Abstract
Background There is a common belief that seniority and gender are associated with clinicians' perceptions of the value of electronic health record (EHR) technology and the propensity for burnout. Insufficient evidence exists on the relationship between these variables.
Objective The aim of this study was to investigate how seniority/years of practice, gender, and screened burnout status are associated with opinions of EHR use on quality, cost, and efficiency of care.
Methods We surveyed ambulatory primary care and subspecialty clinicians at three different institutions to screen for burnout status and to measure their opinions (positive, none, negative, don't know) on how EHR technology has impacted three important attributes of health care: quality, cost, and efficiency of care. We used chi-square tests to analyze association between years of practice (≤10 years or 11+ years), gender, and screened burnout status and the reported attributes. We used a Bonferroni-corrected α = 0.0167 for significance to protect against type I error among multiple comparisons.
Results Overall, 281 clinicians responded from 640 that were surveyed with 44% overall response rate. There were no significant associations of years in practice (≤10 years or 11+ years) or gender ( p > 0.0167 for both) with any of the health care attributes. Clinicians who screened burnout negative ( n = 154, 55%) were more likely to indicate that EHR technology has a positive impact on both the quality ( p = 0.0025) and efficiency ( p = 0.0003) health care attributes compared with those who screened burnout positive ( n = 127, 45%).
Conclusion Burnout status is significantly associated with clinicians' perceived value of EHR technologies, while years of practice and gender are not. This contests the popular notion that junior clinicians view EHR technology more favorably than their more senior counterparts. Hence, burnout status may be an important factor associated with the overall value clinicians ascribe to EHR technologies.
Keywords: people, burnout, documentation burden, workflows and human interactions, human–computer interaction, interfaces and usability, culture, workarounds and unanticipated consequences, user acceptance and resistance
Background and Significance
The rate of physician burnout is alarmingly high. In a 2020 national survey, 38.2% of physicians screened positive for burnout, compared with only 25.2% of the general working U.S. population. 1 2 Professional burnout can have detrimental effects on a physician's mental and physical health, professional performance, job turnover, and quality of care. 3 The comprehensive adoption of electronic health records (EHRs) has triggered many unintended consequences, including those contributing to physician burnout. 3 4 5 6 7 8 9 10 A study conducted with 1,800 physicians from Rhode Island found that burnout prevalence was considerably higher among physicians who used EHRs, with 27.2% of the group reporting one or more burnout symptoms, compared with 13.6% of physicians who did not use EHRs. 2 11 Another study showed that spending more than 6 hours per week on after-hours EHR work was strongly associated with the perception that EHR use affects both work–life balance and burnout. 12 Sinha et al showed how EHR use correlates with physician exhaustion, 9 Marmor et al showed how time spent on EHR use during the day had an inverse relationship with patient satisfaction scores, 13 and Frintner et al showed how EHR use by pediatricians was associated with worse work–life balance, stress in balancing responsibilities, and less career and life satisfaction. 14
Studies have also shown differences between male and female clinicians on various factors associated with EHR use, including stress, frustration, and burnout rates. 15 16 Other factors contributing to workload differences between gender can contribute to overall burnout. Rittenberg et al found that women primary care physicians receive 25% more requests from both office staff and patients, compared with men in the same practice. 17 These differences may be related to aspects outside of EHR usability or efficiency, such as gender biases and discrimination, maternal discrimination, household obligations, and fewer mentoring and sponsoring opportunities available for women when compared with their male colleagues. 15
Similarly, there is a belief that senior physicians view EHRs less favorably and struggle with the technology more than their younger counterparts, a factor that may contribute more to physician burnout in older physicians. 7 18 This notion stems from the “generational divide” between older physicians or “digital immigrants” (individuals who did not grow up with technology but currently use it during their adult life) and younger physicians or “digital natives” (individuals who grew up with technology and are perceived to be familiar with the digital world). 19 There is good evidence that in the general workforce, age seems to be negatively associated with the probability of technology or software adoption and use. 20 21 22 Other studies have also shown that physicians with more EHR experience were more likely to hold positive views on the EHR. 23 Then, are younger physicians who trained with EHRs early on more adept at using the technology, hence making them more resilient to EHR-related burnout? Likewise, does the older generation's experience with paper charts and its use inefficiencies make them more welcoming of the more unified and accessible EHR system?
Insufficient evidence exists on the relationship between clinician seniority, gender, and burnout status and the perceived value of EHR technology.
Objectives
The objective of this study was to investigate how clinician seniority (measured by years of practice), gender, and burnout status associate with clinician perceptions of the EHR's impact on three important attributes of health care: quality, cost, and efficiency of care.
Methods
Survey and Sampling
This study analyzed unpublished survey data from the Minimizing Stress, Maximizing Success of Clinicians' Use of the Electronic Health Record (MS-Squared) study. A copy of the survey instrument 24 and a more detailed description of the methods are freely available. 4 24 In summary, between August 2016 and July 2017, 640 clinicians (physicians, nurse practitioners, and physician assistants) in 5 disciplines (general internal medicine, medical subspecialties, general pediatrics, pediatric subspecialties, and family medicine) at 3 institutions were surveyed as part of the MS-Squared project. Surveys were sent electronically using the survey function in the REDCap (Research Electronic Data Capture) electronic data capture tool. 25 26 All clinicians surveyed worked in the outpatient setting. The institutions employed three different EHR systems (Epic, Cerner, and Meditech). Survey solicitations were first deployed via email with additional paper copies posted to nonresponders. Table 1 summarizes the demographics of those surveyed.
Table 1. Reported demographic characteristics ( n = 281) .
| Characteristic | N (%) |
|---|---|
| Age, y, mean (SD) | 50 (11) |
| NR, N (%) | 5 (1.8%) |
| Gender | |
| Male | 117 (41.6%) |
| Female | 160 (56.9%) |
| NR | 4 (1.4%) |
| Clinician type | |
| MD | 240 (85.5%) |
| PA | 20 (7.1%) |
| NP | 14 (5.0%) |
| DO | 6 (2.1%) |
| NR | 1 (0.4%) |
| Practice type | |
| Primary care | 196 (69.8%) |
| Roles | |
| Full time | 225 (80.1%) |
| Part time | 54 (19.2%) |
| NR | 2 (0.7%) |
Abbreviations: NP, nurse practitioner; NR, no response; PA, physician's assistant; SD, standard deviation.
This analysis focuses on clinician opinions on how EHR technology has impacted three specific attributes of health care (“quality,” “cost,” and “efficiency of care”) based on their background characteristics of years of practice, gender, and screened burnout status. Burnout status was assessed by a validated single-item screening measure of burnout included in the survey questionnaire, in which a score of 3 or more indicates a positive screen for burnout. This single-item measure originated from the Physician Worklife Study 27 and has been validated for clinicians. 28 The MS-Squared survey asked respondents to select “positive,” “none,” “negative,” or “don't know” on how increased EHR use affected the “quality of care you are able to deliver to your patients,” the “cost of care,” and the “efficiency of care.”
Statistical Analysis
We reported categorical data, including gender and clinician opinions on how EHR technology impacted the three specific attributes of health care, as frequency (percent). We converted clinician years of practice into a binary variable with an a priori cutoff of <10 years or 11+ years based on the overall adoption of EHR technology in the clinical settings. 29 We analyzed burnout as a binary variable with scores of 3 or higher indicating a positive burnout screening and scores of 2 or lower indicating a negative burnout screening. Analyses did not use a model that controlled for other clinician characteristics beyond years of practice, gender, and screened burnout status.
Chi-square tests were used to evaluate the association between years of practice, gender, and burnout and the perceived effect increased EHR usage had on each attribute of health care: quality of care, cost of care, and efficiency of care. To protect against type I error that could result from multiple testing across the three E7 survey items, Bonferroni-corrected α of 0.05/3 = 0.0167 was used to declare significance. SAS v9.4 was utilized for data analysis.
Results
Of 640 clinicians surveyed, 281 (44%) responded. One respondent did not answer the quality-of-care item, two did not provide a response for the cost of care, and two did not indicate their years of practice. Four respondents did not indicate their genders. One respondent did not complete the burnout screen. Table 2 contains a summary of the responses from all respondents combined.
Table 2. Frequency (percent) of perceived impact of EHR use on quality, cost, and efficiency from all respondents combined.
| Perceived EHR impact | Overall responses n (%) |
|---|---|
| Quality | |
| Positive | 113 (40.6) |
| None | 43 (15.5) |
| Negative | 98 (35.3) |
| Don't know | 24 (8.63) |
| Cost | |
| Positive | 37 (13.9) |
| None | 64 (24.0) |
| Negative | 92 (34.5) |
| Don't know | 74 (27.7) |
| Efficiency | |
| Positive | 75 (27.0) |
| None | 34 (12.2) |
| Negative | 146 (52.3) |
| Don't know | 24 (8.60) |
Abbreviation: EHR, electronic health record.
Perceived Impact of Increased EHR Use on Quality, Cost, and Efficiency of Care by Clinician Years of Practice
The overall distribution of perceived impact of increased EHR use on quality, cost, and efficiency of care did not differ significantly ( p = 0.3984, 0.7638, and 0.9586, respectively) by clinician years of practice ( α = 0.0167). Sensitivity analysis evaluating years of practice as a continuous variable using analysis of variance (ANOVA) indicated that years of practice did not significantly differ across response options for the perceived impact of increased EHR use on the cost, efficiency, and quality of health care ( Table 3 ).
Table 3. Frequency (percent) of perceived impact of EHR use on quality, cost, and efficiency by clinician years of practice.
| Perceived EHR impact | Years in practice | p -Value a | |
|---|---|---|---|
| ≤10 y ( n = 102) | 11+ y ( n = 177) |
||
| Quality | |||
| Positive | 39 (38.61%) | 74 (41.81%) | 0.3984 |
| None | 20 (19.80%) | 23 (12.99%) | |
| Negative | 32 (31.68%) | 66 (37.29%) | |
| Don't know | 10 (9.90%) | 14 (7.91%) | |
| Cost | |||
| Positive | 15 (14.71%) | 22 (12.57%) | 0.7638 |
| None | 26 (25.49%) | 38 (21.71%) | |
| Negative | 24 (33.33%) | 68 (38.86%) | |
| Don't know | 27 (26.47%) | 47 (26.86%) | |
| Efficiency | |||
| Positive | 27 (26.47%) | 48 (27.12%) | 0.9586 |
| None | 12 (11.76%) | 22 (12.43%) | |
| Negative | 53 (51.96%) | 93 (52.54%) | |
| Don't know | 10 (9.80%) | 14 (7.91%) | |
Abbreviation: EHR, electronic health record.
p -Value provided for chi-square test of independence significant at Bonferroni-corrected α = 0.0167.
Perceived Impact of Increased EHR Use on Quality, Cost, and Efficiency of Care by Gender
The distribution of perceived impact increased EHR use has on quality, cost of care, and efficiency of care did not differ significantly ( p = 0.3497, 0.1229, and 0.0348, respectively) by gender ( α = 0.0167; Table 4 ).
Table 4. Frequency (percent) of perceived impact of EHR use on quality, cost, and efficiency by gender.
| Perceived EHR impact | Gender | p -Value a | |
|---|---|---|---|
| Male ( n = 117) | Female ( n = 160) | ||
| Quality | |||
| Positive | 50 (42.74%) | 64 (40.25%) | 0.3497 |
| None | 19 (16.24%) | 23 (14.47%) | |
| Negative | 42 (35.90%) | 54 (33.96%) | |
| Don't know | 6 (5.13%) | 18 (11.32%) | |
| Cost | |||
| Positive | 16 (13.79%) | 22 (13.84%) | 0.1229 |
| None | 32 (27.59%) | 32 (20.13%) | |
| Negative | 45 (38.79%) | 54 (33.96%) | |
| Don't know | 23 (19.83%) | 51 (32.08%) | |
| Efficiency | |||
| Positive | 37 (31.62%) | 39 (24.38%) | 0.0348 |
| None | 17 (14.53%) | 17 (10.63%) | |
| Negative | 59 (50.43%) | 84 (52.50%) | |
| Don't know | 4 (3.42%) | 20 (12.50%) | |
Abbreviation: EHR, electronic health record.
p -Value provided for chi-square test of independence significant at Bonferroni-corrected α = 0.0167.
Perceived Impact of Increased EHR Use on Quality, Cost, and Efficiency of Care by Burnout Screening
The overall distribution of perceived impact of increased EHR use on quality of care differed significantly ( p = 0.0025) by burnout status. Specifically, a greater proportion of burnout-negative respondents perceived increased EHR use as having a positive impact on quality of care. Moreover, a greater proportion of burnout-positive respondents perceived increased EHR use as having a negative impact on quality of care ( Table 5 ).
Table 5. Frequency (percent) of perceived impact of EHR use on quality, cost, and efficiency by screened burnout status.
| Perceived EHR impact | Burnout screen | p -Value a | |
|---|---|---|---|
| Positive ( n = 127) | Negative ( n = 153) | ||
| Quality | |||
| Positive | 39 (30.71%) | 76 (50.00%) | 0.0025 a |
| None | 22 (17.32%) | 21 (13.82%) | |
| Negative | 57 (44.88%) | 40 (26.32%) | |
| Don't know | 9 (7.09%) | 15 (9.87%) | |
| Cost | |||
| Positive | 10 (8.00%) | 27 (17.65%) | 0.0621 |
| None | 26 (20.80%) | 38 (24.84%) | |
| Negative | 51 (40.80%) | 51 (33.33%) | |
| Don't know | 38 (30.40%) | 37 (24.18%) | |
| Efficiency | |||
| Positive | 23 (18.11%) | 54 (35.29%) | 0.0003 a |
| None | 16 (12.60%) | 18 (11.76%) | |
| Negative | 82 (64.57%) | 63 (41.18%) | |
| Don't know | 6 (4.72%) | 18 (11.76%) | |
Abbreviation: EHR, electronic health record.
p -Value provided for chi-square test of independence significant at Bonferroni-corrected α = 0.0167.
Similarly, the overall distribution of perceived impact increased EHR use has on efficiency differed significantly ( p = 0.0003) by burnout screen. A greater proportion of burnout-negative respondents perceived increased EHR use as having a positive impact on efficiency. Additionally, a greater proportion of burnout-positive respondents perceived increased EHR use as having a negative impact on efficiency of care.
The distribution of perceived impact increased EHR use has on cost of care did not differ significantly for any of the three clinician factors.
Discussion
Overall, approximately half of the respondents felt that the EHR had a negative impact on efficiency and around a third perceived the EHR as having a negative impact on quality and cost of care. Considering the significant financial investment and time commitment of implementing EHRs in the United States alone, these findings indicate many clinicians do not believe this investment is producing meaningful benefits.
Our study shows that positive clinician burnout status was associated with negative impressions of the EHR's impact on quality and efficiency, while lack of burnout was associated with more positive impressions. All other associations between clinician characteristics and health care system attributes did not reach statistical significance. Notably, our results suggest that seniority and gender are not associated with differing perceptions of the impact of EHRs on health care attributes of quality, efficiency, or cost of care. These results contest the idea that junior clinicians have less trouble with the technology compared with their more senior counterparts.
Clinician Years in Practice
Previous studies have shown that clinician age may affect perception of the EHR, 30 with age distribution correlating with years in practice. For example, Williams et al found older and attending-level physicians appeared more likely to report decreased satisfaction with the EHR, with perceived personal efficiency serving as a measure for overall satisfaction and impact on the patient, 31 and Nguyen et al found that older age was associated with lower reported EHR usability. 32 A clinician survey conducted by Emani et al in 2014 found that clinician demographics of age, race, and gender are not significantly associated with their responses on the benefits of Meaningful Use. 33 The one notable exception that was significant was where 33% of clinicians 55 years and over thought Meaningful Use would improve the quality of care, while only 22% of clinicians younger than 55 agreed. Our results indicate otherwise, as we found no significant difference in the perception of the efficiency, quality, or cost of EHR benefits between clinician groups with 10 or fewer years of practice and 11 or more years of practice. This might be explained by other factors the MS-Squared survey did not measure, such as teamwork or homogeneity of age distribution. For instance, Meyer showed in an analysis from 2011 that strong teamwork and a homogeneity of age distribution together fostered group adoption of new technology. 20
Our differing findings may also be due to other factors associated with the perception of EHR systems. One might assume that more junior clinicians have greater proficiency with information technology and therefore hold more positive views regarding the EHR compared with their older counterparts. 31 Similarly, a senior clinician's lesser proficiency in technology may lead them to have more negative views on the functionality and efficiency of the EHR system. However, clinicians who worked prior to the advent of widespread EHR deployment may also more greatly appreciate its positive impact. 21 Conversely, more junior clinicians may be less tolerant of inefficient technology, due to a comparative ease of use in other phone or computer applications in their daily lives, leading to a more negative perception of the EHR. Other factors related to EHR design, such as the accessibility of the EHR system at home, may be relevant as well. For instance, a recent longitudinal study showed that while resident physicians reduced their time spent on the EHR per patient over the course of a year, the proportion of time they spent in the EHR system after hours did not change. 34 Multiple factors may play into how the EHR is percseived, as our study demonstrates that years in practice alone is not associated with a significant difference in perceptions.
Clinician Gender
Our results showed that gender had no significant effect on clinician perceptions toward any of the three potential benefits of EHR. These findings challenge the existing literature, which generally supports that female physicians have different EHR use patterns (EHR use after hours, writing longer notes, documenting a greater number of encounters per day, face time with their patients) compared with their male counterparts. 2 15 17 35 In another study, male physicians reported more frustrations with the EHR, noting lower levels of satisfaction with EHR usability, complexity, and cumbersomeness. 36 These gender-specific use factors may contribute to perceptions of EHR value. Overall, our analysis noted a slightly larger female population of responders and did not measure all the factors listed in these studies, which may explain why the MS-Squared study did not detect a significant difference between genders in the perceived value of EHRs.
Clinician Burnout
The significant association between burnout-positive clinicians and negative perceptions of EHR quality and efficiency may be explained partly by the nature of burnout itself. Burnout alone may lead to poor practice efficiency, and poor EHR design may only be one factor involved. 37 Our prior analysis demonstrated that EHR design factors accounted for only 6.8% of the variance in burnout. When other peri-EHR factors were included in our calculations, such as lack of workload control, lack of attention to work–life balance, chaotic work environments, and ineffective teamwork, the overall variance in burnout rose to 36.2%, still far short of 100%. 4 Clearly, there are areas that contribute more significantly to burnout than merely the technology or other associated factors measured by this survey. 34
A recent systematic review found that negative perception of the EHR was one of the most common EHR-related factors associated with increased clinician burnout. 38 Previously studied variables encompassing clinician perceptions of the EHR included EHR-related frustration, 2 39 40 usability, 41 efficiency of communication, 40 and insufficient EHR support from the clinicians' organization. 42 These variables were all found to be significantly associated with burnout. Given that burnout itself may be associated with poor clinician perceptions of EHR efficiency, it may be desirable to design EHR optimization programs that are specifically designed to not only address improved EHR efficiency but also target reduced clinician burnout. 43 This analysis further expands on the association between burnout and negative perceptions of the EHR by exploring clinician views regarding the EHR's impact on health care. Past research has demonstrated a correlation between burnout and decreased health care efficiency ratings among certain specialists, including adult congenital heart disease and mental health specialists. 40 44 This study's results broaden these findings by including a wider range of specialties, with a primary care majority, and show a similar association between burnout and negative perceptions of the EHR. Clinician burnout is likely another important factor that must be considered when trying to optimize the design, deployment, and ongoing management of the EHR system.
Limitations
Our study sample was limited in number and scope, representing only 281 clinicians from 3 institutions. There was a slight female majority and was limited to a binary conception of gender. Our survey did not measure many clinician-level confounders such as the degree of prior experience or expertise clinicians had with technology overall, which may have influenced how comfortable they were with computers and the EHR in general.
We primarily looked at physicians in primary care and outpatient settings, excluding trainees such as residents or fellows new to the field. Furthermore, the workload and responsibilities may differ by role type (e.g., among nurse practitioners, physician assistants, DOs, and MDs) and thus perceptions of the EHR on quality, efficiency, and cost may differ in this regard. Our survey was not powered to analyze and compare responses by role types. Our sample is not representative of the entire clinician workforce in general and may also represent opinions different from clinicians who work in more specialized or inpatient areas. Assessing how residents and fellows view the EHR may provide more insight into whether opinions on the EHR differ between junior and senior clinicians.
There was no analysis performed comparing survey respondents to nonrespondents. Thus, it is possible that that those who responded to the survey are inherently different, with respect to burnout or additional study variables, than those who did not respond.
Conclusion
Our data showed that despite the EHR's many potential benefits, clinician burnout is associated with more negative clinician perceptions of these benefits. This study suggests that these perceptions are unlikely to be associated with years in practice or gender. Addressing clinician burnout may improve clinician perceptions of the value of EHRs.
Clinical Relevance Statement
EHRs have become integral components of a quality health care system and are key to the future of health care. Addressing clinician burnout may positively influence clinician's perceived value and acceptance of EHRs.
Multiple Choice Questions
-
According to the findings in this study, which of the following factors was/were associated with clinician perceptions of the value of EHRs?
The screened burnout status of the clinician.
The gender of the clinician.
The screened burnout status and the years in practice of the clinician.
The screened burnout status, years in practice, and the gender of the clinician.
Correct Answer: The correct answer is option a. Gender and years in practice were not significantly associated with the respondents' perceptions of the value of EHRs regarding quality, cost, and efficiency. Burnout status was significantly associated with respondent perceptions; those who screened positive for burnout were significantly more likely to rate EHR quality and efficiency negatively than those who screened burnout negative.
-
According to the findings in this study, which of the following factors was/were NOT associated with clinician perceptions of the value of EHRs?
The screened burnout status of the clinician.
The gender of the clinician.
The gender of the clinician and the years in practice of the clinician.
The screened burnout status, years in practice, and the gender of the clinician.
Correct Answer: The correct answer is option c. Gender and years in practice were not significantly associated with the respondents' perceptions of the value of EHRs regarding quality, cost, and efficiency. Burnout status was significantly associated with respondent perceptions; those who screened positive for burnout were significantly more likely to rate EHR quality and efficiency negatively than those who screened burnout negative.
Acknowledgments
We would like to thank Duncan Vos, MS (Western Michigan University Homer Stryker M.D. School of Medicine), who assisted with biostatistics of this project. We also thank the members of the MS-Squared research team Mark Linzer, MD (Hennepin County Medical Center), Sharry Verres (University of Arizona), and Nancy Morioka-Douglas (Stanford University).
Funding Statement
Funding This project was supported in part by grant number R18HS022065 from the Agency for Healthcare Research and Quality (AHRQ), U.S. Department of Health and Human Services.
Conflict of Interest None declared.
Protection of Human and Animal Subjects
This study was determined to be exempt research by the Western Michigan University Homer Stryker M.D. School of Medicine Institutional Review Board.
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