Primary Care Nurse Practitioner Burnout and ED Use and Hospitalizations Among Chronically Ill Medicare Beneficiaries

Abstract

Nurse practitioners (NPs) represent the fastest-growing workforce of primary care clinicians in the United States. Their numbers are projected to grow in the near future. The NP workforce can help the country meet the rising demand for care services due to the aging population and increasing chronic disease burden. Yet, increased burnout among these clinicians may affect their ability to deliver high-quality, safe care. We investigated how NP burnout in primary care practices affects patient outcomes, including emergency department (ED) use and hospitalizations, among older adults with chronic conditions. In 2018-2019, we collected survey data from 1244 primary care NPs from 6 geographically diverse states on their burnout and merged the survey data with data from Medicare claims on ED use and hospitalizations among 467 466 older adults with chronic conditions. 26.3% of NPs reported burnout. Using logistic regression models, we found that with a 1-unit increase in the standardized burnout score, the odds of an ED visit increased by 2.8% (OR = 1.028; P-value = .035); Ambulatory Care Sensitive Conditions (ACSC) ED visit by 3.2% (OR = 1.032; P-value = .019); hospitalization by 3.9% (OR = 1.039; P-value = .001); and ACSC hospitalization by 6.2% (OR = 1.062; P-value = .001). Our findings indicate that if chronically ill older adults receive care in primary care practices with higher NP burnout rates they are more likely to use EDs and hospitals. Policy and practice efforts, such as improving NP working conditions, should be undertaken to reduce NP burnout in primary care practices to potentially prevent acute care use.

• What do we already know about this topic?

• Clinician burnout negatively affects patients, clinicians themselves, and healthcare organizations.

• How does your research contribute to the field?

• Most research focuses on the impact of burnout on patient outcomes among registered nurses and physicians; our findings make novel contributions to the field by producing evidence on the impact of NP burnout on patient outcomes.

• What are your research’s implications toward theory, practice, or policy?

• Given the growing NP workforce and their key role in primary care delivery, our findings will guide policy and practice efforts, such as improving NP work environments to reduce NP burnout and potentially improve patient outcomes.

Nurse practitioners (NPs) represent the fastest-growing segment of primary care clinicians. Their numbers grew to 335 000 in 2022, ¹ with rapid expansion projected in the near future. ² NPs comprise 27% of all primary care clinicians ³ and perform vital primary care functions, including diagnosing and treating patients, prescribing medications, and promoting health education and disease prevention. ¹ The growing NP workforce is viewed as a promising solution to meet the increasing demand for primary care, especially for older patients with chronic diseases. ⁴ For example, patients with chronic diseases cared for by NPs have lower systolic blood pressure and cholesterol levels compared to patients cared for by physicians alone. ⁵ Furthermore, primary care delivered by NPs to older adults with diabetes is associated with a decreased risk of preventable hospitalizations. ⁶ Yet, the NP workforce faces many challenges within their practices, including a lack of support and poor relationships with administrators and experiences high job dissatisfaction and turnover rates.^{7 -9} NPs are also not immune to burnout, an occupational phenomenon typified by feelings of exhaustion, cynicism, and low levels of personal accomplishment. ¹⁰ Research shows that among primary care clinicians (ie, physicians, NPs, and physician assistants), burnout may be as high as 60%, ¹¹ underscoring the critical need to understand better clinician burnout’s implications for patient care delivery. These numbers were reported even before the Covid-19 pandemic, and the widespread burnout among healthcare providers has been front and center during the COVID-19 pandemic. ¹²

Prior research has shown that clinician burnout negatively affects clinicians, organizations, and patients. For example, systematic reviews have linked burnout to poor physical and mental health among workers and higher rates of absenteeism and turnover among nurses.^13,14 Findings from 20 reviewed studies showed that nurse burnout led to poor safety and quality of care and decreased patient satisfaction and nurses’ organizational commitment and productivity. ¹³ Among primary care physicians, burned out physicians reported less satisfaction, more job stress, more time pressure during visits, and more chaotic work conditions. ¹⁵ Similarly, these clinicians are also more likely to leave their jobs, leading to a high rate of organizational turnover.^15,16 There is also compelling evidence suggesting clinician burnout negatively impacts patient outcomes. For example, burnout among physicians has been linked to higher rates of medical errors, poor quality of patient care, and compromised patient safety, ¹⁷ and nurse burnout has been associated with higher levels of patient mortality, lower patient satisfaction, and higher incidence of hospital-acquired infections.^{18 -21} Yet, there is little evidence on how NP burnout affects patient care and outcomes despite the fact that the NP workforce is growing and taking a key role in caring for patients in primary care. Previous research has found that about a quarter of primary care NPs experience burnout and that NP burnout is linked with lower NP-reported quality of care.^11,22 Yet, no studies to date have focused on how NP burnout impacts patient outcomes.

The National Academy of Medicine’s 2022 report, “National Plan for Health Workforce Well-Being,” calls for additional research to understand the impact of clinician burnout on patient outcomes. ²³ To fill these critical gaps in evidence, we assessed the relationship between NP burnout and patient outcomes by linking Medicare claims data on emergency department (ED) visits and hospitalization to NP’s self-reported burnout. We hypothesized that patients receiving care in primary care practices with high rates of NP burnout would have higher rates of ED visits and hospitalizations.

Methods

Conceptual Underpinnings

We used an adapted version of the Clinician Well-Being Model and existing evidence to guide our study.^7,18,24 The Clinician Well-Being Model includes factors associated with clinician burnout and depicts the relationship between burnout and organizational, clinician, and patient outcomes. These factors include practice environments and organizational attributes such as level of support, healthcare roles and regulations (eg, organizational policies, documentation requirements), and society/culture (eg, stigmatization of burnout). The model states that these factors determine clinician burnout, ultimately affecting patient well-being and outcomes. Over the past several decades, robust evidence shows clinician burnout impacts patient care and outcomes, including medical errors and screening for colon cancer and depression among other outcomes.^7,9 Combined, this model and evidence guide our study and selection of our variables. In this study, for the first time, we assess how primary care NP burnout is associated with patient outcomes—ED use and hospitalization among older adults.

Design

We used a cross-sectional survey design to collect data on NP burnout in primary care practices in 6 states. We then merged the survey data with Medicare claims data on ED use and hospitalizations from chronically ill Medicare beneficiaries receiving care in these primary care practices. The study received approval from the Institutional Review Board of Columbia University Irving Medical Center Medical Center.

Nurse Practitioner Survey

Primary care practices from the following 6 states, Arizona (AZ), California (CA), New Jersey (NJ), Pennsylvania (PA), Texas (TX), and Washington (WA), are included in this study. The states were selected for the variation in their NP Scope of Practice (SOP) regulations and their racially and ethnically diverse patient populations. ²⁵ At the time of the study, AZ and WA had full NP SOP, meaning NPs could provide care without physician involvement; NJ and PA had a reduced SOP, meaning NPs must collaborate with physicians to provide care; and CA and TX had a restricted SOP, meaning NPs must practice under physician supervision.

We first identified NPs using IQVIA’s OneKey database, which includes information on U.S. healthcare clinicians and practices, including names, practice locations, contact information, and National Provider Identifiers (NPIs). ²⁶ Primary care practices were defined as having 50% or more physicians with primary care specialties (ie, family medicine, general practice, geriatrics, internal medicine, preventative medicine, or pediatrics). ²⁷ We then selected practices employing at least 1 NP. Finally, to guarantee similar NP counts in each state, we requested a complete sample of NPs from AZ, NJ, and WA, a 75% random sample of NPs in PA, and 50% random samples of NPs from CA and TX.

The survey data collection with NPs was launched in 2018 and completed in 2019. We used a modified Dillman process ²⁸ to conduct the survey, mailing 3 paper surveys and 2 postcard reminders to the 5689 NPs who met the eligibility criteria. We included an online link in the mail survey to allow NPs to complete the survey online or on paper. In total, 1244 NPs completed and returned the surveys, with a response rate of 21.9%. A nonresponse analysis found no substantial differences between responders and eligible non-responders for characteristics such as sex, number of NPs per practice, and practice location. More information on the survey, response rate, and nonresponse bias are reported elsewhere.^>29

Patient Sample and Attribution

Patients who received care at the practices of the surveyed NPs were identified via Medicare claims data (n = 1 226 551). We used a common attribution approach to ensure that patients in the sample received the majority of their care in the NP practices included in the survey. ³⁰ First, for each patient, we calculated the proportion of primary care evaluation and management (E&M) paid amounts billed by each clinician who submitted at least one claim for that patient in the target year (2018). If a clinician delivered the highest proportion of E&M paid amounts, and that clinician accounted for at least 30% of the E&M paid amount, the patient was assigned to them. ³⁰ One clinician was randomly chosen in the rare instances (<1%) of ties selected. Patients without a dominant clinician were excluded from the analysis (n = 937). Finally, patients were attributed to the practice where the assigned clinician worked based on OneKey data.

We then selected only patients ages 65 and older with one or more of the following chronic conditions: asthma, chronic obstructive pulmonary disease (COPD), hypertension, congestive heart failure (CHF), cardiovascular disease (CVD), and diabetes. These 6 chronic conditions are among the most common that affect Medicare beneficiaries, ³¹ and NPs play a key role in chronic care delivery. Patients with chronic conditions were identified in the Centers for Medicare & Medicaid Services Chronic Conditions Warehouse ³² using primary and secondary International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) diagnoses from outpatient and inpatient claims files. Our final sample included 467 466 beneficiaries attributed to 1041 practices with both NP survey and patient data.

Variables

Measures and Explanatory Variable

To assess burnout, NPs were asked to complete a single-item burnout measure and rate their overall level of burnout on an ordinal scale, ranging from 1 to 5 (ie, “1” indicating no symptoms of burnout to “5” indicating completely burned out). This burnout measure is widely used in research, including among primary care clinicians, and has been validated against the Maslach Burnout Inventory’s (MBI) Emotional Exhaustion subscale—the most commonly used subscale of MBI.^{7,33 -35} Given the validity of the single-item survey question and the participant burden and cost of the 22-item MBI, the single item was used. We computed practice-level NP burnout, the explanatory variable in this study. We computed the mean burnout score of all NPs within each practice on the 5-point ordinal scale,^36,37 higher scores indicate higher practice-level burnout. We used the average of NPs’ 5-point Likert scale scores because ordinal variables with 5 or more categories can often be used as continuous variables without harm to the analysis.^{36 -39} The regression models used a standardized score with an average score of 0 and a standard deviation of 1 to ease the interpretation of the results.

Outcome Variables

We measured 4 outcomes: (1) ED visits, (2) ambulatory care sensitive conditions (ACSC) ED visits, (3) all-cause hospitalizations, and (4) ACSC hospitalizations. Each outcome was coded categorically: zero events or 1 or more events. Any visit for Healthcare Common Procedure Coding System codes 99281, 99282, 99283, 99284, and 99285 in Part B claims file was labeled as an ED visit. ⁴⁰ We identified ACSC ED visits as unique ED visits that had evidence of being avoidable or treatable in primary care according to the “New York University ED Algorithm.”^41,42 The algorithm assigns a probability, based on the primary ICD-10-CM diagnosis, that each ED visit is in 1 of 5 categories: 1- Non-Emergent; 2- Emergent, Primary Care Treatable; 3- Emergent, ED Care Needed, Preventable/Avoidable; 4- Emergent, ED Care Needed, Not Preventable/Avoidable; 5- All other. An ED visit was defined as ACSC if it had a nonzero probability of belonging to category 1, 2, or 3 based on the principal ICD-10-CM diagnosis from the Medicare Part B claims data.

We defined hospitalization as any record in the CMS inpatient claims file with a length of stay of more than 1 day during the study period (2018). ACSC hospitalizations are a subset of hospitalizations that can be avoided with high-quality primary care. ⁴³ We defined ACSC hospitalization as a stay for 1 of 9 conditions identified as sensitive to primary care by the Agency for Healthcare Research and Quality “Prevention Quality Indicators” measure, Version 2020. ⁴³

Covariates

We included patient demographic information (ie, age, sex, race) and the type of patients’ dominant primary care clinician (ie, physician, NP). In addition, we included an indicator variable for multimorbidity, including 15 chronic conditions from the U.S. Department of Health and Human Services list of standard chronic conditions (ie, asthma, COPD, hypertension, congestive heart failure, coronary artery disease, cardiac arrhythmias, hyperlipidemia, stroke, arthritis, cancer, chronic kidney disease, dementia, depression, diabetes, osteoporosis). ⁴⁴ To measure multimorbidity, we counted chronic conditions because it is a better predictor of total Medicare expenditures than the cumulative duration of chronic conditions. ⁴⁵ At the practice-level, we included practice location (ie, rural versus urban), practice size (ie, number of physicians and NPs in practice), SOP for each state, and a structural capability index score measuring the presence of practice features (ie, shared systems for communicating with patients, use of care managers, referral to community services, use of electronic health records, care reminders to clinicians, performance feedback to clinicians, patient registries, and after-hours care) that facilitate high-quality care delivery. ⁴⁶ We also included a practice-level work environment score, representing NPs’ perception of the working conditions within their practices. ⁴⁷ We assessed patterns of missingness in the survey data, independent of NPs’ demographic attributes, and found less than 5% of data missing. Thus, case-wise deletion was used. There was no missing data in Master Beneficiary Summary File, from which beneficiaries’ demographic information was extracted.

Statistical Analysis

We first computed descriptive statistics for all patient- and practice-level characteristics for the overall sample. Next, we assessed the difference in outcomes by NP burnout scores using 2-sample t-tests. Finally, we used logistic regression models to assess the relationship between practice-level burnout and each outcome. We first estimated the unadjusted odds ratios (ORs) and then built the final multivariable regression models to estimate adjusted ORs controlling for patient-level and practice-level characteristics. We assessed the multi-collinearity of variables to ensure there was no high multi-collinearity (variance inflation factor < 5.0). We used random effect models to account for the clustering effect of 467 466 patients nested in 1041 practices. For a more straightforward interpretation, practice-level burnout scores were standardized in regression models as a mean of 0 and a standard deviation of 1. We adopted a 2-sided α level of .05. Our practice-level sample size of 1041 is larger than the recommended sample size of 50 at the second level to run random effect models. ⁴⁸ All analyses were conducted in SAS 9.4. ⁴⁹

Results

Patient Sample Characteristics

Table 1 describes the characteristics of patients and primary care practices. Patients averaged 75.9 years of age (SD = 7.6), had, on average, 2.0 chronic conditions (SD = 1.1), and 56.6% of patients were female. NPs were the dominant primary care clinician for 13.8% of patients. Most of the patients were diagnosed with hypertension (86.5%). In addition, many patients were diagnosed with cardiovascular disease (38.5%) and diabetes (37.4%) as well. Under 30% of the patients visited the ED, and 19.4% had ACSC ED visits. Nearly 20% of patients were hospitalized. Just over 85% of practices were in urban settings. On average, each practice employed 2.81 (3.66 SD) NPs and 5.84 (14.31 SD) MDs. Among NPs, 26.3% were burned out (a score from 3 to 5 indicated burned out and a score from 1 to 2 indicated not burned out); the mean practice-level NP burnout score was 2.1 (0.8 SD).

Table 1.

Characteristics of the Patients and Primary Care Practices.

Patient-level characteristics
Characteristics	Total (N = 467 466)
Age (mean, SD)	75.9 (7.6)
Number of chronic conditions (mean, SD)	3.1 (1.8)
Sex
Female	264 425 (56.6)
Male	203 041 (43.4)
Race/Ethnicity
American Indian/Alaska Native	2349 (0.5)
Asian	16 735 (3.6)
Black	22 468 (4.8)
Hispanic	37 758 (8.1)
Non-Hispanic White	377 177 (80.7)
Other	4096 (0.9)
Unknown	6883 (1.5)
Type of primary care clinician
Physician	403 019 (86.2)
Nurse practitioner	64 447 (13.8)
Patient outcomes
ED visits	137 813 (29.5)
ACSC ED visits	90 680 (19.4)
Hospitalizations	88 691 (19.0)
ACSC hospitalizations	12 839 (2.7)
Practice-level characteristics
Characteristics	Total (N = 1041)
Practice setting (n, %)
Rural	152 (14.60)
Urban	889 (85.40)
Number of NPs (mean, SD)	2.81 (3.66)
Number of MDs (mean, SD)	5.84 (14.31)
Structural Capability Index (mean, SD)	0.62 (0.22)
Burnout (mean, SD)	2.12 (0.85)
Work environment (mean, SD)	3.22 (0.51)

Note. Percentages may not total 100 because of rounding. ACSC = ambulatory care sensitive conditions; NP = nurse practitioner; ED = emergency department.

NP Burnout and Patient Outcomes

Table 2 presents practice-level NP burnout scores by each patient outcome. The bivariate analysis demonstrated that practice-level burnout scores were significantly greater in practices where patients experienced more ED visits or hospitalizations (p-values < .001). We observed similar relationships in both bivariable regression models and final multivariable regression models (Table 3). After controlling for the covariates, there was a significant positive relationship between practice-level burnout and all 4 outcomes (ED, ACSC ED, hospitalization, and ACSC hospitalization). With a 1-unit increase in the standardized burnout score, the odds of an ED visit increased by 2.8% (OR = 1.028; P-value = .035); the odds of an ACSC ED visit increased by 3.2% (OR = 1.032; P-value = .019); the odds of hospitalization increased by 3.9% (OR = 1.039; P-value = .001); and the odds of ACSC hospitalization increased by 6.2% (OR = 1.062; P-value = .001).

Table 2.

Practice-Level NP Burnout Scores by Each Patient Outcome (n = 467 466).

	NP burnout Mean ± SD		P
ED visit	Yes (N = 136 716)	No (N = 327 433)
	2.11 ± 0.75	2.09 ± 0.75	<.0001
ACSC ED visit	Yes (N = 89 929)	No (N = 374 220)
	2.11 ± 0.76	2.10 ± 0.75	<.0001
Hospitalization	Yes (N = 87 989)	No (N = 376 160)
	2.12 ± 0.75	2.09 ± 0.75	<.0001
ACSC hospitalization	Yes (N = 12 732)	No (N = 451 417)
	2.13 ± 0.76	2.10 ± 0.75	<.0001

Note. ACSC = ambulatory care sensitive conditions; NP = nurse practitioner; ED = emergency department.

Table 3.

Unadjusted and Adjusted Odds Ratios Demonstrating Relationship Between NP Burnout Score and Patient Outcomes (n = 467 466).

Outcome	Unadjusted		Adjusted a
	Odds ratio (95% CI)	P	Odds ratio (95% CI)	P
ED	1.035 (1.007-1.063)	.011*	1.028 (1.002-1.055)	.035*
ACSC ED	1.042 (1.013-1.072)	.004*	1.032 (1.005-1.059)	.019*
Hospitalization	1.044 (1.020-1.069)	.000*	1.039 (1.016-1.063)	.001***
ACSC hospitalization	1.075 (1.036-1.115)	<.0001*	1.062 (1.025-1.101)	.001***

Note. ED = emergency department visits; ACSC = ambulatory care sensitive conditions.

^aFinal multivariable mixed-effect regression models controlled for patient-level and practice-level characteristics, including: beneficiary age, sex, number of chronic conditions, race, primary care clinician type, work environment, structural capability index, practice size, practice urbanicity, and practice location.

^*P value less than.05. ***P value less than.001.

Discussion

Using a large dataset of survey responses collected from primary care NPs and Medicare claims data from patients in the NP practices, we conducted the first assessment of the relationship between NP burnout and ED use and hospitalization among older adults with chronic conditions. First, we found that a notable proportion of primary care NPs are burned out. Second, we expected that practices with greater levels of NP burnout would have higher ED use and hospitalization rates among patients, and our models supported this hypothesis. Specifically, our findings indicate that primary care practices with higher rates of NP burnout also have higher acute care use among older adults with chronic conditions. Our study is the first to show an association between primary care NP burnout and patient outcomes. Our findings have several important implications.

More than 1 in 4 NPs report burnout, which is concerning given the impact of burnout on patient outcomes and the fact that, to date, little attention has been given to NP burnout as opposed to other clinicians (ie, physicians and registered nurses are some of the most studied in burnout research). ⁵⁰ Most of the burnout research to date has focused on acute care settings and on physicians and registered nurses, with a lack of attention on NPs in primary care. The lack of such studies makes designing evidence-based interventions to reduce NP burnout in primary care practices challenging. For the first time, we show that NPs experience burnout at a similar level as other clinicians, indicating NPs should be a priority for future research, especially given their expanding numbers and critical role in primary care.

We also show that higher levels of NP burnout within primary care practices translate into adverse patient outcomes, consistent with other evidence on clinician burnout’s impact on patients.^11,21,51 For example, hospitals with high nurse burnout rates also had higher odds of patient mortality and prolonged length of stay. ²¹ Among primary care physicians, burnout increases the likelihood of medical errors and poor patient care. ⁵² Together, our findings add to the growing literature that clinician burnout is a patient safety issue and remains an important target to improve patient outcomes. However, one study conducted among primary care physicians produced disparate findings. ⁵³ The study merged survey data from physicians with Medicare claims and showed that there was no association between physician burnout and patient hospital admissions, ED visits, readmissions, or costs. Our findings, along with other findings, contradict this study. Based on NP’s role in primary care, NPs may be in close contact with patients and more involved in chronic disease management by coordinating patient care and supporting patients in symptom management. Such close involvement in patient care coupled with organizational challenges facing NPs within their employment settings may explain the differences in the study results. Some of the patient care responsibilities NPs have may be more sensitive to patient outcomes. Thus, NP burnout affects patients in ways that physician burnout does not.

In burnout research, it is not uncommon to see “clinician-blaming” language, in which studies conclude that clinicians, or NPs in this case, miss essential aspects of patient care because they are burned out. ⁵⁴ An alternative explanation would suggest that high burnout within a practice setting signals much larger organizational failures that pervade workplaces and set clinicians up to fail—meaning that there are only so many organizational failures that clinicians can safely work around before patients are negatively impacted. This is the central idea of the Institute of Medicine’s “To Err is Human” Report, ⁵⁵ which concluded that medical errors are the byproduct of organizational failures rather than individual clinicians. Similar themes appear in the 2019 National Academy of Medicine report on clinician burnout ⁵⁰ and the World Health Organization’s labeling of burnout as an occupational phenomenon ¹⁰ —both of which allude to poor work environments as the leading contributor to burnout. There are well-established organizational interventions to reduce burnout among registered nurses, such as designating hospitals as Magnets. ⁵⁶ In such hospitals, efforts are focused on improving the nurse work environment through enhancing transformational leadership and promoting nurse professional practice. NPs are also susceptible to poor working conditions. ⁷ Therefore, it is critical that we move our interventions away from targeting individual clinicians experiencing burnout to addressing the environmental causes.

For primary care NPs, poor work environments are characterized by a lack of autonomy, inadequate support for care delivery, and poor relationships with practice administrators. ⁵⁷ To date, system-wide change remains elusive and complicated to implement, and so little progress has been made in designing organizational interventions to address NP burnout. However, the natural variation in NP work environments published in other studies^{57 -59} shows that these work environment features are modifiable and subject to organizational interventions. Thus, efforts should be invested in redesigning NP work environment to support NP practice and promote collegial relationships and transformational leadership. Furthermore, future research can focus on how positive work environment features present in high-performing practices (or practices with positively rated work environments) can be implemented in low-performing practices (or practices with poorly rated work environments) to improve NP work environments and potentially reduce NP burnout and improve patient outcomes. Such work would be foundational to the large-scale implementation of meaningful organizational interventions to address NP burnout.

Beyond patient safety, high levels of burnout are also concerning for organizations, as burnout is a strong determinant of turnover among healthcare workers. ¹⁶ In the current climate of primary care workforce shortages, it is critically important to reduce burnout as it depletes primary care practices of necessary workforce resources, which already struggle to find clinicians. At the same time, the remaining clinicians are unable to manage the increased work burden and eventually become burned out in a vicious cycle. Losing NPs, in already understaffed clinics seriously jeopardizes patient care. Developing, testing, and implementing intervention programs to reduce NP burnout may produce a variety of beneficial effects, including better patient outcomes and the retention of NPs in their clinical positions.

Delivering care to the growing population of older adults is a key national priority. Unfortunately, necessary human capital and other organizational resources have not increased correspondingly to population changes. It is important to create structures that better utilize the existing healthcare workforce resources to minimize the gap between needs and resources. Practice administrators can better leverage their existing workforce by improving clinicians’ working conditions to reduce clinician burnout and improve patient outcomes. Administrators should create programs to enhance communication between various teams and address interpersonal conflicts and issues. Future research should be focused on designing and implementing interventions that improve the work environment of NPs—an intervention that would positively reduce burnout and simultaneously improve patient care.

Our findings also have implications for the growing epidemic of clinician burnout globally. Recent systematic reviews among physicians and nurses demonstrate that burnout is prevalent among these clinicians. A systematic review and meta-analysis of 16 studies showed that burnout among general practitioner physicians ranged from 26% to 37%. ⁶⁰ These numbers were different during the Covid 19 pandemic, ranging from 6% to 99.8% among physicians, as reported in a systematic review of 30 studies. ⁶¹ Among global nurses, across 113 studies, an overall pooled prevalence of burnout symptoms was 11.23%, with significant differences between geographical regions, specialties, and types of burnout measurement used. ⁶² Thus, building evidence on the impact of clinician burnout on patient outcomes is important to promote patient safety globally.

Limitations

Our study has several limitations. First, we utilize a cross-sectional design, which limits our ability to determine causal relationships. Yet, in this study, we were not focused on the cause-effect relationship, rather, we tested the association between NP burnout and patient outcomes for the first time. Future studies using longitudinal and other designs should be conducted to investigate this relationship further. Our sample of NPs was also limited to those working in 6 states, and while geographically diverse, our findings might not be nationally representative. We only used data from Medicare claims for patients 65 and older, so our findings might not be generalizable to other patient populations. Our study is also affected by the limitations of Medicare claims data and how NP care is recorded. Medicare’s “incident to billing,” where NP care is reported under the physician’s name, limits our ability to capture NP contributions to patient care fully. ⁶³ NPs often practice in team-based care models and contribute to practice-level patient outcomes, making it difficult to determine individual NP impacts on patient outcomes. In addition, though a large sample size increases the precision of the study, the effect sizes were small, and we should interpret the results cautiously. Finally, our survey was conducted before the COVID-19 pandemic, which significantly exacerbated clinician burnout as working conditions deteriorated. Our findings are even more relevant today, with higher levels of burnout being reported across clinician types. Our results are likely an underestimation of burnout among NPs in the post-COVID era. More up-to-date studies are needed to ascertain the burnout level among primary care NPs and other understudied clinician types.

Conclusion

We found that higher practice-level NP burnout is associated with increased ED visits and hospitalization among older adults. The results of our study join others in the literature on clinician burnout, suggesting burnout’s adverse impacts on patient outcomes. Administrators should take action to reduce NP burnout, through promoting NP work environment, and potentially improving patient care and outcomes.