MODULE6READING-EHR-1.RelatedStressAmongAPNs.pdf

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Applied Nursing Research

journal homepage: www.elsevier.com/locate/apnr

Original article

Estimating the association between burnout and electronic health record-related stress among advanced practice registered nurses

Daniel A. Harris, MPHa,c, Jacqueline Haskell, MSc, Emily Cooper, MPHc,⁎, Nancy Crouse, CNSd,Rebekah Gardner, MDb,c

a Department of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, CanadabWarren Alpert Medical School, Brown University, Providence, RI, United States of AmericacHealthcentric Advisors, Providence, RI, United States of Americad Boston Medical Center, Boston, MA, United States of America

A R T I C L E I N F O

Keywords:APRNBurnoutElectronic health recordHealth information technology

A B S T R A C T

Background: Health information technology (HIT), such as electronic health records (EHRs), is a growing part ofthe clinical landscape. Recent studies among physicians suggest that HIT is associated with a higher prevalenceof burnout. Few studies have investigated the workflow and practice-level predictors of burnout among ad-vanced practice registered nurses (APRNs).Aim: Characterize HIT use and measure associations between EHR-related stress and burnout among APRNs.Methods: An electronic survey was administered to all APRNs licensed in Rhode Island, United States(N= 1197) in May–June 2017. The dependent variable was burnout, measured with the validated Mini zburnout survey. The main independent variables were three EHR-related stress measures: time spent on the EHRat home, daily frustration with the EHR, and time for documentation. Logistic regression was used to measurethe association between EHR-related stress and burnout before and after adjusting for demographics, practice-level characteristics, and the other EHR-related stress measures.Results: Of the 371 participants, 73 (19.8%) reported at least one symptom of burnout. Among participants withan EHR (N=333), 165 (50.3%) agreed or strongly agreed that the EHR added to their daily frustration and 97(32.8%) reported an insufficient amount of time for documentation. After adjustment, insufficient time fordocumentation (AOR=3.72 (1.78–7.80)) and the EHR adding to daily frustration (AOR=2.17 (1.02–4.65))remained predictors of burnout.Conclusions: Results from the present study revealed several EHR-related environmental factors are associatedwith burnout among APRNs. Future studies may explore the impact of addressing these EHR-related factors tomitigate burnout among this population.

1. Introduction

Resulting from chronic job-related stress, burnout is characterizedby emotional exhaustion, depersonalization, and decreased job sa-tisfaction (Maslach, Schaufeli, & Leiter, 2001). Given the high-stressnature of clinical environments, burnout among healthcare workers hasbeen shown to exceed that of the general population (Shanafelt, Boone,Tan, et al., 2012). Among physicians, the first published report of“burnout” emerged in 1981 (Pines, 1981). A nationally representativesurvey of United States physicians revealed that nearly half (45.8%)experienced at least one symptom of burnout (Shanafelt et al., 2012;Shanafelt, Hasan, Dyrbye, et al., 2015). Moreover, results indicated thatover 50% of physicians in “front line” specialties (e.g., emergency

medicine and general internal medicine) reported one or more symp-toms of burnout (Shanafelt et al., 2012). Several studies have identifiedassociations between physician burnout and poorer quality of care(Melville, 1980; Yuguero, Marsal, Esquerda, & Soler-Gonzalez, 2017),reduced patient satisfaction (Haas et al., 2000), and increased risk ofturnover (Williams, Konrad, Scheckler, et al., 2001). However, despitethe breadth of literature investigating burnout among physicians, sig-nificantly fewer studies have explored burnout among advanced prac-tice registered nurses (APRNs) (Hoff, Carabetta, & Collinson, 2017).

In 2010, the Agency for Healthcare Research and Quality estimatedthat over 100,000 APRNs practice in the United States, with over half(52.0%) working in primary care (Agency for Research Health andQuality, 2012). As of 2017, the number of APRNs has grown to 234,000

https://doi.org/10.1016/j.apnr.2018.06.014Received 4 March 2018; Received in revised form 19 June 2018; Accepted 23 June 2018

⁎ Corresponding author at: 235 Promenade Street, Suite 500, Providence, RI, United States of America.E-mail address: [email protected] (E. Cooper).

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in the United States (American Association of Nurse Practitioners, 2017;Hoff et al., 2017). Similar growth of the APRN workforce has beenobserved in the Netherlands, Canada, Australia, Ireland and NewZealand from 2005 to 2015 (Maier, Barnes, Aiken, & Busse, 2016).APRNs comprise a large and crucial component of the clinical work-force especially as physician shortages in both primary and specializedcare settings continue to increase (Hoff et al., 2017; Norful, Swords,Marichal, Cho, & Poghosyan, 2017). Despite the growth of the APRNworkforce in the United States and internationally, few studies haveinvestigated the work-related psychological outcomes experienced bythis population. One study showed that compared to emergency nursesand nurse managers, APRNs tend to experience less burnout (Browning,Ryan, Thomas, Greenberg, & Rolniak, 2007). The authors suggestedthat lower burnout among APRNs may be because they enter the fieldto gain more autonomy (Whelan, 1997), a job characteristic that istypically associated with greater job satisfaction (Tri, 1991). A recentreview of job satisfaction, burnout, and job turnover among APRNs andphysician assistants revealed that although APRNs generally report highjob satisfaction, considerable variation exists across studies (Hoff et al.,2017). The authors also noted that the literature examining burnoutamong APRNs has a number limitations: 1) many studies with samplesizes of less than<200, 2) a predominance of univariable and bivari-able analyses, as opposed to multivariable statistical methods, and 3) alimited consideration of work setting and organizational factors (Hoffet al., 2017).

In the United States, recent changes in the payment landscape (e.g.,Meaningful Use and the Physician Quality Reporting System) and theirconnection to HIT have drawn investigators to explore potential asso-ciations between HIT and burnout among physicians (Shanafelt et al.,2012; Shanafelt, Dyrbye, Sinsky, et al., 2016). One recent survey of anationally representative sample of United States physicians reportedthat overall satisfaction with electronic health records (EHRs) was ty-pically low and that physicians who used EHRs had higher odds ofburnout (Shanafelt et al., 2016). Dissatisfaction with HIT has also beenobserved among physicians and nurses internationally (Griffon et al.,2017; Leslie & Paradis, 2018; Ologeanu-Taddei, Morquin, & Vitari,2017). Similar to physicians, APRNs engage with HIT as part of theirpractice (Bowles, Dykes, & Demiris, 2015; Cooper, Baier, Morphis,Viner-Brown, & Gardner, 2014; Fund TC, 2017); however the re-lationship between HIT and burnout among this population remainsunstudied. Therefore, the current study's primary aim is to addressseveral of the limitations in the literature by estimating the associationbetween EHR-related stress and burnout among APRNs, while adjustingfor demographic and organizational factors using multivariablemethods. To further describe APRN engagement, attitudes and per-ceptions about HIT, our study's secondary aim is to characterize otherdimensions of HIT and EHR use (e.g., office communication). We hy-pothesize that EHR-related stress will be significantly associated withburnout.

2. Methods

Administered by the Rhode Island Department of Health, a state-wide electronic survey was sent to all 1197 APRNs licensed and inpractice in Rhode Island. The survey period was from May 8th, 2017 toJune 12th, 2017. As part of a legislative mandate (State of Rhode IslandPlantations, 1998), the survey measures and publically reports ag-gregated measures of HIT use among physicians, physician assistantsand APRNs in the state. A description of the publically reported mea-sures and survey process has been previously reported (Cooper et al.,2014). A total of 371 APRNs contributed data for a response rate of31.0%. The present study was reviewed by the Rhode Island Depart-ment of Health's Institutional Review Board (IRB) and deemed exempt.

2.1. Sample characteristics

Participant age and gender were obtained through the Rhode IslandDepartment of Health's publically available APRN licensure file andmatched using the participant's self-reported APRN license number. Agewas categorized into three groups (24–40; 41–60; and 61–80 years ofage). Participants also provided information regarding their specialty,practice setting (outpatient/office or inpatient/hospital), practice size,whether they provide primary care and whether they use a medicalscribe (Shanafelt et al., 2012; Shanafelt et al., 2015; Shanafelt et al.,2016). Practice size was categorized into four groups (1–3 clinicians;4–9 clinicians; 10–15 clinicians; 16+ clinicians). Due to the smallnumber of Neonatal specialists (n= 5), their specialty was combinedwith Pediatrics.

2.2. Dependent variable

Burnout was measured using a single question item from the Mini z,a 10-item survey developed from the Physician Work Life Study(McMurray et al., 2000; Puffer, Knight, O'Neill, et al., 2017; Williams,Konrad, Linzer, et al., 1999). Using a 5-point likert scale, participantswere asked to identify their symptoms of burnout (Maslach et al.,2001): 1) “I enjoy my work. I have no symptoms of burnout”; 2) “I amunder stress, and don’t always have as much energy as I did, but I don’tfeel burned out”; 3) “I am definitely burning out and have one or moresymptoms of burnout, e.g., emotional exhaustion”; 4)“The symptoms ofburnout I am experiencing won’t go away. I think about work frustra-tions a lot”; 5) “I feel completely burned out. I am at the point where Imay need to seek help”. Similar to previous studies, we dichotomizedthis measure into no symptoms of burnout (≤2) and 1 or more symp-toms of burnout (≥3) (McMurray et al., 2000; Schmoldt, Freeborn, &Klevit, 1994). This single-item measure has been previously validatedfor physicians (Rohland, Kruse, & Rohrer, 2004) and shown to have asensitivity of 83.2% and specificity of 87.4% when compared to theMaslach Burnout Inventory (Dolan, Mohr, Lempa, et al., 2015).

2.3. Independent variables

The present study's main independent variables of interest are threeEHR-related stress measures: 1) whether the EHR adds to daily frus-tration, 2) sufficiency of time for documentation, and 3) the amount oftime spent on the EHR at home. As with the outcome of interest, thethree EHR-related stress measures were adopted from the Mini z(Williams et al., 1999; Williams et al., 2001). For the first measure,participants rated how much they agreed that EHRs add to their dailyfrustration using a 4-point likert scale (“strongly agree”, “agree”, “dis-agree”, or “strongly disagree”). We dichotomized these responses intotwo categories: agree (combining “agree” with “strongly agree”) anddisagree (combining “disagree” with “strongly disagree”). The secondEHR-related stress measure assessed sufficiency of time for doc-umentation using a 5-point likert scale (“poor”, “marginal”, “satisfac-tory”, “good”, “optimal”). Responses were dichotomized into eitherinsufficient (“poor” and “marginal”) or sufficient (“satisfactory”,“good”, and “optimal”) time for documentation. Last, for the thirdmeasure, participants were asked to rate how much time they spend onthe EHR at home using a 5-point likert scale (“excessive”, “moderatelyhigh”, “satisfactory”, “modest”, or “minimal/none”). Responses werecategorized into three groups: 1) “minimal/none”, 2) “modest” and“satisfactory”, and 3) “moderately high” and “excessive”.

2.4. Additional health information technology use measures

As few studies have explored the distribution, attitudes, and per-ceptions of HIT among APRNs, we included a number of HIT use- andperception-related survey questions. Any EHR use, either at a main orsecondary practice site, was measured with a binary yes/no response.

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Survey questions regarding EHR use were only administered to parti-cipants who reported “yes” to using an EHR. Using a 4-point scale(“strongly agree”, “agree”, “disagree”, or “strongly disagree”), partici-pants were instructed to judge if EHRs 1) improve their clinical work-flow, 2) improve patient care, 3) improve job satisfaction, and 4) im-prove communication among providers and staff. Participants wereasked if they have remote access to their EHR and if they used it, andamong participants who use remote EHR access, the reasons for remoteuse. Last, as medical scribes have been shown to mitigate the burdens ofHIT use among physicians, participants were asked if they used amedical scribe using a dichotomous yes/no response (Gidwani, Nguyen,Kofoed, et al., 2017).

2.5. Data analysis

Bivariable chi-square and Fisher's exact tests were used to measureassociations between burnout, participant demographics, practicecharacteristics, EHR use, and EHR-related stress. Fisher's exact testswere used to measure the association between categorical variableswith a small number (≤5) of participants in a category. Logistic re-gression was used to measure the unadjusted associations betweenburnout, participant demographics (age and gender), practice char-acteristics (practice setting, practice size, use of a medical scribe), andthe three EHR-related stress measures of interest. Multivariable logisticregression was then used to measure the associations between burnoutand each measure of EHR-related stress while controlling for partici-pant demographics, practice characteristics, and the other EHR-relatedstress measures. As the three independent measures of interest requirethe use of an EHR, the regression models only included APRNs whoreported using an EHR (N=333). All statistical analyses were con-ducted using Stata version 14.0 (Stata Statistical Software, 2015).

3. Results

Among the 371 APRN participants in our sample, 73 (19.8%) ex-perienced one or more symptoms of burnout and 333 (89.9%) reportedusing an EHR. Fig. 1 displays the distribution of each APRN specialtyamong those reporting one or more symptoms of burnout. Among the73 APRNs reporting at least one symptom of burnout, 34 (46.6%) wereFamily/Individual APRNs and 16 (21.9%) were Adult/GerontologyAPRNs. Among APRN participants who use EHRs, 64 (19.3%) reportedspending a moderately high to excessive amount of time on their EHR athome, 165 (50.1%) agreed or strongly agreed EHRs add to their dailyfrustration, and 97 (32.8%) reported insufficient time for documenta-tion.

Table 1 stratifies demographic traits, practice characteristics andburnout by EHR use. We note several significant differences in EHR useacross age, practice setting, practice size, specialty, and the ordinalmeasure of burnout (i.e., the 5-point scale identifying symptoms of

0.0%

6.9%

11.0%

13.7%

21.9%

46.6%

0% 20% 40% 60% 80% 100%

Non-prescriptive (n=0)

Women's Health (n=5)

Prediatric (n=8)

Psychiatric (N=10)

Adult/Gerontology (n=16)

Family/Individual (n=34)

APRNs reporting burnoutFig. 1. Distribution of Advanced Practice Registered Nurse (APRN) specialties reporting one or more symptoms of burnout (n=73).

Table 1Sample characteristics of the advanced practice registered nurse (APRN) par-ticipants (N= 371).

Characteristic Does not havean EHR(N=38)n (%)

Has an EHR(N=333)n (%)

p

Age, years 0.00124–40 4 (10.5) 104 (31.2)41–60 17 (44.7) 160 (48.1)61–80 17 (44.7) 69 (20.7)

Gender 0.285Male 2 (5.3) 41 (12.3)Female 36 (94.7) 292 (87.7)

Practice setting 0.015Office/outpatient 33 (86.8) 108 (32.4)Hospital/inpatient 5 (13.2) 225 (67.6)

Practice size 0.0011–3 clinicians 22 (57.9) 74 (22.4)4–9 clinicians 12 (31.6) 96 (29.0)10–15 clinicians 1 (2.6) 43 (13.0)16 or more clinicians 3 (7.9) 118 (35.7)

Primary care providerNo 22 (66.7) 116 (51.6) 0.104Yes 11 (33.3) 109 (48.4)

Specialty/degree type 0.001Adult/Gerontology 6 (15.8) 91 (27.8)Family/Individual 12 (31.6) 154 (46.3)Non-prescriptive 5 (13.16) 2 (0.6)Psychiatric 14 (36.8) 47 (14.1)Women's health/gender related 1 (2.6) 15 (4.5)Pediatric 0 (0.0) 24 (7.2)

Burnout 0.0011. “I enjoy my work. I have nosymptoms of burnout”

28 (73.7) 109 (32.9)

2. “I am under stress, and don'talways have as much energy as Idid, but I don't feel burned out”

6 (15.8) 153 (46.2)

3. “I am definitely burning out andhave one or more symptoms ofburnout, e.g., emotionalexhaustion”

4 (10.53) 59 (17.8)

4. “The symptoms of burnout I amexperiencing won't go away. Ithink about work frustrations alot”

0 (0.0) 8 (2.4)

5. “I feel completely burned out. Iam at the point where I may needto seek help”

0 (0.0) 2 (0.6)

Burned out 0.195No 34 (89.5) 262 (79.2)Yes 4 (10.5) 69 (20.9)

EHR= electronic health record.Notes. Burnout was measured via the Mini z questionnaire. Responses of 3 orabove were considered “burned out”.

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burnout). For example, there are a greater proportion of psychiatricnurse practitioners without EHRs (36.6%), compared to APRNs withEHRs (14.1%). We also observed significant differences in the ordinalmeasurement of burnout when stratified by EHR use, such that APRNswho use EHRs had a greater presence of burnout compared to APRNswho do not use EHRs.

Table 2 presents attitudes and perceptions about EHRs amongAPRNs. More than half of participants agreed or strongly agreed thatEHRs 1) improve their clinical workflow (82.5%), 2) improve patientcare (63.4%), and 3) improve communication among providers andstaff (77.8%). However, less than half of APRNs reported that EHRsimprove their job satisfaction (48.0%). We also noted that among the217 (65.6%) APRNs with remote EHR access, 160 (81.6%) use remoteEHR access because they are unable to complete work during regularwork hours.

Table 3 includes results from both the unadjusted and adjusted lo-gistic regression procedures. All three EHR-related stress measures weresignificantly associated with burnout in the unadjusted model, and tworemained significant after adjusting for confounding factors. In theunadjusted model, participants who agreed that EHRs added to theirdaily frustration had 3.60 (95%CI: 2.0–6.51) times the odds of burnoutcompared to APRNs who disagreed EHRs add to their daily frustration.Similarly, APRNs who reported moderately high to excessive use oftheir EHR at home had 5.02 (95%CI: 2.64–9.56) times the odds of

burnout compared to ARPNs who reported minimal to no use of theirEHR at home before adjustment. In the unadjusted model, APRNs whoreported insufficient time for documentation had 5.15 (95%CI:2.84–9.33) times the odds of burnout compared to APRNs who reporteda sufficient time for documentation. Remote EHR access was also sig-nificantly associated with burnout (OR=2.19, 95%CI: 1.17–4.08) be-fore adjustment.

After adjusting for demographic traits, practice characteristics, andthe three EHR-stress measures, both insufficient time for documenta-tion (AOR=3.72 95%CI: 1.78–7.80) and agreeing that the EHR adds todaily frustration (AOR=2.17, 95%CI: 1.02–4.65) remained sig-nificantly associated with burnout. No other significant effects wereobserved in the adjusted model.

4. Discussion

This study has several key and unique findings. First, to ourknowledge, this is the first study among a growing body of physician-focused literature to characterize HIT use, attitudes, and perceptionsamong APRNs. The APRNs in our sample reported high use of EHRs(90%), similar to that of their physician counterparts (Centers forDisease Control and Prevention, 2017). Second, we estimated the

Table 2Sample characteristics of electronic health record use among advanced practiceregistered nurses who use an EHR (APRNs) (N=333).

EHR characteristic n (%)

EHR adds to the frustration of my dayStrongly disagree 29 (8.8)Disagree 134 (40.6)Agree 125 (38.1)Strongly agree 40 (12.0)

EHR improves my clinical workflowStrongly disagree 26 (7.9)Disagree 90 (27.4)Agree 182 (55.5)Strongly agree 30 (9.2)

EHR improves patient careStrongly disagree 20 (6.1)Disagree 100 (30.5)Agree 180 (54.9)Strongly agree 28 (8.5)

EHR improves my job satisfactionStrongly disagree 53 (16.2)Disagree 117 (35.8)Agree 133 (40.7)Strongly agree 24 (7.3)

EHR improves communication among the providers and staff in myunit or practice

Strongly disagree 16 (4.9)Disagree 57 (17.3)Agree 210 (63.8)Strongly agree 46 (14.0)

Remote EHR useNo, I do not have remote access 77 (23.3)No, I have remote access, but do not use it 37 (11.2)Yes, I use remote EHR access 217 (65.6)

Reason for remote EHR useUnable to complete work during regular work hours 160 (81.6)Have the opportunity to work from home (e.g., to achieve work/life balance)

36 (18.4)

Time spent on the EHR at homeMinimal/None 174 (52.6)Modest/Satisfactory 93 (28.1)Moderately high/Excessive 64 (19.3)

Sufficiency of time for documentationInsufficient 97 (32.8)Sufficient 199 (67.2)

EHR= electronic health record; HIT=health information technology.

Table 3Unadjusted and adjusted odds ratio estimates of the association between elec-tronic health record-related stress and burnout among advanced practice re-gistered nurses (APRNs) with EHRs (N=333).

Characteristic Unadjusted OR(95%CI)

p Adjusted ORa

(95%CI)p

Age, years24–40 Ref Ref41–60 1.00 0.99 0.68 (0.30–1.57) 0.36861–80 1.07 0.86 0.46 (0.16–1.27) 0.132

GenderMale Ref RefFemale 2.59 (0.98–7.54) 0.081 1.37 (0.35–5.33) 0.646

Practice settingHospital/inpatient Ref RefOffice/outpatient 1.76 (0.95–3.26) 0.070 1.30 (0.53–3.24) 0.567

Practice size1–3 clinicians Ref Ref4–9 clinicians 1.48 (0.69–3.16) 0.314 1.41 (0.55–3.63) 0.47610–15 clinicians 2.03 (0.84–4.9) 0.116 2.11 (0.66–6.74) 0.21016 or more clinicians 0.98 (0.45–2.11) 0.954 1.59 (0.54–4.63) 0.400

Uses a medical scribeNo Ref RefYes 0.46 (0.16–1.36) 0.162 0.35 (0.09–1.34) 0.125

EHR adds to dailyfrustration

Strongly disagree/disagree

Ref Ref

Strongly agree/agree 3.60 (2.0–6.51) 0.001 2.17 (1.02–4.65) 0.045Remote EHR useNo Ref RefYes 2.19 (1.17–4.08) 0.014 1.38 (0.51–3.72) 0.531

Time spent on the EHR athome

Minimal/none Ref RefModest/satisfactory 0.93 (0.45–1.90) 0.832 0.53 (0.18–1.54) 0.244Moderately high/excessive

5.02 (2.64–9.56) 0.001 2.66 (0.91–7.80) 0.075

Sufficiency of time fordocumentation

Sufficient Ref RefInsufficient 5.15 (2.84–9.33) 0.001 3.72 (1.78–7.80) 0.001

Notes: Odds Ratio (OR); Confidence interval (CI); Electronic health record(EHR); Pseudo-R2=0.21.

a Factors in the adjusted model included age, gender, practice setting,practice size, use of a medical scribe, EHR adding to daily frustration, remoteEHR use, time spent on the EHR at home, and sufficiency of time for doc-umentation.

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associations between demographic traits, practice characteristics, EHR-related stress, and burnout among APRNs. The unadjusted regressionresults revealed several EHR-related factors that were associated withburnout, such as remote EHR use, the EHR adding to daily frustration,substantial time spent on the EHR at home, and an insufficient amountof time for documentation. After adjusting for confounding factors,insufficient time for documentation and negative attitudes towards EHRremained strongly associated with burnout. Interestingly, and unlikeprevious physician studies, our results did not indicate any significanteffects between demographic traits or practice characteristics andburnout (Shanafelt et al., 2016).

According the Office of the National Coordinator for HealthInformation Technology (ONC), part of the United States Department ofHealth and Human Services, EHRs are designed to improve billing andto have additional co-benefits, such as improvements in patient careand information accessibility (Office of the National Coordinator forHealth Information Technology, 2014). Although some studies haveshown improvements to patient care and associated financial savingsfrom EHRs (Chaudhry, Wang, Wu, et al., 2006; Shekelle, Morton, &Keeler, 2006), the results are mixed (Black, Car, Pagliari, et al., 2011).Moreover, EHRs have been shown to increase the odds of burnoutamong physicians (Shanafelt, Dyrbye, Sinsky, et al., 2016) and nega-tively impact patient-provider interactions (Pelland, Baier, & Gardner,2017). The results from the present study are the first to investigate HITuse among APRNs, a growing and critically important component of thehealthcare delivery system.

Compared to physicians, our results indicated that the APRNs in oursample have more favorable attitudes and perceptions of EHRs. A re-cent study of EHR use and physician burnout indicated that only 36% ofphysicians agreed or strongly agreed that EHRs improve patient care(Shanafelt et al., 2016). However, over 60% of APRNs in our sampleagreed or strongly agreed that EHRs improve patient care. While thesedifferences may be attributed, in part, to differences in training, patientpanel size, and job responsibilities across the provider types, furtherresearch is needed to identify why APRNs may have more favorableopinions of EHRs compared to physicians. However, similar to physi-cians, our results indicated that EHRs and EHR-related stress are asso-ciated with burnout among APRNs.

Results from the bivariable analyses revealed that APRNs with EHRsreported a greater proportion of burnout symptoms compared to APRNswithout EHRs. Additionally, among APRNs with EHRs, results from theregression analyses revealed several EHR-related factors were asso-ciated burnout. First, 217 (66%) of APRNs in our sample indicated theyuse remote EHR access. Before adjusting for other factors, remote EHRuse was significantly associated with burnout. We predict this finding isrelated to the fact that 82% of APRNs reporting remote EHR use do sobecause they are unable to complete patient documentation at work,not for reasons such as improving work/life balance. This interpretationis supported by the relatively high and significant measure of associa-tion between an insufficient amount of time for documentation andburnout in both the unadjusted and adjusted results. Our results high-light the high prevalence of remote EHR use due to insufficient time fordocumentation and its relationship to burnout among APRNs. Similarresults are echoed in the physician literature (Shanafelt et al., 2016).Fortunately, these results do highlight opportunities for quality im-provement, as the conditions of EHR use are modifiable. For example,identifying ways to decrease documentation requirements or to makedocumenting in EHRs less time consuming by making the electronicinterface more provider-friendly.

In the physician literature, medical scribes have been shown to haveseveral significant beneficial effects on overall workplace satisfaction,patient-physician interactions, time for documentation, and doc-umentation quality and accuracy (Gidwani et al., 2017). We did notobserve a significant relationship between the use of a medical scribeand burnout. However, post-hoc bivariable analyses revealed that theproportion of burnout symptoms tended to be lower in APRNs reporting

the use of a medical scribe compared to APRNs who do not use amedical scribe (p=0.055). Our lack of statistical significance may bedue to a small number of APRNs using medical scribes (n=34).However, positive findings from the physician literature and the resultsfrom our post-hoc analyses suggest that scribes may mitigate theburnout associated with documentation. Given these data, future re-search on the use of scribes among APRNs is likely warranted, espe-cially because nearly 20% of APRNs in our sample reported at least onesymptom of burnout.

Burnout among APRNs in our sample appears to be lower than whathas been previously reported in physician samples (Puffer et al., 2017;Shanafelt et al., 2012; Shanafelt et al., 2015). However, the prevalenceof burnout among physicians has been shown to vary widely, from 25%(Puffer et al., 2017) to 46% (Shanafelt et al., 2012). Due to the limitednumber of studies directly quantifying burnout among APRNs (Hoffet al., 2017), it is challenging to report a range. However, one study of48 nurse practitioners reported that 96% reported their job as stressful(Casida & Pastor, 2012). Similarly, emotional exhaustion scores on theMaslach Burnout Inventory were moderately high for nurse practi-tioners in one study, albeit still lower than those of emergency nursesand nurse managers (Browning et al., 2007). The observed variation inphysician and APRN burnout is likely attributed to a number of in-dividual- and practice-level factors, as well as methodological differ-ences across studies. For example, although a validated measure ofburnout, the burnout item from the Mini z has been shown to reportlower rates of burnout compared to the Maslach burnout inventory(Linzer & Poplau, 2017; Linzer, Poplau, Babbott, et al., 2016). Wesuspect that the present study's use of the Mini z and the fact that oursurvey was not anonymous, likely contributed to underreporting of theprevalence of burnout among our sample. As investigators in the phy-sician literature have noted, burnout levels of 20% among healthcareproviders is still high and warrants significant attention from re-searchers as well as payers and policy makers (Linzer & Poplau, 2017;Puffer et al., 2017).

The results from the present study underscore the need to developresources for APRNs experiencing significant burnout symptoms. TheAmerican Medical Association (AMA) not only recognizes widespreadburnout among physicians, but also provides a number of resources forthose experiencing burnout (American Medical Association, 2015), asdoes the American College of Physicians (American College ofPhysicians: New Mexico Chapter, n.d.). To date, we were not able toidentify any publically available and evidence-based resources to ad-dress burnout that are specific to APRNs.

The present study has several limitations. First, the survey was ad-ministered through the Rhode Island Department of Health's legisla-tively mandated healthcare quality reporting program and requiresparticipants to use personal identifiers. Therefore, although individualburnout responses are not publically reported, we predict that someparticipants may not report the extent of their burnout symptoms.Specifically, we predict that our estimation of the prevalence of burnoutis likely lower than truly experienced. Second, although our survey hada response rate typical of electronic surveys, 31% remains less thanpreferred and limits the analytical potential of the data and the gen-eralizability of the results. Last, although over 300 APRNs contributeddata, a larger sample size across more diverse geographic regions willincrease the generalizability of the results.

The present study adds to the field by addressing many of the lim-itations present in the burnout literature. A recent review of studieshighlighted the need for future research to include samples of> 200,use rigorous multivariable statistical techniques, and address organi-zational factors that may be associated with burnout (Hoff et al., 2017).The present study accomplishes these aims and, by estimating the as-sociation between EHR-related stress and burnout, adds to a growingbody of investigation. In addition to the suggestions previously noted,future research should consider potential causal associations betweenHIT use and burnout among all clinician types and should test HIT-

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related interventions to improve burnout among APRNs.

Acknowledgments

The authors report no potential conflicts of interest. Authors DH,EC, and RG participated in the design and dissemination of the surveyinstrument. Authors DH and JH participated in the analysis of thesurvey results. All authors participated in the writing and review of themanuscript. The authors thank Blake Morphis for his invaluable ex-perience with the HIT survey, Chantal Lewis for providing thoughtfulcomments and Samara Viner-Brown from the Rhode Island Departmentof Health for reviewing the manuscript.

Funding

This research did not receive any specific grant from fundingagencies in the public, commercial, or not-for-profit sectors.

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