Hospital Medicine

Study Overview

Objective. To examine the trend of noninvasive and invasive mechanical ventilation at the end of life from 2000 to 2017.

Design. Observational population-based cohort study.

Setting and participants. The study was a population-based cohort study to examine the use of noninvasive and invasive mechanical ventilation among decedents. The study included a random 20% sample of Medicare beneficiaries older than 65 years who were hospitalized in the last 30 days of life and died between January 1, 2000, and December 31, 2017, except for the period October 1, 2015, to December 31, 2015, when the transition from International Classification of Diseases, Ninth Revision (ICD-9) to ICD-10 occurred. Beneficiaries with the primary admitting diagnosis of cardiac arrest or with preexisting tracheostomy were excluded because of expected requirements for ventilatory support. The sample included a total of 2,470,735 Medicare beneficiaries; mean age was 82.2 years, and 54.8% were female. Primary admitting diagnosis codes were used to identify 3 subcohorts: congestive heart failure, chronic obstructive pulmonary disease, and cancer; a fourth subcohort of dementia was identified using the primary admitting diagnosis code or the first 9 secondary diagnosis codes.

Main outcome measures. The study used procedure codes to identify the use of noninvasive ventilation, invasive mechanical ventilation, or none among decedents who were hospitalized in the last 30 days of life. Descriptive statistics to characterize variables by year of hospitalization and ventilatory support were calculated, and the rates of noninvasive and invasive mechanical ventilation use were tabulated. Other outcomes of interest include site of death (in-hospital death), hospice enrollment at death, and hospice enrollment in the last 3 days of life as measures of end-of- life care use. Multivariable logistic regressions were used to examine noninvasive and invasive mechanical ventilation use among decedents, and time trends were examined, with the pattern of use in year 2000 as reference. Subgroup analysis with the subcohort of patients with different diagnoses were conducted to examine trends.

Main results. From 2000 to 2017, 16.3% of decedents had invasive mechanical ventilation, 3.7% had noninvasive ventilation, and 1.0% had both noninvasive and invasive ventilation during their hospital stay. Compared to the reference year 2000, there was a 9-fold increase in noninvasive ventilation use, from 0.8% to 7.1% in 2017, and invasive mechanical ventilation use also increased slightly, from 15.0% to 18.5%. Compared to year 2000, decedents were 2.63 times and 1.04 times (adjusted odds ratio [OR]) more likely to receive noninvasive ventilation and invasive mechanical ventilation, respectively, in 2005, 7.87 times and 1.39 times more likely in 2011, and 11.84 times and 1.63 times more likely in 2017.

Subgroup analysis showed that for congestive heart failure and chronic obstructive pulmonary disease, the increase in noninvasive ventilation use mirrored the trend observed for the overall population, but the use of invasive mechanical ventilation did not increase from 2000 to 2017, with a rate of use of 11.1% versus 7.8% (adjusted OR, 1.07; 95% confidence interval [CI], 0.95-1.19) for congestive heart failure and 17.4% vs 13.2% (OR 1.03, 95% CI, 0.88-1.21) for chronic obstructive pulmonary disease. For the cancer and dementia subgroups, the increase in noninvasive ventilation use from 2000 to 2017 was accompanied by an increase in the use of invasive mechanical ventilation, with a rate of 6.2% versus 7.4% (OR, 1.40; 95% CI, 1.26-1.55) for decedents with cancer and a rate of 5.7% versus 6.2% (OR, 1.28; 95% CI, 1.17-1.41) for decedents with dementia. For other measures of end-of-life care, noninvasive ventilation use when compared to invasive mechanical ventilation use was associated with lower rates of in-hospital (acute care) deaths (50.3% vs 76.7%), hospice enrollment in the last 3 days of life (late hospice enrollment; 57.7% vs 63.0%), and higher rates of hospice enrollment at death (41.3% vs 20.0%).

Conclusion. There was an increase in the use of noninvasive ventilation from 2000 through 2017 among Medicare beneficiaries who died. The findings also suggest that the use of invasive mechanical ventilation did not increase among decedents with congestive heart failure and chronic obstructive pulmonary disease but increased among decedents with cancer and dementia.

Noninvasive ventilation offers an alternative to invasive mechanical ventilation for providing ventilatory support for respiratory failure, and may offer benefits as it could avert adverse effects associated with invasive mechanical ventilation, particularly in the management of respiratory failure due to congestive heart failure and chronic obstructive pulmonary disease.¹ There is evidence for potential benefits of use of noninvasive ventilation in other clinical scenarios, such as pneumonia in older adults with comorbidities, though its clinical utility is not as well established for other diseases.²

As noninvasive ventilation is introduced into clinical practice, it is not surprising that over the period of the study (2000 to 2017) that its use increased substantially. Advance directives that involve discussion of life-sustaining treatments, including in scenarios with respiratory failure, may also result in physician orders that specify whether an individual desires invasive mechanical ventilation versus other medical treatments, including noninvasive ventilation.^3,4 By examining the temporal trends of use of noninvasive and invasive ventilation, this study reveals that invasive mechanical ventilation use among decedents with dementia and cancer has increased, despite increases in the use of noninvasive ventilation. It is important to understand further what would explain these temporal trends and whether the use of noninvasive and also invasive mechanical ventilation at the end of life represents appropriate care with clear goals or whether it may represent overuse. It is also less clear in the end-of-life care scenario what the goals of treatment with noninvasive ventilation would be, especially if it does not avert the use of invasive mechanical ventilation.

The study includes decedents only, thus limiting the ability to draw conclusions about clinically appropriate care.⁵ Further studies should examine a cohort of patients who have serious and life-threatening illness to examine the trends and potential effects of noninvasive ventilation on outcomes and utilization, as individuals who have improved and survived would not be included in this present decedent cohort.

Applications for Clinical Practice

This study highlights changes in the use of noninvasive and invasive ventilation over time and the different trends seen among subgroups with different diagnoses. For older adults with serious comorbid illness such as dementia, it is especially important to have discussions on advance directives so that care at the end of life is concordant with the patient’s wishes and that unnecessary, burdensome care can be averted. Further studies to understand and define the appropriate use of noninvasive and invasive mechanical ventilation for older adults with significant comorbidities who have serious, life-threatening illness are needed to ensure appropriate clinical treatment at the end of life.

–William W. Hung, MD, MPH

Study Overview

Objective. To examine the trend of noninvasive and invasive mechanical ventilation at the end of life from 2000 to 2017.

Design. Observational population-based cohort study.

Setting and participants. The study was a population-based cohort study to examine the use of noninvasive and invasive mechanical ventilation among decedents. The study included a random 20% sample of Medicare beneficiaries older than 65 years who were hospitalized in the last 30 days of life and died between January 1, 2000, and December 31, 2017, except for the period October 1, 2015, to December 31, 2015, when the transition from International Classification of Diseases, Ninth Revision (ICD-9) to ICD-10 occurred. Beneficiaries with the primary admitting diagnosis of cardiac arrest or with preexisting tracheostomy were excluded because of expected requirements for ventilatory support. The sample included a total of 2,470,735 Medicare beneficiaries; mean age was 82.2 years, and 54.8% were female. Primary admitting diagnosis codes were used to identify 3 subcohorts: congestive heart failure, chronic obstructive pulmonary disease, and cancer; a fourth subcohort of dementia was identified using the primary admitting diagnosis code or the first 9 secondary diagnosis codes.

Main outcome measures. The study used procedure codes to identify the use of noninvasive ventilation, invasive mechanical ventilation, or none among decedents who were hospitalized in the last 30 days of life. Descriptive statistics to characterize variables by year of hospitalization and ventilatory support were calculated, and the rates of noninvasive and invasive mechanical ventilation use were tabulated. Other outcomes of interest include site of death (in-hospital death), hospice enrollment at death, and hospice enrollment in the last 3 days of life as measures of end-of- life care use. Multivariable logistic regressions were used to examine noninvasive and invasive mechanical ventilation use among decedents, and time trends were examined, with the pattern of use in year 2000 as reference. Subgroup analysis with the subcohort of patients with different diagnoses were conducted to examine trends.

Main results. From 2000 to 2017, 16.3% of decedents had invasive mechanical ventilation, 3.7% had noninvasive ventilation, and 1.0% had both noninvasive and invasive ventilation during their hospital stay. Compared to the reference year 2000, there was a 9-fold increase in noninvasive ventilation use, from 0.8% to 7.1% in 2017, and invasive mechanical ventilation use also increased slightly, from 15.0% to 18.5%. Compared to year 2000, decedents were 2.63 times and 1.04 times (adjusted odds ratio [OR]) more likely to receive noninvasive ventilation and invasive mechanical ventilation, respectively, in 2005, 7.87 times and 1.39 times more likely in 2011, and 11.84 times and 1.63 times more likely in 2017.

Subgroup analysis showed that for congestive heart failure and chronic obstructive pulmonary disease, the increase in noninvasive ventilation use mirrored the trend observed for the overall population, but the use of invasive mechanical ventilation did not increase from 2000 to 2017, with a rate of use of 11.1% versus 7.8% (adjusted OR, 1.07; 95% confidence interval [CI], 0.95-1.19) for congestive heart failure and 17.4% vs 13.2% (OR 1.03, 95% CI, 0.88-1.21) for chronic obstructive pulmonary disease. For the cancer and dementia subgroups, the increase in noninvasive ventilation use from 2000 to 2017 was accompanied by an increase in the use of invasive mechanical ventilation, with a rate of 6.2% versus 7.4% (OR, 1.40; 95% CI, 1.26-1.55) for decedents with cancer and a rate of 5.7% versus 6.2% (OR, 1.28; 95% CI, 1.17-1.41) for decedents with dementia. For other measures of end-of-life care, noninvasive ventilation use when compared to invasive mechanical ventilation use was associated with lower rates of in-hospital (acute care) deaths (50.3% vs 76.7%), hospice enrollment in the last 3 days of life (late hospice enrollment; 57.7% vs 63.0%), and higher rates of hospice enrollment at death (41.3% vs 20.0%).

Conclusion. There was an increase in the use of noninvasive ventilation from 2000 through 2017 among Medicare beneficiaries who died. The findings also suggest that the use of invasive mechanical ventilation did not increase among decedents with congestive heart failure and chronic obstructive pulmonary disease but increased among decedents with cancer and dementia.

Noninvasive ventilation offers an alternative to invasive mechanical ventilation for providing ventilatory support for respiratory failure, and may offer benefits as it could avert adverse effects associated with invasive mechanical ventilation, particularly in the management of respiratory failure due to congestive heart failure and chronic obstructive pulmonary disease.¹ There is evidence for potential benefits of use of noninvasive ventilation in other clinical scenarios, such as pneumonia in older adults with comorbidities, though its clinical utility is not as well established for other diseases.²

As noninvasive ventilation is introduced into clinical practice, it is not surprising that over the period of the study (2000 to 2017) that its use increased substantially. Advance directives that involve discussion of life-sustaining treatments, including in scenarios with respiratory failure, may also result in physician orders that specify whether an individual desires invasive mechanical ventilation versus other medical treatments, including noninvasive ventilation.^3,4 By examining the temporal trends of use of noninvasive and invasive ventilation, this study reveals that invasive mechanical ventilation use among decedents with dementia and cancer has increased, despite increases in the use of noninvasive ventilation. It is important to understand further what would explain these temporal trends and whether the use of noninvasive and also invasive mechanical ventilation at the end of life represents appropriate care with clear goals or whether it may represent overuse. It is also less clear in the end-of-life care scenario what the goals of treatment with noninvasive ventilation would be, especially if it does not avert the use of invasive mechanical ventilation.

The study includes decedents only, thus limiting the ability to draw conclusions about clinically appropriate care.⁵ Further studies should examine a cohort of patients who have serious and life-threatening illness to examine the trends and potential effects of noninvasive ventilation on outcomes and utilization, as individuals who have improved and survived would not be included in this present decedent cohort.

Applications for Clinical Practice

This study highlights changes in the use of noninvasive and invasive ventilation over time and the different trends seen among subgroups with different diagnoses. For older adults with serious comorbid illness such as dementia, it is especially important to have discussions on advance directives so that care at the end of life is concordant with the patient’s wishes and that unnecessary, burdensome care can be averted. Further studies to understand and define the appropriate use of noninvasive and invasive mechanical ventilation for older adults with significant comorbidities who have serious, life-threatening illness are needed to ensure appropriate clinical treatment at the end of life.

–William W. Hung, MD, MPH

Study Overview

Objective. To examine the trend of noninvasive and invasive mechanical ventilation at the end of life from 2000 to 2017.

Design. Observational population-based cohort study.

Setting and participants. The study was a population-based cohort study to examine the use of noninvasive and invasive mechanical ventilation among decedents. The study included a random 20% sample of Medicare beneficiaries older than 65 years who were hospitalized in the last 30 days of life and died between January 1, 2000, and December 31, 2017, except for the period October 1, 2015, to December 31, 2015, when the transition from International Classification of Diseases, Ninth Revision (ICD-9) to ICD-10 occurred. Beneficiaries with the primary admitting diagnosis of cardiac arrest or with preexisting tracheostomy were excluded because of expected requirements for ventilatory support. The sample included a total of 2,470,735 Medicare beneficiaries; mean age was 82.2 years, and 54.8% were female. Primary admitting diagnosis codes were used to identify 3 subcohorts: congestive heart failure, chronic obstructive pulmonary disease, and cancer; a fourth subcohort of dementia was identified using the primary admitting diagnosis code or the first 9 secondary diagnosis codes.

Main outcome measures. The study used procedure codes to identify the use of noninvasive ventilation, invasive mechanical ventilation, or none among decedents who were hospitalized in the last 30 days of life. Descriptive statistics to characterize variables by year of hospitalization and ventilatory support were calculated, and the rates of noninvasive and invasive mechanical ventilation use were tabulated. Other outcomes of interest include site of death (in-hospital death), hospice enrollment at death, and hospice enrollment in the last 3 days of life as measures of end-of- life care use. Multivariable logistic regressions were used to examine noninvasive and invasive mechanical ventilation use among decedents, and time trends were examined, with the pattern of use in year 2000 as reference. Subgroup analysis with the subcohort of patients with different diagnoses were conducted to examine trends.

Main results. From 2000 to 2017, 16.3% of decedents had invasive mechanical ventilation, 3.7% had noninvasive ventilation, and 1.0% had both noninvasive and invasive ventilation during their hospital stay. Compared to the reference year 2000, there was a 9-fold increase in noninvasive ventilation use, from 0.8% to 7.1% in 2017, and invasive mechanical ventilation use also increased slightly, from 15.0% to 18.5%. Compared to year 2000, decedents were 2.63 times and 1.04 times (adjusted odds ratio [OR]) more likely to receive noninvasive ventilation and invasive mechanical ventilation, respectively, in 2005, 7.87 times and 1.39 times more likely in 2011, and 11.84 times and 1.63 times more likely in 2017.

Subgroup analysis showed that for congestive heart failure and chronic obstructive pulmonary disease, the increase in noninvasive ventilation use mirrored the trend observed for the overall population, but the use of invasive mechanical ventilation did not increase from 2000 to 2017, with a rate of use of 11.1% versus 7.8% (adjusted OR, 1.07; 95% confidence interval [CI], 0.95-1.19) for congestive heart failure and 17.4% vs 13.2% (OR 1.03, 95% CI, 0.88-1.21) for chronic obstructive pulmonary disease. For the cancer and dementia subgroups, the increase in noninvasive ventilation use from 2000 to 2017 was accompanied by an increase in the use of invasive mechanical ventilation, with a rate of 6.2% versus 7.4% (OR, 1.40; 95% CI, 1.26-1.55) for decedents with cancer and a rate of 5.7% versus 6.2% (OR, 1.28; 95% CI, 1.17-1.41) for decedents with dementia. For other measures of end-of-life care, noninvasive ventilation use when compared to invasive mechanical ventilation use was associated with lower rates of in-hospital (acute care) deaths (50.3% vs 76.7%), hospice enrollment in the last 3 days of life (late hospice enrollment; 57.7% vs 63.0%), and higher rates of hospice enrollment at death (41.3% vs 20.0%).

Conclusion. There was an increase in the use of noninvasive ventilation from 2000 through 2017 among Medicare beneficiaries who died. The findings also suggest that the use of invasive mechanical ventilation did not increase among decedents with congestive heart failure and chronic obstructive pulmonary disease but increased among decedents with cancer and dementia.

Noninvasive ventilation offers an alternative to invasive mechanical ventilation for providing ventilatory support for respiratory failure, and may offer benefits as it could avert adverse effects associated with invasive mechanical ventilation, particularly in the management of respiratory failure due to congestive heart failure and chronic obstructive pulmonary disease.¹ There is evidence for potential benefits of use of noninvasive ventilation in other clinical scenarios, such as pneumonia in older adults with comorbidities, though its clinical utility is not as well established for other diseases.²

As noninvasive ventilation is introduced into clinical practice, it is not surprising that over the period of the study (2000 to 2017) that its use increased substantially. Advance directives that involve discussion of life-sustaining treatments, including in scenarios with respiratory failure, may also result in physician orders that specify whether an individual desires invasive mechanical ventilation versus other medical treatments, including noninvasive ventilation.^3,4 By examining the temporal trends of use of noninvasive and invasive ventilation, this study reveals that invasive mechanical ventilation use among decedents with dementia and cancer has increased, despite increases in the use of noninvasive ventilation. It is important to understand further what would explain these temporal trends and whether the use of noninvasive and also invasive mechanical ventilation at the end of life represents appropriate care with clear goals or whether it may represent overuse. It is also less clear in the end-of-life care scenario what the goals of treatment with noninvasive ventilation would be, especially if it does not avert the use of invasive mechanical ventilation.

The study includes decedents only, thus limiting the ability to draw conclusions about clinically appropriate care.⁵ Further studies should examine a cohort of patients who have serious and life-threatening illness to examine the trends and potential effects of noninvasive ventilation on outcomes and utilization, as individuals who have improved and survived would not be included in this present decedent cohort.

Applications for Clinical Practice

This study highlights changes in the use of noninvasive and invasive ventilation over time and the different trends seen among subgroups with different diagnoses. For older adults with serious comorbid illness such as dementia, it is especially important to have discussions on advance directives so that care at the end of life is concordant with the patient’s wishes and that unnecessary, burdensome care can be averted. Further studies to understand and define the appropriate use of noninvasive and invasive mechanical ventilation for older adults with significant comorbidities who have serious, life-threatening illness are needed to ensure appropriate clinical treatment at the end of life.

–William W. Hung, MD, MPH

Overutilization and low-value care are important clinical and policy problems. Their measurement is challenging because it requires detailed clinical information. Additionally, there are inherent difficulties in identifying discretionary services likely to be inappropriate or low-value and demonstrating that certain services produce little/no health benefit. Quantifying “ideal” expected testing rates—ones that would reflect minimization of inappropriate/low-value care without excluding essential, high-yield diagnostic services—presents additional challenges. Consequently, of 521 unique measures specified by national measurement programs and professional guidelines, 91.6% targeted underuse, while only 6.5% targeted overuse.¹

The potential for unintended consequences of implementing measures to eliminate overuse are a barrier to incorporating such measures into practice.² For example, measuring, reporting, and penalizing overuse of inappropriate bone scanning may lead to underuse in patients for whom scanning is crucial.² Most overuse measures based on inappropriate or low-value indications relate to imaging and medications.¹ However, there is increasing interest in overutilization measures based on a broad set of health services. Identifying low-value testing or treatments often requires a substantial degree of clinical detail to avoid the damaging inclusion of beneficial services, which may lead to unintended negative outcomes, creating skepticism among clinicians. Ultimately, getting measurement of low-value care wrong would undermine adoption of interventions to reduce overuse.

To reduce low-value care through expansive measures of provider ordering behavior,³ Ellenbogen et al⁴ derived a novel index to identify hospitals with high rates of low-yield diagnostic testing. This index is based on the concept that, in the presence of nonspecific, symptom-based principal diagnoses, a substantial proportion of (apparently) non-diagnostic related studies were probably ordered despite a low pretest probability of serious disease. Since such symptom-based diagnoses reflect the absence of a more specific diagnosis, the examinations observed are markers of physician-driven decisions leading to discretionary utilization likely to be of low-value to patients. This study fills a critical gap in dual measures of appropriateness and yield, rather than simply utilization, to advance the Choosing Wisely campaign.³

Advantages of this overuse index include its derivation from administrative data, obviating the need for electronic health records, and incorporation of diagnostic yield at the inpatient-encounter level. One study selected procedures identifiable solely with claims from a set deemed overused by professional/consumer groups.⁵ However, the yield of physician decisions in specific cases was not measured. In contrast, this novel index is derived from an assessment of diagnostic yield.⁴ Although test results are not known with certainty, the absence of a specific discharge diagnosis serves as a test result proxy. Measurement of diagnostic examination yield at the patient-level (aggregated to the hospital-level) may be applicable across hospitals with varied patient populations, which include large differences in patient and/or family preferences to seek medical attention and engage in shared decision-making. The role that patient preferences play in decisions creates a limitation in this index—while decisions for the candidate diagnostic tests are physician driven, patient demand may be a confounding factor. This index cannot therefore be considered purely a measure of physician-induced intensity of diagnostic services. Patient-reported data would enhance future analyses by more fully capturing all dimensions of care necessary to identify low-value services. Subjective outcomes are critical in completely measuring the aggregate benefits of tests and interventions judged low-value based on objective metrics. Such data would also aid in quantifying the relative contributions of patient and physician preferences in driving discretionary utilization.

Finally, the derived index is restricted to diagnostic decision-making and may not be applicable to treatment-related practice patterns. However, the literature suggests strong correlations between diagnostic and therapeutic intensity. Application of this novel index will play an important role in reducing low-value discretionary utilization.

Overutilization and low-value care are important clinical and policy problems. Their measurement is challenging because it requires detailed clinical information. Additionally, there are inherent difficulties in identifying discretionary services likely to be inappropriate or low-value and demonstrating that certain services produce little/no health benefit. Quantifying “ideal” expected testing rates—ones that would reflect minimization of inappropriate/low-value care without excluding essential, high-yield diagnostic services—presents additional challenges. Consequently, of 521 unique measures specified by national measurement programs and professional guidelines, 91.6% targeted underuse, while only 6.5% targeted overuse.¹

The potential for unintended consequences of implementing measures to eliminate overuse are a barrier to incorporating such measures into practice.² For example, measuring, reporting, and penalizing overuse of inappropriate bone scanning may lead to underuse in patients for whom scanning is crucial.² Most overuse measures based on inappropriate or low-value indications relate to imaging and medications.¹ However, there is increasing interest in overutilization measures based on a broad set of health services. Identifying low-value testing or treatments often requires a substantial degree of clinical detail to avoid the damaging inclusion of beneficial services, which may lead to unintended negative outcomes, creating skepticism among clinicians. Ultimately, getting measurement of low-value care wrong would undermine adoption of interventions to reduce overuse.

To reduce low-value care through expansive measures of provider ordering behavior,³ Ellenbogen et al⁴ derived a novel index to identify hospitals with high rates of low-yield diagnostic testing. This index is based on the concept that, in the presence of nonspecific, symptom-based principal diagnoses, a substantial proportion of (apparently) non-diagnostic related studies were probably ordered despite a low pretest probability of serious disease. Since such symptom-based diagnoses reflect the absence of a more specific diagnosis, the examinations observed are markers of physician-driven decisions leading to discretionary utilization likely to be of low-value to patients. This study fills a critical gap in dual measures of appropriateness and yield, rather than simply utilization, to advance the Choosing Wisely campaign.³

Advantages of this overuse index include its derivation from administrative data, obviating the need for electronic health records, and incorporation of diagnostic yield at the inpatient-encounter level. One study selected procedures identifiable solely with claims from a set deemed overused by professional/consumer groups.⁵ However, the yield of physician decisions in specific cases was not measured. In contrast, this novel index is derived from an assessment of diagnostic yield.⁴ Although test results are not known with certainty, the absence of a specific discharge diagnosis serves as a test result proxy. Measurement of diagnostic examination yield at the patient-level (aggregated to the hospital-level) may be applicable across hospitals with varied patient populations, which include large differences in patient and/or family preferences to seek medical attention and engage in shared decision-making. The role that patient preferences play in decisions creates a limitation in this index—while decisions for the candidate diagnostic tests are physician driven, patient demand may be a confounding factor. This index cannot therefore be considered purely a measure of physician-induced intensity of diagnostic services. Patient-reported data would enhance future analyses by more fully capturing all dimensions of care necessary to identify low-value services. Subjective outcomes are critical in completely measuring the aggregate benefits of tests and interventions judged low-value based on objective metrics. Such data would also aid in quantifying the relative contributions of patient and physician preferences in driving discretionary utilization.

Finally, the derived index is restricted to diagnostic decision-making and may not be applicable to treatment-related practice patterns. However, the literature suggests strong correlations between diagnostic and therapeutic intensity. Application of this novel index will play an important role in reducing low-value discretionary utilization.

Overutilization and low-value care are important clinical and policy problems. Their measurement is challenging because it requires detailed clinical information. Additionally, there are inherent difficulties in identifying discretionary services likely to be inappropriate or low-value and demonstrating that certain services produce little/no health benefit. Quantifying “ideal” expected testing rates—ones that would reflect minimization of inappropriate/low-value care without excluding essential, high-yield diagnostic services—presents additional challenges. Consequently, of 521 unique measures specified by national measurement programs and professional guidelines, 91.6% targeted underuse, while only 6.5% targeted overuse.¹

The potential for unintended consequences of implementing measures to eliminate overuse are a barrier to incorporating such measures into practice.² For example, measuring, reporting, and penalizing overuse of inappropriate bone scanning may lead to underuse in patients for whom scanning is crucial.² Most overuse measures based on inappropriate or low-value indications relate to imaging and medications.¹ However, there is increasing interest in overutilization measures based on a broad set of health services. Identifying low-value testing or treatments often requires a substantial degree of clinical detail to avoid the damaging inclusion of beneficial services, which may lead to unintended negative outcomes, creating skepticism among clinicians. Ultimately, getting measurement of low-value care wrong would undermine adoption of interventions to reduce overuse.

To reduce low-value care through expansive measures of provider ordering behavior,³ Ellenbogen et al⁴ derived a novel index to identify hospitals with high rates of low-yield diagnostic testing. This index is based on the concept that, in the presence of nonspecific, symptom-based principal diagnoses, a substantial proportion of (apparently) non-diagnostic related studies were probably ordered despite a low pretest probability of serious disease. Since such symptom-based diagnoses reflect the absence of a more specific diagnosis, the examinations observed are markers of physician-driven decisions leading to discretionary utilization likely to be of low-value to patients. This study fills a critical gap in dual measures of appropriateness and yield, rather than simply utilization, to advance the Choosing Wisely campaign.³

Advantages of this overuse index include its derivation from administrative data, obviating the need for electronic health records, and incorporation of diagnostic yield at the inpatient-encounter level. One study selected procedures identifiable solely with claims from a set deemed overused by professional/consumer groups.⁵ However, the yield of physician decisions in specific cases was not measured. In contrast, this novel index is derived from an assessment of diagnostic yield.⁴ Although test results are not known with certainty, the absence of a specific discharge diagnosis serves as a test result proxy. Measurement of diagnostic examination yield at the patient-level (aggregated to the hospital-level) may be applicable across hospitals with varied patient populations, which include large differences in patient and/or family preferences to seek medical attention and engage in shared decision-making. The role that patient preferences play in decisions creates a limitation in this index—while decisions for the candidate diagnostic tests are physician driven, patient demand may be a confounding factor. This index cannot therefore be considered purely a measure of physician-induced intensity of diagnostic services. Patient-reported data would enhance future analyses by more fully capturing all dimensions of care necessary to identify low-value services. Subjective outcomes are critical in completely measuring the aggregate benefits of tests and interventions judged low-value based on objective metrics. Such data would also aid in quantifying the relative contributions of patient and physician preferences in driving discretionary utilization.

Finally, the derived index is restricted to diagnostic decision-making and may not be applicable to treatment-related practice patterns. However, the literature suggests strong correlations between diagnostic and therapeutic intensity. Application of this novel index will play an important role in reducing low-value discretionary utilization.

During the early phase of the novel coronavirus disease 2019 (COVID-19) epidemic in the United States, public health strategies focused on “flattening the curve” to ensure that healthcare systems in hard-hit regions had the ability to care for surges of acutely ill patients. Now, COVID-19 cases and hospitalizations are rising sharply throughout the country, and many healthcare systems are facing intense strain due to an influx of patients.

In this issue of JHM, Horwitz et al provide important insights on evolving inpatient care and healthcare system strain for patients with COVID-19. The authors evaluated 5,121 adults hospitalized with SARS-CoV-2 infection at a 3-hospital health system in New York City from March through August 2020,¹ and found that patients hospitalized later during the time period were much younger and had fewer comorbidities. Importantly, the authors observed a marked decline in adjusted in-hospital mortality or hospice rates, from 25.6% in March to 7.6% in August.

What might explain the dramatic improvement in risk-adjusted mortality? The authors’ use of granular data from the electronic health record allowed them to account for temporal changes in demographics and clinical severity of hospitalized patients, indicating that other factors have contributed to the decline in adjusted mortality. One likely explanation is that increasing clinical experience in the management of patients with COVID-19 has resulted in the delivery of better inpatient care, while the use of evidence-based therapies for COVID-19 has also grown. Although important gains have been made in treatment, the care of patients with COVID-19 largely remains supportive. But supportive care requires an adequate number of hospital beds, healthcare staff, and sufficient critical care resources, at minimum.

Healthcare system strain has undoubtedly played a critical role in the outcomes of hospitalized patients. Horwitz et al found that the number of COVID-19 hospitalizations in March and April, when death rates were highest, was more than 10 times greater than in July and August, when death rates were lowest. As noted in the early epidemic in China, COVID-19 death rates partially reflect access to high-quality medical care.² And, in the US, hospitals’ capacity to care for critically ill patients with COVID-19 is an important predictor of death.³

As COVID-19 cases now surge across the country, ensuring that healthcare systems have the resources needed to care for patients will be paramount. Unfortunately, the spread of COVID-19 is exponential, while hospitals’ ability to scale-up surge capacity over a short timeframe is not. Already, reports are emerging across the country of hospitals reaching bed capacity and experiencing shortages of physicians and nurses.

To curtail escalating healthcare system stress in the coming months, we must minimize the cluster-based super-spreading that drives epidemic surges. Approximately 15% to 20% of infected cases account for up to 80% of disease transmission.⁴ Therefore, strategies must address high-risk scenarios that involve crowding, close prolonged contact, and poor ventilation, such as weddings, sporting events, religious gatherings, and indoor dining and bars.

Without adequate testing or tracing capacity during viral surges, employing nonpharmaceutical interventions to mitigate spread is key. Japan, which created the “3 Cs” campaign (avoid close contact, closed spaces, and crowds), utilized a response framework that specifically targeted super-spreading. The US should follow a similar strategy in the coming months to protect healthcare systems, healthcare workers, and most importantly, our patients.

During the early phase of the novel coronavirus disease 2019 (COVID-19) epidemic in the United States, public health strategies focused on “flattening the curve” to ensure that healthcare systems in hard-hit regions had the ability to care for surges of acutely ill patients. Now, COVID-19 cases and hospitalizations are rising sharply throughout the country, and many healthcare systems are facing intense strain due to an influx of patients.

In this issue of JHM, Horwitz et al provide important insights on evolving inpatient care and healthcare system strain for patients with COVID-19. The authors evaluated 5,121 adults hospitalized with SARS-CoV-2 infection at a 3-hospital health system in New York City from March through August 2020,¹ and found that patients hospitalized later during the time period were much younger and had fewer comorbidities. Importantly, the authors observed a marked decline in adjusted in-hospital mortality or hospice rates, from 25.6% in March to 7.6% in August.

What might explain the dramatic improvement in risk-adjusted mortality? The authors’ use of granular data from the electronic health record allowed them to account for temporal changes in demographics and clinical severity of hospitalized patients, indicating that other factors have contributed to the decline in adjusted mortality. One likely explanation is that increasing clinical experience in the management of patients with COVID-19 has resulted in the delivery of better inpatient care, while the use of evidence-based therapies for COVID-19 has also grown. Although important gains have been made in treatment, the care of patients with COVID-19 largely remains supportive. But supportive care requires an adequate number of hospital beds, healthcare staff, and sufficient critical care resources, at minimum.

Healthcare system strain has undoubtedly played a critical role in the outcomes of hospitalized patients. Horwitz et al found that the number of COVID-19 hospitalizations in March and April, when death rates were highest, was more than 10 times greater than in July and August, when death rates were lowest. As noted in the early epidemic in China, COVID-19 death rates partially reflect access to high-quality medical care.² And, in the US, hospitals’ capacity to care for critically ill patients with COVID-19 is an important predictor of death.³

As COVID-19 cases now surge across the country, ensuring that healthcare systems have the resources needed to care for patients will be paramount. Unfortunately, the spread of COVID-19 is exponential, while hospitals’ ability to scale-up surge capacity over a short timeframe is not. Already, reports are emerging across the country of hospitals reaching bed capacity and experiencing shortages of physicians and nurses.

To curtail escalating healthcare system stress in the coming months, we must minimize the cluster-based super-spreading that drives epidemic surges. Approximately 15% to 20% of infected cases account for up to 80% of disease transmission.⁴ Therefore, strategies must address high-risk scenarios that involve crowding, close prolonged contact, and poor ventilation, such as weddings, sporting events, religious gatherings, and indoor dining and bars.

Without adequate testing or tracing capacity during viral surges, employing nonpharmaceutical interventions to mitigate spread is key. Japan, which created the “3 Cs” campaign (avoid close contact, closed spaces, and crowds), utilized a response framework that specifically targeted super-spreading. The US should follow a similar strategy in the coming months to protect healthcare systems, healthcare workers, and most importantly, our patients.

During the early phase of the novel coronavirus disease 2019 (COVID-19) epidemic in the United States, public health strategies focused on “flattening the curve” to ensure that healthcare systems in hard-hit regions had the ability to care for surges of acutely ill patients. Now, COVID-19 cases and hospitalizations are rising sharply throughout the country, and many healthcare systems are facing intense strain due to an influx of patients.

In this issue of JHM, Horwitz et al provide important insights on evolving inpatient care and healthcare system strain for patients with COVID-19. The authors evaluated 5,121 adults hospitalized with SARS-CoV-2 infection at a 3-hospital health system in New York City from March through August 2020,¹ and found that patients hospitalized later during the time period were much younger and had fewer comorbidities. Importantly, the authors observed a marked decline in adjusted in-hospital mortality or hospice rates, from 25.6% in March to 7.6% in August.

What might explain the dramatic improvement in risk-adjusted mortality? The authors’ use of granular data from the electronic health record allowed them to account for temporal changes in demographics and clinical severity of hospitalized patients, indicating that other factors have contributed to the decline in adjusted mortality. One likely explanation is that increasing clinical experience in the management of patients with COVID-19 has resulted in the delivery of better inpatient care, while the use of evidence-based therapies for COVID-19 has also grown. Although important gains have been made in treatment, the care of patients with COVID-19 largely remains supportive. But supportive care requires an adequate number of hospital beds, healthcare staff, and sufficient critical care resources, at minimum.

Healthcare system strain has undoubtedly played a critical role in the outcomes of hospitalized patients. Horwitz et al found that the number of COVID-19 hospitalizations in March and April, when death rates were highest, was more than 10 times greater than in July and August, when death rates were lowest. As noted in the early epidemic in China, COVID-19 death rates partially reflect access to high-quality medical care.² And, in the US, hospitals’ capacity to care for critically ill patients with COVID-19 is an important predictor of death.³

As COVID-19 cases now surge across the country, ensuring that healthcare systems have the resources needed to care for patients will be paramount. Unfortunately, the spread of COVID-19 is exponential, while hospitals’ ability to scale-up surge capacity over a short timeframe is not. Already, reports are emerging across the country of hospitals reaching bed capacity and experiencing shortages of physicians and nurses.

To curtail escalating healthcare system stress in the coming months, we must minimize the cluster-based super-spreading that drives epidemic surges. Approximately 15% to 20% of infected cases account for up to 80% of disease transmission.⁴ Therefore, strategies must address high-risk scenarios that involve crowding, close prolonged contact, and poor ventilation, such as weddings, sporting events, religious gatherings, and indoor dining and bars.

Without adequate testing or tracing capacity during viral surges, employing nonpharmaceutical interventions to mitigate spread is key. Japan, which created the “3 Cs” campaign (avoid close contact, closed spaces, and crowds), utilized a response framework that specifically targeted super-spreading. The US should follow a similar strategy in the coming months to protect healthcare systems, healthcare workers, and most importantly, our patients.

Hospitalists are known as change agents for their fierce patient advocacy and expertise in hospital systems redesign. The field of hospital medicine has claimed numerous successes and the hospitalist model has been embraced by institutions across the country. Yet the lived experiences of hospitalists surveyed by Bhandari et al in this month’s issue of JHM suggest a grim undertone.¹ Hospital medicine is a field with high physician burnout rates, stark gender inequities in pay, leadership, and academic opportunities, and an unacceptably high prevalence of sexual harassment and gender discrimination. Women hospitalists disproportionately bear the brunt of these inequities. All hospitalists, however, can and should be an integral part of the path forward by recognizing the impact of these inequities on colleagues and hospital systems.

The study by Bhandari et al adds to the increasing body of knowledge documenting high levels of sexual harassment and gender discrimination in medicine and highlights important gender differences in these experiences among hospitalists nationally.^1,2 Among 336 respondents across 18 academic institutions, sexual harassment and gender discrimination were both common and highly problematic within the field of hospital medicine, confirming what prior narratives have only anecdotally shared. Both men and women experienced harassment, from patients and colleagues alike, but women endured higher levels compared with men on all the measures studied.¹

Qualitative comments in this study are noteworthy, including one about a hospitalist’s institution allowing potential faculty to be interviewed about plans for pregnancy, childcare, and personal household division of labor. One might argue that this knowledge is necessary for shift-based inpatient work in the context of a worldwide pandemic in which pregnant workers are likely at higher risk of increased morbidity and mortality. It remains illegal, however, to ask such questions, which are representative of the types of characteristics that constitute a toxic workplace environment. Moreover, such practices are particularly problematic given that pregnancy and childbearing for women in medicine come with their own set of well-documented unique challenges.³

The considerable body of research in this field should help guide new research priorities and targets for intervention. Does the experience of sexual harassment impact hospitalists’ intentions to leave their institutions or the career as a whole? Does sexual harassment originating from colleagues or from patients and families affect patient safety or quality of care? Do interventions in other international hospital settings specifically targeting respectfulness translate to American hospitals?⁴ These questions and a host of others merit our attention.

Hospital system leaders should work with hospital medicine leaders to support wholesale institutional cultural transformation. Implementation of antiharassment measures recommended in the 2018 report on sexual harassment from the National Academies of Sciences, Engineering, and Medicine is critical.² This means supporting diverse, inclusive, and respectful environments at all levels within the organization, improving transparency and accountability for how incidents are handled, striving for strong and diverse leadership, providing meaningful support for targets of harassment, measuring prevalence over time, and encouraging professional societies to adopt similar actions. Furthermore, we believe it is critical to adopt a zero-tolerance policy for harassing behaviors and to hold individuals accountable. Encouraging all individuals within health care systems to uphold their ethical obligations to combat harassment and bias on a personal level is important.⁵ If left unaddressed, the unmet needs of those who are subjected to harassment and bias will continue to be problematic for generations to come, with detrimental effects throughout healthcare systems and the broader populations they serve.

Hospitalists are known as change agents for their fierce patient advocacy and expertise in hospital systems redesign. The field of hospital medicine has claimed numerous successes and the hospitalist model has been embraced by institutions across the country. Yet the lived experiences of hospitalists surveyed by Bhandari et al in this month’s issue of JHM suggest a grim undertone.¹ Hospital medicine is a field with high physician burnout rates, stark gender inequities in pay, leadership, and academic opportunities, and an unacceptably high prevalence of sexual harassment and gender discrimination. Women hospitalists disproportionately bear the brunt of these inequities. All hospitalists, however, can and should be an integral part of the path forward by recognizing the impact of these inequities on colleagues and hospital systems.

The study by Bhandari et al adds to the increasing body of knowledge documenting high levels of sexual harassment and gender discrimination in medicine and highlights important gender differences in these experiences among hospitalists nationally.^1,2 Among 336 respondents across 18 academic institutions, sexual harassment and gender discrimination were both common and highly problematic within the field of hospital medicine, confirming what prior narratives have only anecdotally shared. Both men and women experienced harassment, from patients and colleagues alike, but women endured higher levels compared with men on all the measures studied.¹

Qualitative comments in this study are noteworthy, including one about a hospitalist’s institution allowing potential faculty to be interviewed about plans for pregnancy, childcare, and personal household division of labor. One might argue that this knowledge is necessary for shift-based inpatient work in the context of a worldwide pandemic in which pregnant workers are likely at higher risk of increased morbidity and mortality. It remains illegal, however, to ask such questions, which are representative of the types of characteristics that constitute a toxic workplace environment. Moreover, such practices are particularly problematic given that pregnancy and childbearing for women in medicine come with their own set of well-documented unique challenges.³

The considerable body of research in this field should help guide new research priorities and targets for intervention. Does the experience of sexual harassment impact hospitalists’ intentions to leave their institutions or the career as a whole? Does sexual harassment originating from colleagues or from patients and families affect patient safety or quality of care? Do interventions in other international hospital settings specifically targeting respectfulness translate to American hospitals?⁴ These questions and a host of others merit our attention.

Hospital system leaders should work with hospital medicine leaders to support wholesale institutional cultural transformation. Implementation of antiharassment measures recommended in the 2018 report on sexual harassment from the National Academies of Sciences, Engineering, and Medicine is critical.² This means supporting diverse, inclusive, and respectful environments at all levels within the organization, improving transparency and accountability for how incidents are handled, striving for strong and diverse leadership, providing meaningful support for targets of harassment, measuring prevalence over time, and encouraging professional societies to adopt similar actions. Furthermore, we believe it is critical to adopt a zero-tolerance policy for harassing behaviors and to hold individuals accountable. Encouraging all individuals within health care systems to uphold their ethical obligations to combat harassment and bias on a personal level is important.⁵ If left unaddressed, the unmet needs of those who are subjected to harassment and bias will continue to be problematic for generations to come, with detrimental effects throughout healthcare systems and the broader populations they serve.

Hospitalists are known as change agents for their fierce patient advocacy and expertise in hospital systems redesign. The field of hospital medicine has claimed numerous successes and the hospitalist model has been embraced by institutions across the country. Yet the lived experiences of hospitalists surveyed by Bhandari et al in this month’s issue of JHM suggest a grim undertone.¹ Hospital medicine is a field with high physician burnout rates, stark gender inequities in pay, leadership, and academic opportunities, and an unacceptably high prevalence of sexual harassment and gender discrimination. Women hospitalists disproportionately bear the brunt of these inequities. All hospitalists, however, can and should be an integral part of the path forward by recognizing the impact of these inequities on colleagues and hospital systems.

The study by Bhandari et al adds to the increasing body of knowledge documenting high levels of sexual harassment and gender discrimination in medicine and highlights important gender differences in these experiences among hospitalists nationally.^1,2 Among 336 respondents across 18 academic institutions, sexual harassment and gender discrimination were both common and highly problematic within the field of hospital medicine, confirming what prior narratives have only anecdotally shared. Both men and women experienced harassment, from patients and colleagues alike, but women endured higher levels compared with men on all the measures studied.¹

Qualitative comments in this study are noteworthy, including one about a hospitalist’s institution allowing potential faculty to be interviewed about plans for pregnancy, childcare, and personal household division of labor. One might argue that this knowledge is necessary for shift-based inpatient work in the context of a worldwide pandemic in which pregnant workers are likely at higher risk of increased morbidity and mortality. It remains illegal, however, to ask such questions, which are representative of the types of characteristics that constitute a toxic workplace environment. Moreover, such practices are particularly problematic given that pregnancy and childbearing for women in medicine come with their own set of well-documented unique challenges.³

The considerable body of research in this field should help guide new research priorities and targets for intervention. Does the experience of sexual harassment impact hospitalists’ intentions to leave their institutions or the career as a whole? Does sexual harassment originating from colleagues or from patients and families affect patient safety or quality of care? Do interventions in other international hospital settings specifically targeting respectfulness translate to American hospitals?⁴ These questions and a host of others merit our attention.

Hospital system leaders should work with hospital medicine leaders to support wholesale institutional cultural transformation. Implementation of antiharassment measures recommended in the 2018 report on sexual harassment from the National Academies of Sciences, Engineering, and Medicine is critical.² This means supporting diverse, inclusive, and respectful environments at all levels within the organization, improving transparency and accountability for how incidents are handled, striving for strong and diverse leadership, providing meaningful support for targets of harassment, measuring prevalence over time, and encouraging professional societies to adopt similar actions. Furthermore, we believe it is critical to adopt a zero-tolerance policy for harassing behaviors and to hold individuals accountable. Encouraging all individuals within health care systems to uphold their ethical obligations to combat harassment and bias on a personal level is important.⁵ If left unaddressed, the unmet needs of those who are subjected to harassment and bias will continue to be problematic for generations to come, with detrimental effects throughout healthcare systems and the broader populations they serve.

Historically, bacterial infections in hospitalized children were treated with intravenous (IV) antibiotics for the duration of therapy—frequently with placement of a vascular catheter. Risks associated with vascular catheters and the limitations they impose on a child’s quality of life are increasingly being recognized—including thrombi, catheter dislodgement, and secondary infections as catheters provide a portal of entry for bacteria into the bloodstream (ie, catheter-associated bloodstream infections) or along the catheter wall (ie, phlebitis). This potential for harm underscores the importance of transitioning to oral antibiotic therapy whenever possible.

In this issue of the Journal of Hospital Medicine, Cotter et al used an administrative database to investigate opportunities to transition from IV to oral antibiotics for patients across multiple pediatric hospitals.¹ Their novel metric, “percent opportunity,” represents the percent of days that there was the opportunity to transition from IV to oral antibiotics. They found that over 50% of the time, IV antibiotics could have been switched to equivalent oral agents. Furthermore, there was wide variability across institutions in IV-to-oral transitioning practices; 45% of the variation was seemingly attributable to institution-level preferences.

The large sample size and multicenter nature of this study improve its external validity. However, using administrative data to make assumptions about clinical decision-making has limitations. The definition of opportunity days assumes that any day a child receives other enteral medications provides an “opportunity” to prescribe oral antibiotics instead. This does not account for other reasonable indications to continue IV therapy (eg, endocarditis) and may overestimate true opportunities for conversion to oral therapy. Alternatively, their conservative approach of excluding days when a child received both IV and oral antibiotics may underestimate opportunities for oral transition. Regardless of the precision of their estimates, their findings highlight that there is room to improve the culture of transitioning hospitalized children from IV to oral antibiotic therapy.

Admittedly, the evidence for clinically effective conversion to oral therapy in children remains incomplete. Data support oral antibiotics for hospitalized children with pneumonia, cellulitis, pyelonephritis, and osteoarticular infections—even with associated bacteremia.² There is also evidence for successful conversion to oral therapy for complicated appendicitis, retropharyngeal abscesses, mastoiditis, and orbital cellulitis.²

The decision to transition to oral therapy does not need to be delayed until the time of hospital discharge because each additional day of IV therapy poses a cumulative risk. Rather, prescribers should apply a structured approach, such as the “Four Moments of Antibiotic Decision Making,” on a daily basis for every hospitalized child receiving antibiotics to prompt timely decisions about discontinuing IV therapy, narrowing IV therapy, or transitioning from IV to oral antibiotic therapy.³ We applaud Cotter et al for shedding light on an area in need of standardization of care, which could optimize patient outcomes and minimize harm for a large number of children.¹ The “percent opportunity” to switch from IV to oral antibiotic therapy is a promising antibiotic stewardship metric, and its association with clinical outcomes merits further investigation.

Historically, bacterial infections in hospitalized children were treated with intravenous (IV) antibiotics for the duration of therapy—frequently with placement of a vascular catheter. Risks associated with vascular catheters and the limitations they impose on a child’s quality of life are increasingly being recognized—including thrombi, catheter dislodgement, and secondary infections as catheters provide a portal of entry for bacteria into the bloodstream (ie, catheter-associated bloodstream infections) or along the catheter wall (ie, phlebitis). This potential for harm underscores the importance of transitioning to oral antibiotic therapy whenever possible.

In this issue of the Journal of Hospital Medicine, Cotter et al used an administrative database to investigate opportunities to transition from IV to oral antibiotics for patients across multiple pediatric hospitals.¹ Their novel metric, “percent opportunity,” represents the percent of days that there was the opportunity to transition from IV to oral antibiotics. They found that over 50% of the time, IV antibiotics could have been switched to equivalent oral agents. Furthermore, there was wide variability across institutions in IV-to-oral transitioning practices; 45% of the variation was seemingly attributable to institution-level preferences.

The large sample size and multicenter nature of this study improve its external validity. However, using administrative data to make assumptions about clinical decision-making has limitations. The definition of opportunity days assumes that any day a child receives other enteral medications provides an “opportunity” to prescribe oral antibiotics instead. This does not account for other reasonable indications to continue IV therapy (eg, endocarditis) and may overestimate true opportunities for conversion to oral therapy. Alternatively, their conservative approach of excluding days when a child received both IV and oral antibiotics may underestimate opportunities for oral transition. Regardless of the precision of their estimates, their findings highlight that there is room to improve the culture of transitioning hospitalized children from IV to oral antibiotic therapy.

Admittedly, the evidence for clinically effective conversion to oral therapy in children remains incomplete. Data support oral antibiotics for hospitalized children with pneumonia, cellulitis, pyelonephritis, and osteoarticular infections—even with associated bacteremia.² There is also evidence for successful conversion to oral therapy for complicated appendicitis, retropharyngeal abscesses, mastoiditis, and orbital cellulitis.²

The decision to transition to oral therapy does not need to be delayed until the time of hospital discharge because each additional day of IV therapy poses a cumulative risk. Rather, prescribers should apply a structured approach, such as the “Four Moments of Antibiotic Decision Making,” on a daily basis for every hospitalized child receiving antibiotics to prompt timely decisions about discontinuing IV therapy, narrowing IV therapy, or transitioning from IV to oral antibiotic therapy.³ We applaud Cotter et al for shedding light on an area in need of standardization of care, which could optimize patient outcomes and minimize harm for a large number of children.¹ The “percent opportunity” to switch from IV to oral antibiotic therapy is a promising antibiotic stewardship metric, and its association with clinical outcomes merits further investigation.

Historically, bacterial infections in hospitalized children were treated with intravenous (IV) antibiotics for the duration of therapy—frequently with placement of a vascular catheter. Risks associated with vascular catheters and the limitations they impose on a child’s quality of life are increasingly being recognized—including thrombi, catheter dislodgement, and secondary infections as catheters provide a portal of entry for bacteria into the bloodstream (ie, catheter-associated bloodstream infections) or along the catheter wall (ie, phlebitis). This potential for harm underscores the importance of transitioning to oral antibiotic therapy whenever possible.

In this issue of the Journal of Hospital Medicine, Cotter et al used an administrative database to investigate opportunities to transition from IV to oral antibiotics for patients across multiple pediatric hospitals.¹ Their novel metric, “percent opportunity,” represents the percent of days that there was the opportunity to transition from IV to oral antibiotics. They found that over 50% of the time, IV antibiotics could have been switched to equivalent oral agents. Furthermore, there was wide variability across institutions in IV-to-oral transitioning practices; 45% of the variation was seemingly attributable to institution-level preferences.

The large sample size and multicenter nature of this study improve its external validity. However, using administrative data to make assumptions about clinical decision-making has limitations. The definition of opportunity days assumes that any day a child receives other enteral medications provides an “opportunity” to prescribe oral antibiotics instead. This does not account for other reasonable indications to continue IV therapy (eg, endocarditis) and may overestimate true opportunities for conversion to oral therapy. Alternatively, their conservative approach of excluding days when a child received both IV and oral antibiotics may underestimate opportunities for oral transition. Regardless of the precision of their estimates, their findings highlight that there is room to improve the culture of transitioning hospitalized children from IV to oral antibiotic therapy.

Admittedly, the evidence for clinically effective conversion to oral therapy in children remains incomplete. Data support oral antibiotics for hospitalized children with pneumonia, cellulitis, pyelonephritis, and osteoarticular infections—even with associated bacteremia.² There is also evidence for successful conversion to oral therapy for complicated appendicitis, retropharyngeal abscesses, mastoiditis, and orbital cellulitis.²

The decision to transition to oral therapy does not need to be delayed until the time of hospital discharge because each additional day of IV therapy poses a cumulative risk. Rather, prescribers should apply a structured approach, such as the “Four Moments of Antibiotic Decision Making,” on a daily basis for every hospitalized child receiving antibiotics to prompt timely decisions about discontinuing IV therapy, narrowing IV therapy, or transitioning from IV to oral antibiotic therapy.³ We applaud Cotter et al for shedding light on an area in need of standardization of care, which could optimize patient outcomes and minimize harm for a large number of children.¹ The “percent opportunity” to switch from IV to oral antibiotic therapy is a promising antibiotic stewardship metric, and its association with clinical outcomes merits further investigation.

“Success starts with saying yes. Saying no maintains it.”

—Anonymous

You have just received an opportunity that seems worthwhile. However, you already have a lot on your plate. What do you do? The balance of when to say “yes” and when to say “no” to opportunities, projects, and collaborations is often challenging, especially for busy clinicians. There is a trend, with good basis, to encourage individuals to say “no” more often. While there is much to be said for that, many good opportunities can be missed that way. As Amy Poehler put it, “Saying ‘yes’ doesn’t mean I don’t know how to say no.”

So how does one arrive at a good balance?

Most importantly, figure out who you are, what you want your “brand” to be and where you envision your career going. This is likely the most difficult step. Start with a roadmap and recalibrate as your career unfolds. Early in your career, seek breadth rather than depth.

As your career progresses, the “yes-no” balance may shift. We recommend you say “yes” frequently early on. Be open to opportunities that come up, even if they do not perfectly align with your goals. Explore opportunities beyond the limits of your job description. After all, opportunities beget more opportunities. Consider “stretch opportunities.” If you are offered an opportunity that you may not have 100% of the skills for—and is, therefore, a “stretch”—but which aligns with your career goals, do not turn it down. Consider saying “yes” and learn on the job. A mentor or coach can help you navigate these decisions.

Sometimes, it is important to say “yes” as part of being a “good citizen” in your department. Examples include mentoring learners, serving on a safety committee, teaching student lectures, or coaching a colleague. Often it is possible to align service with career goals.

Another consideration is the benefit of networking: developing alliances and building bridges. In addition to the service or productivity that come with projects or collaborations, these can be powerful networking opportunities. Networking broadly, both within and beyond your field of practice and within and outside your institution, is an important way to create “bonding capital” and “bridging capital,” ie, relationships based on your commonalities and relationships built across differences, respectively.¹

Remember, when you say “yes,” you must deliver: every time, on time, and with excellence. When saying “yes” to more opportunities starts to impact your ability to deliver for what you have already committed to, it is time to say “no.” This will help you maintain balance, avoid burnout, and stay focused.

When juggling a busy schedule, consider effort vs impact. There are many low-effort opportunities that have relatively high impact. For instance, as a junior faculty member interested in medical education, participating in a grading committee is low effort but can help you understand the process, connect you with educational leaders, and open doors to future opportunities. An effective strategy may be to incorporate a combination of low-effort and high-effort activities at any one time, while considering the impact of each, to help maintain balance. The effort-vs-impact balance may shift as you grow in your career.

Know where you are going, explore the opportunities that may get you there, and recalibrate often. The path to success is typically a circuitous one, so enjoy the journey and give it your all every step of the way.

“Success starts with saying yes. Saying no maintains it.”

—Anonymous

You have just received an opportunity that seems worthwhile. However, you already have a lot on your plate. What do you do? The balance of when to say “yes” and when to say “no” to opportunities, projects, and collaborations is often challenging, especially for busy clinicians. There is a trend, with good basis, to encourage individuals to say “no” more often. While there is much to be said for that, many good opportunities can be missed that way. As Amy Poehler put it, “Saying ‘yes’ doesn’t mean I don’t know how to say no.”

So how does one arrive at a good balance?

Most importantly, figure out who you are, what you want your “brand” to be and where you envision your career going. This is likely the most difficult step. Start with a roadmap and recalibrate as your career unfolds. Early in your career, seek breadth rather than depth.

As your career progresses, the “yes-no” balance may shift. We recommend you say “yes” frequently early on. Be open to opportunities that come up, even if they do not perfectly align with your goals. Explore opportunities beyond the limits of your job description. After all, opportunities beget more opportunities. Consider “stretch opportunities.” If you are offered an opportunity that you may not have 100% of the skills for—and is, therefore, a “stretch”—but which aligns with your career goals, do not turn it down. Consider saying “yes” and learn on the job. A mentor or coach can help you navigate these decisions.

Sometimes, it is important to say “yes” as part of being a “good citizen” in your department. Examples include mentoring learners, serving on a safety committee, teaching student lectures, or coaching a colleague. Often it is possible to align service with career goals.

Another consideration is the benefit of networking: developing alliances and building bridges. In addition to the service or productivity that come with projects or collaborations, these can be powerful networking opportunities. Networking broadly, both within and beyond your field of practice and within and outside your institution, is an important way to create “bonding capital” and “bridging capital,” ie, relationships based on your commonalities and relationships built across differences, respectively.¹

Remember, when you say “yes,” you must deliver: every time, on time, and with excellence. When saying “yes” to more opportunities starts to impact your ability to deliver for what you have already committed to, it is time to say “no.” This will help you maintain balance, avoid burnout, and stay focused.

When juggling a busy schedule, consider effort vs impact. There are many low-effort opportunities that have relatively high impact. For instance, as a junior faculty member interested in medical education, participating in a grading committee is low effort but can help you understand the process, connect you with educational leaders, and open doors to future opportunities. An effective strategy may be to incorporate a combination of low-effort and high-effort activities at any one time, while considering the impact of each, to help maintain balance. The effort-vs-impact balance may shift as you grow in your career.

Know where you are going, explore the opportunities that may get you there, and recalibrate often. The path to success is typically a circuitous one, so enjoy the journey and give it your all every step of the way.

“Success starts with saying yes. Saying no maintains it.”

—Anonymous

You have just received an opportunity that seems worthwhile. However, you already have a lot on your plate. What do you do? The balance of when to say “yes” and when to say “no” to opportunities, projects, and collaborations is often challenging, especially for busy clinicians. There is a trend, with good basis, to encourage individuals to say “no” more often. While there is much to be said for that, many good opportunities can be missed that way. As Amy Poehler put it, “Saying ‘yes’ doesn’t mean I don’t know how to say no.”

So how does one arrive at a good balance?

Most importantly, figure out who you are, what you want your “brand” to be and where you envision your career going. This is likely the most difficult step. Start with a roadmap and recalibrate as your career unfolds. Early in your career, seek breadth rather than depth.

As your career progresses, the “yes-no” balance may shift. We recommend you say “yes” frequently early on. Be open to opportunities that come up, even if they do not perfectly align with your goals. Explore opportunities beyond the limits of your job description. After all, opportunities beget more opportunities. Consider “stretch opportunities.” If you are offered an opportunity that you may not have 100% of the skills for—and is, therefore, a “stretch”—but which aligns with your career goals, do not turn it down. Consider saying “yes” and learn on the job. A mentor or coach can help you navigate these decisions.

Sometimes, it is important to say “yes” as part of being a “good citizen” in your department. Examples include mentoring learners, serving on a safety committee, teaching student lectures, or coaching a colleague. Often it is possible to align service with career goals.

Another consideration is the benefit of networking: developing alliances and building bridges. In addition to the service or productivity that come with projects or collaborations, these can be powerful networking opportunities. Networking broadly, both within and beyond your field of practice and within and outside your institution, is an important way to create “bonding capital” and “bridging capital,” ie, relationships based on your commonalities and relationships built across differences, respectively.¹

Remember, when you say “yes,” you must deliver: every time, on time, and with excellence. When saying “yes” to more opportunities starts to impact your ability to deliver for what you have already committed to, it is time to say “no.” This will help you maintain balance, avoid burnout, and stay focused.

When juggling a busy schedule, consider effort vs impact. There are many low-effort opportunities that have relatively high impact. For instance, as a junior faculty member interested in medical education, participating in a grading committee is low effort but can help you understand the process, connect you with educational leaders, and open doors to future opportunities. An effective strategy may be to incorporate a combination of low-effort and high-effort activities at any one time, while considering the impact of each, to help maintain balance. The effort-vs-impact balance may shift as you grow in your career.

Know where you are going, explore the opportunities that may get you there, and recalibrate often. The path to success is typically a circuitous one, so enjoy the journey and give it your all every step of the way.

Many of us are interested in developing or refining our skillsets. To do so, we need mentorship, which in the still-young field of hospital medicine can sometimes be challenging to obtain.

As a physician-investigator and editor, I commonly encounter young and even mid-career physicians wrestling with how to develop or refine their academic skills, and they’re usually pondering the challenges in finding someone in their own division or hospitalist group to help them. When this happens, I talk to them about bagels and cream cheese. I ask them two questions: “What’s your cream cheese?” and “Where’s your bagel?” Their natural reaction of puzzlement, perhaps mixed with hunger if they haven’t yet had breakfast, is similar to the one you’ve likely just experienced, so let me explain.

In medical school, I had a friend who absolutely loved cream cheese. If it had been socially acceptable, he would have simply walked around scooping cream cheese from a large tub. Had he done that, people would likely have given him funny looks and taken a few steps away. So, instead, my friend found an acceptable solution, which is that he would eat a lot of bagels. And those bagels would be piled high with cream cheese because what he wanted was the cream cheese and the bagel provided a reasonable means by which to get it.

So now I ask you: What’s your passion? What is the thing that you want to scoop from the tub (of learning and doing) every day for the rest of your life? That’s the cream cheese. Now, all you have to do is to find your bagel, the vehicle that allows you to get there.

Let’s see those principles in action. Say that you’re a hospitalist who wants to learn how to conduct randomized clinical trials, enhance medication reconciliation, or improve transitions of care. You can read about randomization schemes or improvement cycles but that’s clearly not enough. You need someone to help you frame the question, understand how to navigate the system, and avoid potential pitfalls. You need someone with relevant experience and expertise, someone with whom you can discuss nuances such as the trade-offs between different outcome measures or analytic approaches. You need your bagel.

There may not be anyone in your division with such expertise. You may need to branch out to find that bagel. You talk to a few people and they all point you to a cardiologist who runs clinical trials. What other field has such witty study acronyms as MRFIT or MIRACL or PROVE IT? If you’re interested in medication reconciliation, they may direct you to a pharmacist who studies medication errors. If you’re interested in improving care transitions, they may connect you with a critical care physician with expertise in interhospital transfers. You can meet with these folks to learn about their work. If their personality and mentorship style are a good fit, you can offer to assist in some aspect of their ongoing studies and, in return, ask for mentorship. You may have only a limited interest in the clinical content area, but if there is someone willing to invest their time in teaching, mentoring, and sponsoring you, then you’ve found your bagel.

Think about what you’re hoping to accomplish and keep an open mind to unexpected venues for mentorship and skill development. That bagel may be in your division or department, or it may be somewhere else in your institution, or it may not be in your institution at all but elsewhere regionally or nationally. The sequence is important. What’s your cream cheese? Figured it out? Great, now go find that bagel.

Many of us are interested in developing or refining our skillsets. To do so, we need mentorship, which in the still-young field of hospital medicine can sometimes be challenging to obtain.

As a physician-investigator and editor, I commonly encounter young and even mid-career physicians wrestling with how to develop or refine their academic skills, and they’re usually pondering the challenges in finding someone in their own division or hospitalist group to help them. When this happens, I talk to them about bagels and cream cheese. I ask them two questions: “What’s your cream cheese?” and “Where’s your bagel?” Their natural reaction of puzzlement, perhaps mixed with hunger if they haven’t yet had breakfast, is similar to the one you’ve likely just experienced, so let me explain.

In medical school, I had a friend who absolutely loved cream cheese. If it had been socially acceptable, he would have simply walked around scooping cream cheese from a large tub. Had he done that, people would likely have given him funny looks and taken a few steps away. So, instead, my friend found an acceptable solution, which is that he would eat a lot of bagels. And those bagels would be piled high with cream cheese because what he wanted was the cream cheese and the bagel provided a reasonable means by which to get it.

So now I ask you: What’s your passion? What is the thing that you want to scoop from the tub (of learning and doing) every day for the rest of your life? That’s the cream cheese. Now, all you have to do is to find your bagel, the vehicle that allows you to get there.

Let’s see those principles in action. Say that you’re a hospitalist who wants to learn how to conduct randomized clinical trials, enhance medication reconciliation, or improve transitions of care. You can read about randomization schemes or improvement cycles but that’s clearly not enough. You need someone to help you frame the question, understand how to navigate the system, and avoid potential pitfalls. You need someone with relevant experience and expertise, someone with whom you can discuss nuances such as the trade-offs between different outcome measures or analytic approaches. You need your bagel.

There may not be anyone in your division with such expertise. You may need to branch out to find that bagel. You talk to a few people and they all point you to a cardiologist who runs clinical trials. What other field has such witty study acronyms as MRFIT or MIRACL or PROVE IT? If you’re interested in medication reconciliation, they may direct you to a pharmacist who studies medication errors. If you’re interested in improving care transitions, they may connect you with a critical care physician with expertise in interhospital transfers. You can meet with these folks to learn about their work. If their personality and mentorship style are a good fit, you can offer to assist in some aspect of their ongoing studies and, in return, ask for mentorship. You may have only a limited interest in the clinical content area, but if there is someone willing to invest their time in teaching, mentoring, and sponsoring you, then you’ve found your bagel.

Think about what you’re hoping to accomplish and keep an open mind to unexpected venues for mentorship and skill development. That bagel may be in your division or department, or it may be somewhere else in your institution, or it may not be in your institution at all but elsewhere regionally or nationally. The sequence is important. What’s your cream cheese? Figured it out? Great, now go find that bagel.

Many of us are interested in developing or refining our skillsets. To do so, we need mentorship, which in the still-young field of hospital medicine can sometimes be challenging to obtain.

As a physician-investigator and editor, I commonly encounter young and even mid-career physicians wrestling with how to develop or refine their academic skills, and they’re usually pondering the challenges in finding someone in their own division or hospitalist group to help them. When this happens, I talk to them about bagels and cream cheese. I ask them two questions: “What’s your cream cheese?” and “Where’s your bagel?” Their natural reaction of puzzlement, perhaps mixed with hunger if they haven’t yet had breakfast, is similar to the one you’ve likely just experienced, so let me explain.

In medical school, I had a friend who absolutely loved cream cheese. If it had been socially acceptable, he would have simply walked around scooping cream cheese from a large tub. Had he done that, people would likely have given him funny looks and taken a few steps away. So, instead, my friend found an acceptable solution, which is that he would eat a lot of bagels. And those bagels would be piled high with cream cheese because what he wanted was the cream cheese and the bagel provided a reasonable means by which to get it.

So now I ask you: What’s your passion? What is the thing that you want to scoop from the tub (of learning and doing) every day for the rest of your life? That’s the cream cheese. Now, all you have to do is to find your bagel, the vehicle that allows you to get there.

Let’s see those principles in action. Say that you’re a hospitalist who wants to learn how to conduct randomized clinical trials, enhance medication reconciliation, or improve transitions of care. You can read about randomization schemes or improvement cycles but that’s clearly not enough. You need someone to help you frame the question, understand how to navigate the system, and avoid potential pitfalls. You need someone with relevant experience and expertise, someone with whom you can discuss nuances such as the trade-offs between different outcome measures or analytic approaches. You need your bagel.

There may not be anyone in your division with such expertise. You may need to branch out to find that bagel. You talk to a few people and they all point you to a cardiologist who runs clinical trials. What other field has such witty study acronyms as MRFIT or MIRACL or PROVE IT? If you’re interested in medication reconciliation, they may direct you to a pharmacist who studies medication errors. If you’re interested in improving care transitions, they may connect you with a critical care physician with expertise in interhospital transfers. You can meet with these folks to learn about their work. If their personality and mentorship style are a good fit, you can offer to assist in some aspect of their ongoing studies and, in return, ask for mentorship. You may have only a limited interest in the clinical content area, but if there is someone willing to invest their time in teaching, mentoring, and sponsoring you, then you’ve found your bagel.

Think about what you’re hoping to accomplish and keep an open mind to unexpected venues for mentorship and skill development. That bagel may be in your division or department, or it may be somewhere else in your institution, or it may not be in your institution at all but elsewhere regionally or nationally. The sequence is important. What’s your cream cheese? Figured it out? Great, now go find that bagel.

Bacterial infections are a common reason for pediatric hospital admissions in the United States.¹ Antibiotics are the mainstay of treatment, and whether to administer them intravenously (IV) or enterally is an important and, at times, challenging decision. Not all hospitalized patients with infections require IV antibiotics, and safe, effective early transitions to enteral therapy have been described for numerous infections.^2-7 However, guidelines describing the ideal initial route of antibiotic administration and when to transition to oral therapy are lacking.^5,7,8 This lack of high-quality evidence-based guidance may contribute to overuse of IV antibiotics for many hospitalized pediatric patients, even when safe and effective enteral options exist.⁹

Significant costs and harms are associated with the use of IV antibiotics. In particular, studies have demonstrated longer length of stay (LOS), increased costs, and worsened pain or anxiety related to complications (eg, phlebitis, extravasation injury, thrombosis, catheter-associated bloodstream infections) associated with IV antibiotics.^3,4,10-13 Earlier transition to enteral therapy, however, can mitigate these increased risks and costs.

The Centers for Disease Control and Prevention lists the transition from IV to oral antibiotics as a key stewardship intervention for improving antibiotic use.¹⁴ The Infectious Diseases Society of America (IDSA) antibiotic stewardship program guidelines strongly recommend the timely conversion from IV to oral antibiotics, stating that efforts focusing on this transition should be integrated into routine practice.¹⁵ There are a few metrics in the literature to measure this intervention, but none is universally used, and a modified delphi process could not reach consensus on IV-to-oral transition metrics.¹⁶

Few studies describe the opportunity to transition to enteral antibiotics in hospitalized patients with common bacterial infections or explore variation across hospitals. It is critical to understand current practice of antibiotic administration in order to identify opportunities to optimize patient outcomes and promote high-value care. Furthermore, few studies have evaluated the feasibility of IV-to-oral transition metrics using an administrative database. Thus, the aims of this study were to (1) determine opportunities to transition from IV to enteral antibiotics for pediatric patients hospitalized with common bacterial infections based on their ability to tolerate other enteral medications, (2) describe variation in transition practices among children’s hospitals, and (3) evaluate the feasibility of novel IV-to-oral transition metrics using an administrative database to inform stewardship efforts.

Study Design and Setting

This multicenter, retrospective cohort study used data from the Pediatric Health Information System (PHIS), an administrative and billing database containing encounter-level data from 52 tertiary care pediatric hospitals across the United States affiliated with the Children’s Hospital Association (Lenexa, Kansas). Hospitals submit encounter-level data, including demographics, medications, and diagnoses based on International Classification of Disease, Tenth Revision, Clinical Modification (ICD-10-CM) codes. Data were de-identified at the time of submission, and data quality and reliability were assured by joint efforts between the Children’s Hospital Association and participating hospitals.

Study Population

This study included pediatric patients aged 60 days to 18 years who were hospitalized (inpatient or observation status) at one of the participating hospitals between January 1, 2017, and December 31, 2018, for one of the following seven common bacterial infections: community-acquired pneumonia (CAP), neck infection (superficial and deep), periorbital/orbital infection, urinary tract infection (UTI), osteomyelitis, septic arthritis, or skin and soft tissue infection (SSTI). The diagnosis cohorts were defined based on ICD-10-CM discharge diagnoses adapted from previous studies (Appendix Table 1).^3,17-23 To define a cohort of generally healthy pediatric patients with an acute infection, we excluded patients hospitalized in the intensive care unit, patients with nonhome discharges, and patients with complex chronic conditions.²⁴ We also excluded hospitals with incomplete data during the study period (n=1). The Institutional Review Board at Cincinnati Children’s Hospital Medical Center determined this study to be non–human-subjects research.

Outcomes

The primary outcomes were the number of opportunity days and the percent of days with opportunity to transition from IV to enteral therapy. Opportunity days, or days in which there was a potential opportunity to transition from IV to enteral antibiotics, were defined as days patients received only IV antibiotic doses and at least one enteral nonantibiotic medication, suggesting an ability to take enteral medications.¹³ We excluded days patients received IV antibiotics for which there was no enteral alternative (eg, vancomycin, Appendix Table 2). When measuring opportunity, to be conservative (ie, to underestimate rather than overestimate opportunity), we did not count as an opportunity day any day in which patients received both IV and enteral antibiotics. Percent opportunity, or the percent of days patients received antibiotics in which there was potential opportunity to transition from IV to enteral antibiotics, was defined as the number of opportunity days divided by number of inpatient days patients received enteral antibiotics or IV antibiotics with at least one enteral nonantibiotic medication (antibiotic days). Similar to opportunity days, antibiotic days excluded days patients were on IV antibiotics for which there was no enteral alternative. Based on our definition, a lower percent opportunity indicates that a hospital is using enteral antibiotics earlier during the hospitalization (earlier transition), while a higher percent opportunity represents later enteral antibiotic use (later transition).

Statistical Analysis

Demographic and clinical characteristics were summarized by diagnosis with descriptive statistics, including frequency with percentage, mean with standard deviation, and median with interquartile range (IQR). For each diagnosis, we evaluated aggregate opportunity days (sum of opportunity days among all hospitals), opportunity days per encounter, and aggregate percent opportunity using frequencies, mean with standard deviation, and percentages, respectively. We also calculated aggregate opportunity days for diagnosis-antibiotic combinations. To visually show variation in the percent opportunity across hospitals, we displayed the percent opportunity on a heat map, and evaluated percent opportunity across hospitals using chi-square tests. To compare the variability in the percent opportunity across and within hospitals, we used a generalized linear model with two fixed effects (hospital and diagnosis), and parsed the variability using the sum of squares. We performed a sensitivity analysis and excluded days that patients received antiemetic medications (eg, ondansetron, granisetron, prochlorperazine, promethazine), as these suggest potential intolerance of enteral medications. All statistical analyses were performed using SAS v.9.4 (SAS Institute Inc, Cary, North Carolina) and GraphPad Prism 8.0 (GraphPad Software Inc., San Diego, California), and P values < .05 were considered statistically significant.

During the 2-year study period, 100,103 hospitalizations met our inclusion criteria across 51 hospitals and seven diagnosis categories (Table 1). Diagnosis cohorts ranged in size from 1,462 encounters for septic arthritis to 35,665 encounters for neck infections. Overall, we identified 88,522 aggregate opportunity days on which there was an opportunity to switch from IV to enteral treatment in the majority of participants (percent opportunity, 57%).

Opportunity by Diagnosis

The number of opportunity days (aggregate and mean per encounter) and percent opportunity varied by diagnosis (Table 2). The aggregate number of opportunity days ranged from 3,693 in patients with septic arthritis to 25,359 in patients with SSTI, and mean opportunity days per encounter ranged from 0.9 in CAP to 2.8 in septic arthritis. Percent opportunity was highest for septic arthritis at 72.7% and lowest for CAP at 39.7%.

Variation in Opportunity Among Hospitals

The variation in the percent opportunity across hospitals was statistically significant for all diagnoses (Figure). Within hospitals, we observed similar practice patterns across diagnoses. For example, hospitals with a higher percent opportunity for one diagnosis tended to have higher percent opportunity for the other diagnoses (as noted in the top portion of the Figure), and those with lower percent opportunity for one diagnosis tended to also have lower percent opportunity for the other diagnoses studied (as noted in the bottom portion of the Figure). When evaluating variability in the percent opportunity, 45% of the variability was attributable to the hospital-effect and 35% to the diagnosis; the remainder was unexplained variability. Sensitivity analysis excluding days when patients received an antiemetic medication yielded no differences in our results.

Opportunity by Antibiotic

The aggregate number of opportunity days varied by antibiotic (Table 3). Intravenous antibiotics with the largest number of opportunity days included clindamycin (44,293), ceftriaxone (23,896), and ampicillin-sulbactam (15,484). Antibiotic-diagnosis combinations with the largest number of opportunity days for each diagnosis included ceftriaxone and ampicillin in CAP; clindamycin in cellulitis, SSTI, and neck infections; ceftriaxone in UTI; and cefazolin in osteomyelitis and septic arthritis.

In this multicenter study of pediatric patients hospitalized with common bacterial infections, there was the potential to transition from IV to enteral treatment in over half of the antibiotic days. The degree of opportunity varied by infection, antibiotic, and hospital. Antibiotics with a large aggregate number of opportunity days for enteral transition included clindamycin, which has excellent bioavailability; and ampicillin and ampicillin-sulbactam, which can achieve pharmacodynamic targets with oral equivalents.^25-29 The across-hospital variation for a given diagnosis suggests that certain hospitals have strategies in place which permit an earlier transition to enteral antibiotics compared to other institutions in which there were likely missed opportunities to do so. This variability is likely due to limited evidence, emphasizing the need for robust studies to better understand the optimal initial antibiotic route and transition time. Our findings highlight the need for, and large potential impact of, stewardship efforts to promote earlier transition for specific drug targets. This study also demonstrates the feasibility of obtaining two metrics—percent opportunity and opportunity days—from administrative databases to inform stewardship efforts within and across hospitals.

Opportunity days and percent opportunity varied among diagnoses. The variation in aggregate opportunity days was largely a reflection of the number of encounters: Diagnoses such as SSTI, neck infections, and CAP had a large number of both aggregate opportunity days and encounters. The range of opportunity days per encounter (0.9-2.5) suggests potential missed opportunities to transition to enteral antibiotics across all diagnoses (Table 2). The higher opportunity days per encounter in osteomyelitis and septic arthritis may be related to longer LOS and higher percent opportunity. Percent opportunity likely varied among diagnoses due to differences in admission and discharge readiness criteria, diagnostic evaluation, frequency of antibiotic administration, and evidence on the optimal route of initial antibiotics and when to transition to oral formulations. For example, we hypothesize that certain diagnoses, such as osteomyelitis and septic arthritis, have admission and discharge readiness criteria directly tied to the perceived need for IV antibiotics, which may limit in-hospital days on enteral antibiotics and explain the high percent opportunity that we observed. The high percent opportunity seen in musculoskeletal infections also may be due to delays in initiating targeted treatment until culture results were available. Encounters for CAP had the lowest percent opportunity; we hypothesize that this is because admission and discharge readiness may be determined by factors other than the need for IV antibiotics (eg, need for supplemental oxygen), which may increase days on enteral antibiotics and lead to a lower percent opportunity.³⁰

Urinary tract infection encounters had a high percent opportunity. As with musculoskeletal infection, this may be related to delays in initiating targeted treatment until culture results became available. Another reason for the high percent opportunity in UTI could be the common use of ceftriaxone, which, dosed every 24 hours, likely reduced the opportunity to transition to enteral antibiotics. There is strong evidence demonstrating no difference in outcomes based on antibiotic routes for UTI, and we would expect this to result in a low percent opportunity.^2,31 While the observed high opportunity in UTI may relate to an initial unknown diagnosis or concern for systemic infection, this highlights potential opportunities for quality improvement initiatives to promote empiric oral antibiotics in clinically stable patients hospitalized with suspected UTI.

There was substantial variation in percent opportunity across hospitals for a given diagnosis, with less variation across diagnoses for a given hospital. Variation across hospitals but consistency within individual hospitals suggests that some hospitals may promote earlier transition from IV to enteral antibiotics as standard practice for all diagnoses, while other hospitals continue IV antibiotics for the entire hospitalization, highlighting potential missed opportunities at some institutions. While emerging data suggest that traditional long durations of IV antibiotics are not necessary for many infections, the limited evidence on the optimal time to switch to oral antibiotics may have influenced this variation.^2-7 Many guidelines recommend initial IV antibiotics for hospitalized pediatric patients, but there are few studies comparing IV and enteral therapy.^2,5,9 Limited evidence leaves significant room for hospital culture, antibiotic stewardship efforts, reimbursement considerations, and/or hospital workflow to influence transition timing and overall opportunity at individual hospitals.^7,8,32-34 These findings emphasize the importance of research to identify optimal transition time and comparative effectiveness studies to evaluate whether initial IV antibiotics are truly needed for mild—and even severe—disease presentations. Since many patients are admitted for the perceived need for IV antibiotics, earlier use of enteral antibiotics could reduce rates of hospitalizations, LOS, healthcare costs, and resource utilization.

Antibiotics with a high number of opportunity days included clindamycin, ceftriaxone, ampicillin-sublactam, and ampicillin. Our findings are consistent with another study which found that most bioavailable drugs, including clindamycin, were administered via the IV route and accounted for a large number of antibiotic days.³⁵ The Infectious Diseases Society of America recommends that hospitals promote earlier transition to oral formulations for highly bioavailable drugs.⁷ Given the high bioavailability of clindamycin, its common use in high-frequency encounters such as SSTI and neck infections, and the fact that it accounted for a large number of opportunity days, quality improvement initiatives promoting earlier transition to oral clindamycin could have a large impact across health systems.^25,26 Additionally, although beta-lactam antibiotics such as amoxicillin and amoxicillin-sulbactam are not highly bioavailable, oral dosing can achieve sufficient serum concentrations to reach pharmacodynamic targets for common clinical indications; this could be an important quality improvement initiative.^27-29 Several single-site studies have successfully implemented quality improvement initiatives to promote earlier IV-to-enteral transition, with resulting reductions in costs and no adverse events noted, highlighting the feasibility and impact of such efforts.^13,36-38

This study also demonstrates the feasibility of collecting two metrics (percent opportunity and opportunity days) from administrative databases to inform IV-to-oral transition benchmarking and stewardship efforts. While there are several metrics in the literature for evaluating antibiotic transition (eg, days of IV or oral therapy, percentage of antibiotics given via the oral route, time to switch from IV to oral, and acceptance rate of suggested changes to antibiotic route), none are universally used or agreed upon.^15,16,39 The opportunity metrics used in this study have several strengths, including the feasibility of obtaining them from existing databases and the ability to account for intake of other enteral medications; the latter is not evaluated in other metrics. These opportunity metrics can be used together to identify the percent of time in which there is opportunity to transition and total number of days to understand the full extent of potential opportunity for future interventions. As demonstrated in this study, these metrics can be measured by diagnosis, antibiotic, or diagnosis-antibiotic combination, and they can be used to evaluate stewardship efforts at a single institution over time or compare efforts across hospitals.

These findings should be interpreted in the context of important limitations. First, we attempted to characterize potential opportunity to transition to enteral medications based on a patient’s ability to tolerate nonenteral medications. However, there are other factors that could limit the opportunity to transition that we could not account for with an administrative dataset, including the use of antibiotics prior to admission, disease progression, severity of illness, and malabsorptive concerns. Thus, though we may have overestimated the true opportunity to transition to enteral antibiotics, it is unlikely that this would account for all of the variation in transition times that we observed across hospitals. Second, while our study required patients to have one of seven types of infection, we did not exclude any additional infectious diagnoses (eg, concurrent bacteremia, Clostridioides difficile, otitis media) that could have driven the choice of antibiotic type and modality. Although emerging evidence is supporting earlier transitions to oral therapy, bacteremia is typically treated with IV antibiotics; this may have led to an overestimation of true opportunity.⁴⁰ “Clostridioides” difficile and otitis media are typically treated with enteral therapy; concurrent infections such as these may have led to an underestimation of opportunity given the fact that, based on our definition, the days on which patients received both IV and enteral antibiotics were not counted as opportunity days. Third, because PHIS uses billing days to capture medication use, we were unable to distinguish transitions that occurred early in the day vs those that took place later in the day. This could have led to an underestimation of percent opportunity, particularly for diagnoses with a short LOS; it also likely led to an underestimation of the variability observed across hospitals. Fourth, because we used an administrative dataset, we are unable to understand reasoning behind transitioning time from IV to oral antibiotics, as well as provider, patient, and institutional level factors that influenced these decisions.

Children hospitalized with bacterial infections often receive IV antibiotics, and the timing of transition from IV to enteral antibiotics varies significantly across hospitals. Further research is needed to compare the effectiveness of IV and enteral antibiotics and better define criteria for transition to enteral therapy. We identified ample opportunities for quality improvement initiatives to promote earlier transition, which have the potential to reduce healthcare utilization and promote optimal patient-directed high-value care.

Bacterial infections are a common reason for pediatric hospital admissions in the United States.¹ Antibiotics are the mainstay of treatment, and whether to administer them intravenously (IV) or enterally is an important and, at times, challenging decision. Not all hospitalized patients with infections require IV antibiotics, and safe, effective early transitions to enteral therapy have been described for numerous infections.^2-7 However, guidelines describing the ideal initial route of antibiotic administration and when to transition to oral therapy are lacking.^5,7,8 This lack of high-quality evidence-based guidance may contribute to overuse of IV antibiotics for many hospitalized pediatric patients, even when safe and effective enteral options exist.⁹

Significant costs and harms are associated with the use of IV antibiotics. In particular, studies have demonstrated longer length of stay (LOS), increased costs, and worsened pain or anxiety related to complications (eg, phlebitis, extravasation injury, thrombosis, catheter-associated bloodstream infections) associated with IV antibiotics.^3,4,10-13 Earlier transition to enteral therapy, however, can mitigate these increased risks and costs.

The Centers for Disease Control and Prevention lists the transition from IV to oral antibiotics as a key stewardship intervention for improving antibiotic use.¹⁴ The Infectious Diseases Society of America (IDSA) antibiotic stewardship program guidelines strongly recommend the timely conversion from IV to oral antibiotics, stating that efforts focusing on this transition should be integrated into routine practice.¹⁵ There are a few metrics in the literature to measure this intervention, but none is universally used, and a modified delphi process could not reach consensus on IV-to-oral transition metrics.¹⁶

Few studies describe the opportunity to transition to enteral antibiotics in hospitalized patients with common bacterial infections or explore variation across hospitals. It is critical to understand current practice of antibiotic administration in order to identify opportunities to optimize patient outcomes and promote high-value care. Furthermore, few studies have evaluated the feasibility of IV-to-oral transition metrics using an administrative database. Thus, the aims of this study were to (1) determine opportunities to transition from IV to enteral antibiotics for pediatric patients hospitalized with common bacterial infections based on their ability to tolerate other enteral medications, (2) describe variation in transition practices among children’s hospitals, and (3) evaluate the feasibility of novel IV-to-oral transition metrics using an administrative database to inform stewardship efforts.

Study Design and Setting

This multicenter, retrospective cohort study used data from the Pediatric Health Information System (PHIS), an administrative and billing database containing encounter-level data from 52 tertiary care pediatric hospitals across the United States affiliated with the Children’s Hospital Association (Lenexa, Kansas). Hospitals submit encounter-level data, including demographics, medications, and diagnoses based on International Classification of Disease, Tenth Revision, Clinical Modification (ICD-10-CM) codes. Data were de-identified at the time of submission, and data quality and reliability were assured by joint efforts between the Children’s Hospital Association and participating hospitals.

Study Population

This study included pediatric patients aged 60 days to 18 years who were hospitalized (inpatient or observation status) at one of the participating hospitals between January 1, 2017, and December 31, 2018, for one of the following seven common bacterial infections: community-acquired pneumonia (CAP), neck infection (superficial and deep), periorbital/orbital infection, urinary tract infection (UTI), osteomyelitis, septic arthritis, or skin and soft tissue infection (SSTI). The diagnosis cohorts were defined based on ICD-10-CM discharge diagnoses adapted from previous studies (Appendix Table 1).^3,17-23 To define a cohort of generally healthy pediatric patients with an acute infection, we excluded patients hospitalized in the intensive care unit, patients with nonhome discharges, and patients with complex chronic conditions.²⁴ We also excluded hospitals with incomplete data during the study period (n=1). The Institutional Review Board at Cincinnati Children’s Hospital Medical Center determined this study to be non–human-subjects research.

Outcomes

The primary outcomes were the number of opportunity days and the percent of days with opportunity to transition from IV to enteral therapy. Opportunity days, or days in which there was a potential opportunity to transition from IV to enteral antibiotics, were defined as days patients received only IV antibiotic doses and at least one enteral nonantibiotic medication, suggesting an ability to take enteral medications.¹³ We excluded days patients received IV antibiotics for which there was no enteral alternative (eg, vancomycin, Appendix Table 2). When measuring opportunity, to be conservative (ie, to underestimate rather than overestimate opportunity), we did not count as an opportunity day any day in which patients received both IV and enteral antibiotics. Percent opportunity, or the percent of days patients received antibiotics in which there was potential opportunity to transition from IV to enteral antibiotics, was defined as the number of opportunity days divided by number of inpatient days patients received enteral antibiotics or IV antibiotics with at least one enteral nonantibiotic medication (antibiotic days). Similar to opportunity days, antibiotic days excluded days patients were on IV antibiotics for which there was no enteral alternative. Based on our definition, a lower percent opportunity indicates that a hospital is using enteral antibiotics earlier during the hospitalization (earlier transition), while a higher percent opportunity represents later enteral antibiotic use (later transition).

Statistical Analysis

Demographic and clinical characteristics were summarized by diagnosis with descriptive statistics, including frequency with percentage, mean with standard deviation, and median with interquartile range (IQR). For each diagnosis, we evaluated aggregate opportunity days (sum of opportunity days among all hospitals), opportunity days per encounter, and aggregate percent opportunity using frequencies, mean with standard deviation, and percentages, respectively. We also calculated aggregate opportunity days for diagnosis-antibiotic combinations. To visually show variation in the percent opportunity across hospitals, we displayed the percent opportunity on a heat map, and evaluated percent opportunity across hospitals using chi-square tests. To compare the variability in the percent opportunity across and within hospitals, we used a generalized linear model with two fixed effects (hospital and diagnosis), and parsed the variability using the sum of squares. We performed a sensitivity analysis and excluded days that patients received antiemetic medications (eg, ondansetron, granisetron, prochlorperazine, promethazine), as these suggest potential intolerance of enteral medications. All statistical analyses were performed using SAS v.9.4 (SAS Institute Inc, Cary, North Carolina) and GraphPad Prism 8.0 (GraphPad Software Inc., San Diego, California), and P values < .05 were considered statistically significant.

During the 2-year study period, 100,103 hospitalizations met our inclusion criteria across 51 hospitals and seven diagnosis categories (Table 1). Diagnosis cohorts ranged in size from 1,462 encounters for septic arthritis to 35,665 encounters for neck infections. Overall, we identified 88,522 aggregate opportunity days on which there was an opportunity to switch from IV to enteral treatment in the majority of participants (percent opportunity, 57%).

Opportunity by Diagnosis

The number of opportunity days (aggregate and mean per encounter) and percent opportunity varied by diagnosis (Table 2). The aggregate number of opportunity days ranged from 3,693 in patients with septic arthritis to 25,359 in patients with SSTI, and mean opportunity days per encounter ranged from 0.9 in CAP to 2.8 in septic arthritis. Percent opportunity was highest for septic arthritis at 72.7% and lowest for CAP at 39.7%.

Variation in Opportunity Among Hospitals

The variation in the percent opportunity across hospitals was statistically significant for all diagnoses (Figure). Within hospitals, we observed similar practice patterns across diagnoses. For example, hospitals with a higher percent opportunity for one diagnosis tended to have higher percent opportunity for the other diagnoses (as noted in the top portion of the Figure), and those with lower percent opportunity for one diagnosis tended to also have lower percent opportunity for the other diagnoses studied (as noted in the bottom portion of the Figure). When evaluating variability in the percent opportunity, 45% of the variability was attributable to the hospital-effect and 35% to the diagnosis; the remainder was unexplained variability. Sensitivity analysis excluding days when patients received an antiemetic medication yielded no differences in our results.

Opportunity by Antibiotic

The aggregate number of opportunity days varied by antibiotic (Table 3). Intravenous antibiotics with the largest number of opportunity days included clindamycin (44,293), ceftriaxone (23,896), and ampicillin-sulbactam (15,484). Antibiotic-diagnosis combinations with the largest number of opportunity days for each diagnosis included ceftriaxone and ampicillin in CAP; clindamycin in cellulitis, SSTI, and neck infections; ceftriaxone in UTI; and cefazolin in osteomyelitis and septic arthritis.

In this multicenter study of pediatric patients hospitalized with common bacterial infections, there was the potential to transition from IV to enteral treatment in over half of the antibiotic days. The degree of opportunity varied by infection, antibiotic, and hospital. Antibiotics with a large aggregate number of opportunity days for enteral transition included clindamycin, which has excellent bioavailability; and ampicillin and ampicillin-sulbactam, which can achieve pharmacodynamic targets with oral equivalents.^25-29 The across-hospital variation for a given diagnosis suggests that certain hospitals have strategies in place which permit an earlier transition to enteral antibiotics compared to other institutions in which there were likely missed opportunities to do so. This variability is likely due to limited evidence, emphasizing the need for robust studies to better understand the optimal initial antibiotic route and transition time. Our findings highlight the need for, and large potential impact of, stewardship efforts to promote earlier transition for specific drug targets. This study also demonstrates the feasibility of obtaining two metrics—percent opportunity and opportunity days—from administrative databases to inform stewardship efforts within and across hospitals.

Opportunity days and percent opportunity varied among diagnoses. The variation in aggregate opportunity days was largely a reflection of the number of encounters: Diagnoses such as SSTI, neck infections, and CAP had a large number of both aggregate opportunity days and encounters. The range of opportunity days per encounter (0.9-2.5) suggests potential missed opportunities to transition to enteral antibiotics across all diagnoses (Table 2). The higher opportunity days per encounter in osteomyelitis and septic arthritis may be related to longer LOS and higher percent opportunity. Percent opportunity likely varied among diagnoses due to differences in admission and discharge readiness criteria, diagnostic evaluation, frequency of antibiotic administration, and evidence on the optimal route of initial antibiotics and when to transition to oral formulations. For example, we hypothesize that certain diagnoses, such as osteomyelitis and septic arthritis, have admission and discharge readiness criteria directly tied to the perceived need for IV antibiotics, which may limit in-hospital days on enteral antibiotics and explain the high percent opportunity that we observed. The high percent opportunity seen in musculoskeletal infections also may be due to delays in initiating targeted treatment until culture results were available. Encounters for CAP had the lowest percent opportunity; we hypothesize that this is because admission and discharge readiness may be determined by factors other than the need for IV antibiotics (eg, need for supplemental oxygen), which may increase days on enteral antibiotics and lead to a lower percent opportunity.³⁰

Urinary tract infection encounters had a high percent opportunity. As with musculoskeletal infection, this may be related to delays in initiating targeted treatment until culture results became available. Another reason for the high percent opportunity in UTI could be the common use of ceftriaxone, which, dosed every 24 hours, likely reduced the opportunity to transition to enteral antibiotics. There is strong evidence demonstrating no difference in outcomes based on antibiotic routes for UTI, and we would expect this to result in a low percent opportunity.^2,31 While the observed high opportunity in UTI may relate to an initial unknown diagnosis or concern for systemic infection, this highlights potential opportunities for quality improvement initiatives to promote empiric oral antibiotics in clinically stable patients hospitalized with suspected UTI.

There was substantial variation in percent opportunity across hospitals for a given diagnosis, with less variation across diagnoses for a given hospital. Variation across hospitals but consistency within individual hospitals suggests that some hospitals may promote earlier transition from IV to enteral antibiotics as standard practice for all diagnoses, while other hospitals continue IV antibiotics for the entire hospitalization, highlighting potential missed opportunities at some institutions. While emerging data suggest that traditional long durations of IV antibiotics are not necessary for many infections, the limited evidence on the optimal time to switch to oral antibiotics may have influenced this variation.^2-7 Many guidelines recommend initial IV antibiotics for hospitalized pediatric patients, but there are few studies comparing IV and enteral therapy.^2,5,9 Limited evidence leaves significant room for hospital culture, antibiotic stewardship efforts, reimbursement considerations, and/or hospital workflow to influence transition timing and overall opportunity at individual hospitals.^7,8,32-34 These findings emphasize the importance of research to identify optimal transition time and comparative effectiveness studies to evaluate whether initial IV antibiotics are truly needed for mild—and even severe—disease presentations. Since many patients are admitted for the perceived need for IV antibiotics, earlier use of enteral antibiotics could reduce rates of hospitalizations, LOS, healthcare costs, and resource utilization.

Antibiotics with a high number of opportunity days included clindamycin, ceftriaxone, ampicillin-sublactam, and ampicillin. Our findings are consistent with another study which found that most bioavailable drugs, including clindamycin, were administered via the IV route and accounted for a large number of antibiotic days.³⁵ The Infectious Diseases Society of America recommends that hospitals promote earlier transition to oral formulations for highly bioavailable drugs.⁷ Given the high bioavailability of clindamycin, its common use in high-frequency encounters such as SSTI and neck infections, and the fact that it accounted for a large number of opportunity days, quality improvement initiatives promoting earlier transition to oral clindamycin could have a large impact across health systems.^25,26 Additionally, although beta-lactam antibiotics such as amoxicillin and amoxicillin-sulbactam are not highly bioavailable, oral dosing can achieve sufficient serum concentrations to reach pharmacodynamic targets for common clinical indications; this could be an important quality improvement initiative.^27-29 Several single-site studies have successfully implemented quality improvement initiatives to promote earlier IV-to-enteral transition, with resulting reductions in costs and no adverse events noted, highlighting the feasibility and impact of such efforts.^13,36-38

This study also demonstrates the feasibility of collecting two metrics (percent opportunity and opportunity days) from administrative databases to inform IV-to-oral transition benchmarking and stewardship efforts. While there are several metrics in the literature for evaluating antibiotic transition (eg, days of IV or oral therapy, percentage of antibiotics given via the oral route, time to switch from IV to oral, and acceptance rate of suggested changes to antibiotic route), none are universally used or agreed upon.^15,16,39 The opportunity metrics used in this study have several strengths, including the feasibility of obtaining them from existing databases and the ability to account for intake of other enteral medications; the latter is not evaluated in other metrics. These opportunity metrics can be used together to identify the percent of time in which there is opportunity to transition and total number of days to understand the full extent of potential opportunity for future interventions. As demonstrated in this study, these metrics can be measured by diagnosis, antibiotic, or diagnosis-antibiotic combination, and they can be used to evaluate stewardship efforts at a single institution over time or compare efforts across hospitals.

These findings should be interpreted in the context of important limitations. First, we attempted to characterize potential opportunity to transition to enteral medications based on a patient’s ability to tolerate nonenteral medications. However, there are other factors that could limit the opportunity to transition that we could not account for with an administrative dataset, including the use of antibiotics prior to admission, disease progression, severity of illness, and malabsorptive concerns. Thus, though we may have overestimated the true opportunity to transition to enteral antibiotics, it is unlikely that this would account for all of the variation in transition times that we observed across hospitals. Second, while our study required patients to have one of seven types of infection, we did not exclude any additional infectious diagnoses (eg, concurrent bacteremia, Clostridioides difficile, otitis media) that could have driven the choice of antibiotic type and modality. Although emerging evidence is supporting earlier transitions to oral therapy, bacteremia is typically treated with IV antibiotics; this may have led to an overestimation of true opportunity.⁴⁰ “Clostridioides” difficile and otitis media are typically treated with enteral therapy; concurrent infections such as these may have led to an underestimation of opportunity given the fact that, based on our definition, the days on which patients received both IV and enteral antibiotics were not counted as opportunity days. Third, because PHIS uses billing days to capture medication use, we were unable to distinguish transitions that occurred early in the day vs those that took place later in the day. This could have led to an underestimation of percent opportunity, particularly for diagnoses with a short LOS; it also likely led to an underestimation of the variability observed across hospitals. Fourth, because we used an administrative dataset, we are unable to understand reasoning behind transitioning time from IV to oral antibiotics, as well as provider, patient, and institutional level factors that influenced these decisions.

Children hospitalized with bacterial infections often receive IV antibiotics, and the timing of transition from IV to enteral antibiotics varies significantly across hospitals. Further research is needed to compare the effectiveness of IV and enteral antibiotics and better define criteria for transition to enteral therapy. We identified ample opportunities for quality improvement initiatives to promote earlier transition, which have the potential to reduce healthcare utilization and promote optimal patient-directed high-value care.

Bacterial infections are a common reason for pediatric hospital admissions in the United States.¹ Antibiotics are the mainstay of treatment, and whether to administer them intravenously (IV) or enterally is an important and, at times, challenging decision. Not all hospitalized patients with infections require IV antibiotics, and safe, effective early transitions to enteral therapy have been described for numerous infections.^2-7 However, guidelines describing the ideal initial route of antibiotic administration and when to transition to oral therapy are lacking.^5,7,8 This lack of high-quality evidence-based guidance may contribute to overuse of IV antibiotics for many hospitalized pediatric patients, even when safe and effective enteral options exist.⁹

Significant costs and harms are associated with the use of IV antibiotics. In particular, studies have demonstrated longer length of stay (LOS), increased costs, and worsened pain or anxiety related to complications (eg, phlebitis, extravasation injury, thrombosis, catheter-associated bloodstream infections) associated with IV antibiotics.^3,4,10-13 Earlier transition to enteral therapy, however, can mitigate these increased risks and costs.

The Centers for Disease Control and Prevention lists the transition from IV to oral antibiotics as a key stewardship intervention for improving antibiotic use.¹⁴ The Infectious Diseases Society of America (IDSA) antibiotic stewardship program guidelines strongly recommend the timely conversion from IV to oral antibiotics, stating that efforts focusing on this transition should be integrated into routine practice.¹⁵ There are a few metrics in the literature to measure this intervention, but none is universally used, and a modified delphi process could not reach consensus on IV-to-oral transition metrics.¹⁶

Few studies describe the opportunity to transition to enteral antibiotics in hospitalized patients with common bacterial infections or explore variation across hospitals. It is critical to understand current practice of antibiotic administration in order to identify opportunities to optimize patient outcomes and promote high-value care. Furthermore, few studies have evaluated the feasibility of IV-to-oral transition metrics using an administrative database. Thus, the aims of this study were to (1) determine opportunities to transition from IV to enteral antibiotics for pediatric patients hospitalized with common bacterial infections based on their ability to tolerate other enteral medications, (2) describe variation in transition practices among children’s hospitals, and (3) evaluate the feasibility of novel IV-to-oral transition metrics using an administrative database to inform stewardship efforts.

Study Design and Setting

This multicenter, retrospective cohort study used data from the Pediatric Health Information System (PHIS), an administrative and billing database containing encounter-level data from 52 tertiary care pediatric hospitals across the United States affiliated with the Children’s Hospital Association (Lenexa, Kansas). Hospitals submit encounter-level data, including demographics, medications, and diagnoses based on International Classification of Disease, Tenth Revision, Clinical Modification (ICD-10-CM) codes. Data were de-identified at the time of submission, and data quality and reliability were assured by joint efforts between the Children’s Hospital Association and participating hospitals.

Study Population

This study included pediatric patients aged 60 days to 18 years who were hospitalized (inpatient or observation status) at one of the participating hospitals between January 1, 2017, and December 31, 2018, for one of the following seven common bacterial infections: community-acquired pneumonia (CAP), neck infection (superficial and deep), periorbital/orbital infection, urinary tract infection (UTI), osteomyelitis, septic arthritis, or skin and soft tissue infection (SSTI). The diagnosis cohorts were defined based on ICD-10-CM discharge diagnoses adapted from previous studies (Appendix Table 1).^3,17-23 To define a cohort of generally healthy pediatric patients with an acute infection, we excluded patients hospitalized in the intensive care unit, patients with nonhome discharges, and patients with complex chronic conditions.²⁴ We also excluded hospitals with incomplete data during the study period (n=1). The Institutional Review Board at Cincinnati Children’s Hospital Medical Center determined this study to be non–human-subjects research.

Outcomes

The primary outcomes were the number of opportunity days and the percent of days with opportunity to transition from IV to enteral therapy. Opportunity days, or days in which there was a potential opportunity to transition from IV to enteral antibiotics, were defined as days patients received only IV antibiotic doses and at least one enteral nonantibiotic medication, suggesting an ability to take enteral medications.¹³ We excluded days patients received IV antibiotics for which there was no enteral alternative (eg, vancomycin, Appendix Table 2). When measuring opportunity, to be conservative (ie, to underestimate rather than overestimate opportunity), we did not count as an opportunity day any day in which patients received both IV and enteral antibiotics. Percent opportunity, or the percent of days patients received antibiotics in which there was potential opportunity to transition from IV to enteral antibiotics, was defined as the number of opportunity days divided by number of inpatient days patients received enteral antibiotics or IV antibiotics with at least one enteral nonantibiotic medication (antibiotic days). Similar to opportunity days, antibiotic days excluded days patients were on IV antibiotics for which there was no enteral alternative. Based on our definition, a lower percent opportunity indicates that a hospital is using enteral antibiotics earlier during the hospitalization (earlier transition), while a higher percent opportunity represents later enteral antibiotic use (later transition).

Statistical Analysis

Demographic and clinical characteristics were summarized by diagnosis with descriptive statistics, including frequency with percentage, mean with standard deviation, and median with interquartile range (IQR). For each diagnosis, we evaluated aggregate opportunity days (sum of opportunity days among all hospitals), opportunity days per encounter, and aggregate percent opportunity using frequencies, mean with standard deviation, and percentages, respectively. We also calculated aggregate opportunity days for diagnosis-antibiotic combinations. To visually show variation in the percent opportunity across hospitals, we displayed the percent opportunity on a heat map, and evaluated percent opportunity across hospitals using chi-square tests. To compare the variability in the percent opportunity across and within hospitals, we used a generalized linear model with two fixed effects (hospital and diagnosis), and parsed the variability using the sum of squares. We performed a sensitivity analysis and excluded days that patients received antiemetic medications (eg, ondansetron, granisetron, prochlorperazine, promethazine), as these suggest potential intolerance of enteral medications. All statistical analyses were performed using SAS v.9.4 (SAS Institute Inc, Cary, North Carolina) and GraphPad Prism 8.0 (GraphPad Software Inc., San Diego, California), and P values < .05 were considered statistically significant.

During the 2-year study period, 100,103 hospitalizations met our inclusion criteria across 51 hospitals and seven diagnosis categories (Table 1). Diagnosis cohorts ranged in size from 1,462 encounters for septic arthritis to 35,665 encounters for neck infections. Overall, we identified 88,522 aggregate opportunity days on which there was an opportunity to switch from IV to enteral treatment in the majority of participants (percent opportunity, 57%).

Opportunity by Diagnosis

The number of opportunity days (aggregate and mean per encounter) and percent opportunity varied by diagnosis (Table 2). The aggregate number of opportunity days ranged from 3,693 in patients with septic arthritis to 25,359 in patients with SSTI, and mean opportunity days per encounter ranged from 0.9 in CAP to 2.8 in septic arthritis. Percent opportunity was highest for septic arthritis at 72.7% and lowest for CAP at 39.7%.

Variation in Opportunity Among Hospitals

The variation in the percent opportunity across hospitals was statistically significant for all diagnoses (Figure). Within hospitals, we observed similar practice patterns across diagnoses. For example, hospitals with a higher percent opportunity for one diagnosis tended to have higher percent opportunity for the other diagnoses (as noted in the top portion of the Figure), and those with lower percent opportunity for one diagnosis tended to also have lower percent opportunity for the other diagnoses studied (as noted in the bottom portion of the Figure). When evaluating variability in the percent opportunity, 45% of the variability was attributable to the hospital-effect and 35% to the diagnosis; the remainder was unexplained variability. Sensitivity analysis excluding days when patients received an antiemetic medication yielded no differences in our results.

Opportunity by Antibiotic

The aggregate number of opportunity days varied by antibiotic (Table 3). Intravenous antibiotics with the largest number of opportunity days included clindamycin (44,293), ceftriaxone (23,896), and ampicillin-sulbactam (15,484). Antibiotic-diagnosis combinations with the largest number of opportunity days for each diagnosis included ceftriaxone and ampicillin in CAP; clindamycin in cellulitis, SSTI, and neck infections; ceftriaxone in UTI; and cefazolin in osteomyelitis and septic arthritis.

In this multicenter study of pediatric patients hospitalized with common bacterial infections, there was the potential to transition from IV to enteral treatment in over half of the antibiotic days. The degree of opportunity varied by infection, antibiotic, and hospital. Antibiotics with a large aggregate number of opportunity days for enteral transition included clindamycin, which has excellent bioavailability; and ampicillin and ampicillin-sulbactam, which can achieve pharmacodynamic targets with oral equivalents.^25-29 The across-hospital variation for a given diagnosis suggests that certain hospitals have strategies in place which permit an earlier transition to enteral antibiotics compared to other institutions in which there were likely missed opportunities to do so. This variability is likely due to limited evidence, emphasizing the need for robust studies to better understand the optimal initial antibiotic route and transition time. Our findings highlight the need for, and large potential impact of, stewardship efforts to promote earlier transition for specific drug targets. This study also demonstrates the feasibility of obtaining two metrics—percent opportunity and opportunity days—from administrative databases to inform stewardship efforts within and across hospitals.

Opportunity days and percent opportunity varied among diagnoses. The variation in aggregate opportunity days was largely a reflection of the number of encounters: Diagnoses such as SSTI, neck infections, and CAP had a large number of both aggregate opportunity days and encounters. The range of opportunity days per encounter (0.9-2.5) suggests potential missed opportunities to transition to enteral antibiotics across all diagnoses (Table 2). The higher opportunity days per encounter in osteomyelitis and septic arthritis may be related to longer LOS and higher percent opportunity. Percent opportunity likely varied among diagnoses due to differences in admission and discharge readiness criteria, diagnostic evaluation, frequency of antibiotic administration, and evidence on the optimal route of initial antibiotics and when to transition to oral formulations. For example, we hypothesize that certain diagnoses, such as osteomyelitis and septic arthritis, have admission and discharge readiness criteria directly tied to the perceived need for IV antibiotics, which may limit in-hospital days on enteral antibiotics and explain the high percent opportunity that we observed. The high percent opportunity seen in musculoskeletal infections also may be due to delays in initiating targeted treatment until culture results were available. Encounters for CAP had the lowest percent opportunity; we hypothesize that this is because admission and discharge readiness may be determined by factors other than the need for IV antibiotics (eg, need for supplemental oxygen), which may increase days on enteral antibiotics and lead to a lower percent opportunity.³⁰

Urinary tract infection encounters had a high percent opportunity. As with musculoskeletal infection, this may be related to delays in initiating targeted treatment until culture results became available. Another reason for the high percent opportunity in UTI could be the common use of ceftriaxone, which, dosed every 24 hours, likely reduced the opportunity to transition to enteral antibiotics. There is strong evidence demonstrating no difference in outcomes based on antibiotic routes for UTI, and we would expect this to result in a low percent opportunity.^2,31 While the observed high opportunity in UTI may relate to an initial unknown diagnosis or concern for systemic infection, this highlights potential opportunities for quality improvement initiatives to promote empiric oral antibiotics in clinically stable patients hospitalized with suspected UTI.

There was substantial variation in percent opportunity across hospitals for a given diagnosis, with less variation across diagnoses for a given hospital. Variation across hospitals but consistency within individual hospitals suggests that some hospitals may promote earlier transition from IV to enteral antibiotics as standard practice for all diagnoses, while other hospitals continue IV antibiotics for the entire hospitalization, highlighting potential missed opportunities at some institutions. While emerging data suggest that traditional long durations of IV antibiotics are not necessary for many infections, the limited evidence on the optimal time to switch to oral antibiotics may have influenced this variation.^2-7 Many guidelines recommend initial IV antibiotics for hospitalized pediatric patients, but there are few studies comparing IV and enteral therapy.^2,5,9 Limited evidence leaves significant room for hospital culture, antibiotic stewardship efforts, reimbursement considerations, and/or hospital workflow to influence transition timing and overall opportunity at individual hospitals.^7,8,32-34 These findings emphasize the importance of research to identify optimal transition time and comparative effectiveness studies to evaluate whether initial IV antibiotics are truly needed for mild—and even severe—disease presentations. Since many patients are admitted for the perceived need for IV antibiotics, earlier use of enteral antibiotics could reduce rates of hospitalizations, LOS, healthcare costs, and resource utilization.

Antibiotics with a high number of opportunity days included clindamycin, ceftriaxone, ampicillin-sublactam, and ampicillin. Our findings are consistent with another study which found that most bioavailable drugs, including clindamycin, were administered via the IV route and accounted for a large number of antibiotic days.³⁵ The Infectious Diseases Society of America recommends that hospitals promote earlier transition to oral formulations for highly bioavailable drugs.⁷ Given the high bioavailability of clindamycin, its common use in high-frequency encounters such as SSTI and neck infections, and the fact that it accounted for a large number of opportunity days, quality improvement initiatives promoting earlier transition to oral clindamycin could have a large impact across health systems.^25,26 Additionally, although beta-lactam antibiotics such as amoxicillin and amoxicillin-sulbactam are not highly bioavailable, oral dosing can achieve sufficient serum concentrations to reach pharmacodynamic targets for common clinical indications; this could be an important quality improvement initiative.^27-29 Several single-site studies have successfully implemented quality improvement initiatives to promote earlier IV-to-enteral transition, with resulting reductions in costs and no adverse events noted, highlighting the feasibility and impact of such efforts.^13,36-38

This study also demonstrates the feasibility of collecting two metrics (percent opportunity and opportunity days) from administrative databases to inform IV-to-oral transition benchmarking and stewardship efforts. While there are several metrics in the literature for evaluating antibiotic transition (eg, days of IV or oral therapy, percentage of antibiotics given via the oral route, time to switch from IV to oral, and acceptance rate of suggested changes to antibiotic route), none are universally used or agreed upon.^15,16,39 The opportunity metrics used in this study have several strengths, including the feasibility of obtaining them from existing databases and the ability to account for intake of other enteral medications; the latter is not evaluated in other metrics. These opportunity metrics can be used together to identify the percent of time in which there is opportunity to transition and total number of days to understand the full extent of potential opportunity for future interventions. As demonstrated in this study, these metrics can be measured by diagnosis, antibiotic, or diagnosis-antibiotic combination, and they can be used to evaluate stewardship efforts at a single institution over time or compare efforts across hospitals.

These findings should be interpreted in the context of important limitations. First, we attempted to characterize potential opportunity to transition to enteral medications based on a patient’s ability to tolerate nonenteral medications. However, there are other factors that could limit the opportunity to transition that we could not account for with an administrative dataset, including the use of antibiotics prior to admission, disease progression, severity of illness, and malabsorptive concerns. Thus, though we may have overestimated the true opportunity to transition to enteral antibiotics, it is unlikely that this would account for all of the variation in transition times that we observed across hospitals. Second, while our study required patients to have one of seven types of infection, we did not exclude any additional infectious diagnoses (eg, concurrent bacteremia, Clostridioides difficile, otitis media) that could have driven the choice of antibiotic type and modality. Although emerging evidence is supporting earlier transitions to oral therapy, bacteremia is typically treated with IV antibiotics; this may have led to an overestimation of true opportunity.⁴⁰ “Clostridioides” difficile and otitis media are typically treated with enteral therapy; concurrent infections such as these may have led to an underestimation of opportunity given the fact that, based on our definition, the days on which patients received both IV and enteral antibiotics were not counted as opportunity days. Third, because PHIS uses billing days to capture medication use, we were unable to distinguish transitions that occurred early in the day vs those that took place later in the day. This could have led to an underestimation of percent opportunity, particularly for diagnoses with a short LOS; it also likely led to an underestimation of the variability observed across hospitals. Fourth, because we used an administrative dataset, we are unable to understand reasoning behind transitioning time from IV to oral antibiotics, as well as provider, patient, and institutional level factors that influenced these decisions.

Children hospitalized with bacterial infections often receive IV antibiotics, and the timing of transition from IV to enteral antibiotics varies significantly across hospitals. Further research is needed to compare the effectiveness of IV and enteral antibiotics and better define criteria for transition to enteral therapy. We identified ample opportunities for quality improvement initiatives to promote earlier transition, which have the potential to reduce healthcare utilization and promote optimal patient-directed high-value care.

There is substantial geographic variation in intensity of healthcare use in the United States,¹ yet areas with higher healthcare utilization do not demonstrate superior clinical outcomes.² Low-value care exposes patients to unnecessary anxiety, radiation, and risk for adverse events.

Previous research has focused on measuring low-value care at the level of hospital referral regions,^3-6 metropolitan statistical areas,⁷ provider organizations,⁸ and individual physicians.^9,10 Hospital referral regions designate regional healthcare markets for tertiary care and generally include at least one major referral center.¹¹ Well-calibrated and validated hospital-level measures of diagnostic overuse are lacking.

We sought to construct a novel index to measure hospital level overuse of diagnostic testing. We focused on diagnostic intensity rather than other forms of overuse such as screening or treatment intensity. Moreover, we aimed to create a parsimonious index—one that is simple, relies on a small number of inputs, is derived from readily available administrative data without the need for chart review or complex logic, and does not require exclusion criteria.

Conceptual Framework for Choosing Index Components

To create our overuse index, we took advantage of the requirements for International Classification of Diseases, 9th Revision-Clinical Modification (ICD-9-CM) billing codes 780-796; these codes are based on “symptoms, signs, and ill-defined conditions” and can only be listed as the primary discharge diagnosis if no more specific diagnosis is made.¹² As such, when coupled with expensive tests, a high prevalence of these symptom-based diagnosis codes at discharge may serve as a proxy for low-value care. One of the candidate metrics we selected was based on Choosing Wisely^® recommendations.¹³ The other candidate metrics were based on clinical experience and consensus of the study team.

Data Sources

We used hospital-level data on primary discharge diagnosis codes and utilization of testing data from the State Inpatient Databases (SID), which are part of the Agency for Healthcare Research and Quality Healthcare Cost and Utilization Project (HCUP). Our derivation cohort used data from acute care hospitals in Maryland, New Jersey, and Washington state. Our validation cohort used data from acute care hospitals in Kentucky, North Carolina, New York, and West Virginia. States were selected based on availability of data (certain states lacked complete testing utilization data) and cost of data acquisition. The SID contains hospital-level utilization of computed tomography (CT) scans (CT of the body and head) and diagnostic testing, including stress testing and esophagogastroduodenoscopy (EGD).

Data on three prespecified Dartmouth Atlas of Health Care metrics at the hospital service area (HSA) level were obtained from the Dartmouth Atlas website.¹⁴ These metrics were (1) rate of inpatient coronary angiograms per 1,000 Medicare enrollees, (2) price-adjusted physician reimbursement per fee-for-service Medicare enrollee per year (adjusted for patient sex, race, and age), and (3) mean inpatient spending per decedent in the last 6 months of life.¹⁵ Data on three prespecified Medicare metrics at the county level were obtained from the Centers for Medicare & Medicaid Services (CMS) website.¹⁶ These metrics were standardized per capita cost per (1) procedure, (2) imaging, and (3) test of Medicare fee-for-service patients. The CMS uses the Berenson-Eggers Type of Service Codes to classify fee-generating interventions into a number of categories, including procedure, imaging, and test.¹⁷

Components of the Overuse Index

We tested five candidate metrics for index inclusion (Table 1). We utilized Clinical Classifications Software (CCS) codes provided by HCUP, which combine several ICD-9-CM codes into a single primary CCS discharge code for ease of use. The components were (1) primary CCS diagnosis of “nausea and vomiting” coupled with body CT scan or EGD, (2) primary CCS diagnosis of abdominal pain and body CT scan or EGD, (3) primary CCS diagnosis of “nonspecific chest pain” and body CT scan or stress test, (4) primary CCS diagnosis of syncope and stress test, and (5) primary CCS diagnosis for syncope and CT of the brain. For a given metric, the denominator was all patients with the particular primary CCS discharge diagnosis code. The numerator was patients with the diagnostic code who also had the specific test or procedure. We characterized the denominators of each metric in terms of mean, SD, and range.

Index Inclusion Criteria and Construction

Specialty, pediatric, rehabilitation, and long-term care hospitals were excluded. Moreover, any hospital with an overall denominator (for the entire index, not an individual metric) of five or fewer observations was excluded. Admissions to acute care hospitals between January 2011 and September 2015 (time of transition from ICD-9-CM to ICD-10-CM) that had one of the specified diagnosis codes were included. For a given hospital, the value of each of the five candidate metrics was defined as the ratio of all admissions that had the given testing and all admissions during the observation period with inclusion CCS diagnosis codes.

Derivation and Validation of the Index

In our derivation cohort (hospitals in Maryland, New Jersey, and Washington state), we tested the temporal stability of each candidate metric by year using the intraclass correlation coefficient (ICC). Using exploratory factor analysis (EFA) and Cronbach’s alpha, we then tested internal consistency of the index candidate components to ensure that all measured a common underlying factor (ie, diagnostic overuse). To standardize data, test rates for both of these analyses were converted to z-scores. For the EFA, we expected that if the index was reflecting only a single underlying factor, the Eigenvalue for one factor should be much higher (typically above 1.0) than that for multiple factors. We calculated item-test correlation for each candidate metric and Cronbach’s alpha for the entire index. A high and stable value for item-test correlation for each index component, as well as a high Cronbach’s alpha, suggests that index components measure a single common factor. Given the small number of test items, we considered a Cronbach’s alpha above 0.6 to be satisfactory.

This analysis showed satisfactory temporal stability of each candidate metric and good internal consistency of the candidate metrics in the derivation cohort. Therefore, we decided to keep all metrics rather than discard any of them. This same process was repeated with the validation cohort (Kentucky, New York, North Carolina, and West Virginia) and then with the combined group of seven states. Tests on the validation and entire cohort further supported our decision to keep all five metrics.

To determine the overall index value for a hospital, all of its metric numerators and denominators were added to calculate one fraction. In this way for a given hospital, a metric for which there were no observations was effectively excluded from the index. This essentially weights each index component by frequency. We chose to count syncope admissions only once in the denominator to avoid the index being unduly influenced by this diagnosis. The hospital index values were combined into their HSAs by adding numerators and denominators from each hospital to calculate HSA index values, effectively giving higher weight to hospitals with more observations. Spearman’s correlation coefficients were measured for these Dartmouth Atlas metrics, also at the HSA level. For the county level analysis, we used a hospital-county crosswalk (available from the American Hospital Association [AHA] Annual Survey; https://www.ahadata.com/aha-annual-survey-database) to link a hospital overuse index value to a county level cost value rather than aggregating data at the county level. We felt this was appropriate, as HSAs were constructed to represent a local healthcare market, whereas counties are less likely to be homogenous from a healthcare perspective.

Analysis of Entire Hospital Sample

The mean index value and SD were calculated for the entire sample of hospitals and for each state. The mean index value for each year of data was calculated to measure the temporal change of the index (representing a change in diagnostic intensity over the study period) using linear regression. We divided the cohort of hospitals into tertiles based on their index value. This is consistent with the CMS categorization of hospital payments and value of care as being “at,” “significantly above,” or “significantly below” a mean value.¹⁸ The characteristics of hospitals by tertile were described by mean total hospital beds, mean annual admissions, teaching status (nonteaching hospital, minor teaching hospital, major teaching hospital), and critical access hospital (yes/no). We utilized the AHA Annual Survey for data on hospital characteristics. We calculated P values using analysis of variance for hospital bed size and a chi-square test for teaching status and critical access hospital.

The entire group of hospitals from seven states was then used to apply the index to the HSA level. Numerators and denominators for each hospital in an HSA were added to calculate an HSA-level proportion. Thus, the HSA level index value, though unweighted, is dominated by hospitals with larger numbers of observations. For each of the Dartmouth metrics, the adjusted odds ratio of being in a higher diagnostic overuse index tertile given being in a certain Dartmouth Atlas metric tertile was calculated using ordinal logistic regression. This model controlled for the mean number of beds of hospitals in the HSA (continuous variable), mean Elixhauser Comorbidity Index (ECI) score (continuous variable; unweighted average among hospitals in an HSA), whether the HSA had a major or minor teaching hospital (yes/no) or was a critical access hospital (yes/no), and state fixed effects. The ECI score is a validated score that uses the presence or absence of 29 comorbidities to predict in-hospital mortality.¹⁹ For discriminant validity, we also tested two variables not expected to be associated with overuse—hospital ownership and affiliation with the Catholic Church.

For the county-level analysis, ordinal logistic regression was used to predict the adjusted odds ratio of being in a higher diagnostic overuse index tertile given being in a certain tertile of a given county-level spending metric. This model controlled for hospital bed size (continuous variable), hospital ECI score (continuous variable), teaching status (major, minor, nonteaching), critical access hospital status (yes/no), and state fixed effects.

Descriptive Statistics for Metrics

A total of 620 acute care hospitals were included in the index. Thirteen hospitals were excluded because their denominator was five or fewer. The vast majority of HSAs (85.9%) had only one hospital, 8.2% had two hospitals, and 2.4% had three hospitals. Similarly, the majority of counties (68.7%) had only one hospital, 15.1% had two hospitals, and 6.6% had three hospitals (Appendix Tables 1.1 and 1.2). Nonspecific chest pain was the metric with largest denominator mean (650), SD (1,012), and range (0-10,725) (Appendix Table 2). Overall, the metric denominators were a small fraction of total hospital discharges, with means at the hospital level ranging from 0.69% for nausea and vomiting to 5.81% for nonspecific chest pain, suggesting that our index relies on a relatively small fraction of discharges.

Tests for Temporal Stability and Internal Consistency by Derivation and Validation Strategy

Overall, the ICCs for the derivation, validation, and entire cohort suggested strong temporal stability (Appendix Table 3). The EFA of the derivation, validation, and entire cohort showed high Eigenvalues for one principal component, with no other factors close to 1, indicating strong internal consistency (Appendix Table 4). The Cronbach’s alpha analysis also suggested strong internal consistency, with alpha values ranging from 0.73 for the validation cohort to 0.80 for the derivation cohort (Table 2).

Correlation With External Validation Measures

For the entire cohort, the Spearman’s rho for correlation between our overuse index and inpatient rate of coronary angiography at the HSA level was 0.186 (95% CI, 0.089-0.283), Medicare reimbursement at the HSA level was 0.355 (95% CI, 0.272-0.437), and Medicare spending during the last 6 months of life at the HSA level was 0.149 (95% CI, 0.061-0.236) (Appendix Figures 5.1-5.3). The Spearman’s rho for correlation between our overuse index and county level standardized procedure cost was 0.284 (95% CI, 0.210-0.358), imaging cost was 0.268 (95% CI, 0.195-0.342), and testing cost was 0.226 (95% CI, 0.152-0.300) (Appendix Figures 6.1-6.3).

Overall Index Values and Change Over Time

The mean hospital index value was 0.541 (SD, 0.178) (Appendix Table 7). There was a slight but statistically significant annual increase in the overall mean index value over the study period, suggesting a small rise in overuse of diagnostic testing (coefficient 0.011; P <.001) (Appendix Figure 8).

Diagnostic Overuse Index Tertiles

Hospitals in the lowest tertile of the index tended to be smaller (based on number of beds) (P < .0001) and were more likely to be critical access hospitals (P <.0001). There was a significant difference in the proportion of nonteaching, minor teaching, and major teaching hospitals, with more nonteaching hospitals in tertile 1 (P = .001) (Table 3). The median ECI score was not significantly different among tertiles. Neither of the variables tested for discriminant validity (hospital ownership and Catholic Church affiliation) was associated with our index.

Adjusted Multilevel Mixed-Effects Ordinal Logistic Regression

Our overuse index correlated most closely with physician reimbursement, with an odds ratio of 2.02 (95% CI, 1.11-3.66) of being in a higher tertile of the overuse index when comparing tertiles 3 and 1 of this Dartmouth metric. Of the Medicare county-level metrics, our index correlated most closely with cost of procedures, with an odds ratio of 2.03 (95% CI, 1.21-3.39) of being in a higher overuse index tertile when comparing tertiles 3 and 1 of the cost per procedure metric (Figure 1).

Previous research shows variation among hospitals for overall physician spending,²⁰ noninvasive cardiac imaging,²¹ and the rate of finding obstructive lesions during elective coronary angiography.²² However, there is a lack of standardized methods to study a broad range of diagnostic overuse at the hospital level. To our knowledge, no studies have attempted to develop a diagnostic overuse index at the hospital level. We used a derivation-validation approach to achieve our goal. Although the five metrics represent a range of conditions, the EFA and Cronbach’s alpha tests suggest that they measure a common phenomenon. To avoid systematically excluding smaller hospitals, we limited the extent to which we eliminated hospitals with few observations. Our findings suggest that it may be reasonable to make generalizations on the diagnostic intensity of a hospital based on a relatively small number of discharges. Moreover, our index is a proof of concept that rates of negative diagnostic testing can serve as a proxy for estimating diagnostic overuse.

Our hospital-level index values extrapolated to the HSA level weakly correlated with prespecified Dartmouth Atlas metrics. In a multivariate ordinal regression, there was a significant though weak association between hospitals in higher tertiles of the Dartmouth Atlas metrics and categorization in higher tertiles of our diagnostic overuse index. Similarly, our hospital-level index correlated with two of the three county-level metrics in a multivariate ordinal regression.

We do not assume that all of the metrics in our index track together. However, our results, including the wide dispersion of index values among the tertiles (Table 3), suggest that at least some hospitals are outliers in multiple metrics. We did not assume ex ante that our index should correlate with Dartmouth overuse metrics or Medicare county-level spending; however, we did believe that an association with these measures would assist in validating our index. Given that our index utilizes four common diagnoses, while the Dartmouth and Medicare cost metrics are based on a much broader range of conditions, we would not expect more than a weak correlation even if our index is a valid way to measure overuse.

All of the metrics were based on the concept that hospitals with high rates of negative testing are likely providing large amounts of low-value care. Prior studies on diagnostic yield of CT scans in the emergency department for pulmonary embolus (PE) found an increase in testing and decrease in yield over time; these studies also showed that physicians with more experience ordered fewer CT scans and had a higher yield.²³ A review of electronic health records and billing data also showed that hospitals with higher rates of D-dimer testing had higher yields on CT scans ordered to test for PE.²⁴

We took advantage of the coding convention that certain diagnoses only be listed as the primary discharge diagnosis if no more specific diagnosis is made. This allowed us to identify hospitals that likely had high rates of negative tests without granular data. Of course, the metrics are not measuring rates of negative testing per se, but a proxy for this, based instead on the proportion of patients with a symptom-based primary discharge diagnosis who underwent diagnostic testing.

Measuring diagnostic overuse at the hospital level may help to understand factors that drive overuse, given that institutional incentives and culture likely play important roles in ordering tests. There is evidence that financial incentives drive physicians’ decisions,^25-27 and there is also evidence that institutional culture impacts outcomes.²⁸ Further, quality improvement projects are typically designed at the hospital level and may be an effective way to curb overuse.^29,30

Previous studies have focused on measuring variation among providers and identifying outlier physicians.^9,10,20 Providing feedback to underperforming physicians has been shown to change practice habits.^31,32 Efforts to improve the practice habits of outlier hospitals may have a number of advantages, including economies of scale and scope and the added benefit of improving the habits of all providers—not just those who are underperforming.

Ordering expensive diagnostic tests on patients with a low pretest probability of having an organic etiology for their symptoms contributes to high healthcare costs. Of course, we do not believe that the ideal rate of negative testing is zero. However, hospitals with high rates of negative diagnostic testing are more likely to be those with clinicians who use expensive tests as a substitute for clinical judgment or less-expensive tests (eg, D-dimer testing to rule out PE).

One challenge we faced is that there is no gold standard of hospital-level overuse with which to validate our index. Our index is weakly correlated with a number of regional metrics that may be proxies for overuse. We are reassured that there is a statistically significant correlation with measures at both HSA and county levels. These correlations are weak, but these regional metrics are themselves imperfect surrogates for overuse. Furthermore, our index is preliminary and will need refinement in future studies.

Limitations

Our analysis has multiple limitations. First, since it relies heavily on primary ICD discharge diagnosis codes, biases could exist due to variations in coding practices. Second, the SID does not include observation stays or tests conducted in the ED, so differential use of observation stays among hospitals might impact results. Finally, based on utilization data, we were not able to distinguish between CT scans of the chest, abdomen, and pelvis because the SID labels each of these as body CT.

We developed a novel index to measure diagnostic intensity at the hospital level. This index relies on the concept that high rates of negative diagnostic testing likely indicate some degree of overuse. Our index is parsimonious, does not require granular claims data, and measures a range of potentially overused tests for common clinical scenarios. Our next steps include further refining the index, testing it with granular data, and validating it with other datasets. Thereafter, this index may be useful at identifying positive and negative outliers to understand what processes of care contribute to outlier high and low levels of diagnostic testing. We suspect our index is more useful at identifying extremes than comparing hospitals in the middle of the utilization curve. Additionally, exploring the relationship among individual metrics and the relationship between our index and quality measures like mortality and readmissions may be informative.

There is substantial geographic variation in intensity of healthcare use in the United States,¹ yet areas with higher healthcare utilization do not demonstrate superior clinical outcomes.² Low-value care exposes patients to unnecessary anxiety, radiation, and risk for adverse events.

Previous research has focused on measuring low-value care at the level of hospital referral regions,^3-6 metropolitan statistical areas,⁷ provider organizations,⁸ and individual physicians.^9,10 Hospital referral regions designate regional healthcare markets for tertiary care and generally include at least one major referral center.¹¹ Well-calibrated and validated hospital-level measures of diagnostic overuse are lacking.

We sought to construct a novel index to measure hospital level overuse of diagnostic testing. We focused on diagnostic intensity rather than other forms of overuse such as screening or treatment intensity. Moreover, we aimed to create a parsimonious index—one that is simple, relies on a small number of inputs, is derived from readily available administrative data without the need for chart review or complex logic, and does not require exclusion criteria.

Conceptual Framework for Choosing Index Components

To create our overuse index, we took advantage of the requirements for International Classification of Diseases, 9th Revision-Clinical Modification (ICD-9-CM) billing codes 780-796; these codes are based on “symptoms, signs, and ill-defined conditions” and can only be listed as the primary discharge diagnosis if no more specific diagnosis is made.¹² As such, when coupled with expensive tests, a high prevalence of these symptom-based diagnosis codes at discharge may serve as a proxy for low-value care. One of the candidate metrics we selected was based on Choosing Wisely^® recommendations.¹³ The other candidate metrics were based on clinical experience and consensus of the study team.

Data Sources

We used hospital-level data on primary discharge diagnosis codes and utilization of testing data from the State Inpatient Databases (SID), which are part of the Agency for Healthcare Research and Quality Healthcare Cost and Utilization Project (HCUP). Our derivation cohort used data from acute care hospitals in Maryland, New Jersey, and Washington state. Our validation cohort used data from acute care hospitals in Kentucky, North Carolina, New York, and West Virginia. States were selected based on availability of data (certain states lacked complete testing utilization data) and cost of data acquisition. The SID contains hospital-level utilization of computed tomography (CT) scans (CT of the body and head) and diagnostic testing, including stress testing and esophagogastroduodenoscopy (EGD).

Data on three prespecified Dartmouth Atlas of Health Care metrics at the hospital service area (HSA) level were obtained from the Dartmouth Atlas website.¹⁴ These metrics were (1) rate of inpatient coronary angiograms per 1,000 Medicare enrollees, (2) price-adjusted physician reimbursement per fee-for-service Medicare enrollee per year (adjusted for patient sex, race, and age), and (3) mean inpatient spending per decedent in the last 6 months of life.¹⁵ Data on three prespecified Medicare metrics at the county level were obtained from the Centers for Medicare & Medicaid Services (CMS) website.¹⁶ These metrics were standardized per capita cost per (1) procedure, (2) imaging, and (3) test of Medicare fee-for-service patients. The CMS uses the Berenson-Eggers Type of Service Codes to classify fee-generating interventions into a number of categories, including procedure, imaging, and test.¹⁷

Components of the Overuse Index

We tested five candidate metrics for index inclusion (Table 1). We utilized Clinical Classifications Software (CCS) codes provided by HCUP, which combine several ICD-9-CM codes into a single primary CCS discharge code for ease of use. The components were (1) primary CCS diagnosis of “nausea and vomiting” coupled with body CT scan or EGD, (2) primary CCS diagnosis of abdominal pain and body CT scan or EGD, (3) primary CCS diagnosis of “nonspecific chest pain” and body CT scan or stress test, (4) primary CCS diagnosis of syncope and stress test, and (5) primary CCS diagnosis for syncope and CT of the brain. For a given metric, the denominator was all patients with the particular primary CCS discharge diagnosis code. The numerator was patients with the diagnostic code who also had the specific test or procedure. We characterized the denominators of each metric in terms of mean, SD, and range.

Index Inclusion Criteria and Construction

Specialty, pediatric, rehabilitation, and long-term care hospitals were excluded. Moreover, any hospital with an overall denominator (for the entire index, not an individual metric) of five or fewer observations was excluded. Admissions to acute care hospitals between January 2011 and September 2015 (time of transition from ICD-9-CM to ICD-10-CM) that had one of the specified diagnosis codes were included. For a given hospital, the value of each of the five candidate metrics was defined as the ratio of all admissions that had the given testing and all admissions during the observation period with inclusion CCS diagnosis codes.

Derivation and Validation of the Index

In our derivation cohort (hospitals in Maryland, New Jersey, and Washington state), we tested the temporal stability of each candidate metric by year using the intraclass correlation coefficient (ICC). Using exploratory factor analysis (EFA) and Cronbach’s alpha, we then tested internal consistency of the index candidate components to ensure that all measured a common underlying factor (ie, diagnostic overuse). To standardize data, test rates for both of these analyses were converted to z-scores. For the EFA, we expected that if the index was reflecting only a single underlying factor, the Eigenvalue for one factor should be much higher (typically above 1.0) than that for multiple factors. We calculated item-test correlation for each candidate metric and Cronbach’s alpha for the entire index. A high and stable value for item-test correlation for each index component, as well as a high Cronbach’s alpha, suggests that index components measure a single common factor. Given the small number of test items, we considered a Cronbach’s alpha above 0.6 to be satisfactory.

This analysis showed satisfactory temporal stability of each candidate metric and good internal consistency of the candidate metrics in the derivation cohort. Therefore, we decided to keep all metrics rather than discard any of them. This same process was repeated with the validation cohort (Kentucky, New York, North Carolina, and West Virginia) and then with the combined group of seven states. Tests on the validation and entire cohort further supported our decision to keep all five metrics.

To determine the overall index value for a hospital, all of its metric numerators and denominators were added to calculate one fraction. In this way for a given hospital, a metric for which there were no observations was effectively excluded from the index. This essentially weights each index component by frequency. We chose to count syncope admissions only once in the denominator to avoid the index being unduly influenced by this diagnosis. The hospital index values were combined into their HSAs by adding numerators and denominators from each hospital to calculate HSA index values, effectively giving higher weight to hospitals with more observations. Spearman’s correlation coefficients were measured for these Dartmouth Atlas metrics, also at the HSA level. For the county level analysis, we used a hospital-county crosswalk (available from the American Hospital Association [AHA] Annual Survey; https://www.ahadata.com/aha-annual-survey-database) to link a hospital overuse index value to a county level cost value rather than aggregating data at the county level. We felt this was appropriate, as HSAs were constructed to represent a local healthcare market, whereas counties are less likely to be homogenous from a healthcare perspective.

Analysis of Entire Hospital Sample

The mean index value and SD were calculated for the entire sample of hospitals and for each state. The mean index value for each year of data was calculated to measure the temporal change of the index (representing a change in diagnostic intensity over the study period) using linear regression. We divided the cohort of hospitals into tertiles based on their index value. This is consistent with the CMS categorization of hospital payments and value of care as being “at,” “significantly above,” or “significantly below” a mean value.¹⁸ The characteristics of hospitals by tertile were described by mean total hospital beds, mean annual admissions, teaching status (nonteaching hospital, minor teaching hospital, major teaching hospital), and critical access hospital (yes/no). We utilized the AHA Annual Survey for data on hospital characteristics. We calculated P values using analysis of variance for hospital bed size and a chi-square test for teaching status and critical access hospital.

The entire group of hospitals from seven states was then used to apply the index to the HSA level. Numerators and denominators for each hospital in an HSA were added to calculate an HSA-level proportion. Thus, the HSA level index value, though unweighted, is dominated by hospitals with larger numbers of observations. For each of the Dartmouth metrics, the adjusted odds ratio of being in a higher diagnostic overuse index tertile given being in a certain Dartmouth Atlas metric tertile was calculated using ordinal logistic regression. This model controlled for the mean number of beds of hospitals in the HSA (continuous variable), mean Elixhauser Comorbidity Index (ECI) score (continuous variable; unweighted average among hospitals in an HSA), whether the HSA had a major or minor teaching hospital (yes/no) or was a critical access hospital (yes/no), and state fixed effects. The ECI score is a validated score that uses the presence or absence of 29 comorbidities to predict in-hospital mortality.¹⁹ For discriminant validity, we also tested two variables not expected to be associated with overuse—hospital ownership and affiliation with the Catholic Church.

For the county-level analysis, ordinal logistic regression was used to predict the adjusted odds ratio of being in a higher diagnostic overuse index tertile given being in a certain tertile of a given county-level spending metric. This model controlled for hospital bed size (continuous variable), hospital ECI score (continuous variable), teaching status (major, minor, nonteaching), critical access hospital status (yes/no), and state fixed effects.

Descriptive Statistics for Metrics

A total of 620 acute care hospitals were included in the index. Thirteen hospitals were excluded because their denominator was five or fewer. The vast majority of HSAs (85.9%) had only one hospital, 8.2% had two hospitals, and 2.4% had three hospitals. Similarly, the majority of counties (68.7%) had only one hospital, 15.1% had two hospitals, and 6.6% had three hospitals (Appendix Tables 1.1 and 1.2). Nonspecific chest pain was the metric with largest denominator mean (650), SD (1,012), and range (0-10,725) (Appendix Table 2). Overall, the metric denominators were a small fraction of total hospital discharges, with means at the hospital level ranging from 0.69% for nausea and vomiting to 5.81% for nonspecific chest pain, suggesting that our index relies on a relatively small fraction of discharges.

Tests for Temporal Stability and Internal Consistency by Derivation and Validation Strategy

Overall, the ICCs for the derivation, validation, and entire cohort suggested strong temporal stability (Appendix Table 3). The EFA of the derivation, validation, and entire cohort showed high Eigenvalues for one principal component, with no other factors close to 1, indicating strong internal consistency (Appendix Table 4). The Cronbach’s alpha analysis also suggested strong internal consistency, with alpha values ranging from 0.73 for the validation cohort to 0.80 for the derivation cohort (Table 2).

Correlation With External Validation Measures

For the entire cohort, the Spearman’s rho for correlation between our overuse index and inpatient rate of coronary angiography at the HSA level was 0.186 (95% CI, 0.089-0.283), Medicare reimbursement at the HSA level was 0.355 (95% CI, 0.272-0.437), and Medicare spending during the last 6 months of life at the HSA level was 0.149 (95% CI, 0.061-0.236) (Appendix Figures 5.1-5.3). The Spearman’s rho for correlation between our overuse index and county level standardized procedure cost was 0.284 (95% CI, 0.210-0.358), imaging cost was 0.268 (95% CI, 0.195-0.342), and testing cost was 0.226 (95% CI, 0.152-0.300) (Appendix Figures 6.1-6.3).

Overall Index Values and Change Over Time

The mean hospital index value was 0.541 (SD, 0.178) (Appendix Table 7). There was a slight but statistically significant annual increase in the overall mean index value over the study period, suggesting a small rise in overuse of diagnostic testing (coefficient 0.011; P <.001) (Appendix Figure 8).

Diagnostic Overuse Index Tertiles

Hospitals in the lowest tertile of the index tended to be smaller (based on number of beds) (P < .0001) and were more likely to be critical access hospitals (P <.0001). There was a significant difference in the proportion of nonteaching, minor teaching, and major teaching hospitals, with more nonteaching hospitals in tertile 1 (P = .001) (Table 3). The median ECI score was not significantly different among tertiles. Neither of the variables tested for discriminant validity (hospital ownership and Catholic Church affiliation) was associated with our index.

Adjusted Multilevel Mixed-Effects Ordinal Logistic Regression

Our overuse index correlated most closely with physician reimbursement, with an odds ratio of 2.02 (95% CI, 1.11-3.66) of being in a higher tertile of the overuse index when comparing tertiles 3 and 1 of this Dartmouth metric. Of the Medicare county-level metrics, our index correlated most closely with cost of procedures, with an odds ratio of 2.03 (95% CI, 1.21-3.39) of being in a higher overuse index tertile when comparing tertiles 3 and 1 of the cost per procedure metric (Figure 1).

Previous research shows variation among hospitals for overall physician spending,²⁰ noninvasive cardiac imaging,²¹ and the rate of finding obstructive lesions during elective coronary angiography.²² However, there is a lack of standardized methods to study a broad range of diagnostic overuse at the hospital level. To our knowledge, no studies have attempted to develop a diagnostic overuse index at the hospital level. We used a derivation-validation approach to achieve our goal. Although the five metrics represent a range of conditions, the EFA and Cronbach’s alpha tests suggest that they measure a common phenomenon. To avoid systematically excluding smaller hospitals, we limited the extent to which we eliminated hospitals with few observations. Our findings suggest that it may be reasonable to make generalizations on the diagnostic intensity of a hospital based on a relatively small number of discharges. Moreover, our index is a proof of concept that rates of negative diagnostic testing can serve as a proxy for estimating diagnostic overuse.

Our hospital-level index values extrapolated to the HSA level weakly correlated with prespecified Dartmouth Atlas metrics. In a multivariate ordinal regression, there was a significant though weak association between hospitals in higher tertiles of the Dartmouth Atlas metrics and categorization in higher tertiles of our diagnostic overuse index. Similarly, our hospital-level index correlated with two of the three county-level metrics in a multivariate ordinal regression.

We do not assume that all of the metrics in our index track together. However, our results, including the wide dispersion of index values among the tertiles (Table 3), suggest that at least some hospitals are outliers in multiple metrics. We did not assume ex ante that our index should correlate with Dartmouth overuse metrics or Medicare county-level spending; however, we did believe that an association with these measures would assist in validating our index. Given that our index utilizes four common diagnoses, while the Dartmouth and Medicare cost metrics are based on a much broader range of conditions, we would not expect more than a weak correlation even if our index is a valid way to measure overuse.

All of the metrics were based on the concept that hospitals with high rates of negative testing are likely providing large amounts of low-value care. Prior studies on diagnostic yield of CT scans in the emergency department for pulmonary embolus (PE) found an increase in testing and decrease in yield over time; these studies also showed that physicians with more experience ordered fewer CT scans and had a higher yield.²³ A review of electronic health records and billing data also showed that hospitals with higher rates of D-dimer testing had higher yields on CT scans ordered to test for PE.²⁴

We took advantage of the coding convention that certain diagnoses only be listed as the primary discharge diagnosis if no more specific diagnosis is made. This allowed us to identify hospitals that likely had high rates of negative tests without granular data. Of course, the metrics are not measuring rates of negative testing per se, but a proxy for this, based instead on the proportion of patients with a symptom-based primary discharge diagnosis who underwent diagnostic testing.

Measuring diagnostic overuse at the hospital level may help to understand factors that drive overuse, given that institutional incentives and culture likely play important roles in ordering tests. There is evidence that financial incentives drive physicians’ decisions,^25-27 and there is also evidence that institutional culture impacts outcomes.²⁸ Further, quality improvement projects are typically designed at the hospital level and may be an effective way to curb overuse.^29,30

Previous studies have focused on measuring variation among providers and identifying outlier physicians.^9,10,20 Providing feedback to underperforming physicians has been shown to change practice habits.^31,32 Efforts to improve the practice habits of outlier hospitals may have a number of advantages, including economies of scale and scope and the added benefit of improving the habits of all providers—not just those who are underperforming.

Ordering expensive diagnostic tests on patients with a low pretest probability of having an organic etiology for their symptoms contributes to high healthcare costs. Of course, we do not believe that the ideal rate of negative testing is zero. However, hospitals with high rates of negative diagnostic testing are more likely to be those with clinicians who use expensive tests as a substitute for clinical judgment or less-expensive tests (eg, D-dimer testing to rule out PE).

One challenge we faced is that there is no gold standard of hospital-level overuse with which to validate our index. Our index is weakly correlated with a number of regional metrics that may be proxies for overuse. We are reassured that there is a statistically significant correlation with measures at both HSA and county levels. These correlations are weak, but these regional metrics are themselves imperfect surrogates for overuse. Furthermore, our index is preliminary and will need refinement in future studies.

Limitations

Our analysis has multiple limitations. First, since it relies heavily on primary ICD discharge diagnosis codes, biases could exist due to variations in coding practices. Second, the SID does not include observation stays or tests conducted in the ED, so differential use of observation stays among hospitals might impact results. Finally, based on utilization data, we were not able to distinguish between CT scans of the chest, abdomen, and pelvis because the SID labels each of these as body CT.

We developed a novel index to measure diagnostic intensity at the hospital level. This index relies on the concept that high rates of negative diagnostic testing likely indicate some degree of overuse. Our index is parsimonious, does not require granular claims data, and measures a range of potentially overused tests for common clinical scenarios. Our next steps include further refining the index, testing it with granular data, and validating it with other datasets. Thereafter, this index may be useful at identifying positive and negative outliers to understand what processes of care contribute to outlier high and low levels of diagnostic testing. We suspect our index is more useful at identifying extremes than comparing hospitals in the middle of the utilization curve. Additionally, exploring the relationship among individual metrics and the relationship between our index and quality measures like mortality and readmissions may be informative.

There is substantial geographic variation in intensity of healthcare use in the United States,¹ yet areas with higher healthcare utilization do not demonstrate superior clinical outcomes.² Low-value care exposes patients to unnecessary anxiety, radiation, and risk for adverse events.

Previous research has focused on measuring low-value care at the level of hospital referral regions,^3-6 metropolitan statistical areas,⁷ provider organizations,⁸ and individual physicians.^9,10 Hospital referral regions designate regional healthcare markets for tertiary care and generally include at least one major referral center.¹¹ Well-calibrated and validated hospital-level measures of diagnostic overuse are lacking.

We sought to construct a novel index to measure hospital level overuse of diagnostic testing. We focused on diagnostic intensity rather than other forms of overuse such as screening or treatment intensity. Moreover, we aimed to create a parsimonious index—one that is simple, relies on a small number of inputs, is derived from readily available administrative data without the need for chart review or complex logic, and does not require exclusion criteria.

Conceptual Framework for Choosing Index Components

To create our overuse index, we took advantage of the requirements for International Classification of Diseases, 9th Revision-Clinical Modification (ICD-9-CM) billing codes 780-796; these codes are based on “symptoms, signs, and ill-defined conditions” and can only be listed as the primary discharge diagnosis if no more specific diagnosis is made.¹² As such, when coupled with expensive tests, a high prevalence of these symptom-based diagnosis codes at discharge may serve as a proxy for low-value care. One of the candidate metrics we selected was based on Choosing Wisely^® recommendations.¹³ The other candidate metrics were based on clinical experience and consensus of the study team.

Data Sources

We used hospital-level data on primary discharge diagnosis codes and utilization of testing data from the State Inpatient Databases (SID), which are part of the Agency for Healthcare Research and Quality Healthcare Cost and Utilization Project (HCUP). Our derivation cohort used data from acute care hospitals in Maryland, New Jersey, and Washington state. Our validation cohort used data from acute care hospitals in Kentucky, North Carolina, New York, and West Virginia. States were selected based on availability of data (certain states lacked complete testing utilization data) and cost of data acquisition. The SID contains hospital-level utilization of computed tomography (CT) scans (CT of the body and head) and diagnostic testing, including stress testing and esophagogastroduodenoscopy (EGD).

Data on three prespecified Dartmouth Atlas of Health Care metrics at the hospital service area (HSA) level were obtained from the Dartmouth Atlas website.¹⁴ These metrics were (1) rate of inpatient coronary angiograms per 1,000 Medicare enrollees, (2) price-adjusted physician reimbursement per fee-for-service Medicare enrollee per year (adjusted for patient sex, race, and age), and (3) mean inpatient spending per decedent in the last 6 months of life.¹⁵ Data on three prespecified Medicare metrics at the county level were obtained from the Centers for Medicare & Medicaid Services (CMS) website.¹⁶ These metrics were standardized per capita cost per (1) procedure, (2) imaging, and (3) test of Medicare fee-for-service patients. The CMS uses the Berenson-Eggers Type of Service Codes to classify fee-generating interventions into a number of categories, including procedure, imaging, and test.¹⁷

Components of the Overuse Index

We tested five candidate metrics for index inclusion (Table 1). We utilized Clinical Classifications Software (CCS) codes provided by HCUP, which combine several ICD-9-CM codes into a single primary CCS discharge code for ease of use. The components were (1) primary CCS diagnosis of “nausea and vomiting” coupled with body CT scan or EGD, (2) primary CCS diagnosis of abdominal pain and body CT scan or EGD, (3) primary CCS diagnosis of “nonspecific chest pain” and body CT scan or stress test, (4) primary CCS diagnosis of syncope and stress test, and (5) primary CCS diagnosis for syncope and CT of the brain. For a given metric, the denominator was all patients with the particular primary CCS discharge diagnosis code. The numerator was patients with the diagnostic code who also had the specific test or procedure. We characterized the denominators of each metric in terms of mean, SD, and range.

Index Inclusion Criteria and Construction

Specialty, pediatric, rehabilitation, and long-term care hospitals were excluded. Moreover, any hospital with an overall denominator (for the entire index, not an individual metric) of five or fewer observations was excluded. Admissions to acute care hospitals between January 2011 and September 2015 (time of transition from ICD-9-CM to ICD-10-CM) that had one of the specified diagnosis codes were included. For a given hospital, the value of each of the five candidate metrics was defined as the ratio of all admissions that had the given testing and all admissions during the observation period with inclusion CCS diagnosis codes.

Derivation and Validation of the Index

In our derivation cohort (hospitals in Maryland, New Jersey, and Washington state), we tested the temporal stability of each candidate metric by year using the intraclass correlation coefficient (ICC). Using exploratory factor analysis (EFA) and Cronbach’s alpha, we then tested internal consistency of the index candidate components to ensure that all measured a common underlying factor (ie, diagnostic overuse). To standardize data, test rates for both of these analyses were converted to z-scores. For the EFA, we expected that if the index was reflecting only a single underlying factor, the Eigenvalue for one factor should be much higher (typically above 1.0) than that for multiple factors. We calculated item-test correlation for each candidate metric and Cronbach’s alpha for the entire index. A high and stable value for item-test correlation for each index component, as well as a high Cronbach’s alpha, suggests that index components measure a single common factor. Given the small number of test items, we considered a Cronbach’s alpha above 0.6 to be satisfactory.

This analysis showed satisfactory temporal stability of each candidate metric and good internal consistency of the candidate metrics in the derivation cohort. Therefore, we decided to keep all metrics rather than discard any of them. This same process was repeated with the validation cohort (Kentucky, New York, North Carolina, and West Virginia) and then with the combined group of seven states. Tests on the validation and entire cohort further supported our decision to keep all five metrics.

To determine the overall index value for a hospital, all of its metric numerators and denominators were added to calculate one fraction. In this way for a given hospital, a metric for which there were no observations was effectively excluded from the index. This essentially weights each index component by frequency. We chose to count syncope admissions only once in the denominator to avoid the index being unduly influenced by this diagnosis. The hospital index values were combined into their HSAs by adding numerators and denominators from each hospital to calculate HSA index values, effectively giving higher weight to hospitals with more observations. Spearman’s correlation coefficients were measured for these Dartmouth Atlas metrics, also at the HSA level. For the county level analysis, we used a hospital-county crosswalk (available from the American Hospital Association [AHA] Annual Survey; https://www.ahadata.com/aha-annual-survey-database) to link a hospital overuse index value to a county level cost value rather than aggregating data at the county level. We felt this was appropriate, as HSAs were constructed to represent a local healthcare market, whereas counties are less likely to be homogenous from a healthcare perspective.

Analysis of Entire Hospital Sample

The mean index value and SD were calculated for the entire sample of hospitals and for each state. The mean index value for each year of data was calculated to measure the temporal change of the index (representing a change in diagnostic intensity over the study period) using linear regression. We divided the cohort of hospitals into tertiles based on their index value. This is consistent with the CMS categorization of hospital payments and value of care as being “at,” “significantly above,” or “significantly below” a mean value.¹⁸ The characteristics of hospitals by tertile were described by mean total hospital beds, mean annual admissions, teaching status (nonteaching hospital, minor teaching hospital, major teaching hospital), and critical access hospital (yes/no). We utilized the AHA Annual Survey for data on hospital characteristics. We calculated P values using analysis of variance for hospital bed size and a chi-square test for teaching status and critical access hospital.

The entire group of hospitals from seven states was then used to apply the index to the HSA level. Numerators and denominators for each hospital in an HSA were added to calculate an HSA-level proportion. Thus, the HSA level index value, though unweighted, is dominated by hospitals with larger numbers of observations. For each of the Dartmouth metrics, the adjusted odds ratio of being in a higher diagnostic overuse index tertile given being in a certain Dartmouth Atlas metric tertile was calculated using ordinal logistic regression. This model controlled for the mean number of beds of hospitals in the HSA (continuous variable), mean Elixhauser Comorbidity Index (ECI) score (continuous variable; unweighted average among hospitals in an HSA), whether the HSA had a major or minor teaching hospital (yes/no) or was a critical access hospital (yes/no), and state fixed effects. The ECI score is a validated score that uses the presence or absence of 29 comorbidities to predict in-hospital mortality.¹⁹ For discriminant validity, we also tested two variables not expected to be associated with overuse—hospital ownership and affiliation with the Catholic Church.

For the county-level analysis, ordinal logistic regression was used to predict the adjusted odds ratio of being in a higher diagnostic overuse index tertile given being in a certain tertile of a given county-level spending metric. This model controlled for hospital bed size (continuous variable), hospital ECI score (continuous variable), teaching status (major, minor, nonteaching), critical access hospital status (yes/no), and state fixed effects.

Descriptive Statistics for Metrics

A total of 620 acute care hospitals were included in the index. Thirteen hospitals were excluded because their denominator was five or fewer. The vast majority of HSAs (85.9%) had only one hospital, 8.2% had two hospitals, and 2.4% had three hospitals. Similarly, the majority of counties (68.7%) had only one hospital, 15.1% had two hospitals, and 6.6% had three hospitals (Appendix Tables 1.1 and 1.2). Nonspecific chest pain was the metric with largest denominator mean (650), SD (1,012), and range (0-10,725) (Appendix Table 2). Overall, the metric denominators were a small fraction of total hospital discharges, with means at the hospital level ranging from 0.69% for nausea and vomiting to 5.81% for nonspecific chest pain, suggesting that our index relies on a relatively small fraction of discharges.

Tests for Temporal Stability and Internal Consistency by Derivation and Validation Strategy

Overall, the ICCs for the derivation, validation, and entire cohort suggested strong temporal stability (Appendix Table 3). The EFA of the derivation, validation, and entire cohort showed high Eigenvalues for one principal component, with no other factors close to 1, indicating strong internal consistency (Appendix Table 4). The Cronbach’s alpha analysis also suggested strong internal consistency, with alpha values ranging from 0.73 for the validation cohort to 0.80 for the derivation cohort (Table 2).

Correlation With External Validation Measures

For the entire cohort, the Spearman’s rho for correlation between our overuse index and inpatient rate of coronary angiography at the HSA level was 0.186 (95% CI, 0.089-0.283), Medicare reimbursement at the HSA level was 0.355 (95% CI, 0.272-0.437), and Medicare spending during the last 6 months of life at the HSA level was 0.149 (95% CI, 0.061-0.236) (Appendix Figures 5.1-5.3). The Spearman’s rho for correlation between our overuse index and county level standardized procedure cost was 0.284 (95% CI, 0.210-0.358), imaging cost was 0.268 (95% CI, 0.195-0.342), and testing cost was 0.226 (95% CI, 0.152-0.300) (Appendix Figures 6.1-6.3).

Overall Index Values and Change Over Time

The mean hospital index value was 0.541 (SD, 0.178) (Appendix Table 7). There was a slight but statistically significant annual increase in the overall mean index value over the study period, suggesting a small rise in overuse of diagnostic testing (coefficient 0.011; P <.001) (Appendix Figure 8).

Diagnostic Overuse Index Tertiles

Hospitals in the lowest tertile of the index tended to be smaller (based on number of beds) (P < .0001) and were more likely to be critical access hospitals (P <.0001). There was a significant difference in the proportion of nonteaching, minor teaching, and major teaching hospitals, with more nonteaching hospitals in tertile 1 (P = .001) (Table 3). The median ECI score was not significantly different among tertiles. Neither of the variables tested for discriminant validity (hospital ownership and Catholic Church affiliation) was associated with our index.

Adjusted Multilevel Mixed-Effects Ordinal Logistic Regression

Our overuse index correlated most closely with physician reimbursement, with an odds ratio of 2.02 (95% CI, 1.11-3.66) of being in a higher tertile of the overuse index when comparing tertiles 3 and 1 of this Dartmouth metric. Of the Medicare county-level metrics, our index correlated most closely with cost of procedures, with an odds ratio of 2.03 (95% CI, 1.21-3.39) of being in a higher overuse index tertile when comparing tertiles 3 and 1 of the cost per procedure metric (Figure 1).

Previous research shows variation among hospitals for overall physician spending,²⁰ noninvasive cardiac imaging,²¹ and the rate of finding obstructive lesions during elective coronary angiography.²² However, there is a lack of standardized methods to study a broad range of diagnostic overuse at the hospital level. To our knowledge, no studies have attempted to develop a diagnostic overuse index at the hospital level. We used a derivation-validation approach to achieve our goal. Although the five metrics represent a range of conditions, the EFA and Cronbach’s alpha tests suggest that they measure a common phenomenon. To avoid systematically excluding smaller hospitals, we limited the extent to which we eliminated hospitals with few observations. Our findings suggest that it may be reasonable to make generalizations on the diagnostic intensity of a hospital based on a relatively small number of discharges. Moreover, our index is a proof of concept that rates of negative diagnostic testing can serve as a proxy for estimating diagnostic overuse.

Our hospital-level index values extrapolated to the HSA level weakly correlated with prespecified Dartmouth Atlas metrics. In a multivariate ordinal regression, there was a significant though weak association between hospitals in higher tertiles of the Dartmouth Atlas metrics and categorization in higher tertiles of our diagnostic overuse index. Similarly, our hospital-level index correlated with two of the three county-level metrics in a multivariate ordinal regression.

We do not assume that all of the metrics in our index track together. However, our results, including the wide dispersion of index values among the tertiles (Table 3), suggest that at least some hospitals are outliers in multiple metrics. We did not assume ex ante that our index should correlate with Dartmouth overuse metrics or Medicare county-level spending; however, we did believe that an association with these measures would assist in validating our index. Given that our index utilizes four common diagnoses, while the Dartmouth and Medicare cost metrics are based on a much broader range of conditions, we would not expect more than a weak correlation even if our index is a valid way to measure overuse.

All of the metrics were based on the concept that hospitals with high rates of negative testing are likely providing large amounts of low-value care. Prior studies on diagnostic yield of CT scans in the emergency department for pulmonary embolus (PE) found an increase in testing and decrease in yield over time; these studies also showed that physicians with more experience ordered fewer CT scans and had a higher yield.²³ A review of electronic health records and billing data also showed that hospitals with higher rates of D-dimer testing had higher yields on CT scans ordered to test for PE.²⁴

We took advantage of the coding convention that certain diagnoses only be listed as the primary discharge diagnosis if no more specific diagnosis is made. This allowed us to identify hospitals that likely had high rates of negative tests without granular data. Of course, the metrics are not measuring rates of negative testing per se, but a proxy for this, based instead on the proportion of patients with a symptom-based primary discharge diagnosis who underwent diagnostic testing.

Measuring diagnostic overuse at the hospital level may help to understand factors that drive overuse, given that institutional incentives and culture likely play important roles in ordering tests. There is evidence that financial incentives drive physicians’ decisions,^25-27 and there is also evidence that institutional culture impacts outcomes.²⁸ Further, quality improvement projects are typically designed at the hospital level and may be an effective way to curb overuse.^29,30

Previous studies have focused on measuring variation among providers and identifying outlier physicians.^9,10,20 Providing feedback to underperforming physicians has been shown to change practice habits.^31,32 Efforts to improve the practice habits of outlier hospitals may have a number of advantages, including economies of scale and scope and the added benefit of improving the habits of all providers—not just those who are underperforming.

Ordering expensive diagnostic tests on patients with a low pretest probability of having an organic etiology for their symptoms contributes to high healthcare costs. Of course, we do not believe that the ideal rate of negative testing is zero. However, hospitals with high rates of negative diagnostic testing are more likely to be those with clinicians who use expensive tests as a substitute for clinical judgment or less-expensive tests (eg, D-dimer testing to rule out PE).

One challenge we faced is that there is no gold standard of hospital-level overuse with which to validate our index. Our index is weakly correlated with a number of regional metrics that may be proxies for overuse. We are reassured that there is a statistically significant correlation with measures at both HSA and county levels. These correlations are weak, but these regional metrics are themselves imperfect surrogates for overuse. Furthermore, our index is preliminary and will need refinement in future studies.

Limitations

Our analysis has multiple limitations. First, since it relies heavily on primary ICD discharge diagnosis codes, biases could exist due to variations in coding practices. Second, the SID does not include observation stays or tests conducted in the ED, so differential use of observation stays among hospitals might impact results. Finally, based on utilization data, we were not able to distinguish between CT scans of the chest, abdomen, and pelvis because the SID labels each of these as body CT.

We developed a novel index to measure diagnostic intensity at the hospital level. This index relies on the concept that high rates of negative diagnostic testing likely indicate some degree of overuse. Our index is parsimonious, does not require granular claims data, and measures a range of potentially overused tests for common clinical scenarios. Our next steps include further refining the index, testing it with granular data, and validating it with other datasets. Thereafter, this index may be useful at identifying positive and negative outliers to understand what processes of care contribute to outlier high and low levels of diagnostic testing. We suspect our index is more useful at identifying extremes than comparing hospitals in the middle of the utilization curve. Additionally, exploring the relationship among individual metrics and the relationship between our index and quality measures like mortality and readmissions may be informative.

Gender-based discrimination refers to “any distinction, exclusion or restriction made on the basis of socially constructed gender roles and norms which prevents a person from enjoying full human rights.”¹ Similarly, sexual harassment encompasses a spectrum of sexual conduct that includes “unwelcome sexual advances, requests for sexual favors, and other verbal or physical harassment of a sexual nature,” as defined by the US Equal Employment Opportunity Commission.² Gender-based discrimination and sexual harassment can be “overt,” which includes explicitly recognizable behaviors, or they can be “implicit,” which includes verbal and nonverbal behaviors that often go unrecognized but convey hostility, objectification, or exclusion of another person. Gender-based discrimination and sexual harassment are commonly described and likely more prevalent in academic settings.^3-6 Female physicians are disproportionately affected by gender-based discrimination and sexual harassment, compared with their male peers.^4,7

Female physicians face workplace harassment from both patients and coworkers. A study in Canada reported that more than 75% of female physicians experienced sexual harassment from their patients.⁸ A recent study showed almost half of the physicians who reported harassment, which was three times more often among female physicians, described other physician colleagues as perpetrators.⁹ In a study among clinician-researchers in the field of academic medicine, 30% of females reported having experienced sexual harassment, compared with 4% of males.⁷ Among females who reported harassment in this study, 47% stated that these experiences adversely affected their opportunities for career advancement. Career stage may also affect experiences or perceptions of gender-based discrimination and sexual harassment, with females in earlier career stages reporting a less favorable environment of gender equity.¹⁰

Hospital medicine is a young and evolving specialty, and the number of hospitalists has increased substantially from a few hundred at the time of inception to over 50,000 as of 2016.¹¹ The proportion of female hospitalists increased from 31% in 2012 to 52% in 2014, reflecting equal gender representation in hospital medicine.¹² Available evidence shows gender disparities exist in hospital medicine disproportionately affecting female hospitalists in their career advancement, including leadership and scholarship opportunities.¹³ However, there remains a knowledge gap regarding the prevalence of gender-based discrimination and sexual harassment experienced by hospitalists.

Our study examines the experiences of academic hospitalists regarding gender-based discrimination and sexual harassment and the impact of gender on career satisfaction and advancement.

Study Design and Participants

An online survey was developed and approved by the institutional board review (IRB) at the Medical College of Wisconsin in Milwaukee. University-based academic centers with hospitalist programs, identified through personal connections, from across the continental United Stated were identified as potential study sites, and leaders at each institution were contacted to ascertain potential participation in the survey. The survey was distributed to Internal Medicine hospitalists at 18 participating academic institutions from January 2019 to June 2019. Participation was voluntary. The cover letter explained the purpose of the study and provided a link to the survey. To maintain anonymity, none of the questionnaires requested identifying information from participants. A web-based Qualtrics online-based survey platform was used.

Survey Elements

The survey aimed to assess several elements of gender-based discrimination and sexual harassment. All questions about these experiences distinguished encounters with patients from those with colleagues, and questions about occurrences either over a career or in the last 30 days were intended to capture both distant and recent timeframes. The theme for the questions for the survey was based on previous studies.^4,7,8 The wording of questions was simplified to make them easily understandable, and the brevity of the survey was maintained to prevent possible nonresponses.¹⁴ Additional questions (mistaken for a healthcare provider other than a physician, feeling respected by patients and colleagues, referred to by terms such as “honey,” “dear,” “sweetheart,” “sugar,” or equivalent), which were deemed relevant in day-to-day clinical practice through consensus among study investigators and discussions among peer hospitalists, were incorporated into the final survey (Appendix). Survey questions were intended to capture several elements regarding interactions with patients and with colleagues or other healthcare providers (HCPs).

Questions on gender-based discrimination included:

• Has a patient [colleague or other healthcare provider] mistaken you for a healthcare provider other than a physician?
• Has a patient [colleague or other healthcare provider] asked you to do something not at your level of training?
• Do you feel respected?
Do you perceive your gender has impacted opportunities for your career advancement?

Questions on sexual harassment included:

• Has a patient [colleague or other healthcare provider] touched you inappropriately, made sexual remarks or gestures, or made suggestive looks?
• Has a patient [colleague or other healthcare provider] referred to you by terms such as “honey,” “dear,” “sweetheart,” “sugar,” or equivalent?

In addition, the survey sought demographic information of the participants (age, gender, and race/ethnicity) and information about their individual institutions (names and locations) (Appendix). The geographical locations of the institutions were further categorized into four different regions according to the United States Census Bureau (Northwest, Midwest, South, and West). At the end of the survey, participants were given an opportunity to provide any additional comments.

Statistical Analysis

Standard descriptive summary statistics were used for demographic data. Associations between the variables were analyzed using chi-square test or Fischer’s exact test, as appropriate, for categorical variables and t test for continuous variables. The variations among institution-based responses were presented in the form of inter-quartile range (IQR). All tests were 2-sided, and P values less than .05 were considered statistically significant. All analyses were performed using IBM^® SPSS^® Statistics software version 24. Relevant responses representative of the overall respondents’ sentiments as provided under additional comment section were discussed and cited.

Eighteen different academic institutions across the United States participated in the study, with 336 individual responses. The majority of respondents were females (57%), in younger age categories (58% were 30-39 years old), Caucasian (59%), and early-career hospitalists (>50% working as hospitalists for ≤5 years) (Table 1). Regarding the overall geographic distribution, the largest number of responses were from the Midwest (n = 115; 35.6%) (Table 1 and Appendix).

Gender Discrimination

Interactions With Patients

Over their careers, 69% of hospitalists reported being mistaken for an HCP other than a physician by patients. This was more common among females than among males (99% vs 29%, respectively; P < .001) (Table 2). Almost half (48%) reported this had occurred in the last 30 days, more frequently by females (76% vs 10%; P < .001).

Of responding hospitalists, 96% stated that, over their careers, they have been asked by patients to do something they did not consider to be at their level of training (eg, help get food or water, help with a bed pan), with a higher prevalence of such experiences among females than males (99% vs 93%, respectively; P = .004) (Table 2). Most (71%) said they had experienced this in the last 30 days, which was again more frequently reported by females (78% vs 62%; P = .001).

The responses from female hospitalists regarding their career-long experiences of being mistaken for an HCP or asked to do something not at their level of training by their patients had both the highest number of positive responses across institutions (median of hospital proportions, 100%) and the least institutional variation since both had the narrowest IQR) (Table 2).

Interactions With Colleagues or Other HCPs

Among hospitalists responding to the survey, 46% felt that, over their careers, they had been mistaken for nonphysician HCPs by colleagues or other HCPs. This was more prevalent among females than among males (65% vs 20%; P < .001) (Table 2). Among respondents, 14% reported these events had occurred in the last 30 days, which was again more common among females (21% vs 5%; P < .001).

Over their careers, 26% of hospitalists reported they have been asked by a colleague or HCP to do something not at their level of training (eg, clean up the physician’s work room, make coffee, take notes in a meeting), with similar prevalence among females and males (29% vs 23%; P = .228). Ten percent reported these occurrences in the last 30 days, which was similar between females and males (12% vs 9%; P = .330).

Feelings of Respect and Opportunities for Career Advancement

When asked to rate the statement “I feel respected by patients” on a 5-point Likert scale, female hospitalists overall scored significantly lower as compared with their male counterparts (mean score, 3.73 vs 4.04; P < .001) (Table 3); this was also true when asked about feelings of respect by physicians (mean score, 3.84 vs 4.15; P < .001). Female hospitalists were more likely than males to report that their gender has more negatively impacted their opportunities for career advancement (mean score, 2.73 vs 3.34; P < .001).

Sexual Harassment

Interactions With Patients

Over half (57%) of hospitalists reported career-long experiences of patient(s) touching them inappropriately, making sexual remarks or gestures, or making suggestive looks. These experiences were more prevalent among females than among males (72% vs 36%, respectively; P < .001) (Table 2). Fifteen percent said they had such experience in rhe last 30 days, which was also more common among females (22% vs 6%; P < .001).

Most hospitalists (84%) reported that patients have referred to them by inappropriately familiar terms such as “honey,” “dear,” “sweetheart,” “sugar,” or equivalent over their careers, with females more frequently reporting these behaviors (95% vs 68%; P < .001). Experiencing this during the last 30 days was reported by 48%, which was again more common among females (68% vs 23%; P < .001).

Interactions With Colleagues or Other HCPs

Within their careers, 15% of hospitalists reported at least one experience of a colleague or HCP touching them inappropriately or making sexual remarks, gestures, or suggestive looks. This was more prevalent for females than males (18% vs 10%, respectively; P = .033). Only 2% of both females and males reported these experiences in the last 30 days (2% vs 2%; P = .981).

Almost one-third of participants (32%) affirmed that another HCP has referred to them by terms such as “honey,” “dear,” “sweetheart,” “sugar,” or equivalent in their career, with a higher proportion of females than males reporting these events (39% vs 23%; P = .002) (Table 2). Experiencing this during the last 30 days was reported by 10%, which was similar between females and males (12% vs 7%; P = .112).

Additional Comments From Respondents

• “Throughout my training and now into my professional career, there are nearly weekly incidents of elderly male patients referring to me as “honey/dear/sweetie” or even by my first name, even though I introduce myself as their physician and politely correct them. They will often refer to me as a nurse and ask me to do something not at my level of training. Sometimes even despite correcting the patient, they continue to refer to me as such. Throughout the years, other female MDs and I have discussed that this is ‘status quo’ for female physicians and observe that this is not an experience that male MDs share.”
• “I frequently round with a male nurse practitioner and the patients almost always, despite introducing ourselves and our roles, turn to him and ask him questions instead of addressing them to me.”
• “Our institution allows female faculty to be interviewed about childcare, household labor division, plans for pregnancy. One professor asks women private details about their private relationships such as what they do with spouse on date night or weekends away.”
• “It’s hard to answer questions related to my level of training. I don’t think it’s unreasonable for people to ask me to do things, no matter my level of training. . . . I don’t think being a doctor means that I am above this, or that it is inappropriate to be asked to do this.”

This survey demonstrated that gender-based discrimination and sexual harassment in the academic hospitalist healthcare environment are common, both in more distant and recent time frames. Notably, these experiences are shared by female and male physicians in interactions with both patients and colleagues, though male hospitalists report most of these experiences at significantly lower frequencies than females. These results support past work showing that female physicians are significantly more likely to be subjected to gender-based discrimination and sexual harassment, but also challenges the perception that gender-based discrimination and sexual harassment are uniquely experienced by females.

A startling number of females and males in the study reported sexual harassment (inappropriate touching, remarks, gestures, and looks) when interacting with patients throughout their careers and in last 30 days. Many males and females reported that patients had referred to them with inappropriately familiar, and potentially demeaning, terms of endearment. For both overt and implicit sexual harassment, females were significantly more likely than males to report experiencing these behaviors when interacting with patients. Although some of these experiences may seem less harmful than others, a meta-analysis demonstrated that frequent, less intense experiences of gender-based discrimination and sexual harassment have a similar impact on female’s well-being as do less frequent, more intense experiences.¹⁵ Although the person using the terms of endearment like “honey,” “sugar,” or “sweetheart” may feel the terms are harmless, such expressions can be inappropriate and constitute sexual harassment according to the U.S. Department of the Interior’s Office of Civil Rights.¹⁶ The Sexual Harassment/Assault Response and Prevention Program (SHARP) also classifies such terms into verbal categories of sexual harrassment.¹⁷

Of female physicians surveyed, 99% reported that they had been mistaken for HCPs other than physicians by their patients over their careers. Although this was also reported by male physicians, the experience was 3.4 times as likely for female physicians. Misidentification by patients may represent a disconnect between the growing female representation in the physician workforce and patients’ conceptions of the traditional image of a physician.

In parallel with this finding of misidentification, an interesting area of the study was the question regarding being asked to do “something not at your level of training.” A recurring theme in the comments was a rejection of the notion that certain tasks were “beneath a level of training,” suggesting a common view that acts of caregiving are not bounded by hierarchy. Analysis of qualitative responses showed that 40% of these responses had comments regarding this question. An example was “It’s hard to answer questions related to my level of training. . . . I don’t think being a doctor means that I am above this, or that it is inappropriate to be asked to do this.” Notably, however, a larger number of female than male physicians responded yes to this question in both study time frames. This points to a differential in how female physicians are viewed by patients, both in frequent misidentification and in behaviors more frequently asked of female physicians than their male counterparts. Given the comments, it may also suggest a difference in how female and male physicians perceive the fluidity of bounds on their care-taking roles set by their “level of training.”

A large number of study participants were early-career hospitalists, which may in part explain some of the study results. In a previous study of gender equity in an Internal Medicine department, physicians practicing medicine for more than 15 years perceived the departmental culture as more favorable than physicians with shorter careers.¹⁰ Additionally, the perception of cultures was most discordant between senior male physicians and junior female physicians.¹⁰ Because many hospitalists are early-career physicians, they may have trained in an environment that had heightened awareness surrounding gender-based discrimination and sexual harassment, which affects the overall study results.

Multiple qualitative comments, mentioned above, were submitted by participants describing their experiences in all categories. Such comments paint a picture of insidious bias and cultural norms affecting the quality of female physicians’ work lives.

Two questions focused on career satisfaction and the sense of respect from patients and colleagues. In both responses, there was a statistically different response between males and females, with females less likely to report that they felt respected and that their gender adversely impacted their opportunities for career advancement. This is disturbing information and warrants more investigation.

The reasons for the observed prevalence of gender-based discrimination and sexual harassment in this broad survey of academic hospitalists are uncertain. Multiple studies to date have demonstrated that gender-based discrimination and sexual harassment have historically existed in medicine and continue to even today. Unlike physicians with long-term relationships with patients, hospitalists may face more exposure due to a lack of long-term continuity with patients. The absence of an established trust in the relationship also may make them more vulnerable to inappropriate behaviors when interacting with patients. Hospital medicine, however, is a young specialty with equal gender representation and should be at the forefront of addressing and solving these issues of gender-based discrimination and sexual harassment.

The survey had a good distribution between female and male participants. Additionally, the survey reflected the general distribution of the national hospitalist workforce in gender, age, and ethnic/racial distribution, as well as number of years in practice.¹² The study surveyed respondents regarding experiences in both long- and short-term time frames, as well as experiences with patients and colleagues.

Our study reflects a cross-sectional snapshot of hospitalists’ perceptions with no longitudinal follow-up. Since the survey was limited to academic medical centers, it may not reflect experiences in community/private practice settings. The small number of participants limited the ability to perform subgroup analyses by age, race, or years in practice, which may play a role in interactions with patients and colleagues. Since the number of respondents varied greatly by institution, a minority of institutions could have influenced some of the findings. Narrow IQRs of the hospital proportions as shown in Table 2 would suggest similar responses across institutions, whereas wide IQRs would suggest that a smaller number of institutions were possibly driving the findings. Because of the survey distribution method, it is unknown how many physicians received the survey and a response rate could not be calculated. Further, selection, recall, and detection biases cannot be ruled out.

This survey shows that gender-based discrimination and sexual harassment in the academic hospitalist healthcare environment are common and more frequently experienced by female physicians, both in interactions with patients and colleagues. Our study highlights the need to address this prevalent issue among academic hospitalists.

Gender-based discrimination refers to “any distinction, exclusion or restriction made on the basis of socially constructed gender roles and norms which prevents a person from enjoying full human rights.”¹ Similarly, sexual harassment encompasses a spectrum of sexual conduct that includes “unwelcome sexual advances, requests for sexual favors, and other verbal or physical harassment of a sexual nature,” as defined by the US Equal Employment Opportunity Commission.² Gender-based discrimination and sexual harassment can be “overt,” which includes explicitly recognizable behaviors, or they can be “implicit,” which includes verbal and nonverbal behaviors that often go unrecognized but convey hostility, objectification, or exclusion of another person. Gender-based discrimination and sexual harassment are commonly described and likely more prevalent in academic settings.^3-6 Female physicians are disproportionately affected by gender-based discrimination and sexual harassment, compared with their male peers.^4,7

Female physicians face workplace harassment from both patients and coworkers. A study in Canada reported that more than 75% of female physicians experienced sexual harassment from their patients.⁸ A recent study showed almost half of the physicians who reported harassment, which was three times more often among female physicians, described other physician colleagues as perpetrators.⁹ In a study among clinician-researchers in the field of academic medicine, 30% of females reported having experienced sexual harassment, compared with 4% of males.⁷ Among females who reported harassment in this study, 47% stated that these experiences adversely affected their opportunities for career advancement. Career stage may also affect experiences or perceptions of gender-based discrimination and sexual harassment, with females in earlier career stages reporting a less favorable environment of gender equity.¹⁰

Hospital medicine is a young and evolving specialty, and the number of hospitalists has increased substantially from a few hundred at the time of inception to over 50,000 as of 2016.¹¹ The proportion of female hospitalists increased from 31% in 2012 to 52% in 2014, reflecting equal gender representation in hospital medicine.¹² Available evidence shows gender disparities exist in hospital medicine disproportionately affecting female hospitalists in their career advancement, including leadership and scholarship opportunities.¹³ However, there remains a knowledge gap regarding the prevalence of gender-based discrimination and sexual harassment experienced by hospitalists.

Our study examines the experiences of academic hospitalists regarding gender-based discrimination and sexual harassment and the impact of gender on career satisfaction and advancement.

Study Design and Participants

An online survey was developed and approved by the institutional board review (IRB) at the Medical College of Wisconsin in Milwaukee. University-based academic centers with hospitalist programs, identified through personal connections, from across the continental United Stated were identified as potential study sites, and leaders at each institution were contacted to ascertain potential participation in the survey. The survey was distributed to Internal Medicine hospitalists at 18 participating academic institutions from January 2019 to June 2019. Participation was voluntary. The cover letter explained the purpose of the study and provided a link to the survey. To maintain anonymity, none of the questionnaires requested identifying information from participants. A web-based Qualtrics online-based survey platform was used.

Survey Elements

The survey aimed to assess several elements of gender-based discrimination and sexual harassment. All questions about these experiences distinguished encounters with patients from those with colleagues, and questions about occurrences either over a career or in the last 30 days were intended to capture both distant and recent timeframes. The theme for the questions for the survey was based on previous studies.^4,7,8 The wording of questions was simplified to make them easily understandable, and the brevity of the survey was maintained to prevent possible nonresponses.¹⁴ Additional questions (mistaken for a healthcare provider other than a physician, feeling respected by patients and colleagues, referred to by terms such as “honey,” “dear,” “sweetheart,” “sugar,” or equivalent), which were deemed relevant in day-to-day clinical practice through consensus among study investigators and discussions among peer hospitalists, were incorporated into the final survey (Appendix). Survey questions were intended to capture several elements regarding interactions with patients and with colleagues or other healthcare providers (HCPs).

Questions on gender-based discrimination included:

• Has a patient [colleague or other healthcare provider] mistaken you for a healthcare provider other than a physician?
• Has a patient [colleague or other healthcare provider] asked you to do something not at your level of training?
• Do you feel respected?
Do you perceive your gender has impacted opportunities for your career advancement?

Questions on sexual harassment included:

• Has a patient [colleague or other healthcare provider] touched you inappropriately, made sexual remarks or gestures, or made suggestive looks?
• Has a patient [colleague or other healthcare provider] referred to you by terms such as “honey,” “dear,” “sweetheart,” “sugar,” or equivalent?

In addition, the survey sought demographic information of the participants (age, gender, and race/ethnicity) and information about their individual institutions (names and locations) (Appendix). The geographical locations of the institutions were further categorized into four different regions according to the United States Census Bureau (Northwest, Midwest, South, and West). At the end of the survey, participants were given an opportunity to provide any additional comments.

Statistical Analysis

Standard descriptive summary statistics were used for demographic data. Associations between the variables were analyzed using chi-square test or Fischer’s exact test, as appropriate, for categorical variables and t test for continuous variables. The variations among institution-based responses were presented in the form of inter-quartile range (IQR). All tests were 2-sided, and P values less than .05 were considered statistically significant. All analyses were performed using IBM^® SPSS^® Statistics software version 24. Relevant responses representative of the overall respondents’ sentiments as provided under additional comment section were discussed and cited.

Eighteen different academic institutions across the United States participated in the study, with 336 individual responses. The majority of respondents were females (57%), in younger age categories (58% were 30-39 years old), Caucasian (59%), and early-career hospitalists (>50% working as hospitalists for ≤5 years) (Table 1). Regarding the overall geographic distribution, the largest number of responses were from the Midwest (n = 115; 35.6%) (Table 1 and Appendix).

Gender Discrimination

Interactions With Patients

Over their careers, 69% of hospitalists reported being mistaken for an HCP other than a physician by patients. This was more common among females than among males (99% vs 29%, respectively; P < .001) (Table 2). Almost half (48%) reported this had occurred in the last 30 days, more frequently by females (76% vs 10%; P < .001).

Of responding hospitalists, 96% stated that, over their careers, they have been asked by patients to do something they did not consider to be at their level of training (eg, help get food or water, help with a bed pan), with a higher prevalence of such experiences among females than males (99% vs 93%, respectively; P = .004) (Table 2). Most (71%) said they had experienced this in the last 30 days, which was again more frequently reported by females (78% vs 62%; P = .001).

The responses from female hospitalists regarding their career-long experiences of being mistaken for an HCP or asked to do something not at their level of training by their patients had both the highest number of positive responses across institutions (median of hospital proportions, 100%) and the least institutional variation since both had the narrowest IQR) (Table 2).

Interactions With Colleagues or Other HCPs

Among hospitalists responding to the survey, 46% felt that, over their careers, they had been mistaken for nonphysician HCPs by colleagues or other HCPs. This was more prevalent among females than among males (65% vs 20%; P < .001) (Table 2). Among respondents, 14% reported these events had occurred in the last 30 days, which was again more common among females (21% vs 5%; P < .001).

Over their careers, 26% of hospitalists reported they have been asked by a colleague or HCP to do something not at their level of training (eg, clean up the physician’s work room, make coffee, take notes in a meeting), with similar prevalence among females and males (29% vs 23%; P = .228). Ten percent reported these occurrences in the last 30 days, which was similar between females and males (12% vs 9%; P = .330).

Feelings of Respect and Opportunities for Career Advancement

When asked to rate the statement “I feel respected by patients” on a 5-point Likert scale, female hospitalists overall scored significantly lower as compared with their male counterparts (mean score, 3.73 vs 4.04; P < .001) (Table 3); this was also true when asked about feelings of respect by physicians (mean score, 3.84 vs 4.15; P < .001). Female hospitalists were more likely than males to report that their gender has more negatively impacted their opportunities for career advancement (mean score, 2.73 vs 3.34; P < .001).

Sexual Harassment

Interactions With Patients

Over half (57%) of hospitalists reported career-long experiences of patient(s) touching them inappropriately, making sexual remarks or gestures, or making suggestive looks. These experiences were more prevalent among females than among males (72% vs 36%, respectively; P < .001) (Table 2). Fifteen percent said they had such experience in rhe last 30 days, which was also more common among females (22% vs 6%; P < .001).

Most hospitalists (84%) reported that patients have referred to them by inappropriately familiar terms such as “honey,” “dear,” “sweetheart,” “sugar,” or equivalent over their careers, with females more frequently reporting these behaviors (95% vs 68%; P < .001). Experiencing this during the last 30 days was reported by 48%, which was again more common among females (68% vs 23%; P < .001).

Interactions With Colleagues or Other HCPs

Within their careers, 15% of hospitalists reported at least one experience of a colleague or HCP touching them inappropriately or making sexual remarks, gestures, or suggestive looks. This was more prevalent for females than males (18% vs 10%, respectively; P = .033). Only 2% of both females and males reported these experiences in the last 30 days (2% vs 2%; P = .981).

Almost one-third of participants (32%) affirmed that another HCP has referred to them by terms such as “honey,” “dear,” “sweetheart,” “sugar,” or equivalent in their career, with a higher proportion of females than males reporting these events (39% vs 23%; P = .002) (Table 2). Experiencing this during the last 30 days was reported by 10%, which was similar between females and males (12% vs 7%; P = .112).

Additional Comments From Respondents

• “Throughout my training and now into my professional career, there are nearly weekly incidents of elderly male patients referring to me as “honey/dear/sweetie” or even by my first name, even though I introduce myself as their physician and politely correct them. They will often refer to me as a nurse and ask me to do something not at my level of training. Sometimes even despite correcting the patient, they continue to refer to me as such. Throughout the years, other female MDs and I have discussed that this is ‘status quo’ for female physicians and observe that this is not an experience that male MDs share.”
• “I frequently round with a male nurse practitioner and the patients almost always, despite introducing ourselves and our roles, turn to him and ask him questions instead of addressing them to me.”
• “Our institution allows female faculty to be interviewed about childcare, household labor division, plans for pregnancy. One professor asks women private details about their private relationships such as what they do with spouse on date night or weekends away.”
• “It’s hard to answer questions related to my level of training. I don’t think it’s unreasonable for people to ask me to do things, no matter my level of training. . . . I don’t think being a doctor means that I am above this, or that it is inappropriate to be asked to do this.”

This survey demonstrated that gender-based discrimination and sexual harassment in the academic hospitalist healthcare environment are common, both in more distant and recent time frames. Notably, these experiences are shared by female and male physicians in interactions with both patients and colleagues, though male hospitalists report most of these experiences at significantly lower frequencies than females. These results support past work showing that female physicians are significantly more likely to be subjected to gender-based discrimination and sexual harassment, but also challenges the perception that gender-based discrimination and sexual harassment are uniquely experienced by females.

A startling number of females and males in the study reported sexual harassment (inappropriate touching, remarks, gestures, and looks) when interacting with patients throughout their careers and in last 30 days. Many males and females reported that patients had referred to them with inappropriately familiar, and potentially demeaning, terms of endearment. For both overt and implicit sexual harassment, females were significantly more likely than males to report experiencing these behaviors when interacting with patients. Although some of these experiences may seem less harmful than others, a meta-analysis demonstrated that frequent, less intense experiences of gender-based discrimination and sexual harassment have a similar impact on female’s well-being as do less frequent, more intense experiences.¹⁵ Although the person using the terms of endearment like “honey,” “sugar,” or “sweetheart” may feel the terms are harmless, such expressions can be inappropriate and constitute sexual harassment according to the U.S. Department of the Interior’s Office of Civil Rights.¹⁶ The Sexual Harassment/Assault Response and Prevention Program (SHARP) also classifies such terms into verbal categories of sexual harrassment.¹⁷

Of female physicians surveyed, 99% reported that they had been mistaken for HCPs other than physicians by their patients over their careers. Although this was also reported by male physicians, the experience was 3.4 times as likely for female physicians. Misidentification by patients may represent a disconnect between the growing female representation in the physician workforce and patients’ conceptions of the traditional image of a physician.

In parallel with this finding of misidentification, an interesting area of the study was the question regarding being asked to do “something not at your level of training.” A recurring theme in the comments was a rejection of the notion that certain tasks were “beneath a level of training,” suggesting a common view that acts of caregiving are not bounded by hierarchy. Analysis of qualitative responses showed that 40% of these responses had comments regarding this question. An example was “It’s hard to answer questions related to my level of training. . . . I don’t think being a doctor means that I am above this, or that it is inappropriate to be asked to do this.” Notably, however, a larger number of female than male physicians responded yes to this question in both study time frames. This points to a differential in how female physicians are viewed by patients, both in frequent misidentification and in behaviors more frequently asked of female physicians than their male counterparts. Given the comments, it may also suggest a difference in how female and male physicians perceive the fluidity of bounds on their care-taking roles set by their “level of training.”

A large number of study participants were early-career hospitalists, which may in part explain some of the study results. In a previous study of gender equity in an Internal Medicine department, physicians practicing medicine for more than 15 years perceived the departmental culture as more favorable than physicians with shorter careers.¹⁰ Additionally, the perception of cultures was most discordant between senior male physicians and junior female physicians.¹⁰ Because many hospitalists are early-career physicians, they may have trained in an environment that had heightened awareness surrounding gender-based discrimination and sexual harassment, which affects the overall study results.

Multiple qualitative comments, mentioned above, were submitted by participants describing their experiences in all categories. Such comments paint a picture of insidious bias and cultural norms affecting the quality of female physicians’ work lives.

Two questions focused on career satisfaction and the sense of respect from patients and colleagues. In both responses, there was a statistically different response between males and females, with females less likely to report that they felt respected and that their gender adversely impacted their opportunities for career advancement. This is disturbing information and warrants more investigation.

The reasons for the observed prevalence of gender-based discrimination and sexual harassment in this broad survey of academic hospitalists are uncertain. Multiple studies to date have demonstrated that gender-based discrimination and sexual harassment have historically existed in medicine and continue to even today. Unlike physicians with long-term relationships with patients, hospitalists may face more exposure due to a lack of long-term continuity with patients. The absence of an established trust in the relationship also may make them more vulnerable to inappropriate behaviors when interacting with patients. Hospital medicine, however, is a young specialty with equal gender representation and should be at the forefront of addressing and solving these issues of gender-based discrimination and sexual harassment.

The survey had a good distribution between female and male participants. Additionally, the survey reflected the general distribution of the national hospitalist workforce in gender, age, and ethnic/racial distribution, as well as number of years in practice.¹² The study surveyed respondents regarding experiences in both long- and short-term time frames, as well as experiences with patients and colleagues.

Our study reflects a cross-sectional snapshot of hospitalists’ perceptions with no longitudinal follow-up. Since the survey was limited to academic medical centers, it may not reflect experiences in community/private practice settings. The small number of participants limited the ability to perform subgroup analyses by age, race, or years in practice, which may play a role in interactions with patients and colleagues. Since the number of respondents varied greatly by institution, a minority of institutions could have influenced some of the findings. Narrow IQRs of the hospital proportions as shown in Table 2 would suggest similar responses across institutions, whereas wide IQRs would suggest that a smaller number of institutions were possibly driving the findings. Because of the survey distribution method, it is unknown how many physicians received the survey and a response rate could not be calculated. Further, selection, recall, and detection biases cannot be ruled out.

This survey shows that gender-based discrimination and sexual harassment in the academic hospitalist healthcare environment are common and more frequently experienced by female physicians, both in interactions with patients and colleagues. Our study highlights the need to address this prevalent issue among academic hospitalists.

Gender-based discrimination refers to “any distinction, exclusion or restriction made on the basis of socially constructed gender roles and norms which prevents a person from enjoying full human rights.”¹ Similarly, sexual harassment encompasses a spectrum of sexual conduct that includes “unwelcome sexual advances, requests for sexual favors, and other verbal or physical harassment of a sexual nature,” as defined by the US Equal Employment Opportunity Commission.² Gender-based discrimination and sexual harassment can be “overt,” which includes explicitly recognizable behaviors, or they can be “implicit,” which includes verbal and nonverbal behaviors that often go unrecognized but convey hostility, objectification, or exclusion of another person. Gender-based discrimination and sexual harassment are commonly described and likely more prevalent in academic settings.^3-6 Female physicians are disproportionately affected by gender-based discrimination and sexual harassment, compared with their male peers.^4,7

Female physicians face workplace harassment from both patients and coworkers. A study in Canada reported that more than 75% of female physicians experienced sexual harassment from their patients.⁸ A recent study showed almost half of the physicians who reported harassment, which was three times more often among female physicians, described other physician colleagues as perpetrators.⁹ In a study among clinician-researchers in the field of academic medicine, 30% of females reported having experienced sexual harassment, compared with 4% of males.⁷ Among females who reported harassment in this study, 47% stated that these experiences adversely affected their opportunities for career advancement. Career stage may also affect experiences or perceptions of gender-based discrimination and sexual harassment, with females in earlier career stages reporting a less favorable environment of gender equity.¹⁰

Hospital medicine is a young and evolving specialty, and the number of hospitalists has increased substantially from a few hundred at the time of inception to over 50,000 as of 2016.¹¹ The proportion of female hospitalists increased from 31% in 2012 to 52% in 2014, reflecting equal gender representation in hospital medicine.¹² Available evidence shows gender disparities exist in hospital medicine disproportionately affecting female hospitalists in their career advancement, including leadership and scholarship opportunities.¹³ However, there remains a knowledge gap regarding the prevalence of gender-based discrimination and sexual harassment experienced by hospitalists.

Our study examines the experiences of academic hospitalists regarding gender-based discrimination and sexual harassment and the impact of gender on career satisfaction and advancement.

Study Design and Participants

An online survey was developed and approved by the institutional board review (IRB) at the Medical College of Wisconsin in Milwaukee. University-based academic centers with hospitalist programs, identified through personal connections, from across the continental United Stated were identified as potential study sites, and leaders at each institution were contacted to ascertain potential participation in the survey. The survey was distributed to Internal Medicine hospitalists at 18 participating academic institutions from January 2019 to June 2019. Participation was voluntary. The cover letter explained the purpose of the study and provided a link to the survey. To maintain anonymity, none of the questionnaires requested identifying information from participants. A web-based Qualtrics online-based survey platform was used.

Survey Elements

The survey aimed to assess several elements of gender-based discrimination and sexual harassment. All questions about these experiences distinguished encounters with patients from those with colleagues, and questions about occurrences either over a career or in the last 30 days were intended to capture both distant and recent timeframes. The theme for the questions for the survey was based on previous studies.^4,7,8 The wording of questions was simplified to make them easily understandable, and the brevity of the survey was maintained to prevent possible nonresponses.¹⁴ Additional questions (mistaken for a healthcare provider other than a physician, feeling respected by patients and colleagues, referred to by terms such as “honey,” “dear,” “sweetheart,” “sugar,” or equivalent), which were deemed relevant in day-to-day clinical practice through consensus among study investigators and discussions among peer hospitalists, were incorporated into the final survey (Appendix). Survey questions were intended to capture several elements regarding interactions with patients and with colleagues or other healthcare providers (HCPs).

Questions on gender-based discrimination included:

• Has a patient [colleague or other healthcare provider] mistaken you for a healthcare provider other than a physician?
• Has a patient [colleague or other healthcare provider] asked you to do something not at your level of training?
• Do you feel respected?
Do you perceive your gender has impacted opportunities for your career advancement?

Questions on sexual harassment included:

• Has a patient [colleague or other healthcare provider] touched you inappropriately, made sexual remarks or gestures, or made suggestive looks?
• Has a patient [colleague or other healthcare provider] referred to you by terms such as “honey,” “dear,” “sweetheart,” “sugar,” or equivalent?

In addition, the survey sought demographic information of the participants (age, gender, and race/ethnicity) and information about their individual institutions (names and locations) (Appendix). The geographical locations of the institutions were further categorized into four different regions according to the United States Census Bureau (Northwest, Midwest, South, and West). At the end of the survey, participants were given an opportunity to provide any additional comments.

Statistical Analysis

Standard descriptive summary statistics were used for demographic data. Associations between the variables were analyzed using chi-square test or Fischer’s exact test, as appropriate, for categorical variables and t test for continuous variables. The variations among institution-based responses were presented in the form of inter-quartile range (IQR). All tests were 2-sided, and P values less than .05 were considered statistically significant. All analyses were performed using IBM^® SPSS^® Statistics software version 24. Relevant responses representative of the overall respondents’ sentiments as provided under additional comment section were discussed and cited.

Eighteen different academic institutions across the United States participated in the study, with 336 individual responses. The majority of respondents were females (57%), in younger age categories (58% were 30-39 years old), Caucasian (59%), and early-career hospitalists (>50% working as hospitalists for ≤5 years) (Table 1). Regarding the overall geographic distribution, the largest number of responses were from the Midwest (n = 115; 35.6%) (Table 1 and Appendix).

Gender Discrimination

Interactions With Patients

Over their careers, 69% of hospitalists reported being mistaken for an HCP other than a physician by patients. This was more common among females than among males (99% vs 29%, respectively; P < .001) (Table 2). Almost half (48%) reported this had occurred in the last 30 days, more frequently by females (76% vs 10%; P < .001).

Of responding hospitalists, 96% stated that, over their careers, they have been asked by patients to do something they did not consider to be at their level of training (eg, help get food or water, help with a bed pan), with a higher prevalence of such experiences among females than males (99% vs 93%, respectively; P = .004) (Table 2). Most (71%) said they had experienced this in the last 30 days, which was again more frequently reported by females (78% vs 62%; P = .001).

The responses from female hospitalists regarding their career-long experiences of being mistaken for an HCP or asked to do something not at their level of training by their patients had both the highest number of positive responses across institutions (median of hospital proportions, 100%) and the least institutional variation since both had the narrowest IQR) (Table 2).

Interactions With Colleagues or Other HCPs

Among hospitalists responding to the survey, 46% felt that, over their careers, they had been mistaken for nonphysician HCPs by colleagues or other HCPs. This was more prevalent among females than among males (65% vs 20%; P < .001) (Table 2). Among respondents, 14% reported these events had occurred in the last 30 days, which was again more common among females (21% vs 5%; P < .001).

Over their careers, 26% of hospitalists reported they have been asked by a colleague or HCP to do something not at their level of training (eg, clean up the physician’s work room, make coffee, take notes in a meeting), with similar prevalence among females and males (29% vs 23%; P = .228). Ten percent reported these occurrences in the last 30 days, which was similar between females and males (12% vs 9%; P = .330).

Feelings of Respect and Opportunities for Career Advancement

When asked to rate the statement “I feel respected by patients” on a 5-point Likert scale, female hospitalists overall scored significantly lower as compared with their male counterparts (mean score, 3.73 vs 4.04; P < .001) (Table 3); this was also true when asked about feelings of respect by physicians (mean score, 3.84 vs 4.15; P < .001). Female hospitalists were more likely than males to report that their gender has more negatively impacted their opportunities for career advancement (mean score, 2.73 vs 3.34; P < .001).

Sexual Harassment

Interactions With Patients

Over half (57%) of hospitalists reported career-long experiences of patient(s) touching them inappropriately, making sexual remarks or gestures, or making suggestive looks. These experiences were more prevalent among females than among males (72% vs 36%, respectively; P < .001) (Table 2). Fifteen percent said they had such experience in rhe last 30 days, which was also more common among females (22% vs 6%; P < .001).

Most hospitalists (84%) reported that patients have referred to them by inappropriately familiar terms such as “honey,” “dear,” “sweetheart,” “sugar,” or equivalent over their careers, with females more frequently reporting these behaviors (95% vs 68%; P < .001). Experiencing this during the last 30 days was reported by 48%, which was again more common among females (68% vs 23%; P < .001).

Interactions With Colleagues or Other HCPs

Within their careers, 15% of hospitalists reported at least one experience of a colleague or HCP touching them inappropriately or making sexual remarks, gestures, or suggestive looks. This was more prevalent for females than males (18% vs 10%, respectively; P = .033). Only 2% of both females and males reported these experiences in the last 30 days (2% vs 2%; P = .981).

Almost one-third of participants (32%) affirmed that another HCP has referred to them by terms such as “honey,” “dear,” “sweetheart,” “sugar,” or equivalent in their career, with a higher proportion of females than males reporting these events (39% vs 23%; P = .002) (Table 2). Experiencing this during the last 30 days was reported by 10%, which was similar between females and males (12% vs 7%; P = .112).

Additional Comments From Respondents

• “Throughout my training and now into my professional career, there are nearly weekly incidents of elderly male patients referring to me as “honey/dear/sweetie” or even by my first name, even though I introduce myself as their physician and politely correct them. They will often refer to me as a nurse and ask me to do something not at my level of training. Sometimes even despite correcting the patient, they continue to refer to me as such. Throughout the years, other female MDs and I have discussed that this is ‘status quo’ for female physicians and observe that this is not an experience that male MDs share.”
• “I frequently round with a male nurse practitioner and the patients almost always, despite introducing ourselves and our roles, turn to him and ask him questions instead of addressing them to me.”
• “Our institution allows female faculty to be interviewed about childcare, household labor division, plans for pregnancy. One professor asks women private details about their private relationships such as what they do with spouse on date night or weekends away.”
• “It’s hard to answer questions related to my level of training. I don’t think it’s unreasonable for people to ask me to do things, no matter my level of training. . . . I don’t think being a doctor means that I am above this, or that it is inappropriate to be asked to do this.”

This survey demonstrated that gender-based discrimination and sexual harassment in the academic hospitalist healthcare environment are common, both in more distant and recent time frames. Notably, these experiences are shared by female and male physicians in interactions with both patients and colleagues, though male hospitalists report most of these experiences at significantly lower frequencies than females. These results support past work showing that female physicians are significantly more likely to be subjected to gender-based discrimination and sexual harassment, but also challenges the perception that gender-based discrimination and sexual harassment are uniquely experienced by females.

A startling number of females and males in the study reported sexual harassment (inappropriate touching, remarks, gestures, and looks) when interacting with patients throughout their careers and in last 30 days. Many males and females reported that patients had referred to them with inappropriately familiar, and potentially demeaning, terms of endearment. For both overt and implicit sexual harassment, females were significantly more likely than males to report experiencing these behaviors when interacting with patients. Although some of these experiences may seem less harmful than others, a meta-analysis demonstrated that frequent, less intense experiences of gender-based discrimination and sexual harassment have a similar impact on female’s well-being as do less frequent, more intense experiences.¹⁵ Although the person using the terms of endearment like “honey,” “sugar,” or “sweetheart” may feel the terms are harmless, such expressions can be inappropriate and constitute sexual harassment according to the U.S. Department of the Interior’s Office of Civil Rights.¹⁶ The Sexual Harassment/Assault Response and Prevention Program (SHARP) also classifies such terms into verbal categories of sexual harrassment.¹⁷

Of female physicians surveyed, 99% reported that they had been mistaken for HCPs other than physicians by their patients over their careers. Although this was also reported by male physicians, the experience was 3.4 times as likely for female physicians. Misidentification by patients may represent a disconnect between the growing female representation in the physician workforce and patients’ conceptions of the traditional image of a physician.

In parallel with this finding of misidentification, an interesting area of the study was the question regarding being asked to do “something not at your level of training.” A recurring theme in the comments was a rejection of the notion that certain tasks were “beneath a level of training,” suggesting a common view that acts of caregiving are not bounded by hierarchy. Analysis of qualitative responses showed that 40% of these responses had comments regarding this question. An example was “It’s hard to answer questions related to my level of training. . . . I don’t think being a doctor means that I am above this, or that it is inappropriate to be asked to do this.” Notably, however, a larger number of female than male physicians responded yes to this question in both study time frames. This points to a differential in how female physicians are viewed by patients, both in frequent misidentification and in behaviors more frequently asked of female physicians than their male counterparts. Given the comments, it may also suggest a difference in how female and male physicians perceive the fluidity of bounds on their care-taking roles set by their “level of training.”

A large number of study participants were early-career hospitalists, which may in part explain some of the study results. In a previous study of gender equity in an Internal Medicine department, physicians practicing medicine for more than 15 years perceived the departmental culture as more favorable than physicians with shorter careers.¹⁰ Additionally, the perception of cultures was most discordant between senior male physicians and junior female physicians.¹⁰ Because many hospitalists are early-career physicians, they may have trained in an environment that had heightened awareness surrounding gender-based discrimination and sexual harassment, which affects the overall study results.

Multiple qualitative comments, mentioned above, were submitted by participants describing their experiences in all categories. Such comments paint a picture of insidious bias and cultural norms affecting the quality of female physicians’ work lives.

Two questions focused on career satisfaction and the sense of respect from patients and colleagues. In both responses, there was a statistically different response between males and females, with females less likely to report that they felt respected and that their gender adversely impacted their opportunities for career advancement. This is disturbing information and warrants more investigation.

The reasons for the observed prevalence of gender-based discrimination and sexual harassment in this broad survey of academic hospitalists are uncertain. Multiple studies to date have demonstrated that gender-based discrimination and sexual harassment have historically existed in medicine and continue to even today. Unlike physicians with long-term relationships with patients, hospitalists may face more exposure due to a lack of long-term continuity with patients. The absence of an established trust in the relationship also may make them more vulnerable to inappropriate behaviors when interacting with patients. Hospital medicine, however, is a young specialty with equal gender representation and should be at the forefront of addressing and solving these issues of gender-based discrimination and sexual harassment.

The survey had a good distribution between female and male participants. Additionally, the survey reflected the general distribution of the national hospitalist workforce in gender, age, and ethnic/racial distribution, as well as number of years in practice.¹² The study surveyed respondents regarding experiences in both long- and short-term time frames, as well as experiences with patients and colleagues.

Our study reflects a cross-sectional snapshot of hospitalists’ perceptions with no longitudinal follow-up. Since the survey was limited to academic medical centers, it may not reflect experiences in community/private practice settings. The small number of participants limited the ability to perform subgroup analyses by age, race, or years in practice, which may play a role in interactions with patients and colleagues. Since the number of respondents varied greatly by institution, a minority of institutions could have influenced some of the findings. Narrow IQRs of the hospital proportions as shown in Table 2 would suggest similar responses across institutions, whereas wide IQRs would suggest that a smaller number of institutions were possibly driving the findings. Because of the survey distribution method, it is unknown how many physicians received the survey and a response rate could not be calculated. Further, selection, recall, and detection biases cannot be ruled out.

This survey shows that gender-based discrimination and sexual harassment in the academic hospitalist healthcare environment are common and more frequently experienced by female physicians, both in interactions with patients and colleagues. Our study highlights the need to address this prevalent issue among academic hospitalists.