Physician perceptions of documentation methods in electronic health records

Abstract

This study sought to determine physician, specialty and practice factors influencing choice of method for electronic health record (EHR) documentation: direct typing (DT), electronic transcription (ET), human transcription (HT), and scribes. A survey assessing physician documentation practices was developed and distributed online. The primary outcome was the proportion of physicians using each method. Secondary outcomes were provider-rated accuracy, efficiency, and ease of navigation on a 1-5 Likert scale. Means were compared using linear mixed models with Bonferroni adjustment. The 818 respondents were mostly outpatient (46%) adult (79%) physicians, practiced for a mean 15.8 years, and used DT for EHR documentation (72%). Emergency physicians were more likely to use scribes (p < 0.0001). DT was rated less efficient than all other methods (p < 0.0001). ET was rated less accurate than DT (p < 0.001) and HT (p < 0.001). HT was rated less easy to navigate than DT (p = 0.002) and scribe (p < 0.001), and ET less than scribe (p = 0.002). Two hundred and forty-three respondents provided free-text comments that further described opinions. DT was the most commonly used EHR method but rated least efficient. Scribes were rated easy to navigate and efficient but infrequently used outside of emergency settings. Further innovation is needed to design systems responsive to all physician EHR needs.

Keywords

documentation electronic health record voice recognition medical scribe

Introduction and background

The Health Information Technology for Economic and Clinical Health (HITECH) Act became law in February 2009 as a way to incentivize hospitals to use health information technology.¹ Due to these incentives to adopt electronic health records (EHRs), the percentage of office-based physicians using an EHR system has increased to 86.9%.^2,3 While EHR was created as a way to improve efficiency and charting, many studies have found that physicians are spending increased time in the EHR, thus taking time away from patients and potentially reducing quality of care.^4–6 EHRs have been shown to negatively impact both physician professional satisfaction and patient satisfaction as EHR technology is leading to time-consumption, inefficiency, and potentially worsening clinical documentation.⁴ Inaccuracies were noted to be most prevalent in documentation of current medications, medical history, and medical allergies.⁷ Inaccurate documentation is especially concerning as medical errors are now the third leading cause of death in the United States.⁸

The modes by which physicians interact with the EHR are changing and may offer some solutions to these concerns. For example, automatic translation of voice into text (i.e. electronic transcription) offers promise of improved documentation efficiency as well as decreased documentation time. It is seen as an attractive option for physicians who are untrained in EHR use or who find the EHR interface difficult to navigate.^9,10 Electronic transcription also has a faster turnaround time than human transcription services.⁹ The use of medical scribes, who are staff whose role is to transcribe clinical visit information directly into the EHR in real time under physician supervision, has increased in prevalence among multiple specialties.^11,12 This strategy led to less self-reported after-hours EHR documentation by physicians as well as a higher likelihood of physicians interacting with the patient throughout the visit.^11–13 However, there are limited studies comparing current rates of use or opinions regarding these modes of EHR interface across medical specialties.

Therefore, a survey study was conducted to identify physician factors and practice factors associated with the use of various methods, as well as physician attitudes toward different methods of documentation in an EHR.

Methods

Survey development

As there are no established validated surveys examining satisfaction with modes of documentation, a survey was developed through an iterative process, including discussions with colleagues and members of the study team. The twelve-question survey (Supplemental Table 1) was reviewed and refined for content validity by three currently practicing physicians (co-authors M.T., A.B., A.D.).

The survey assessed medical specialty, number of years in practice, practice type, and employment type. Physicians who responded that they do not use an EHR were considered to have completed the survey and no further answers were recorded. Physicians were next asked to rank which category describes how the majority of their documentation is done: direct typing (DT), electronic transcription (ET), human transcription (HT), scribe, or other. Categories were clarified in the survey by providing these examples: DT = “point and click”; ET = “that is, Dragon Dictation”; HT = “over the phone.” Respondents were asked to choose the method they primarily use and then rank on a 1-5 Likert scale the accuracy, efficiency, and ease of use of each documentation method. Participants could choose the “N/A” option if they had never used that specific type of documentation.

The primary method of accessing and completing the survey was via the Internet using online survey software (Qualtrics). The survey was only able to be accessed and completed once per respondent. The study was reviewed and determined to be exempt from requiring written consent by the institutional review board (IRB) at Quinnipiac University.

Participants

Participants included a national sample of currently practicing inpatient, outpatient and subspecialty physicians. Medical trainees (i.e. medical students, residents, and fellows) were excluded. No identifying data from the participants was recorded.

Survey administration

The survey was distributed using email and medical online forums. Emails were sent to physicians at five health systems: Frank H. Netter School of Medicine at Quinnipiac University in North Haven, CT; St. Vincent’s Medical Center in Bridgeport, CT; USC Keck School of Medicine in Los Angeles, CA; Mount Sinai Health System in New York, NY; SSM St. Mary’s Hospital in St. Louis, MO. Online forum requests to complete the survey were submitted to the following: Society of General Internal Medicine (SGIM); American College of Emergency Physicians (ACEP); Emergency Medicine Residents’ Association (EMRA); Society of Hospital Medicine (SHM); American Academy of Family Physicians (AAFP). All email invitations and medical online forum invitations included eligibility criteria for participating physicians and the statement of waved consent.

Statistical analysis

Descriptive statistics included frequencies and percentages for categorical variables and means and standard deviations for quantitative variables. Subspecialties were grouped into broader categories due to the large number of subspecialties represented. Physician and practice factors were then compared across primary documentation using chi-square or ANOVA.

Response rate was not calculated as the number of participants who opened the email and chose not to respond is unknown. Provider-rated accuracy, efficiency, and ease of navigation on a 1-5 Likert scale were compared across documentation groups using linear mixed models with a within subject factor method (method rated) and a between subject factor documentation group. The Bonferroni adjustment for multiple comparisons was used for post-hoc analysis following a significant group effect. Analyses were conducted in SAS v9.4 and statistical significance was set at 0.05.

Responses to an open-ended question (“Please provide any additional information or commentary in the following space provided”) were reviewed using qualitative methodology via content analysis.¹⁴ Two authors (M.T. and N.M.) individually reviewed responses to identify key content organized into categories.

Results

Demographics

Table 1 shows the demographic characteristics of the 818 participants. Respondents were mostly outpatient providers (46%), treated primarily adults (79%), mostly specialized in internal (25%) or family medicine (24%) and practiced for an average of 15.8 years. The most common mode for interacting with EHR was DT (72%) followed by ET (18%). The most often used EHR software was Epic (43%).

Table 1.

Demographic characteristics of physician survey participants.

Characteristic	N (%)
Characteristic	Total = 818 (100%)
Practice setting
Outpatient	378 (46%)
Inpatient	271 (33%)
ER/Urgent	157 (19%)
Other	12 (2%)
Patient population
Adults	645 (79%)
Pediatrics	54 (7%)
Equally split	119 (14%)
Specialty
Internal medicine	208 (25%)
Family medicine	194 (24%)
Medical sub-specialties	148 (18%)
Emergency medicine	143 (17%)
Pediatrics	29 (4%)
Surgery and sub-specialties	29 (4%)
Multiple specialties	44 (5%)
Other	23 (3%)
Years in practice
Mean ± SD	15.8 ± 11.5
Employment type
Employed	657 (80%)
Self-employed	161 (20%)
Primary mode of documentation
Direct typing	588 (72%)
Electronic transcription	144 (18%)
Human transcription over phone	20 (2%)
Scribe	47 (6%)
Other	6 (0%)
Nonresponse	13 (2%)
Type of EHR used
Epic	354 (43%)
Cerner	116 (15%)
AllScripts	60 (7%)
Others	243 (30%)
More than 1 type	35 (4%)
Do not use EHR	10 (1%)

Table 2 shows how mode of EHR documentation primarily used by survey participants varied by practice characteristics. There were statistically significant associations for all characteristics. Physicians practicing in emergency/urgent care and those self-employed were the most likely group to use scribes. Pediatricians were more likely to use DT than physicians caring for adults. Physicians who mostly used HT have practiced longer (M = 28.0, SD = 12.3 years) compared to those who used DT (M = 15.5, SD = 11.3), ET (M = 16.1, SD = 11.4), and scribe (M = 11.2, SD = 10.1).

Table 2.

Primary mode of documentation varies across practice characteristics.

Characteristic	Primarily used documentation				p-value
	Direct typing	Electronic transcription	Human transcription	Scribe
Practice setting					<0.001*
Inpatient	201 (76%)	51 (19%)	9 (3%)	5 (2%)
Outpatient	302 (81%)	51 (14%)	10 (3%)	9 (2%)
ER/Urgent	76 (50%)	42 (28%)	1 (1%)	32 (21%)
Patient population					0.031*
Adults	456 (72%)	120 (19%)	19 (3%)	38 (6%)
Pediatrics	47 (94%)	3 (6%)	0 (0%)	0 (0%)
Equally split	85 (73%)	21 (18%)	1 (1%)	9 (8%)
Specialty					<0.001*
Internal medicine	175 (84%)	21 (10%)	6 (3%)	6 (3%)
Pediatrics	25 (96%)	1 (4%)	0 (0%)	0 (0%)
Family medicine	157 (82%)	24 (13%)	4 (2%)	6 (3%)
Medical subspecialties	102 (72%)	30 (21%)	10 (7%)	0 (0%)
Emergency medicine	58 (42%)	45 (33%)	0 (0%)	34 (25%)
Surgery and subspecialties	15 (52%)	13 (45%)	0 (0%)	1 (3%)
Multiple specialties	38 (86%)	6 (18%)	0 (0%)	0 (0%)
Employment type					0.009*
Employed	476 (74%)	119 (18%)	20 (3%)	31 (5%)
Self-employed	112 (73%)	25 (16%)	0 (0%)	16 (11%)
Years in practice					<0.001
Mean ± SD	15.5 ± 11.3	16.1 ± 11.4	28.0 ± 12.3	11.2 ± 10.1

Exact test p-value used because of small cell sizes.

Physician opinions: Likert-scales

Participants rated their impressions about the different documentation methods on a 1-5 Likert scale, with a “5” being the most favorable score. Across all participants, irrespective of primary documentation method, there were significant differences in the mean Likert-scale scores given to methods on time efficiency, accuracy, and ease of navigation (p < 0.001 for all). DT was considered less efficient than ET (2.7 vs 3.6; p < 0.001), HT (2.7 vs 3.1; p < 0.001), and scribe (2.7 vs 3.9; p < 0.001), while the latter three methods did not significantly differ from each other. ET was rated less accurate than DT (2.9 vs 3.6; p < 0.001) and HT (2.9 vs 3.1; p < 0.001). For ease of navigation, HT was rated less easy to navigate than DT (2.7 vs 3.5; p = 0.002) and scribe (2.7 vs 3.5; p < 0.001), and ET was rated less than scribe (3 vs 3.5; p = 0.002).

In order to determine if opinions were affected by experience with the various methods, Likert ratings were separated based on what respondents reported was their primary documentation method. In general, the highest rating for a method was given by physicians who primarily used that method. For example, respondents who used scribes in their own practice rated scribes the most time efficient. The only exceptions were in the “accurate” category. Physicians using DT rated scribe to be just as accurate as DT, while physicians using scribe rated scribe and ET to be equivalent in accuracy (Supplemental Table 2).

Physician opinions: Free-text responses

Two hundred and forty-three respondents (29.7%) provided detailed responses to a final open-ended question. Most comments explored personal opinions about negative or positive aspects of either EHR in general or the specific documentation methods referenced in the survey (Table 3). Comments were also used to provide feedback on special situations the survey did not address that may have prevented gathering the most nuanced data. Finally, some comments tried to explore concepts that could lead to a more ideal EHR.

Table 3.

Free-text responses regarding EHR and documentation method opinions.

Negative	Positive
Electronic health systems in general
Non-relevant documentation: Billing/Legal/Insurance requirements “Note bloat” Copy/paste, errors perpetuatedBurnout Detracts from doctor/patient interaction Doctors working below license (clerical work) Time consuming, prolongs workday Deters trainees from documentation-heavy specialtiesTechnology flaws: Not user friendly Lack connectivity between systems At risk for hacking	LegibilityAnalytics and data trendingContinuity of patient’s story over timeFaster documentation of certain components
Scribes
Expense Detract from doctor/patient relationship Variable quality between scribes “Workaround,” not a true method Still needs proofreading	EfficientAllows provider to see more patientsPhysical member of the healthcare team
Transcription
Turnaround time can be prolonged	More narrative format
Still needs proofreading	Less errors versus dictation
Dictation
Extensive editing needed	More narrative format
Extensive editing needed	Efficient
Direct typing
Non-narrative; breaks in clinical reasoning	Accurate
Slow	Allows editing in real time

General EHR

Regarding the EHR in general, negative comments largely centered on the burdensome volume of non-patient care documentation required for insurance, billing, or legal reasons. For example, one respondent commented:

“The burden of documentation for billing purposes is heavy. Often lost in the effort to click all the right boxes for billing is the actual narrative of the patient’s story, what the [doctors] impression of what is actually going on with the patient is.”

While many other negative opinions about EHR were identified, the most common comments focused on notes being excessively long and filled with errors, provider burnout including long workdays and less direct communication with patients, and technology issues such as different types of EHR not interfacing with each other. The following comments illustrate these concerns:

“Documentation is the worst part of my job as an emergency medicine physician. I know this sounds like hyperbole, but I’m serious. . . .I have to stand at the computer in the room and chart as I speak with the patient to maintain efficiency, and I am always constantly apologizing for looking at the computer when doing so. Charting takes away from time with patients, including history taking and education, and likely leads to poorer outcomes. Charting makes me feel like a data entry technician. Having to closely review my charts to ensure they hits all the necessary elements for billing makes the whole process even worse. Why is the value of my work determined by the volume of information in my chart? EHR charting is, I believe, a big part of physician burnout.”

“EHR’s are extremely flawed and force the physician to be dependent on IT companies who often do not share the same sense of urgency over patient care. Physicians are forced by government bodies to use EHR’s. Unfortunately, there is no regulatory government body to police failed, inaccurate, or inaccessible patient data in case of server crash or other EHR update. Different EHR’s, at this time, do not communicate with each other. Data is often not shared between primary care and specialist referrals, laboratories and radiologic centers. This data may be lost to the patient chart and compromise patient care.”

Some respondents commented on positive aspects including improvements in legibility and cohesiveness of a patient’s medical history, as well as the ability from an informatics standpoint to trend and analyze data over time for both direct patient care and research applications.

Documentation methods

Regarding positive and negative aspects of the documentation methods, free-text responses generally agreed with that of the Likert-scale responses that highlighted DT as least efficient and scribes as most efficient. However, additional nuance to the experience of using these methods were identified. Most comments about scribes focused on the high cost associated with their use, as well as variable quality between scribes affecting their utility. However, scribes being an additional physical presence on the health care team was seen as a benefit by one respondent:

“Scribes are not merely human Dragon speak. They provide a second set of eyes to perform checklists and reduce error rates for care delivery, become part of team-based care delivery, serve as care coordinators between visits, and reduce burnout for the physician. They also can move patients forward to transition to checkout for ‘warm handoffs’ and provide review of the care plan - allowing the physician to move to the next patient. None of the other methods listed above perform these functions.”

Another major topic that emerged when comparing different documentation methods centered around editing mistakes. It was noted that DT allowed direct editing at the time of entry which led to increased accuracy and potentially efficiency, while HT, ET, and scribe all necessitated editing by the provider after the document was completed.

Finally, a new concept that emerged from the comments was that of preserving the patient narrative. Respondents felt that with DT, components of the history were often separated into text-entry boxes that broke up and disconnected the patient story. However, ET and HT allowed the provider to verbalize the patient story smoothly and create a more cohesive narrative. For example, one respondent noted:

“Human transcription allows structuring medical information and thoughts the way a physician would normally do. Clicking and filling out lists breaks the thoughts up and into ‘billable pieces,’ which is excellent for financial reasons but is far from a logical, comprehensive, empathetic way of the human medical (physician-like) thought process.”

Factors not included in survey

Several respondents took the opportunity of a free-text question to comment on special situations not addressed in the survey (Table 4). Most commonly, respondents said they used several methods of EHR charting, or even several separate EHR systems, in the course of a day which made focusing their answers difficult. Contrastingly, institutional choices may restrict provider access to only one option for EHR or documentation method, which limits their ability to provide broad responses. Finally, it was noted that the survey did not fully explore how individual provider factors such as technologic knowledge or experience with a particular EHR product may influence the responses received.

Table 4.

Free-text responses not related to opinions about EHR.

Additional factors influencing choice of documentation method

People may use multiple methods

Local regulations or administrative decisions dictate which methods are available to providers

High variability between brands/versions of EHR used influences preference of methods

Duration of time using one method

The user’s personal abilities (computer savviness and editing skills) influence success of methods

Designing the ideal system

Customization—allow physicians to change templates to match specialty or patient story

Voice recognition that automatically places components into correct fields and does orders

Common operating system that enables inter-communication among EHR’s

“Write in charts as before and have someone enter the data”

More patient-facing documentation devices

The Ideal EHR

A subset of comments featured survey responders trying to imagine or design what an improved EHR system would look like (Table 4). The majority of these ideas centered around more physician involvement in designing the templates, with increased customizability for individual providers. Another very common suggestion was improved voice recognition that integrates directly into the charting system. Finally, many respondents expressed a desire that different EHR systems could interface with and share data with each other, allowing more cohesion to a patient story between healthcare systems.

Discussion

In this large national survey, direct typing was the most commonly used documentation method despite being rated the least time efficient. Use of more innovative methods, such as scribes, was associated with more recently entering the workforce. The sample consisted of physicians with a relatively long mean time in practice (15.8 years), and therefore supports other studies indicating that older physicians may be less comfortable adopting new systems or technologies.^15,16 Primary users tended to like their current method best, although free-text comments pointed out that providers often lack the option of changing to a new system based on hospital or clinic regulations and cost. New technologies were also identified as having challenges: electronic transcription was rated less accurate and human transcription rated as less easy to navigate. This information will be useful for physicians or hospitals who are trying to advocate for a change in documentation methods.

Approximately one in five practices that use an EHR utilize scribes.¹⁷ Though practice-level data from the survey respondents was not collected, the frequency of scribe use in the sample was lower than expected. This survey showed that scribe use was associated with physicians practicing in emergency room settings, and a higher proportion were self-employed. Most of the existing studies about scribe efficacy also come from emergency room settings and focus on benefits associated with increased throughput in these busy clinical environments.¹⁸ The results suggest that scribes are not often utilized in inpatient settings. Qualitative analysis of the data identified the expense associated with hiring scribes and perceived variation in quality between scribes being likely reasons for lack of uptake outside of emergency settings.

Another important nuance was captured by the high proportion of written short response answers in the survey (>200 comments). Many studies have identified an increase in physician burnout associated with EHR use which was echoed in the qualitative data.^19–21 Exactly why and which aspects of EHR are most associated with burnout, and if those factors are modifiable, is not fully understood. The comments in the study identified dis-satisfaction with working beneath the level of a physician license, that is, doing clerical activities instead of clinical decision making, as an important factor. While scribes may be one potential solution, the study also revealed some of the limitations to that approach. Another aspect of burnout related to EHR use identified by the survey was the impression that medical trainees are being negatively affected by exposure to physician frustration with EHR. However, one study of medical students showed that students readily engage with EHR and did not see it impairing interaction with patients.²² With increasing numbers of pre-medical students working as scribes, the effect of EHR exposure on long-term trainee outcomes and specialty choice is deserving of further study.

What are solutions to problems with EHR and documentation? Ideas for improvement did emerge from the large number of unprompted suggestions from the survey participants. Many hoped that improvements in speech recognition technology might be the “silver bullet” that removed identified problems with current methods of dictation and transcription, including high error rates and needing to use a third-party system. Although the survey did not capture many radiologists, that specialty has long relied on voice recognition to dictate image reads and radiology literature supports increased speed but continued problems with accuracy.^23,24 While voice recognition has been improved over the years, it was recently found that errors are much higher in speech recognition compared to transcription and often lead to decreased productivity due to an increased amount of time to produce a chart or report.²⁴ At the most cutting-edge, speech recognition combined with artificial intelligence and the ability to navigate via voice command (e.g. “get patient data”) does exist in the virtual assistants used by large technology corporations like Amazon and Google. These tools have yet to make the leap to health care due to concerns about privacy when dealing with protected health information (since they use unsecured “cloud” storage), potential legal ramifications of errors, and lack of structure to much of the data currently stored in EHR that hinders sorting and navigation.²⁵ Physicians, and especially those with training in medical informatics, should encourage healthcare leaders to embrace innovation.

There are several limitations to this study. As identified in the qualitative data, choice of EHR is often not up to the provider and rather dictated by their employer or practice. Since the study occurred in the United States, there are many national health information technology initiatives through organizations such as the Center for Medicare and Medicaid Services (CMS) that may alter how practitioners use EHR and the survey did not specifically address those influences. Although the survey was refined through review by experts for face validity, respondent interpretation of certain questions could lead to response bias. Due to the broad anonymous survey distribution, information regarding physicians who were invited but chose to not participate in the study was not collected. Similarly, the total number who saw the email but chose not to respond is unknown, and thus no response rate can be reported. There may have been selection bias as the participant pool was skewed towards certain subspecialties. Participants were not surveyed regarding their baseline comfort with technology, which may be a factor in uptake of newer technologies. Physicians were also asked to provide opinions on all documentation methods, however some physicians may not have had experience with all methods. Stratifying by primary documentation type used was the attempt to control for this. To address some of these issues and expand upon the current findings, future research may include surveying practices outside of the United States, or exploring the attitudes of medical students and other trainees.

Conclusion

This survey of over 800 practicing physicians shows that most physicians interact with EHR through direct typing, despite the existence of more innovative methods such as transcription and scribes. Direct typing was viewed as the least time efficient. Scribes were used primarily in emergency room settings. Survey comments expressed improvements since transitioning from paper records but continued significant limitations associated with all currently available methods for interacting with the EHR. Institutional and health-systems leaders can use this information to advocate for continued technological improvements that allow improved voice recognition, customization, and interconnectedness between EHR systems.

Supplemental Material

sj-docx-1-jhi-10.1177_1460458221989399 – Supplemental material for Physician perceptions of documentation methods in electronic health records

Supplemental material, sj-docx-1-jhi-10.1177_1460458221989399 for Physician perceptions of documentation methods in electronic health records by Nicole E Exeni McAmis, Andrew S Dunn, Richard S Feinn, Aaron W Bernard and Margaret J Trost in Health Informatics Journal

Footnotes

Acknowledgements

A special thank you to the Emergency Medicine Residents’ Association (EMRA) for their support and assistance in distributing the survey.

Contributors

All authors made substantial contributions to the conception and design of the work or the acquisition, analysis, or interpretation of data for the work; drafted the work or revised it critically for important intellectual content; had final approval of the version to be published; and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Declaration of conflicting interests

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

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Nicole E Exeni McAmis

Margaret J Trost

Supplemental material

Supplemental material for this article is available online.

References

USDoHaH Services. HITECH Act enforcement interim final rule, http://www.hhs.gov/ocr/privacy/hipaa/administrative/enforcementrule/hitechenforcementifr.html (accessed 25 February 2020).

NCfH Statistics. Centers for Disease Control and Prevention, www.cdc.gov/nchs/fastats/electronic-medical-records.html (2017, accessed 25 February 2020).

Jamoom

Yang

Hing

Adoption of certified electronic health record systems and electronic information sharing in physician offices: United States, 2013 and 2014. NCHS Data Brief 2016; 236: 1–8.

Friedberg

Chen

Van Busum

, et al. Factors affecting physician professional satisfaction and their implications for patient care, health systems, and health policy. Rand Health Q 2014; 3(4): 1.

Zhou

Soran

Jenter

, et al. The relationship between electronic health record use and quality of care over time. J Am Med Inform Assoc 2009; 16(4): 457–464.

Manca

DP.

Do electronic medical records improve quality of care? Yes. Can Fam Physician 2015; 61(10): 846–851.

Hong

Kaur

Farrokhyar

, et al. Accuracy and completeness of electronic medical records obtained from referring physicians in a Hamilton, Ontario, plastic surgery practice: a prospective feasibility study. Plast Surg (Oakv) 2015; 23(1): 48–50.

Makary

Daniel

Medical error-the third leading cause of death in the US. BMJ 2016; 353: i2139.

Hodgson

Magrabi

Coiera

Efficiency and safety of speech recognition for documentation in the electronic health record. J Am Med Inform Assoc 2017; 24(6): 1127–1133.

10.

Zhou

Blackley

Kowalski

, et al. Analysis of errors in dictated clinical documents assisted by speech recognition software and professional transcriptionists. JAMA Netw Open 2018; 1(3): e180530.

11.

Shultz

Holmstrom

HL.

The use of medical scribes in health care settings: a systematic review and future directions. J Am Board Fam Med 2015; 28(3): 371–381.

12.

Bank

Obetz

Konrardy

, et al. Impact of scribes on patient interaction, productivity, and revenue in a cardiology clinic: a prospective study. Clinicoecon Outcomes Res 2013; 5: 399–406.

13.

Mishra

Kiang

Grant

RW.

Association of medical scribes in primary care with physician workflow and patient experience. JAMA Intern Med 2018; 178(11): 1467–1472.

14.

Hsieh

Shannon

SE.

Three approaches to qualitative content analysis. Qual Health Res 2005; 15(9): 1277–1288.

15.

Williams

Warren

Ebeling

, et al. Physician use of electronic health records: survey study assessing factors associated with provider reported satisfaction and perceived patient impact. JMIR Med Inform 2019; 7(2): e10949.

16.

O’Donnell

Kaner

Shaw

, et al. Primary care physicians’ attitudes to the adoption of electronic medical records: a systematic review and evidence synthesis using the clinical adoption framework. BMC Med Inform Decis Mak 2018; 18(1): 101.

17.

Gellert

Ramirez

Webster

SL.

The rise of the medical scribe industry: implications for the advancement of electronic health records. J Am Med Assoc 2015; 313(13): 1315–1316.

18.

Shultz

Holmstrom

HL.

The use of medical scribes in health care settings: a systematic review and future directions. J Am Board Fam Med 2015; 28(3): 371–381.

19.

Gardner

Cooper

Haskell

, et al. Physician stress and burnout: the impact of health information technology. J Am Med Inform Assoc 2019; 26(2): 106–114.

20.

Melnick

Dyrbye

Sinsky

, et al. The association between perceived electronic health record usability and professional burnout among US physicians. Mayo Clin Proc 2019; 95(3): 476–487.

21.

Gregory

Russo

Singh

Electronic health record alert-related workload as a predictor of burnout in primary care providers. Appl Clin Inform 2017; 8(3): 686–697.

22.

Cheng

Scodellaro

Uahwatanasakul

, et al. An electronic medical record in pediatric medical education: survey of medical students’ expectations and experiences. Appl Clin Inform 2018; 9(4): 809–816.

23.

Rana

Hurst

Shepstone

, et al. Voice recognition for radiology reporting: is it good enough? Clin Radiol 2005; 60(11): 1205–1212.

24.

Hammana

Lepanto

Poder

, et al. Speech recognition in the radiology department: a systematic review. Health Inf Manag 2015; 44(2): 4–10.

25.

Kumah-Crystal

Pirtle

Whyte

, et al. Electronic health record interactions through voice: a review. Appl Clin Inform 2018; 9(3): 541–552.

Supplementary Material

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