Typing Proficiency among Physicians in Internal Medicine: A Pilot Study of Speed and Performance

Abstract

Background

Electronic health records (EHRs) are widely implemented and consume nearly half of physicians' work time. Despite the importance of efficient data entry, physicians' typing skills—potential contributors to documentation burden—remain poorly studied.

Objective

This study aims to evaluate the typing skills of physicians and their associations with demographic characteristics and professional roles.

Methods

This cross-sectional pilot study included a convenience sample of physicians (residents, chief residents, and attending physicians) from the internal medicine division of an academic hospital. Participants completed a 1-minute typing test under supervised conditions. The primary outcome was raw typing speed, measured in words per minute (WPM). The secondary outcome was a performance score calculated by subtracting 50 points for each error from the total number of characters typed per minute.

Results

Participation rate was 100% (82/82 physicians). The mean age was 33.7 ± 7.3 years; 7.2 ± 7.1 years since graduation; and 45.1% female. The mean typing speed was 53.4 WPM (range: 31–91 WPM), with 57.3% (47/82) of participants exceeding 50 WPM, a threshold commonly considered professional. Bivariate analysis showed a significant negative association with age (Spearman's ρ = −0.281, p = 0.011), which was not sustained in the multivariable analysis. No significant association was observed with sex, country of diploma, or role. Upon multivariable analysis, performance score showed a significant negative association with age (β = −17.724, p = 0.009) but a positive association with years since graduation (β = 16.850, p = 0.021), suggesting a generation- and experience-related interaction.

Conclusion

Nearly half of physicians exhibited professional-level typing skills, yet overall performance varied widely and was influenced by both generational factors and clinical experience. Given that documentation burden affects clinicians across all skill levels, both individual and systemic strategies—such as improved EHR design and alternative input methods—should be explored.

Keywords

electronic health records - documentation burden - medicine - human–computer interaction

Background and Significance

Burnout is a psychological syndrome characterized by emotional exhaustion, depersonalization, and reduced personal accomplishment, resulting from prolonged exposure to occupational stress. Physicians exhibit higher rates of burnout. Shanafelt et al.[1] reported that over 54.4% of U.S. physicians had at least one burnout symptom.

Electronic health record (EHR) use is a significant contributor to physician stress and burnout, primarily due to excessive and increased documentation burden, poor system usability, cognitive overload, and alert fatigue.[2] These factors reduce work efficiency, increase after-hours workload, and negatively impact clinician well-being.[3] [4] [5] [6] [7] [8] [9] [10]

Li et al.'s[3] scoping review underlined the association between EHR use in health care providers and burnout, due to many factors, including time spent on EHR. For example, on average, internal medicine residents spent over 5.1 hours per day on a computer when practicing in a hospital,[11] [12] [13] [14] 5.9 hours in ambulatory care,[15] and dedicated 2 hours per day (18% of day shifts) to typing documentation. Such tasks, including typing discharge summaries and notes, were among the most time-consuming activities, highlighting the administrative burden faced by medical professionals in clinical practice. While the EHR has many benefits,[16] being slow at documentation appears to increase the risk of burnout.[17]

Overhage et al.[18] found that the greatest variability in EHR usage occurred at an individual physician level, rather than at a system or practice level. Differences in typing speed could account for some of this individual variability, particularly impacting documentation time.

Objectives

The keyboard remains the primary input device; however, physicians' typing skills have been insufficiently studied. We hypothesized that better typists may be better adapted to practicing in an EHR-based health care system.

Our aim was to delineate the typing skills of physicians in internal medicine, and the association between these skills and their demographic characteristics.

Methods

Study Design

The study was designed as a cross-sectional, monocentric, pilot study including a convenience sample of 82 physicians from the Internal Medicine Division of the Lausanne University Hospital, an academic hospital in the French-speaking region of Switzerland. All typing tests were performed between June 21 and October 27, 2018, alongside the second phase of the MEDAY study,[14] designed to observe the workday of residents. A 50 Swiss Franc voucher was offered as reward for the best performance.

Typing Test

We used the TAKI test, developed by Gian Paolo Trivulzio, and available online on www.intersteno.org. We selected a 1-minute typing test to balance practicality and reliability. The widely accessible TAKI test is a standardized tool used in international competitions organized by Intersteno, ensuring assessment of typing proficiency and promoting reporting, text, and information processing, and secretarial skills. While longer tests may provide a more exhaustive assessment, a 1-minute duration limits fatigue and maintains participant engagement.

The selected test parameters were “Switzerland—French”, QWERTZ keyboard layout, and 1-minute duration. The given instruction was to reproduce a random text as fast and precisely as possible. The physicians performed the test in a quiet environment, on standard hospital office computers, hardwired on Ethernet, without any latency issues. An investigator supervised all tests. Participants were allowed to repeat the test once. The better result was selected for analysis.

Number of characters per minute and the number of errors were recorded. Raw typing speed was expressed in words per minute (WPM), calculated as the number of characters per minute divided by 5. WPM is the most widely used metric for assessing text entry skills and allows for a standardized comparison across different input methods. Considering various professional benchmarks such as the ECDL Typing Certificate, we considered 50 WPM as an expert level.[19] [20] [21] [22]

A performance score was calculated, as part of the standard TAKI test, by subtracting 50 points for each error from the total number of characters typed per minute. The 50-point penalty aims to be a sufficient deterrent against random or meaningless typing. The resulting score provides an integrated measure of typing efficiency, taking into account both speed and accuracy. The performance score does not map directly onto such benchmarks and is intended primarily for internal comparison within the TAKI test framework.[22]

Demographic Characteristics

We categorized physicians as residents, chief residents, or attending physicians and recorded age, sex, clinical experience, and country of diploma delivery. Chief residents are physicians who have completed or are nearing the completion of their postgraduate training and hold supervisory responsibilities over typically three residents.

Inclusion and Exclusion Criteria

All physicians working in the Internal Medicine Division were eligible for inclusion. Physicians on rotation in other specialties or on maternity leave were excluded.

Statistical Analysis

Statistical analyses were performed using Stata version 18 (StataCorp, College Station, TX). Results were expressed as mean ± standard deviation for continuous data and number of participants (percentage) for categorical data. Due to the non-normal distribution of typing speed and errors (Shapiro–Wilk test: p = 0.00194), non-parametric tests were used (Mann–Whitney U, Kruskal–Wallis, chi-square). Correlations were assessed using Spearman's rank coefficient. To quantify associations, bivariate analyses included regression coefficients to estimate effect sizes. Multivariable analysis was performed using robust regression models adjusting for age, sex, hierarchical role, and years since graduation. Statistical significance was set at p < 0.05.

Ethical Statement

As the field of research is not subject to the Human Research Act (LRH), the Local Ethics Commission confirmed that no formal informed consent was required. Participants were informed of the purposes and use of the results. All data were de-identified before analysis.

Results

Population and Characteristics

Out of 97 eligible physicians, 8 were on maternity leave and 7 on rotation in other specialties. Consequently, 82 participants were included, who all completed the typing test. [Table 1] presents the demographic characteristics of the participants and their role distribution within the Internal Medicine Division. While women represented 58.1% (25/43) of residents, they were underrepresented in senior roles, with only 8.3% (1/12) among attending physicians.

Table 1 Demographic characteristics

	All	Role
		Residents	Chief residents	Attending physicians	p-Value
N	82	43 (52.4%)	27 (32.9%)	12 (14.6%)	–
Age (y)	33.7 ± 7.3	29.8 ± 2.2	33.4 ± 3.0	48.5 ± 7.4	<0.001
Sex (female)	37 (45.1%)	25 (58.1%)	11 (40.7%)	1 (8.3%)	0.008
Diploma delivered in Switzerland	59 (72.0%)	30 (69.8%)	18 (66.7%)	11 (91.7%)	0.250
Years since graduation	7.2 ± 7.1	3.8 ± 1.6	7.0 ± 2.4	21.8 ± 7.0	<0.001

Note: Demographic characteristics of the study participants, stratified by role. Data are presented as N (%) for categorical variables and mean ± standard deviation (SD) for continuous variables. p-Values correspond to comparisons among the three role groups using appropriate statistical tests.

Typing Speed

Participants had a mean typing speed of 53.4 ± 14.3 WPM. Typing speeds ranged from 31 to 91 WPM, reflecting a nearly threefold difference between the slowest and fastest typists. Over half of participants (57.3%, 47/82) exceeded 50 WPM.

Residents (53.9 ± 13.8 WPM) and chief residents (55.9 ± 15.9 WPM) tended to be faster than attending physicians (45.9 ± 13.8 WPM). Post hoc Mann–Whitney tests indicate no statistically significant differences between residents and attending physicians (p = 0.078), nor between chief residents and attending physicians (p = 0.059, [Fig. 1]).

Fig. 1 Typing speed distribution by role. Box plot illustrating the distribution of typing speed, expressed in words per minute (WPM), stratified by role among 82 physicians: residents (n = 43), chief residents (n = 27), and attending physicians (n = 12). The horizontal red dashed line at 50 WPM represents the expert-level threshold for the ECDL Typing Certificate. Post hoc Mann–Whitney tests indicate no statistically significant differences between residents and attending physicians (p = 0.078), nor between chief residents and attending physicians (p = 0.059).

[Table 2] presents the results of the bivariate and multivariable analyses examining the associations between typing speed and demographic characteristics. Typing speed was negatively associated with age (Spearman's ρ = −0.281, p = 0.011, see [Fig. 2]), indicating a lower performance with increasing age. A similar trend was observed for years since graduation (Spearman's ρ = −0.205, p = 0.065), though it did not reach statistical significance. No significant associations were found with sex (p = 0.789), country of diploma delivery (p = 0.684), or professional role (p = 0.139).

Table 2 Bivariate and multivariable analysis of typing speed and demographic characteristics (N = 82)

	Bivariate			Multivariable
	Coefficient (β)	95% CI	p-Value	Coefficient (β)	95% CI	p-Value
Age (y)	−0.491	[−0.913; −0.070]	0.023	−1.978	[−4.105; 0.149]	0.068
Female (vs. male)	2.029	[−4.312; 8.371]	0.526	0.612	[−7.377; 6.154]	0.858
Diploma delivered in Switzerland (vs. abroad)	−0.091	[−7.134; 6.952]	0.980	1.897	[−5.323; 9.117]	0.602
Years since graduation (y)	−0.432	[−0.871; 0.009]	0.055	1.659	[−0.725; 4.042]	0.170
Role
Chief residents (vs. residents)	2.076	[−4.818; 8.969]	0.551	2.887	[−5.104; 10.878]	0.474
Attending physicians (vs. residents)	−7.998	[−17.165; 1.168]	0.086	2.457	[−16.957; 12.043]	0.737

Note: Each coefficient represents the estimated effect of the corresponding variable on typing speed, with 95% confidence intervals and p-values. Bivariate analyses were conducted using Spearman's rank correlation for age (ρ = −0.281, p = 0.011) and years since graduation (ρ = −0.205, p = 0.065), the Mann–Whitney U test for sex (p = 0.789), and Kruskal–Wallis tests for role (p = 0.139) and country of diploma delivery (p = 0.684). Multivariable analyses adjusted for age, sex, role, country of diploma delivery, and years since graduation.

Fig. 2 Association between typing speed and age. Scatter plot illustrating the relationship between typing speed, expressed in words per minute (WPM), and age among 82 physicians. Each point represents an individual participant, categorized as residents (●), chief residents (+), or attending physicians (◇). The continuous line represents a LOWESS-smoothed trend, highlighting a decline in typing speed with increasing age. The Spearman correlation coefficient (ρ = −0.281, p = 0.011) indicates a statistically significant negative association.

Typing Efficiency: Performance Score

Participants had a mean performance score of 130.0 ± 114.0 points. Attending physicians (28.0 ± 76.0) had a significantly lower performance than chief residents (160.7 ± 114.0, p < 0.001) and residents (139.1 ± 108.7, p < 0.001).

[Table 3] presents the results of the bivariate and multivariable analyses examining the associations between performance score and demographic characteristics. Performance score was negatively associated with age (β = −5.673, p = 0.001), indicating a decline in typing efficiency with increasing age. In the multivariable model ([Fig. 3]), the association between age and performance score remained significant (β = −17.724, p = 0.009), whereas the effect of years since graduation became positive (β = 16.850, p = 0.021), suggesting a complex interaction between experience and typing efficiency. No significant associations were found with sex or country of diploma delivery.

Table 3 Bivariate and multivariable analysis of performance score and demographic characteristics

	Bivariate			Multivariable
	Coefficient (β)	95% CI	p-Value	Coefficient (β)	95% CI	p-Value
Age (y)	−5.673	[−8.906; −2.441]	0.001	−17.724	[−30.918; −4.529]	0.009
Female (vs. male)	16.494	[−34.015; 67.002]	0.518	−14.477	[−68.380; 39.428]	0.594
Diploma delivered in Switzerland (vs. abroad)	6.915	[−49.159; 62.989]	0.807	33.290	[−21.754; 88.335]	0.232
Years since graduation (years)	−5.264	[−8.657; −1.872]	0.003	16.850	[2.653; 31.048]	0.021
Role
Chief residents (vs. residents)	21.624	[−30.483; 73.732]	0.411	20.363	[−38.412; 79.137]	0.492
Attending physicians (vs. residents)	−111.116	[−180.398; −41.834]	0.002	−106.412	[−240.585; 27.760]	0.118

Note: Performance score is calculated as number of characters per minute minus 50 characters per error. Each coefficient represents the estimated effect of the corresponding variable on performance score, with 95% confidence intervals and p-values. Bivariate analyses were conducted using Spearman's rank correlation for age (ρ = −0.266, p = 0.016) and years since graduation (ρ = −0.200, p = 0.072), the Mann–Whitney U test for sex (p = 0.562), and Kruskal–Wallis tests for role (p = 0.001) and country of diploma delivery (p = 0.808). Multivariable analyses adjusted for age, sex, role, country of diploma delivery, and years since graduation.

Fig. 3 Demographic and professional characteristics influencing performance score. Forest plot displaying the adjusted coefficients (95% confidence intervals) from the multivariable regression analysis assessing the association between demographic and professional characteristics and the performance score. Negative coefficients indicate lower performance scores relative to the reference group. The confidence interval for “attending physicians versus residents” extends beyond −150 and is truncated for readability, as indicated by the blue cross symbol.

Discussion

In this monocentric pilot study, we found that only half of physicians achieved a professional typing speed (>50 WPM). In this small cohort, multivariable analysis revealed that typing efficiency, accounting for errors, was negatively associated with age but positively associated with years since graduation, suggesting a complex interaction between experience and typing efficiency.

Typing Speed of Physicians

Only a few studies have focused on physician typing speed. Kalava et al.[23] found that residents had a lower median typing skill of 30.4 WPM. Schuurman et al.[24] analyzed a cohort of 2,690 health care workers in the Netherlands and reported a mean typing speed of 60.1 WPM. Health care workers in internal medicine exhibited the highest typing speeds, highlighting specialty-related variations in proficiency. These results align with ours and confirm that a large number of physicians potentially suffer from low typing speed compared to historic pencil usage in medical records.

Association of Typing Efficiency and Age

The association between age and typing efficiency, observed both in Schuurman's study and in ours, likely reflects generational differences in digital exposure rather than a pure age-related decline.[24] Younger physicians may benefit from earlier and more frequent use of computers, while older physicians were trained in an era where documentation was primarily handwritten or dictated.

In our multivariable analysis, age remained negatively associated with performance score, while years since graduation reversed to a positive effect—despite both showing negative associations in bivariate analysis. This likely reflects a suppression effect due to multicollinearity (ρ = 0.978) and suggests that professional experience may help compensate for less training in typing or electronic workflows.

Therefore, older physicians may benefit the most from alternative text input methods, such as dictation. However, optimizing documentation efficiency is likely to impact all physicians by reducing workload and improving practice satisfaction.

Electronic Health Record Documentation Time and Burnout

Documentation burden is a well-recognized contributor to physician burnout. Focus groups of residents showed that time was a major determinant of their daily experience, and on no account did they want to waste it.[25] Any way of improving efficiency, such as time spent on EHR documentation, should be explored.[26]

Improvement Opportunities

Further research has explored how to improve EHR documentation to reduce physician burnout. Kang and Sarkar reviewed literature and found 44 articles describing interventions to reduce EHR-related burnout, including scribes, EHR training and modifications. Subjective improvements were noted, but objective data remain limited.[27]

Misurac et al. showed that ambient artificial intelligence allows for reducing documentation burden through technologies that summarize patient encounters into clinical notes, thus decreasing burnout in health care providers.[28]

Speech recognition technology has a theoretical speed of 100 to 150 WPM. Performed after patient encounter or in front of them with the benefit of reformulation by patients themselves, this technique could be beneficial to physicians.[29]

Moreover, the synthesis of reports and the limitation of their number should be encouraged. Indeed, time spent on documentation is closely associated with the volume of data entry. Kroth et al. showed that excessive data entry, redundant notes, and lengthy documentation contribute significantly to clinician stress and burnout, but also to physical discomfort.[2]

Finally, only clinical data should be entered by physicians. Other entries, such as regulatory and accreditation requirements that contribute to data redundancy, should be delegated to data managers, secretaries, or automated. Unfortunately, the development of EHR has shifted many clerical tasks towards physicians.[4] Indeed, the Harris Poll showed that 69% of primary care physicians felt that most EHR clerical tasks that they completed did not require the skills of a qualified physician.[30]

However, despite such interventions being generally well perceived, best practices for documentation and new EHR tools that significantly reduce the documentation burden are still lacking.[31]

While structured typing training has not been formally studied in physicians, it may be considered for individuals with low baseline skills. Yet, scalable interventions targeting documentation burden more broadly remain a more promising path for systemic impact.

Study Strengths and Limitations

Our pilot study reached a 100% participation rate across all hierarchical levels. Moreover, it is easily reproducible as TAKI is a freely available online test. This study also has some limitations. Firstly, the performance score, while embedded in the standardized TAKI test, relies on a heuristic penalty of 50 characters per error that has not been clinically validated. As such, interpretations based on this score should be viewed as exploratory and contextual to the test framework. Moreover, typing skills required during a medical workday are more complex than those assessed by the test. In addition, mother tongue was not included as a variable. Furthermore, due to their small number, attendings could not easily be compared to other hierarchical groups. Finally, the study was performed at a single hospital in one country and results may not be generalizable.

Conclusion

This pilot study highlights substantial variability in physicians' typing skills. While over half of participants demonstrated professional-level typing speed, a significant proportion exhibited slower performance, potentially increasing their EHR documentation burden. Typing proficiency thus emerges as a relevant but often overlooked component of EHR usability and clinical efficiency. However, given that documentation burden affects physicians regardless of their baseline skills, system-level strategies such as improved EHR design, structured data entry, and voice-based technologies should also be prioritized. Addressing both individual and systemic factors is essential to reduce administrative load and improve clinician experience.

Clinical Relevance Statement

This cross-sectional single-center pilot study included 82 physicians, with a median typing speed of 50.9 WPM and a three-fold variation between the fastest and slowest typists.

Multiple-Choice Questions

1. Which factor was found to have a significant negative correlation with typing performance in the study?

   • Sex

   • Country of medical diploma delivery

   • Age

   • Typing test duration

   Correct Answer: The correct answer is option c. The study found a significant negative correlation between age and performance score, with younger physicians typing faster than older ones.

2. What was the primary finding regarding typing speed among physicians?

   • Most physicians had a typing speed below 30 WPM.

   • Over half of the physicians achieved a typing speed of 50 WPM or higher.

   • Typing speed was unaffected by age or sex.

   • Physicians with international diplomas had significantly faster typing speeds.

   Correct Answer: The correct answer is option b. The study revealed that 57% of the physicians achieved a typing speed of over 50 WPM, which is considered a professional level. This finding highlights the variability in typing skills among physicians.

Conflict of Interest

None declared.

Protection of Human and Animal Subjects

As the research focus involves typing performance and does not entail clinical intervention or access to sensitive patient information, the study is not subject to the Swiss Human Research Act (HRA). The local ethics commission confirmed that no formal ethical approval or informed consent was required. All participants were fully informed of the study's objectives, and their data were anonymized prior to analysis to ensure confidentiality.

^* These authors contributed equally to this work.

• References

• 1 Shanafelt TD, Boone S, Tan L. et al. Burnout and satisfaction with work-life balance among US physicians relative to the general US population. Arch Intern Med 2012; 172 (18) 1377-1385
• 2 Kroth PJ, Morioka-Douglas N, Veres S. et al. Association of electronic health record design and use factors with clinician stress and burnout. JAMA Netw Open 2019; 2 (08) e199609
• 3 Li C, Parpia C, Sriharan A, Keefe DT. Electronic medical record-related burnout in healthcare providers: a scoping review of outcomes and interventions. BMJ Open 2022; 12 (08) e060865
• 4 Budd J. Burnout related to electronic health record use in primary care. J Prim Care Community Health 2023 ;14:21501319231166921
• 5 Alobayli F, O'Connor S, Holloway A, Cresswell K. Electronic health record stress and burnout among clinicians in hospital settings: A systematic review. Digit Health 2023 ;9:20552076231220241
• 6 Muhiyaddin R, Elfadl A, Mohamed E. et al. Electronic health records and physician burnout: A scoping review. Stud Health Technol Inform 2022; 289: 481-484
• 7 Wu Y, Wu M, Wang C, Lin J, Liu J, Liu S. Evaluating the prevalence of burnout among health care professionals related to electronic health record use: Systematic review and meta-analysis. JMIR Med Inform 2024; 12: e54811
• 8 Kroth PJ, Morioka-Douglas N, Veres S. et al. The electronic elephant in the room: Physicians and the electronic health record. JAMIA Open 2018; 1 (01) 49-56
• 9 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
• 10 Friedberg MW, Chen PG, Van Busum KR. et al. Factors affecting physician professional satisfaction and their implications for patient care, health systems, and health policy. Rand Health Q 2014; 3 (04) 1
• 11 Wang JK, Ouyang D, Hom J, Chi J, Chen JH. Characterizing electronic health record usage patterns of inpatient medicine residents using event log data. PLoS ONE 2019; 14 (02) e0205379
• 12 Chen L, Guo U, Illipparambil LC. et al. Racing against the clock: Internal medicine residents' time spent on electronic health records. J Grad Med Educ 2016; 8 (01) 39-44
• 13 Fletcher KE, Visotcky AM, Slagle JM, Tarima S, Weinger MB, Schapira MM. The composition of intern work while on call. J Gen Intern Med 2012; 27 (11) 1432-1437
• 14 Wenger N, Méan M, Castioni J, Marques-Vidal P, Waeber G, Garnier A. Allocation of internal medicine resident time in a Swiss hospital: A time and motion study of day and evening shifts. Ann Intern Med 2017; 166 (08) 579-586
• 15 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
• 16 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
• 17 Gardner RL, Cooper E, Haskell J. et al. Physician stress and burnout: The impact of health information technology. J Am Med Inform Assoc 2019; 26 (02) 106-114
• 18 Overhage JM, Qeadan F, Choi EHE, Vos D, Kroth PJ. Explaining variability in electronic health record effort in primary care ambulatory encounters. Appl Clin Inform 2024; 15 (02) 212-219
• 19 Nottrodt J. What is a good WPM for typing? The Predictive Index. January 28, 2025
• 20 Dhakal V, Feit AM, Kristensson PO, Oulasvirta A. Observations on Typing from 136 Million Keystrokes. Paper presented at: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems; 2018 ; Montreal, Quebec, Canada
• 21 Kaushik A. What is a Good Typing Speed for Employees & Why? WECP. January 28, 2025. Accessed May 30, 2025 at: https://www.wecreateproblems.com/blog/good-typing-speed#heading-3
• 22 Digital Literacy Inc. Typing skills certificate. Accessed March 27, 2025. Accessed May 30, 2025 at: https://www.ecdl.ch/en/typing/
• 23 Kalava A, Ravindranath S, Bronshteyn I, Munjal RS, Schianodicola J, Yarmush JM. Typing skills of physicians in training. J Grad Med Educ 2014; 6 (01) 155-157
• 24 Schuurman AR, Baarsma ME, Wiersinga WJ, Hovius JW. Digital disparities among healthcare workers in typing speed between generations, genders, and medical specialties: Cross-sectional study. BMJ 2022; 379: e072784
• 25 Céline B, Matteo M, Michael S. et al. Running against the clock: a qualitative study of internal medicine residents' work experience. Swiss Med Wkly 2022; 152: w30216
• 26 West CP. The paradox of burnout from physicians' most meaningful roles. JAMA Netw Open 2024; 7 (12) e2452106-e2452106
• 27 Kang C, Sarkar IN. Interventions to reduce electronic health record-related burnout: A systematic review. Appl Clin Inform 2024; 15 (01) 10-25
• 28 Misurac J, Knake LA, Blum JM. The effect of ambient artificial intelligence notes on provider burnout. Appl Clin Inform 2025; 16 (02) 252-258
• 29 Kumah-Crystal YA, Pirtle CJ, Whyte HM, Goode ES, Anders SH, Lehmann CU. Electronic health record interactions through voice: A review. Appl Clin Inform 2018; 9 (03) 541-552
• 30 Stanford Medicine ǀ The Harris Poll. How Doctors Feel about Electronic Health Records. Accessed May 30, 2025 at: https://med.stanford.edu/content/dam/sm/ehr/documents/EHR-Poll-Presentation.pdf
• 31 Shah S, Bedgood M, Devon-Sand A. et al. Effect of an electronic health record-based intervention on documentation practices. Appl Clin Inform 2024; 15 (04) 771-777

Publication History

Received: 18 November 2024

Accepted: 20 May 2025

Accepted Manuscript online:
26 May 2025

Article published online:
17 October 2025