processes, and structures of oversight, coalition-building, regulation, design, and accountability[21]), and (12) outcome(s). We identified categories of documentation burden reduction efforts based on the primary stated purpose of the effort. We created a table of evidence, highlighting documentation burden reduction effort, measure(s), governance, and outcome(s) using Microsoft Excel ([Supplementary Table S1], available in online version only).

Our initial search of the six databases on August 3, 2022 yielded a total of 178 articles. An additional 9 articles that met inclusion criteria were identified through scanning reference lists, Google Scholar, and gray literature searches. After removing duplicates, we reviewed the titles and abstracts of 73 articles for inclusion. We excluded 21 articles based on title and abstract review and reviewed full texts of the remaining 52 articles. We repeated our search of the six databases on October 19, 2022 but did not identify any additional articles for inclusion. In total, 34 articles and publications were identified where data were extracted and synthesized for this review. [Fig. 1] provides an overview of our process for record identification and inclusion.

Fig. 1 Review process overview: record identification and inclusion.

Results

The articles included in this scoping review were published between 2016 and 2022, with more than half (n = 19, 55.9%) published in the last 2 years.[5] [8] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38] The majority of articles (n = 31, 91.2%) described documentation burden reduction efforts in the United States,[5] [8] [22] [23] [24] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] [41] [42] [43] [44] [45] [46] [47] [48] [49] [50] [51] [52] [53] although the articles covered a variety of care settings and clinician populations. Many of the documentation burden reduction efforts targeted physicians or advanced practice providers (APPs) (n = 18, 52.9%), with some also including medical students.[22] [26] [27] [30] [33] [34] [36] [37] [39] [40] [41] [42] [43] [44] [45] [47] [50] [51] Eleven articles (32.4%) discussed nursing efforts and nursing documentation.[5] [8] [23] [24] [25] [28] [29] [32] [35] [38] [46] The remaining articles described efforts aimed at multiple clinician types, including attendings, fellows, and nurses (n = 5, 14.7%).[8] [31] [38] [46] [48] [Table 1] provides a summary of article characteristics.

Table 1 Summary of article characteristics

Characteristics	Number of studies (n = 34)	%
Year of publication
2016	1	2.94
2017	2	5.88
2018	4	11.76
2019	3	8.82
2020	5	14.71
2021	12	35.29
2022[a]	7	20.59
Geographic location
United States	32	5.88
Canada	2	94.12
Care setting
Academic medical center	9	26.47
Community hospital	3	8.82
Inpatient—facility type not specified	7	20.59
Outpatient—primary or specialty	7	20.59
Multisite/national	3	8.82
Not specified/unknown	5	14.71
Target population
Physicians, APPs, medical students	18	52.94
Registered nurses	11	32.35
Multiple clinician types	5	14.71

Abbreviation: APP, advanced practice provider.

^a Repeat search was conducted on October 19, 2022.

Documentation Burden Reduction Efforts

Documentation burden reduction efforts described in the articles can be categorized as follows: medical scribes (in-person or virtual) (n = 7, 20.6%),[26] [33] [36] [42] [43] [44] [47] reducing volume and/or improving efficiencies (e.g., note bloat, flowsheet redesign) (n = 10, 29.4%),[5] [22] [28] [29] [30] [32] [40] [49] [51] [52] educational (n = 4, 11.8%),[23] [45] [46] [48] user-driven (n = 2, 5.9%),[25] [31] technology-based (e.g., natural language processing (NLP), digital assistant) (n = 4, 11.8%),[24] [37] [39] [41] combination approaches (n = 4, 11.8%),[8] [27] [38] [50] and other (n = 3, 8.8%).[34] [35] [53] [Table 2] displays our classification of documentation burden reduction and target populations.

Table 2 Classification of documentation burden reduction efforts and target population

Category (overall %) and subcategory (category %)	Target population
Medical scribes (n = 7, 20.6%)  In-person (n = 6, 85.7%)  Virtual (n = 1, 14.3%)	MDs, APPs
Documentation volume reduction and/or improving efficiencies (n = 10, 29.4%)  Removal of unnecessary data fields (n = 6, 60.0%)  Workflow simplification (e.g., data autopopulation) (n = 3, 30.0%)  Template/note efficiency (n = 1, 10.0%)	MDs, APPs RNs
Educational (n = 4, 11.8%)  General information on documentation burden (n = 2, 50%)  Focus on EHR tools (n = 2, 50%)	MDs, APPs, medical students RNs
User driven (n = 2, 5.9%)	RNs Multiple clinician types
Technology based (n = 4, 11.8%)  NLP (n = 2, 50.0%)  Digital assistant (n = 1, 25.0%)  Sensor based (n = 1, 25.0%)	MDs, APPs RNs
Combination (n = 4, 11.8%)	MDs, APPs Multiple clinician types
Other  Standardized datasets (n = 2, 66.7%)  Communication tool (n = 1, 33.3%)	MDs, APPs RNs

Abbreviations: APP, advanced practice provider; EHR, electronic health record; MD, Doctor of Medicine; NLP, natural language processing; RN, registered nurse.

We categorized efforts involving documentation volume reduction and/or improving efficiencies as those specifically targeting or lessening the total amount of information entered into the EHR, whether through flowsheet fields, clicks, or free text notes. Specific examples of efforts identified as reducing total documentation volume and/or improving clinical efficiencies ranged from reducing note bloat through implementation of a template to improve note writing efficiency[22] to reducing data fields for nursing admission documentation.[52] Many efforts in this category were evaluated by reduction in click counts, time spent documenting, or both.[5] [22] [29] [30] [32] [40] [49] [51] [52] Efforts categorized as educational included coaching or educational sessions that provided information on documentation burden reduction strategies and improving documentation efficiency. In particular, two of the education-based included content how clinicians could leverage specific EHR tools in their documentation workflow.[23] [46] User-driven efforts used surveys, interviews, and focus groups to engage and seek insight into opportunities for documentation and/or workflow improvements.[25] [31] Technology-based efforts were categorized as such based on the use of digital tools or artificial intelligence (AI) approaches that included NLP,[39] [41] digital assistant (Suki),[37] and automatic sensor that documented patient repositioning.[24] Combination approaches describes efforts that included more than one effort type, like documentation volume reduction and/or improving efficiencies, education, and technology. Other approaches included the development of a standardized dataset[35] [53] and the development of a communication tool.[34]

Governance Structures

About one third of the articles (n = 11, 32.4%) described governance structures and/or processes associated with the documentation burden reduction effort.[5] [22] [25] [28] [29] [31] [32] [46] [49] [52] [53] A majority of efforts that referenced governance structures were classified as reducing volume and/or improving efficiencies (n = 7, 63.6%,[5] [22] [28] [29] [32] [49] [52] the outcomes of which showed reduction in time to completion, click counts, and/or data fields, and most efforts specifically targeted nurses (n = 8, 72.2%).[5] [25] [28] [29] [32] [49] [52] [53] Of the articles that described governance structures, the amount of provided detail varied. Nearly all of the articles that referenced governance structures described a process of involving clinical end users or informaticians in the development and/or implementation of the effort.[25] [29] [31] [46] [49] [52] [53] Three mentioned following a shared-governance structure, specifically.[5] [29] [52] Several described health systems leadership involvement including the chief medical officer (CMO), chief medical informatics/information officer (CMIO), chief nursing officer (CNO), and/or chief nursing informatics/information officer (CNIO).[5] [22] [32]

Measures

The methods used to evaluate outcomes of the documentation burden reduction efforts varied across articles and included quantitative, qualitative, or both. A majority (n = 20, 58.8%) of studies reported quantitative, EHR-related metrics such as time spent documenting, reduction in and/or number of documentation fields, click counts, or length of notes.[5] [22] [23] [25] [26] [29] [31] [32] [34] [35] [39] [40] [43] [44] [47] [49] [50] [51] [52] [53] Other EHR-related metrics included time spent documenting after-hours.[33] [43] [50] Clinical process evaluations included time-motion data captured through video studies as well as measures of clinical volume, completed patient encounters, and time to task completion.[5] [24] [30] [32] [37] [47] [50] [52] Others used validated measures including the Consumer Assessment of Healthcare Physicians and Systems Clinician & Group Survey (CG-CAHPS)[44] and a 19-item survey developed to assess documentation reduction strategies implemented during the coronavirus disease 2019 (COVID-19) pandemic.[8]

Several articles (n = 4, 11.8%) conducted user-interviews or focus groups to obtain feedback on the effort.[31] [34] [36] [37] Other studies (n = 12, 35.3%) collected data on clinician satisfaction through surveys.[8] [23] [30] [31] [32] [33] [35] [37] [42] [43] [44] [46] Two studies (5.9%) examined financial impacts of the documentation burden reduction efforts. One article discussed a planned evaluation of the impact of the effort on cost reduction, although did not report actual outcomes,[31] whereas another examined missed billing opportunities due to insufficient documentation.[51] Other outcome measures included patient satisfaction and outcomes like the Hospital Consumer Assessment of Healthcare Providers and Systems.[26] [31] [42] [43] [44]

Outcomes

The reported outcomes of documentation burden reduction efforts were generally positive[5] [22] [23] [24] [28] [29] [32] [33] [34] [35] [37] [39] [40] [42] [43] [45] [46] [49] [51] [52] [53] (n = 21, 61.8%), although there was also report of mixed outcomes or outcomes that were not statistically significant (n = 6, 17.6%).[8] [30] [36] [44] [47] [50] As previously noted, we did include articles that did not specifically report outcomes (n = 7, 17.6%).[25] [26] [27] [31] [38] [41] [48] Of the efforts that reported outcomes, most showed through qualitative and quantitative assessment reduced documentation time and/or click counts as well as improved workflow efficiency, provider satisfaction, and patient interactions. Specifically, efforts classified as reducing documentation volume and/or improving efficiencies resulted in decreased documentation time[5] [22] [32] [35] [52] [53] and click burden,[5] [22] [35] [40] [49] [53] allowed providers to spend more time with and/or interact with patients,[40] and resulted in improved clinician satisfaction.[30] [32] [45] [46]

Medical scribes were associated with decreased physician EHR documentation burden, as well as improved work efficiency and visit interactions.[33] [36] [42] [43] [44] [47] One article examined how the provider–scribe relationship influenced provider burnout.[36] That study, set across five different sites ranging from acute care to outpatient clinic around the United States, found that while medical scribes increased job satisfaction and reduced burnout among providers, including documentation-related burnout, medical scribes themselves may also experience burnout.[36] Another study reported that while there was a decrease in documentation time, the length of the note increased.[47]

Other strategies, such as educational and technological approaches, were found to also result in improved satisfaction and be associated with lower volume of documentation.[23] [24] [39] [45] [46] Specifically, technology-based approaches including NLP digital assistants were associated with decreased documentation time.[37] [39] Educational efforts, such as individual coaching sessions and trainings on EHR efficiencies, resulted in higher clinician satisfaction,[23] [46] decreased clinician burnout,[46] and decreased documentation time.[23]

Discussion

This scoping review of the literature provides a comprehensive summary of documentation burden reduction efforts to date. The majority of articles identified for inclusion were published within the last 2 years, which illustrates the timeliness, importance, and ongoing necessity to address documentation burden. Regarding effectiveness, a large majority of articles reported outcomes that demonstrated decreased volume of documentation and/or improved efficiencies as a result of the documentation burden reduction efforts; however, only a small number of articles used validated measures. Further, many of the articles reported on anecdotal findings, outcomes from quality improvement efforts, or did not report outcomes at all. As most of the efforts that reported efficacy targeted reducing documentation volume and/or improving efficiency, more evidence is needed to identify strategies that go beyond primarily volume-reducing documentation burden reduction efforts.

The majority of documentation burden reduction efforts were classified as reducing documentation volume and/or improving efficiencies. Many of these efforts targeted nurses, specifically, reducing documentation volume and/or data fields on nursing flowsheets and admission assessments. While these efforts showed effectiveness, it is important to note that these types of efforts may not address underlying causes of or contributors to documentation burden, such as poor user design or challenges related to EHR information retrieval. To address these other contributors to clinical workflow inefficiencies, researchers are developing and building redesigned EHRs that promote communication and facilitate team-based approaches to clinical care.[11] [54]

Similarly, several of the studies describing medical scribe use noted that it did not inherently reflect a reduction in total documentation volume but rather a transfer of documentation burden to another member of the care team. Additionally, several of the efforts were aimed at educating clinicians to improve efficiency in documenting rather than reducing the total volume of documentation. While education is an important component of documentation burden reduction efforts, it is necessary to ensure that educational programs are effective at reducing overall documentation volume and do not contribute to additional burden due to participation.

The technology-based efforts reported in this review are limited, with only two focused on NLP, one that reported on a digital assistant, and one that used sensors. Notably absent are efforts that incorporate large language models (LLMs) and generative AI, like Chat-GPT, into clinical workflow. This may be due to our search concluding prior to the acceleration of and focus on LLM capabilities. However, there is push toward incorporating innovative technologies like generative into the EHR or clinical workflow with the recognition that these may be useful tools to improve clinical documentation and related workflows.[55] [56] [57] One consideration for this type of documentation burden reduction effort is who is impacted; given the function of generative AI to compose text, and when combined with ambient listening, use cases will likely focus on providers (MDs, APPs). It is important to explore other applications for LLMs and generative AI beyond assisting with note composition, such as facilitating information retrieval from the patient's entire record, populating structured data fields during nursing assessments, or to meet regulatory requirements. Of course, the need for comprehensive evaluation of these technology-based approaches remains to ensure appropriate and successful integration of these efforts into routine health care delivery.

Regarding governance structures, several articles highlighted the involvement of clinicians or end users in the development and implementation of the efforts, often following a shared-governance model. Moreover, health system leadership, including the CMO, CMIO, CNO, and CNIO, were frequently mentioned as key stakeholders in these efforts. These findings underscore the importance of considering clinician time and involving them in the decision-making process to ensure the success and sustainability of documentation burden reduction efforts. The purpose of including governance structures in our review was to catalog approaches that accompanied efforts and used in the implementation of documentation burden reduction efforts. Indeed, key takeaways from this review regarding governance include the preponderance of governance approaches that involved clinician end users. That said, an important consideration for implementation of user-engaged documentation burden reduction efforts is the organizational and time commitment required from clinicians to maintain their engagement and participation in addition to their clinical responsibilities.

The articles included references to various metrics to measure documentation burden including EHR-related metrics, process evaluation, qualitative data, and financial measures. Of note, very few articles mentioned financial measures, specifically. While the measures identified through this scoping review illustrate what is being done currently to assess or quantify documentation burden, it is important to note that there is little consensus and the measures used may not actually capture or fully quantify documentation burden levels. Thus, further work is needed on the measurement of documentation burden.[3] Specifically, future studies should aim to identify valid, reliable metrics for documentation burden. It is important to recognize that there is not likely one single measure that will fully capture the construct that is documentation burden across all clinician types, clinical roles, and practice settings; thus, determining which measures appropriately assess documentation burden given a specific context, and thus can capture accurate changes to documentation burden based on reduction efforts, should be a priority.

Limitations

This was a review specifically targeted at documentation burden reduction efforts. We relied on keyword search to identify articles, as MeSH terms for documentation burden or related concepts do not exist. Further, we did not search the literature on EHR systems design or redesign, nor did we search for the use of specific technologies such as speech recognition as we were interested in articles that specifically addressed solutions to the problem of documentation burden reduction. Thus, we did not include articles that focus on EHR systems build or redesign and may have missed studies that did not use keywords related to documentation burden. Notably, reluctance to report or publish results that did not demonstrate positive outcomes of documentation burden reduction efforts, or publication bias, may be why the outcomes of the included studies were generally positive. Several of the articles we identified included citations to specific technology and the measures that were used to assess their use. There may be other literature related to measuring documentation burden in clinicians that were not captured in this publication because they were not reported in conjunction with a specific documentation burden reduction effort.

Implications

This scoping review of the literature reveals several key implications regarding documentation burden reduction efforts. First, as demonstrated in several of the studies we reported, the involvement of clinicians and health system leadership in governance structures is an important consideration for those planning documentation burden reduction efforts and may contribute to the success and sustainability of these efforts. Indeed, clinician involvement from the beginning of the design process and throughout implementation of EHRs and related workflows may minimize inefficiencies and the need to later pursue documentation burden reduction efforts. Furthermore, the measurement of documentation burden requires further development, with current metrics falling short in capturing the concept of documentation burden. Future research should focus on refining measurement methods to accurately quantify documentation burden and inform targeted interventions. While the outcomes reported in the majority of articles highlight the potential effectiveness of these efforts, more rigorous evidence is needed to identify and establish strategies, particularly those that leverage emerging technologies like LLMS or generative AI, which reduce documentation burden.

Conclusion

This scoping review identified and synthesized the existing literature on efforts specifically targeted at reducing documentation burden experienced by health care workers. The articles described various approaches to documentation burden reduction efforts, including the use of medical scribes, improving documentation efficiencies, education, and other interventions. The implications of documentation burden reduction efforts extend beyond alleviating the workload of health care workers and have the potential to enhance clinician satisfaction, reduce burnout, improve patient–provider interactions, and optimize overall health care delivery. Future studies are needed to establish evidence and identify effective interventions for reducing documentation burden. By understanding and implementing effective strategies to mitigate documentation burden, health care organizations can improve the work environment for clinicians while promoting high-quality patient care.

Clinical Relevance Statement

As awareness of the burden of clinical documentation associated with EHR use grows, there are increasing numbers of efforts to combat documentation burden appearing in the literature. Documentation burden reduction efforts to date vary in target population, setting, and type and have included the use of medical scribes, improving documentation efficiencies, education, and other interventions. More rigorous evidence is needed to identify and establish strategies that are effective at reducing documentation burden.

Multiple-Choice Questions

1. Among articles that reported using governance structures, what governance approach was commonly utilized?

   • Corporate governance

   • Shared-governance involving clinicians and other key stakeholders

   • Advisory-board governance

   • Management governance

   Correct Answer: The correct answer is option b. Many of the articles that included reference to governance approaches emphasized the importance of involving clinicians and using shared-governance approaches in documentation burden reduction efforts.

2. Which of the following strategies should health care organizations apply to ensure the sustained success of documentation burden reduction efforts?

   • Do not disseminate findings

   • Implement efforts slowly

   • Involve clinicians while also being mindful of clinician time

   • Use technological tools that add to clinician burden

   Correct Answer: The correct answer is option c. The discussion emphasizes the importance of considering clinician time and involving them in the decision-making process for the success and sustainability of documentation burden reduction efforts.

3. What was one consideration reported in the literature that health care organizations should contemplate when implementing medical scribes to reduce documentation burden?

   • Patient confidentiality and privacy

   • Scribe burnout

   • Cost-effectiveness

   • Availability of technological tools

   Correct Answer: The correct answer is option b. One article reporting on the use of medical scribes reported that scribes may also experience burnout. Additionally, one study found that medical scribe use was associated with longer note length.

Conflict of Interest

None declared.

Protection of Human and Animal Subjects

No human subjects were involved in the project.

• References

• 1 Murthy VH. Addressing health worker burnout. US Department of Health and Human Services; 2022 . Accessed April 29, 2014 at: https://www.hhs.gov/surgeongeneral/priorities/health-worker-burnout/index.html
• 2 Hobensack M, Levy DR, Cato K. et al. 25 × 5 symposium to reduce documentation burden: report-out and call for action. Appl Clin Inform 2022; 13 (02) 439-446
• 3 Moy AJ, Schwartz JM, Chen R. et al. Measurement of clinical documentation burden among physicians and nurses using electronic health records: a scoping review. J Am Med Inform Assoc 2021; 28 (05) 998-1008
• 4 Rossetti SC, Rosenbloom S, Levy D. et al. Summary Report from the 25 By 5: Symposium Series to Reduce Documentation Burden on U.S. Clinicians by 75% by 2025; 2021. Accessed April 29, 2014 at: https://www.dbmi.columbia.edu/wp-content/uploads/2021/12/25×5-Summary-Report.pdf
• 5 Lindsay MR, Lytle K. Implementing best practices to redesign workflow and optimize nursing documentation in the electronic health record. Appl Clin Inform 2022; 13 (03) 711-719
• 6 Gesner E, Gazarian P, Dykes P. The burden and burnout in documenting patient care: an integrative literature review. Stud Health Technol Inform 2019; 264: 1194-1198
• 7 Collins S, Couture B, Kang MJ. et al. Quantifying and visualizing nursing flowsheet documentation burden in acute and critical care. AMIA Annu Symp Proc AMIA Symp 2018 ;2018: 348-357
• 8 Moy AJ, Schwartz JM, Withall J. et al. Clinician and health care leaders' experiences with-and perceptions of COVID-19 documentation reduction policies and practices. Appl Clin Inform 2021; 12 (05) 1061-1073
• 9 Levy DR, Sloss EA, Chartash D. et al. Reflections on the documentation burden reduction AMIA plenary session through the lens of 25 × 5. Appl Clin Inform 2023; 14 (01) 11-15
• 10 Steinkamp J, Kantrowitz JJ, Airan-Javia S. Prevalence and sources of duplicate information in the electronic medical record. JAMA Netw Open 2022; 5 (09) e2233348
• 11 Steinkamp J, Sharma A, Bala W, Kantrowitz JJ. A fully collaborative, noteless electronic medical record designed to minimize information chaos: software design and feasibility study. JMIR Form Res 2021; 5 (11) e23789
• 12 AMIA 25 × 5 | AMIA - American Medical Informatics Association. Accessed September 21, 2023 at: https://amia.org/about-amia/amia-25x5
• 13 Sengstack P, Adrian B, David RB, Boyd L, Davis A, Hook M. The six domains of burden: a conceptual framework to address the burden of documentation in the electronic health record. Position Paper of the American Nursing Informatics Association Board of Directors; 2020 . Accessed September 26, 2023 at: https://www.ania.org/assets/documents/position/ehrBurdenPosition.pdf
• 14 Ashton M. Getting Rid of Stupid Stuff: Reduce the Unnecessary Burdens for Clinicians. American Medical Association. 2019 . Accessed May 4, 2023 at: https://edhub.ama-assn.org/steps-forward/module/2757858
• 15 Bodenheimer T, Sinsky C. From triple to quadruple aim: care of the patient requires care of the provider. Ann Fam Med 2014; 12 (06) 573-576
• 16 Strategy on reducing burden relating to the use of health IT and EHRs | HealthIT.gov. Accessed May 4, 2023 at: https://www.healthit.gov/topic/usability-and-provider-burden/strategy-reducing-burden-relating-use-health-it-and-ehrs
• 17 Peters MDJ, Godfrey CM, Khalil H, McInerney P, Parker D, Soares CB. Guidance for conducting systematic scoping reviews. Int J Evid-Based Healthc 2015; 13 (03) 141-146
• 18 Tricco AC, Lillie E, Zarin W. et al. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med 2018; 169 (07) 467-473
• 19 Munn Z, Peters MDJ, Stern C, Tufanaru C, McArthur A, Aromataris E. Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach. BMC Med Res Methodol 2018; 18 (01) 143
• 20 Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A. Rayyan-a web and mobile app for systematic reviews. Syst Rev 2016; 5 (01) 210
• 21 Health Systems Governance. Accessed January 30, 2024 at: https://www.who.int/health-topics/health-systems-governance
• 22 Kaizuka S, Maguire J, Sridhar S, Devine M, Nicandri G, Fogarty CT. Notes 2.0: reducing documentation burden. Fam Pract Manag 2022; 29 (04) 19-24
• 23 McDonnell B. Nurse EHR documentation coaching effort review and evaluation: a case study. J Inform Nurs 2022; 7 (01) 25-28
• 24 Rose A, Cooley A, Yap TL. et al. Increasing nursing documentation efficiency with wearable sensors for pressure injury prevention. Crit Care Nurse 2022; 42 (02) 14-22
• 25 Strudwick G, Jeffs L, Kemp J. et al. Identifying and adapting interventions to reduce documentation burden and improve nurses' efficiency in using electronic health record systems (The IDEA Study): protocol for a mixed methods study. BMC Nurs 2022; 21 (01) 213
• 26 Benko S, Idarraga AJ, Bohl DD, Hamid KS. Virtual scribe services decrease documentation burden without affecting patient satisfaction: a randomized controlled trial. Foot Ankle Spec 2022; 15 (03) 252-257
• 27 Tajirian T, Jankowicz D, Lo B. et al. Tackling the burden of electronic health record use among physicians in a mental health setting: physician engagement strategy. J Med Internet Res 2022; 24 (03) e32800
• 28 Englebright J, Michel S, Boyd DL, Hulett SL. A framework for national collaboration to reduce documentation burden and design for usable and reusable data. J Nurs Adm 2021; 51 (03) 162-167
• 29 Phillips T, Baur K. Nursing praxis for reducing documentation burden within nursing admission assessments. Comput Inform Nurs 2021; 39 (11) 627-633
• 30 Pallansch J, Belyshev V, Longerstaey O. Streamlined point-of-care ultrasound electronic worksheet effects on provider satisfaction and completion time. Acad Emerg Med 2021; 28: S357
• 31 Otokiti AU, Craven CK, Shetreat-Klein A, Cohen S, Darrow B. Beyond getting rid of stupid stuff in the electronic health record (Beyond-GROSS): protocol for a user-centered, mixed-method intervention to improve the electronic health record system. JMIR Res Protoc 2021; 10 (03) e25148
• 32 McIlreevy J, Rylee TL, Shields-Tettamanti T, Gee PM. Interdisciplinary optimization of admission documentation: reducing the bloat. Comput Inform Nurs 2021; 39 (05) 248-256
• 33 Lee SK, Chang JH, Lee J, Seng SS, Namm JP, Lum SS. Medical scribes-help or hindrance? Attending and trainee satisfaction with scribes in outpatient academic surgery clinics. J Am Coll Surg 2021; 233 (05) S227
• 34 Kumah-Crystal YA, Stein PM, Chen Q. et al. Before-visit questionnaire: a tool to augment communication and decrease provider documentation burden in pediatric diabetes. Appl Clin Inform 2021; 12 (05) 969-978
• 35 Horn JJ, Doucette JN, Sweeney NL. An essential clinical dataset intervention for nursing documentation of a pediatric admission history database. J Pediatr Nurs 2021; 59: 110-114
• 36 Corby S, Ash JS, Mohan V. et al. A qualitative study of provider burnout: do medical scribes hinder or help?. JAMIA Open 2021; 4 (03) ooab047
• 37 AAFP innovation lab: reducing documentation burden through the use of a digital assistant. Fam Pract Manag 2021; 28 (04) 8-11
• 38 Dymek C, Kim B, Melton GB, Payne TH, Singh H, Hsiao CJ. Building the evidence-base to reduce electronic health record-related clinician burden. J Am Med Inform Assoc 2021; 28 (05) 1057-1061
• 39 Kaufman DR, Sheehan B, Stetson P. et al. Natural language processing-enabled and conventional data capture methods for input to electronic health records: a comparative usability study. JMIR Med Inform 2016; 4 (04) e35
• 40 Guo U, Chen L, Mehta PH. Electronic health record innovations: Helping physicians - one less click at a time. HIM J 2017; 46 (03) 140-144
• 41 Leventhal R. How natural language processing is helping to revitalize physician documentation. Healthc Inform 2017; 34 (05) 8-13
• 42 Mehta S, Johnston R, Yadav S, Cadotte R, Maranda M, Mogannam J. Scribes in hospital medicine-a powerful value-added resource!. J Hosp Med 2018; 13 (04) U2-L629665584
• 43 Mishra P, Kiang JC, Grant RW. Association of medical scribes in primary care with physician workflow and patient experience. JAMA Intern Med 2018; 178 (11) 1467-1472
• 44 Pozdnyakova A, Laiteerapong N, Volerman A. et al. Impact of medical scribes on physician and patient satisfaction in primary care. J Gen Intern Med 2018; 33 (07) 1109-1115
• 45 Tan GS, Rogers R, Stark R. Making epic your friend: a curricular innovation to improve the EMR experience in the clinic. J Gen Intern Med 2018; 33 (02) 725-726
• 46 Sieja A, Markley K, Pell J. et al. Optimization sprints: improving clinician satisfaction and teamwork by rapidly reducing electronic health record burden. Mayo Clin Proc 2019; 94 (05) 793-802
• 47 Dusek HL, Goldstein I, Hribar M, Chiang MF. Electronic health records in ophthalmology: impact of scribes on office visit length, documentation time, and note length. Invest Ophthalmol Vis Sci 2019; 60 (09) 5503
• 48 Kim A. 23.2 Working with medical scribes, medical assistants, and registered nurses to improve outpatient clinic workflow and support physician wellness. J Am Acad Child Adolesc Psychiatry 2019; 58 (10) S33-S34
• 49 Swietlek M, Sengstack PP. An evaluation of nursing admission assessment documentation to identify opportunities for burden reduction. J Inform Nurs 2020; 5 (03) 6-11
• 50 Baxter SL, Gali HE, Chiang MF. et al. Promoting quality face-to-face communication during ophthalmology encounters in the electronic health record era. Appl Clin Inform 2020; 11 (01) 130-141
• 51 Hunt RJ, Fletcher S, Rock JP, Air EL. More time for doctoring: reduction of resident daily documentation burden. Neurosurgery 2020; 67 (Suppl. 01)
• 52 Russell CK, Floyd CT, Ledwell L, Morgan S. A community hospital's approach to alleviate documentation burden. J Inform Nurs 2020; 5 (02) 8-12
• 53 Sutton DE, Fogel JR, Giard AS, Gulker LA, Ivory CH, Rosa AM. Defining an essential clinical dataset for admission patient history to reduce nursing documentation burden. Appl Clin Inform 2020; 11 (03) 464-473
• 54 Soegaard Ballester JM, Bass GD, Urbani R. et al. A mobile, electronic health record-connected application for managing team workflows in inpatient care. Appl Clin Inform 2021; 12 (05) 1120-1134
• 55 Zhang P, Kamel Boulos MN. Generative AI in medicine and healthcare: promises, opportunities and challenges. Future Internet 2023; 15 (09) 286
• 56 Xue VW, Lei P, Cho WC. The potential impact of ChatGPT in clinical and translational medicine. Clin Transl Med 2023; 13 (03) e1216
• 57 Wachter RM, Brynjolfsson E. Will generative artificial intelligence deliver on its promise in health care?. JAMA 2024; 331 (01) 65-69

Publication History

Received: 30 November 2023

Accepted: 17 April 2024

Article published online:
05 June 2024