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DOI: 10.1055/a-2437-0185
Taking a Team Approach: Keep Up with the EHR with a Training and Optimization Program
- Abstract
- Background and Significance
- Objectives
- Methods
- Results
- Discussion
- Conclusion
- Clinical Relevance Statement
- Multiple-Choice Questions
- References
Abstract
Objectives This study aimed to describe the current landscape of electronic health record (EHR) training and optimization programs (ETOPs) and their impact on health care workers' (HCWs) experience with the EHR.
Methods A 72-question electronic survey was developed and distributed to health care organization (HCO) leaders in clinical informatics (Chief Medical Information Officer [CMIO]/Chief Nursing Information Officer [CNIO]/Medical Informatics Executive, Associate CMIO/Medical Director of, Clinical/Nurse/Physician Informaticist) through various channels such as national informatics conferences, social media, and email distribution lists of vendors and informatics associations. The survey collected data on the characteristics, resourcing, approach, and outcomes of ETOPs. Descriptive statistics were applied to analyze the data.
Results There were 193 responses from 147 distinct HCOs. Of these, 69% offer ongoing EHR training, and 52% offer some version of an ETOP. Offered ETOPs vary in their timing, modality, audience, team composition, duration, and EHR build strategy. The most commonly measured outcomes were EHR satisfaction, efficiency, and provider burnout, and most ETOPs reported improvement in these areas.
Conclusion The findings suggest that ETOPs are inconsistently implemented across HCOs, and while there are some commonalities, there is a wide variety of designs and methods of evaluation for the programs. Though the problems to solve (EHR efficiency, proficiency, and satisfaction) are the same, the organizational structure and culture of HCOs vary widely, which may partially explain the variability seen in reported ETOPs. When considering the measured outcomes, ETOPs may have direct and indirect effects on HCW burnout by improving EHR efficiency and satisfaction, as well as driving organizational culture toward teamwork and flexible problem-solving. For this reason, ETOPs may also serve as a model for addressing other challenges in health care delivery. ETOPs are a promising intervention to enhance HCW experience with the EHR and reduce burnout. More research is needed to identify the optimal features, methods, and outcomes of ETOPs, and to disseminate them across HCOs.
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Keywords
clinical informatics - electronic health record - burnout - health care workers - organizational cultureBackground and Significance
Electronic health records (EHRs) are ubiquitous and have become an essential component of health care delivery in the United States and many other countries. EHRs have the potential to improve patient safety, facilitate population-based care, and prevent duplication of testing and procedures through interoperability.[1] [2] They also have the potential to negatively impact health care workers (HCWs) by creating administrative burdens or shifting administrative tasks from nonclinicians to clinicians, especially when health care organizations (HCOs) do not invest in EHR optimization.[3] [4] Moon et al define EHR optimization as “the ongoing process making the implemented EHR more efficient and usable for end user clinicians that results in improved efficiency in clinicians' practice and satisfaction.”[5] Clinicians spend half of their workday in the EHR, which leaves little time for more meaningful, direct patient care; there is a dose–response relationship between the time spent on meaningful work and burnout in HCWs.[6] [7] [8] [9] [10]
While EHR burden is frequently cited as a cause of HCW burnout,[11] [12] [13] [14] [15] [16] there are several other factors such as organizational culture, leadership values alignment, and job autonomy that also contribute.[17] It is now understood that addressing burnout means more than teaching EHR proficiency or efficiency. It is critical to also tackle larger issues like communication, teamwork, and workflows.[18] [19] Uniquely positioned to address some of these issues are physician informaticist (PI) and information technology (IT) leaders who can impact organizational culture and inform strategic decisions impacting EHR deployment, training, upgrades, and optimizations at the health system level.[20] [21] [22]
In the early years of widespread EHR deployment, training modeled other forms of basic software education which was largely focused on screen personalization and unidirectional “how to” actions: how to order, how to document, how to find information. Prior to EHR deployment, because HCOs had never been forced to contemplate the values of specific workflows unless they supported federal regulations, billing, or compliance, the adage “because I said so” often prevailed. The climate for EHR optimization was similarly bleak with a high bar for achieving software changes unless the change clearly and directly correlated with business metrics such as organizational growth.[23] [24]
In recent years, widespread recognition of HCW burnout as well as studies demonstrating the benefits of robust EHR training and optimization programs (ETOPs),[5] [25] [26] [27] [28] [29] [30] [31] [32] [33] have led more HCOs to invest in these comprehensive programs beyond initial implementation and clinician onboarding. This case review aims to discover how commonly ETOPs are employed, develop an understanding of the strategies, people, and processes they employ, and start a discussion about what types of outcomes are being measured and attained. Specific descriptions and measured outcomes of a variety of ETOPs (such as Sprint, Home for Dinner, and Practice Experience Program [PEP]) have been previously published and were not the focus of this survey.[26] [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38]
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Objectives
This study aimed to better understand ways in which HCOs can impact HCWs experience with the EHR. This descriptive analysis aims to describe the current ETOP landscape with respect to both resourcing and approach.
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Methods
A 72-question survey was developed, and responses were collected within the University of Vermont instance of Research Electronic Data Capture (REDCap) software. The project was determined to not meet the federal regulatory definition of research by the University of Vermont Institutional Review Board. It was distributed electronically to HCO leaders in clinical informatics (CMIO/CNIO/Medical Informatics Executive, Associate CMIO/Medical Director of, Clinical/Nurse/PI) through a variety of means including several professional organizational and EHR vendor email listservs, author presentation(s) at national conferences, and personal and professional social media accounts of the authors and author contacts. The survey was voluntary and respondent names were anonymous unless participants preferred to provide their contact information to be direct recipients of survey results.
In the case of multiple responses from the same HCO, the response from the most senior self-reported role was included in the findings because the study's purpose was to obtain the perspective of organizational leaders. Possible discordance between HCOs with multiple responders was not analyzed given the small numbers when compared with the total number of responses. The survey was open for 6 weeks. Data were analyzed using Stata 18 (StataCorp, College Station, TX) and descriptive statistics were applied. Surveys with missing values or incomplete data were included when the determination was made that the partial data available on those surveys contributed to the overall study questions.
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Results
Health Care Organization Characteristics
The survey received 193 total responses from 147 distinct HCOs. As seen in [Table 1], most (140/147, 95%) HCO respondents were in the United States and the majority (92/147, 63%) included an academic center. Utilization of a single EHR was most common (131/147, 89%) and Epic was the most frequent EHR in use overall (138/147). Cerner (Oracle Health) was the second most commonly used EHR (15/147). The various HCO sizes were represented, with responses from 67 small (0–3 hospitals) HCOs, 41 medium (4–10 hospitals) HCOs, and 37 large (11+ hospitals) HCOs. Within the large HCO category, two of those surveyed reported that their system included more than 50 hospitals. Seventy-three percent of survey respondents provided their email addresses rather than remaining anonymous.
Total distinct health care organizations |
n = 147 |
---|---|
Respondent role |
|
CMIO/CNIO/Medical Informatics Executive |
65 (44%) |
Clinical/Nurse/Physician Informaticist |
45 (31%) |
Associate CMIO/Medical Director of Informatics |
23 (16%) |
Other[a] |
14 (9%) |
Location |
|
Within the United States |
140 (95%) |
Outside the United States |
7 (5%) |
Size of health care organization |
|
Small (0–3 hospitals) |
67 (46%) |
Medium (4–10 hospitals) |
41 (28%) |
Large (>11 hospitals) |
37 (25%) |
Don't know |
2 (1%) |
Number of EHR systems in use |
|
Single EHR |
131 (89%) |
More than one EHR |
16 (11%) |
EHR system(s) in use (select all that apply) |
|
Epic |
138 |
Cerner |
15 |
Other[b] |
5 |
AllScripts |
4 |
Meditech |
3 |
GE |
2 |
McKesson |
1 |
Abbreviation: EHR, electronic health record.
a Director of oversight, risk, and ethics; medical director; practicing physician; physician builder, former physician informaticist; IT project manager; chief quality officer; project manager; physician assistant, associate director of optimization and clinical informatics; informatics director; trainer; physician champion; program director of analytics and informatics.
b NexGen, eCW, Athena; CPRS; Systoc (occupational medicine); Athena; Accuro.
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Ongoing Training
Of the 147 survey responses, 69% (101/147) of HCOs offer ongoing EHR training, defined as training programs beyond onboarding and/or implementation. Of the 101 HCOs that offer this type of ongoing training, 52% (52/101) offer an ETOP such as Sprint, Home for Dinner, or PEP ([Fig. 1]).
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Electronic Health Record Training and Optimization Programs
Survey responses showed that HCOs offering ETOPs are more likely to be large organizations and less likely to be public, and HCOs that do not offer ETOPs are more likely to have an academic component ([Table 2]). In addition, whether ETOP is offered at an HCO impacts the number of annual hours of dedicated EHR training offered to providers but does not impact the number of annual hours of dedicated EHR training offered to clinical (nurses, medical assistants, or similar roles) or nonclinical staff (registration, patient scheduling, check-in role or similar; [Fig. 2]).
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Abbreviation: ETOP, electronic health record training and optimization program.
The timing and mode of delivery of ETOPs varied across HCOs but there were some commonalities: 71% (37/52) offer some training sessions during clinic hours, and 86% (44/52) include a virtual component. The survey did not ask how many hours of training sessions were offered to each participant; this has been described elsewhere in the literature.[26] [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38] All HCOs that offer ETOPs reported that the program serves providers, most also serve clinical staff 86% (44/52), and less than half serve nonclinical staff 46% (24/52). ETOP teams are composed of multiple roles that vary by HCO. Nearly all include EHR trainers and provider informaticists and they are less likely to include a project manager ([Table 3]). Of the 52 organizations that have ETOPs, 79% (41/52) offer the program to one clinic or specialty at a time. Of these, the duration varies widely but shorter programs are more common (29 offered for less than 4 weeks, 17 for 2–6 weeks, 7 for more than 6 weeks).
Abbreviations: EHR, electronic health record; ETOP, EHR training and optimization program.
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Electronic Health Record System Build
Of the 52 HCOs that offer ETOPs, 62% (34/52) complete EHR build as a component of the program; only 50% (17/34) deliver the build within the time constraints of the training and optimization event. EHR analysts (28/34, 82%) and provider builders (30/34, 88%) are the most common ETOP team members who perform EHR builds. Nurse builders (11/34, 32%), trainers (8/34, 24%), and trainer–analyst combined roles (7/34, 21%) were reported as less likely to build in the system. Seventy-four percent (25/34) of programs require all build to pass through IT governance channels. Build strategies in this context were reported as largely similar, with small variations by ETOP team composition ([Fig. 3]). Overall, the least likely strategy to be utilized is “creating a burning platform” (i.e., time-limited sense of urgency as described in Kotter's 8 step change model) for build to get done.[39]
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Electronic Health Record Training and Optimization Program Outcomes
Seventy-nine percent (41/52) of the organizations that offer ETOPs reported measuring program outcomes. Of these, the most measured outcome is EHR satisfaction which is measured by 90% (37/41) of organizations, followed by EHR efficiency at 88% (36/41), and provider burnout at 71% (29/41; [Fig. 4]). Of the 29 organizations that reported measuring burnout, just over half reported showing improvement in this outcome.
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Discussion
The EHR plays a central and continuously expanding role in health care in the United States and abroad, and there is research to support the advantages of ongoing EHR training and optimization for HCWs,[5] [26] [27] [28] [29] [30] [31] [32] [33] including a trend toward reduction in HCW burnout from such efforts.[26] [27] [32] In this survey of the EHR training and optimization landscape at 147 HCOs, we discover variability of program composition and delivery but similarities in reported effectiveness and goals.
Surprisingly, one-third of survey respondents reported that their HCO offered no EHR training beyond onboarding and implementation. Of the 101 HCOs that offer some ongoing training, including those with ETOPs, approximately 50% of their HCWs receive none or less than 1 hour of dedicated EHR training per year. Additionally, nearly three-quarters of ETOPs engage with HCWs at least partially during patient care hours. In our experience, this can lead to shortened and sometimes hurried training sessions due to the inherent unpredictability of the clinical time and space. In 2019, a study of 72,000 EHR users found that investment in training was critical to end-user EHR satisfaction, and the authors suggested that 3 to 5 hours of EHR training per year for HCWs was optimal.[29] When considering frequent upgrades and optimizations as well as high HCW turnover, HCOs that use ETOPs for ongoing training need to have consistent funding to revisit clinical sites on a regular cadence to accomplish these goals. Given the continuously evolving nature of both clinical care and the health IT required to support it, we call on HCO leaders to resist focusing on lost clinical revenue incurred from ongoing EHR training and optimization and instead consider the time spent by HCWs to be important for their clinical efficiency, EHR satisfaction, overall well-being, and ultimately the best interests of the HCO. One-third of survey respondents who do offer ongoing training reported that their HCO does not invest in formal ETOPs. In 2018, Moon et al reported that “dedicated resources were the biggest facilitator and the second biggest barrier to [EHR] optimization.”[5] Some smaller HCOs cite the prohibitive cost of investing in these programs, but using a number of hospitals as a surrogate for HCO size, we find the presence of ETOPs in our survey to be well-distributed among HCOs of varying size. At UCHealth, the cost of a 10-person, dedicated, multidisciplinary Sprint team that provides intervention to 1,000 clinicians and staff each year costs $1.2 million per year. At the University of Vermont Health Network, the Sprint team and associated cost is simply scaled down by half (5 team members, serves 450 clinicians and staff per year). While the investment in ETOPs can be scaled to the size of the HCO, the $500,000 to 1 million cost of replacing one burned-out physician is fixed.[40]
The value proposition for elevating end-user and care team voice in the EHR goes beyond the Return on Investment (ROI) on HCW burnout; PIs often lead ETOPs and have a sphere of influence that extends outside of IT and into the clinical and operational leadership of HCOs.[41] [42] For this reason, they are well-positioned to empathize, engage, and promote change within an HCO.[43] [44] Training for PIs is variable, so it is not surprising to find that ETOPs that include PIs were employing basic principles of informatics such as starting with the why, creating a burning platform, facilitating productive conversations, and building relationships only 50% of the time. Perhaps more intriguing is that ETOP teams that include IT analysts use these tactics more than 50% of the time. Despite this finding, ETOPs were more likely to employ PIs (90%, 46/51) than IT analysts (57%, 29/51). Survey numbers were insufficient to provide an analysis of team member composition and program success, though based on prior findings from KLAS Arch Collaborative, it is likely that the specific composition of the program team matters less than the goals, philosophy, and general approach.[45] [46] In our opinion, this should include at a minimum trainers and analysts with a clinical background whenever possible, a project manager, and a physician or clinical informaticist leader who is attentive to problems and working to implement interventions that fit within the organizational culture. In this survey, we noted that only 60% of ETOPs conducted workflow analysis and optimization and only 56% offered EHR build as part of their program.
The most commonly measured outcome for ETOPs is EHR satisfaction, which was measured 90% of the time and showed improvement in all cases. This was followed closely by EHR efficiency, measured 88% of the time, and when measured, was reported to improve 78% of the time. Burnout was measured by two-thirds of ETOPs. Since drivers of burnout are varied and multifaceted (including but not limited to efficiency of practice, resources, organizational culture/values, control, flexibility, meaning in work, workload, specific job demands, work–life integration, social support, and community work) and require complex intervention,[17] [47] [48] it is notable that half of the ETOPs measuring burnout report an improvement ([Fig. 4]). EHR training alone primarily addresses EHR efficiency and proficiency, and to impact other drivers of burnout, we need high-performing teams who can meet HCWs where they are and move them forward.
Despite the inherent complexity of HCW burnout, it is encouraging that many HCOs that offer ETOPs are measuring burnout and most that measured reported finding improvement. One possibility is that the mere existence of an ETOP with clearly stated goals of reducing EHR burden may function to impact burnout both directly (by improving EHR satisfaction and efficiency) and indirectly, by recognizing and validating HCWs, demonstrating organizational level shared core values, and offering professional development opportunities.[48]
Several factors are likely contributing to this proposed indirect impact. First, most ETOPs bring a large and coordinated group of resources to HCWs, rather than asking unsatisfied and burned-out HCWs to independently seek help. Second, the intensive training sessions with a focus on personalization that are characteristic of ETOPs empower HCWs to change their relationship with the EHR by imparting both practical knowledge and self-help strategies that allow participants to get top-of-mind questions answered and complaints aired, which helps reclaim control over the ways the technology is used to deliver care. Lastly, successful problem-solving of inefficient EHR workflows as a clinical and operational team under the guidance of ETOP leadership can lead to improvements in organizational culture. We have seen this occur when the cycle of depersonalization and hopelessness inherent in HCW burnout[48] is broken and the clinical and operational team in a clinical setting starts to drive together toward the improvement of both EHR and non-EHR-related challenges. In this way, solving a relatively small EHR-related problem opens the door for team formation and alignment. Trends toward improved teamwork have been described,[26] and the measurement of whether ETOPs can improve health care team functioning and performance is an important area for ongoing study. Using this framework, we can start to expand from the Institute of Medicine concept of the EHR as an essential component of the “learning health care system”[49] and consider that the lessons learned from impacting change within EHR-related workflows can serve as a model for making changes in the health care delivery system as a whole.
Limitations
Survey distribution through informal professional networks and electronic communication groups such as email listservs, does not allow for a survey response rate calculation. There is no single list of unique HCOs nationwide (especially given the current environment of frequent mergers and acquisitions in U.S.-based HCOs) and no definitive list of informatics executives at each HCO. The American Hospital Association approximates that there are 404 “health systems” (broadly defined as either a multihospital system of two or more hospitals or a single diversified hospital system of one hospital and three or more pre- or postacute HCOs) in the United States.[50] Using this estimate, the survey response rate for U.S. HCOs is 35% (140/404). There were also seven international respondents; it would be nearly impossible to generate a response rate for this subset. An additional limitation is this heavy skewing toward U.S. HCOs and HCOs where Epic is the EHR in use. Response bias is also possible given that those who feel most opinionated or invested in either the authors (due to a prior professional relationship or collaboration on an ETOP) or the survey topic as evidenced by the title (“Keeping up with the EHR”) as it came into their email inbox were more likely to take the time to respond. An additional limitation is that the process for ensuring nonduplication of specific HCOs relied on including the responses from the most senior role; this is therefore a functional rather than statistical method for de-duplication of responses. Lastly, there is a wide variety of clinical informatics programs and ETOPs around the country such that drawing direct comparisons between them is difficult. The survey was not exhaustive in collecting ETOP details. For example, it was not asked whether the ETOP was mandatory or optional, or whether clinician time to participate was “protected” or compensated. Both of these factors could have a significant impact on program outcomes and costs. We have therefore attempted to summarize and extract thematic patterns in a descriptive manner rather than undertake a quantitative exercise of statistical analysis.
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Conclusion
ETOPs are a promising intervention to enhance HCW experience with the EHR and to reduce burnout. Additional research is needed to identify the optimal team composition, delivery method, and outcomes of ETOPs.
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Clinical Relevance Statement
This study suggests that ETOPs can have a positive impact on physicians by improving their satisfaction and efficiency with the EHR and potentially reducing burnout. ETOPs may have both direct and indirect effects on physician burnout by driving organizational culture toward teamwork and flexible problem-solving. PIs, uniquely situated in the space between clinical, operational, and IT components of HCOs, are key drivers of these programs.
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Multiple-Choice Questions
Question 1: What proportion of surveyed HCOs offer some ongoing EHR training beyond onboarding and implementation?
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All of them.
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One-third
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Two-thirds
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None of them.
Correct Answer: The correct answer is option c. Approximately two-thirds of surveyed HCOs offer some ongoing EHR training.
Question 2: What was the most commonly measured outcome for ETOPs?
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HCW burnout
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EHR satisfaction
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EHR efficiency
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No outcomes were measured.
Correct Answer: The correct answer is option b. Ninety percent of HCOs that offer ETOPs measured EHR satisfaction as an outcome of the program.
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Conflict of Interest
None declared.
Protection of Human and Animal Subjects
The project was determined to not meet the federal regulatory definition of research by the University of Vermont Institutional Review Board.
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References
- 1 Chaudhry B, Wang J, Wu S. et al. Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. Ann Intern Med 2006; 144 (10) 742-752
- 2 King J, Patel V, Jamoom EW, Furukawa MF. Clinical benefits of electronic health record use: national findings. Health Serv Res 2014; 49 (1 Pt 2): 392-404
- 3 Colicchio TK, Cimino JJ, Del Fiol G. Unintended consequences of nationwide electronic health record adoption: challenges and opportunities in the post-meaningful use era. J Med Internet Res 2019; 21 (06) e13313
- 4 Poissant L, Pereira J, Tamblyn R, Kawasumi Y. The impact of electronic health records on time efficiency of physicians and nurses: a systematic review. J Am Med Inform Assoc 2005; 12 (05) 505-516
- 5 Moon MC, Hills R, Demiris G. Understanding optimization processes of electronic health records (EHR) in select leading hospitals: a qualitative study. J Innov Health Inform 2018; 25 (02) 109-125
- 6 Arndt BG, Beasley JW, Watkinson MD. et al. Tethered to the EHR: primary care physician workload assessment using EHR event log data and time-motion observations. Ann Fam Med 2017; 15 (05) 419-426
- 7 Pinevich Y, Clark KJ, Harrison AM, Pickering BW, Herasevich V. Interaction time with electronic health records: a systematic review. Appl Clin Inform 2021; 12 (04) 788-799
- 8 Shanafelt TD. Enhancing meaning in work: a prescription for preventing physician burnout and promoting patient-centered care. JAMA 2009; 302 (12) 1338-1340
- 9 Shanafelt TD, West CP, Sloan JA. et al. Career fit and burnout among academic faculty. Arch Intern Med 2009; 169 (10) 990-995
- 10 Young RA, Burge SK, Kumar KA, Wilson JM, Ortiz DF. A time-motion study of primary care physicians' work in the electronic health record era. Fam Med 2018; 50 (02) 91-99
- 11 Nguyen OT, Jenkins NJ, Khanna N. et al. A systematic review of contributing factors of and solutions to electronic health record-related impacts on physician well-being. J Am Med Inform Assoc 2021; 28 (05) 974-984
- 12 Shanafelt TD, Dyrbye LN, Sinsky C. et al. Relationship between clerical burden and characteristics of the electronic environment with physician burnout and professional satisfaction. Mayo Clin Proc 2016; 91 (07) 836-848
- 13 Tai-Seale M, Olson CW, Li J. et al. Electronic health record logs indicate that physicians split time evenly between seeing patients and desktop medicine. Health Aff (Millwood) 2017; 36 (04) 655-662
- 14 Thomas Craig KJ, Willis VC, Gruen D, Rhee K, Jackson GP. The burden of the digital environment: a systematic review on organization-directed workplace interventions to mitigate physician burnout. J Am Med Inform Assoc 2021; 28 (05) 985-997
- 15 West CP, Dyrbye LN, Sinsky C. et al. Resilience and burnout among physicians and the general us working population. JAMA Netw Open 2020; 3 (07) e209385
- 16 Yan Q, Jiang Z, Harbin Z, Tolbert PH, Davies MG. Exploring the relationship between electronic health records and provider burnout: a systematic review. J Am Med Inform Assoc 2021; 28 (05) 1009-1021
- 17 Shanafelt TD, Noseworthy JH. Executive leadership and physician well-being: Nine organizational strategies to promote engagement and reduce burnout. Mayo Clin Proc 2017; 92 (01) 129-146
- 18 Nguyen OT, Turner K, Apathy NC. et al. Primary care physicians' electronic health record proficiency and efficiency behaviors and time interacting with electronic health records: a quantile regression analysis. J Am Med Inform Assoc 2022; 29 (03) 461-471
- 19 West CP, Dyrbye LN, Erwin PJ, Shanafelt TD. Interventions to prevent and reduce physician burnout: a systematic review and meta-analysis. Lancet 2016; 388 (10057): 2272-2281
- 20 Lin CT, Bookman K, Sieja A. et al. Clinical informatics accelerates health system adaptation to the COVID-19 pandemic: examples from Colorado. J Am Med Inform Assoc 2020; 27 (12) 1955-1963
- 21 Meehan R. Health informatics workforce in the digital health ecosystem. Stud Health Technol Inform 2024; 310: 1226-1230
- 22 Veinot TC, Ancker JS, Bakken S. Health informatics and health equity: improving our reach and impact. J Am Med Inform Assoc 2019; 26 (8-9): 689-695
- 23 Shah T, Kitts AB, Gold JA. et al. Electronic health record optimization and clinician well-being: a potential roadmap toward action. NAM Perspect 2020; 2020
- 24 Office of the National Coordinator for Health Information Technology. Strategy on reducing regulatory and administrative burden relating to the use of health IT and EHRs. 2020 . Accessed September 5, 2024 at: https://www.healthit.gov/sites/default/files/page/2020-02/BurdenReport_0.pdf
- 25 Chen J, Chi WN, Ravichandran U. et al. Sprint-inspired one-on-one post-go-live training session (mini-sprint) improves provider electronic health record efficiency and satisfaction. Appl Clin Inform 2024; 15 (02) 313-319
- 26 English EF, Holmstrom H, Kwan BW. et al. Virtual sprint outpatient electronic health record training and optimization effect on provider burnout. Appl Clin Inform 2022; 13 (01) 10-18
- 27 Eschenroeder HC, Manzione LC, Adler-Milstein J. et al. Associations of physician burnout with organizational electronic health record support and after-hours charting. J Am Med Inform Assoc 2021; 28 (05) 960-966
- 28 Kang C, Sarkar IN. Interventions to reduce electronic health record-related burnout: A systematic review. Appl Clin Inform 2024; 15 (01) 10-25
- 29 Longhurst CA, Davis T, Maneker A. et al; Arch Collaborative. Local investment in training drives electronic health record user satisfaction. Appl Clin Inform 2019; 10 (02) 331-335
- 30 McAlearney AS, Song PH, Robbins J. et al. Moving from good to great in ambulatory electronic health record implementation. J Healthc Qual 2010; 32 (05) 41-50
- 31 Rungvivatjarus T, Bialostozky M, Chong AZ, Huang JS, Kuelbs CL. Preparing future pediatric care providers with a clinical informatics elective. Appl Clin Inform 2024; 15 (03) 437-445
- 32 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
- 33 Touson JC, Azad N, Beirne J. et al. Application of the consolidated framework for implementation research model to design and implement an optimization methodology within an ambulatory setting. Appl Clin Inform 2022; 13 (01) 123-131
- 34 DiAngi YT, Stevens LA, Halpern-Felsher B, Pageler NM, Lee TC. Electronic health record (EHR) training program identifies a new tool to quantify the EHR time burden and improves providers' perceived control over their workload in the EHR. JAMIA Open 2019; 2 (02) 222-230
- 35 Lourie EM, Stevens LA, Webber EC. Measuring success: perspectives from three optimization programs on assessing impact in the age of burnout. JAMIA Open 2020; 3 (04) 492-495
- 36 Lourie EM, Utidjian LH, Ricci MF, Webster L, Young C, Grenfell SM. Reducing electronic health record-related burnout in providers through a personalized efficiency improvement program. J Am Med Inform Assoc 2021; 28 (05) 931-937
- 37 Robinson KE, Kersey JA. Novel electronic health record (EHR) education intervention in large healthcare organization improves quality, efficiency, time, and impact on burnout. Medicine (Baltimore) 2018; 97 (38) e12319
- 38 Sieja A, Kim E, Holmstrom H. et al. Multidisciplinary sprint program achieved specialty-specific EHR optimization in 20 clinics. Appl Clin Inform 2021; 12 (02) 329-339
- 39 ChangeQuest. Kotter's 8 step change model. Accessed September 5, 2024 at: https://www.changequest.co.uk/resources/kotters-8-step-change-model/
- 40 AMA ǀ ED Hub. Professional Well-Being: Organizational Cost of Physician Burnout. 2024 . Accessed February 28, 2024 at: https://edhub.ama-assn.org/steps-forward/interactive/16830405
- 41 Mann DM, Stevens ER, Testa P, Mherabi N. From silos to synergy: integrating academic health informatics with operational IT for healthcare transformation. NPJ Digit Med 2024; 7 (01) 185
- 42 McEntee RK, McDougall C, Seija A. High performing teams: Best of 14 years of sprint EHR training and optimization. Presented at Epic User Group Meeting, Aug 20, 2024, Verona, WI. Accessed September 5, 2024 at https://eventarchive.epic.com/Past%20Events/2024%20Events/UGM/Executive%20Breakouts/EXEC12%20Physician%20Executive%20Forum%20-%20High-Performing%20Teams%20-%20The%20Best%20Of%2014%20Years%20of%20Sprint%20Training%20and%20Optimization.pdf
- 43 Iavin FRC, Shah A, Devers K. Lessons from the literature on electronic health record implementation: A study funded by the office of the national coordinator for health information technology of the U.S. Department of Health and Human Services. 2013 . Urban Institute. Accessed November 20, 2015 at: https://www.healthit.gov/sites/default/files/hit_lessons_learned_lit_review_final_08-01-2013.pdf
- 44 Yu Jr FB, O'Brien A. Celebrating clinical informatics as a specialty practice. Appl Clin Inform 2020; 11 (02) 303-304
- 45 Hendricks T, Manzione L, Bice C. Arch Collaborative Provider Guidebook. 2023 . KLAS Research. 2024 . Accessed October 17, 2024 at: https://klasresearch.com/archcollaborative/report/arch-collaborative-provider-guidebook-2023/536
- 46 Stevens LA, DiAngi YT, Schremp JD. et al. Designing an individualized EHR learning plan for providers. Appl Clin Inform 2017; 8 (03) 924-935
- 47 Trockel M, Corcoran D, Minor LB, Shanafelt TD. Advancing physician well-being: a population health framework. Mayo Clin Proc 2020; 95 (11) 2350-2355
- 48 West CP, Dyrbye LN, Shanafelt TD. Physician burnout: contributors, consequences and solutions. J Intern Med 2018; 283 (06) 516-529
- 49 Institute of Medicine Roundtable on Evidence-Based Medicine. Olsen L, Aisner D, McGinnis JM. eds. The learning healthcare system: workshop summary. Washington (DC):: National Academies Press (US);; 2007
- 50 American Hospital Association. Fast facts: U.S. Health Systems Infographic. 2024 . Accessed September 5, 2024 at: https://www.aha.org/infographics/2021-01-15-fast-facts-us-health-systems-infographic
Address for correspondence
Publication History
Received: 22 May 2024
Accepted: 08 October 2024
Accepted Manuscript online:
09 October 2024
Article published online:
12 February 2025
© 2025. Thieme. All rights reserved.
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References
- 1 Chaudhry B, Wang J, Wu S. et al. Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. Ann Intern Med 2006; 144 (10) 742-752
- 2 King J, Patel V, Jamoom EW, Furukawa MF. Clinical benefits of electronic health record use: national findings. Health Serv Res 2014; 49 (1 Pt 2): 392-404
- 3 Colicchio TK, Cimino JJ, Del Fiol G. Unintended consequences of nationwide electronic health record adoption: challenges and opportunities in the post-meaningful use era. J Med Internet Res 2019; 21 (06) e13313
- 4 Poissant L, Pereira J, Tamblyn R, Kawasumi Y. The impact of electronic health records on time efficiency of physicians and nurses: a systematic review. J Am Med Inform Assoc 2005; 12 (05) 505-516
- 5 Moon MC, Hills R, Demiris G. Understanding optimization processes of electronic health records (EHR) in select leading hospitals: a qualitative study. J Innov Health Inform 2018; 25 (02) 109-125
- 6 Arndt BG, Beasley JW, Watkinson MD. et al. Tethered to the EHR: primary care physician workload assessment using EHR event log data and time-motion observations. Ann Fam Med 2017; 15 (05) 419-426
- 7 Pinevich Y, Clark KJ, Harrison AM, Pickering BW, Herasevich V. Interaction time with electronic health records: a systematic review. Appl Clin Inform 2021; 12 (04) 788-799
- 8 Shanafelt TD. Enhancing meaning in work: a prescription for preventing physician burnout and promoting patient-centered care. JAMA 2009; 302 (12) 1338-1340
- 9 Shanafelt TD, West CP, Sloan JA. et al. Career fit and burnout among academic faculty. Arch Intern Med 2009; 169 (10) 990-995
- 10 Young RA, Burge SK, Kumar KA, Wilson JM, Ortiz DF. A time-motion study of primary care physicians' work in the electronic health record era. Fam Med 2018; 50 (02) 91-99
- 11 Nguyen OT, Jenkins NJ, Khanna N. et al. A systematic review of contributing factors of and solutions to electronic health record-related impacts on physician well-being. J Am Med Inform Assoc 2021; 28 (05) 974-984
- 12 Shanafelt TD, Dyrbye LN, Sinsky C. et al. Relationship between clerical burden and characteristics of the electronic environment with physician burnout and professional satisfaction. Mayo Clin Proc 2016; 91 (07) 836-848
- 13 Tai-Seale M, Olson CW, Li J. et al. Electronic health record logs indicate that physicians split time evenly between seeing patients and desktop medicine. Health Aff (Millwood) 2017; 36 (04) 655-662
- 14 Thomas Craig KJ, Willis VC, Gruen D, Rhee K, Jackson GP. The burden of the digital environment: a systematic review on organization-directed workplace interventions to mitigate physician burnout. J Am Med Inform Assoc 2021; 28 (05) 985-997
- 15 West CP, Dyrbye LN, Sinsky C. et al. Resilience and burnout among physicians and the general us working population. JAMA Netw Open 2020; 3 (07) e209385
- 16 Yan Q, Jiang Z, Harbin Z, Tolbert PH, Davies MG. Exploring the relationship between electronic health records and provider burnout: a systematic review. J Am Med Inform Assoc 2021; 28 (05) 1009-1021
- 17 Shanafelt TD, Noseworthy JH. Executive leadership and physician well-being: Nine organizational strategies to promote engagement and reduce burnout. Mayo Clin Proc 2017; 92 (01) 129-146
- 18 Nguyen OT, Turner K, Apathy NC. et al. Primary care physicians' electronic health record proficiency and efficiency behaviors and time interacting with electronic health records: a quantile regression analysis. J Am Med Inform Assoc 2022; 29 (03) 461-471
- 19 West CP, Dyrbye LN, Erwin PJ, Shanafelt TD. Interventions to prevent and reduce physician burnout: a systematic review and meta-analysis. Lancet 2016; 388 (10057): 2272-2281
- 20 Lin CT, Bookman K, Sieja A. et al. Clinical informatics accelerates health system adaptation to the COVID-19 pandemic: examples from Colorado. J Am Med Inform Assoc 2020; 27 (12) 1955-1963
- 21 Meehan R. Health informatics workforce in the digital health ecosystem. Stud Health Technol Inform 2024; 310: 1226-1230
- 22 Veinot TC, Ancker JS, Bakken S. Health informatics and health equity: improving our reach and impact. J Am Med Inform Assoc 2019; 26 (8-9): 689-695
- 23 Shah T, Kitts AB, Gold JA. et al. Electronic health record optimization and clinician well-being: a potential roadmap toward action. NAM Perspect 2020; 2020
- 24 Office of the National Coordinator for Health Information Technology. Strategy on reducing regulatory and administrative burden relating to the use of health IT and EHRs. 2020 . Accessed September 5, 2024 at: https://www.healthit.gov/sites/default/files/page/2020-02/BurdenReport_0.pdf
- 25 Chen J, Chi WN, Ravichandran U. et al. Sprint-inspired one-on-one post-go-live training session (mini-sprint) improves provider electronic health record efficiency and satisfaction. Appl Clin Inform 2024; 15 (02) 313-319
- 26 English EF, Holmstrom H, Kwan BW. et al. Virtual sprint outpatient electronic health record training and optimization effect on provider burnout. Appl Clin Inform 2022; 13 (01) 10-18
- 27 Eschenroeder HC, Manzione LC, Adler-Milstein J. et al. Associations of physician burnout with organizational electronic health record support and after-hours charting. J Am Med Inform Assoc 2021; 28 (05) 960-966
- 28 Kang C, Sarkar IN. Interventions to reduce electronic health record-related burnout: A systematic review. Appl Clin Inform 2024; 15 (01) 10-25
- 29 Longhurst CA, Davis T, Maneker A. et al; Arch Collaborative. Local investment in training drives electronic health record user satisfaction. Appl Clin Inform 2019; 10 (02) 331-335
- 30 McAlearney AS, Song PH, Robbins J. et al. Moving from good to great in ambulatory electronic health record implementation. J Healthc Qual 2010; 32 (05) 41-50
- 31 Rungvivatjarus T, Bialostozky M, Chong AZ, Huang JS, Kuelbs CL. Preparing future pediatric care providers with a clinical informatics elective. Appl Clin Inform 2024; 15 (03) 437-445
- 32 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
- 33 Touson JC, Azad N, Beirne J. et al. Application of the consolidated framework for implementation research model to design and implement an optimization methodology within an ambulatory setting. Appl Clin Inform 2022; 13 (01) 123-131
- 34 DiAngi YT, Stevens LA, Halpern-Felsher B, Pageler NM, Lee TC. Electronic health record (EHR) training program identifies a new tool to quantify the EHR time burden and improves providers' perceived control over their workload in the EHR. JAMIA Open 2019; 2 (02) 222-230
- 35 Lourie EM, Stevens LA, Webber EC. Measuring success: perspectives from three optimization programs on assessing impact in the age of burnout. JAMIA Open 2020; 3 (04) 492-495
- 36 Lourie EM, Utidjian LH, Ricci MF, Webster L, Young C, Grenfell SM. Reducing electronic health record-related burnout in providers through a personalized efficiency improvement program. J Am Med Inform Assoc 2021; 28 (05) 931-937
- 37 Robinson KE, Kersey JA. Novel electronic health record (EHR) education intervention in large healthcare organization improves quality, efficiency, time, and impact on burnout. Medicine (Baltimore) 2018; 97 (38) e12319
- 38 Sieja A, Kim E, Holmstrom H. et al. Multidisciplinary sprint program achieved specialty-specific EHR optimization in 20 clinics. Appl Clin Inform 2021; 12 (02) 329-339
- 39 ChangeQuest. Kotter's 8 step change model. Accessed September 5, 2024 at: https://www.changequest.co.uk/resources/kotters-8-step-change-model/
- 40 AMA ǀ ED Hub. Professional Well-Being: Organizational Cost of Physician Burnout. 2024 . Accessed February 28, 2024 at: https://edhub.ama-assn.org/steps-forward/interactive/16830405
- 41 Mann DM, Stevens ER, Testa P, Mherabi N. From silos to synergy: integrating academic health informatics with operational IT for healthcare transformation. NPJ Digit Med 2024; 7 (01) 185
- 42 McEntee RK, McDougall C, Seija A. High performing teams: Best of 14 years of sprint EHR training and optimization. Presented at Epic User Group Meeting, Aug 20, 2024, Verona, WI. Accessed September 5, 2024 at https://eventarchive.epic.com/Past%20Events/2024%20Events/UGM/Executive%20Breakouts/EXEC12%20Physician%20Executive%20Forum%20-%20High-Performing%20Teams%20-%20The%20Best%20Of%2014%20Years%20of%20Sprint%20Training%20and%20Optimization.pdf
- 43 Iavin FRC, Shah A, Devers K. Lessons from the literature on electronic health record implementation: A study funded by the office of the national coordinator for health information technology of the U.S. Department of Health and Human Services. 2013 . Urban Institute. Accessed November 20, 2015 at: https://www.healthit.gov/sites/default/files/hit_lessons_learned_lit_review_final_08-01-2013.pdf
- 44 Yu Jr FB, O'Brien A. Celebrating clinical informatics as a specialty practice. Appl Clin Inform 2020; 11 (02) 303-304
- 45 Hendricks T, Manzione L, Bice C. Arch Collaborative Provider Guidebook. 2023 . KLAS Research. 2024 . Accessed October 17, 2024 at: https://klasresearch.com/archcollaborative/report/arch-collaborative-provider-guidebook-2023/536
- 46 Stevens LA, DiAngi YT, Schremp JD. et al. Designing an individualized EHR learning plan for providers. Appl Clin Inform 2017; 8 (03) 924-935
- 47 Trockel M, Corcoran D, Minor LB, Shanafelt TD. Advancing physician well-being: a population health framework. Mayo Clin Proc 2020; 95 (11) 2350-2355
- 48 West CP, Dyrbye LN, Shanafelt TD. Physician burnout: contributors, consequences and solutions. J Intern Med 2018; 283 (06) 516-529
- 49 Institute of Medicine Roundtable on Evidence-Based Medicine. Olsen L, Aisner D, McGinnis JM. eds. The learning healthcare system: workshop summary. Washington (DC):: National Academies Press (US);; 2007
- 50 American Hospital Association. Fast facts: U.S. Health Systems Infographic. 2024 . Accessed September 5, 2024 at: https://www.aha.org/infographics/2021-01-15-fast-facts-us-health-systems-infographic
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