Hospital Medicine

Inspired by the ABIM Foundation’s Choosing Wisely^® campaign, the “Things We Do for No Reason^™” (TWDFNR) series reviews practices that have become common parts of hospital care but may provide little value to our patients. Practices reviewed in the TWDFNR series do not represent clear-cut conclusions or clinical practice standards but are meant as a starting place for research and active discussions among hospitalists and patients. We invite you to be part of that discussion.

A 54-year-old immunocompetent man admitted to the hospital for non–ST-segment elevation myocardial infarction develops profuse watery diarrhea after his third day of admission. He denies prior episodes of diarrhea. He does not have any fevers, blood in the stool, recent travel, or antibiotic use. Vital signs include a blood pressure of 128/82 mm Hg, heart rate of 120 beats per minute, respiratory rate of 16 breaths per min, oxygen saturation of 100% on room air, and temperature of 36.9 °C. His physical examination is normal, without signs of abdominal tenderness, rebound, or guarding. Complete blood count is normal, without eosinophilia. The comprehensive metabolic panel shows mild hypokalemia of 3.3 mmol/L. The hospitalist resuscitates him with normal saline, provides oral potassium repletion, and orders a stool culture, Clostridioides difficile test, and an ova and parasite (O&P) test. Loperamide and time resolve his symptoms in 2 days. Results of his stool culture, C difficile, and O&P tests return negative in 3 days.

Acute diarrhea is a common complaint in both inpatient and outpatient settings. It is defined as the passage of three or more liquid or poorly formed stools in a 24-hour period lasting less than 14 days. Persistent diarrhea lasts from 14 to 29 days, while chronic diarrhea lasts longer than 30 days. There are 47.8 million cases of acute diarrhea per year in the United States, costing $150 million in US health expenditures.¹ Viral pathogens remain the most common cause of acute diarrhea in the United States.^1,2 Standard O&P testing consists of applying a stool sample to a slide with either saline or iodine (wet mount) and evaluating the specimen with a microscope.

Giardia and Cryptosporidium remain the most commonly implicated parasitic pathogens in acute diarrheal episodes in the United States.³Cryptosporidium cases in the United States range from 2.2 to 3.9 per 100,000 persons,⁴ and Giardia cases in the United States range from 5.8 to 6.4 per 100,000 persons.⁵ To avoid missing potentially treatable causes, providers often order O&P tests reflexively as part of a standard workup for acute diarrhea. From 2001 to 2007, Associated Regional and University Pathologists Laboratories experienced a 379% increase in O&P testing.⁶ Many providers ordering these tests assume that standard O&P testing covers most, if not all, parasites and that a negative test will rule out a parasitic cause of disease. Furthermore, providers are unaware that more sensitive tests to detect certain parasites have replaced standard O&P microscopy.³

Most hospitalized patients do not have a parasitic infection

In a review of 5,681 completed O&P tests from a tertiary care medical center in Canada over a 5-year period, only 1.4% of tests were positive.⁷ In a 3-year retrospective analysis of stool samples obtained after 3 days of hospitalization, positive results were found in only 1 of 191 stool cultures and in 0 of 90 O&P samples.⁸ Current practice guidelines suggest not testing patients with stool studies in cases of acute diarrhea lasting less than 7 days in the absence of significant risk factors for parasitic disease because it has been shown to be a rare event and most cases will self-resolve with supportive care only.^1,9

The stool O&P test has low sensitivity

Classically ordered stool O&P tests have low sensitivity for the detection of Giardia and Cryptosporidium, the two most common parasites in the United States.^6,10,11 O&P studies detect Giardia in only 66% to 79% of specimen samples and Cryptosporidium in less than 5% of specimens. Diagnostic yields can be improved with the use of special stains such as modified acid-fast stain (MAF).⁶ Despite use of MAF staining, though, sensitivity for Cryptosporidium detection has remained at only 55%.¹² Additionally, several studies have shown that physicians are generally unaware of the test characteristics of stool O&P tests and they do not know to order the newer more sensitive enzyme immunoassays (EIA) or direct fluorescent antibody (DFA) tests even in situations when testing for a parasitic infection is appropriate.^10,11,13,14 As stated earlier, a parasitic infection without significant risk factors is a rare event. A negative test with low or moderate sensitivity is not additive to such a low clinical suspicion because it does not significantly change posttest probability.

Testing can have adverse consequences

In addition to the low yield, O&P testing is technically complex, is time intensive, and requires an experienced technician’s interpretation. Inappropriate testing increases the cost of care and staff workload without much benefit.⁶ As such, some institutions have opted to send the O&P tests to labs with experienced technicians. Other institutions have adopted a restrictive stool O&P testing approach that reduces healthcare time and costs and improves the rate of positive tests.^13,15 A study at a single tertiary care medical center demonstrated an estimated cost savings of $21,931 annually by implementing a computer-based algorithm to restrict testing for stool cultures and O&P tests to patients with higher probabilities of infection.¹⁵ The algorithm directed clinicians to provide further information when attempting to order stool culture, O&P, or other specific stool tests. For patients hospitalized for more than 3 days, the system did not allow certain testing. For patients with worrisome features like severe symptoms or an immunocompromised state, the algorithm directed the clinician to place an infectious disease or gastroenterology consult rather than order stool tests. Decreased laboratory costs of all stool studies (including O&P) in adult inpatient locations led to the cost savings. Additionally, the study authors felt that they likely underestimated the cost savings because they did not account for other expenses in the analysis, such as nursing workload and supplies.¹⁵

Clinicians should evaluate patients on a case-by-case basis and determine the need to test based on the presence of high-risk features (Table).

When performing parasitic testing in patients without a recent travel history but with other high-risk features, test for the most prevalent parasites in the United States (ie, Giardia, Cryptosporidium, and Entamoeba histolytica) with DFA or EIA tests.³ DFA testing for Giardia is 99% sensitive.¹² In patients with symptoms lasting more than 7 days and recent travel, in addition to the above DFA/EIA tests, perform O&P testing with wet mount, modified acid-fast bacilli stain to detect rare parasites such as helminths, Strongyloides, Cyclospora, and Cystoisospora.³ In patients who live or travel to endemic areas (about 10% of traveler’s diarrhea is caused by parasitic infections), have unexplained eosinophilia, or are part of a community outbreak (eg, childcare institutions or drinking water/food outbreaks), test for Giardia, Cryptosporidium, Cyclospora, Cystoisospora, Entamoeba histolytica, and Isospora belli.⁹ In addition, among patients with AIDS or immunosuppression, testing should include assays for Microsporidia, Strongyloides, and Mycobacterium avium complex (Figure).^9,16 Newer tests, such as the multiplex real-time polymerase chain reaction assay, can also simultaneously detect Entamoeba histolytica, Giardia lamblia, and Cryptosporidium parvum. For more information on parasitic testing, we suggest reading the review article “Beyond O&P times three.”³ It is important to familiarize yourself with the parasitic tests available at your respective clinics/hospital so the optimal test can be used.

• Prescribe a trial of “wait and see” for patients without high-risk features for parasitic disease.
• Test patients with high-risk features for parasitic disease by utilizing targeted testing.
• For patients with high-risk features but no travel history, first perform DFA, EIA, or multiplex real-time polymerase chain reaction testing to evaluate for Giardia, Cryptosporidium, and Entamoeba histolytica.
• If DFA/EIA testing is negative, obtain O&P tests with and without stains, such as acid-fast bacilli, for detection of other rare parasites.

Hospitalists should risk-stratify patients to determine when O&P testing is appropriate. Employ more targeted testing, especially use of DFA/EIA tests when evaluating for parasites. Avoid parasitic testing if symptoms have lasted less than 7 days and the patient has no other high-risk features. Become familiar with the tests available at your institution and their sensitivities. As in our clinical scenario, most acute cases of diarrhea resolve without intervention and should be managed and treated conservatively.

Do you think this is a low-value practice? Is this truly a “Thing We Do for No Reason”? Share what you do in your practice and join in the conversation online by retweeting it on Twitter (#TWDFNR) and liking it on Facebook. We invite you to propose ideas for other “Things We Do for No Reason ^™ ” topics by emailing [email protected] .

The authors thank Dr Lenny Feldman for his assistance with editing the manuscript.

Inspired by the ABIM Foundation’s Choosing Wisely^® campaign, the “Things We Do for No Reason^™” (TWDFNR) series reviews practices that have become common parts of hospital care but may provide little value to our patients. Practices reviewed in the TWDFNR series do not represent clear-cut conclusions or clinical practice standards but are meant as a starting place for research and active discussions among hospitalists and patients. We invite you to be part of that discussion.

A 54-year-old immunocompetent man admitted to the hospital for non–ST-segment elevation myocardial infarction develops profuse watery diarrhea after his third day of admission. He denies prior episodes of diarrhea. He does not have any fevers, blood in the stool, recent travel, or antibiotic use. Vital signs include a blood pressure of 128/82 mm Hg, heart rate of 120 beats per minute, respiratory rate of 16 breaths per min, oxygen saturation of 100% on room air, and temperature of 36.9 °C. His physical examination is normal, without signs of abdominal tenderness, rebound, or guarding. Complete blood count is normal, without eosinophilia. The comprehensive metabolic panel shows mild hypokalemia of 3.3 mmol/L. The hospitalist resuscitates him with normal saline, provides oral potassium repletion, and orders a stool culture, Clostridioides difficile test, and an ova and parasite (O&P) test. Loperamide and time resolve his symptoms in 2 days. Results of his stool culture, C difficile, and O&P tests return negative in 3 days.

Acute diarrhea is a common complaint in both inpatient and outpatient settings. It is defined as the passage of three or more liquid or poorly formed stools in a 24-hour period lasting less than 14 days. Persistent diarrhea lasts from 14 to 29 days, while chronic diarrhea lasts longer than 30 days. There are 47.8 million cases of acute diarrhea per year in the United States, costing $150 million in US health expenditures.¹ Viral pathogens remain the most common cause of acute diarrhea in the United States.^1,2 Standard O&P testing consists of applying a stool sample to a slide with either saline or iodine (wet mount) and evaluating the specimen with a microscope.

Giardia and Cryptosporidium remain the most commonly implicated parasitic pathogens in acute diarrheal episodes in the United States.³Cryptosporidium cases in the United States range from 2.2 to 3.9 per 100,000 persons,⁴ and Giardia cases in the United States range from 5.8 to 6.4 per 100,000 persons.⁵ To avoid missing potentially treatable causes, providers often order O&P tests reflexively as part of a standard workup for acute diarrhea. From 2001 to 2007, Associated Regional and University Pathologists Laboratories experienced a 379% increase in O&P testing.⁶ Many providers ordering these tests assume that standard O&P testing covers most, if not all, parasites and that a negative test will rule out a parasitic cause of disease. Furthermore, providers are unaware that more sensitive tests to detect certain parasites have replaced standard O&P microscopy.³

Most hospitalized patients do not have a parasitic infection

In a review of 5,681 completed O&P tests from a tertiary care medical center in Canada over a 5-year period, only 1.4% of tests were positive.⁷ In a 3-year retrospective analysis of stool samples obtained after 3 days of hospitalization, positive results were found in only 1 of 191 stool cultures and in 0 of 90 O&P samples.⁸ Current practice guidelines suggest not testing patients with stool studies in cases of acute diarrhea lasting less than 7 days in the absence of significant risk factors for parasitic disease because it has been shown to be a rare event and most cases will self-resolve with supportive care only.^1,9

The stool O&P test has low sensitivity

Classically ordered stool O&P tests have low sensitivity for the detection of Giardia and Cryptosporidium, the two most common parasites in the United States.^6,10,11 O&P studies detect Giardia in only 66% to 79% of specimen samples and Cryptosporidium in less than 5% of specimens. Diagnostic yields can be improved with the use of special stains such as modified acid-fast stain (MAF).⁶ Despite use of MAF staining, though, sensitivity for Cryptosporidium detection has remained at only 55%.¹² Additionally, several studies have shown that physicians are generally unaware of the test characteristics of stool O&P tests and they do not know to order the newer more sensitive enzyme immunoassays (EIA) or direct fluorescent antibody (DFA) tests even in situations when testing for a parasitic infection is appropriate.^10,11,13,14 As stated earlier, a parasitic infection without significant risk factors is a rare event. A negative test with low or moderate sensitivity is not additive to such a low clinical suspicion because it does not significantly change posttest probability.

Testing can have adverse consequences

In addition to the low yield, O&P testing is technically complex, is time intensive, and requires an experienced technician’s interpretation. Inappropriate testing increases the cost of care and staff workload without much benefit.⁶ As such, some institutions have opted to send the O&P tests to labs with experienced technicians. Other institutions have adopted a restrictive stool O&P testing approach that reduces healthcare time and costs and improves the rate of positive tests.^13,15 A study at a single tertiary care medical center demonstrated an estimated cost savings of $21,931 annually by implementing a computer-based algorithm to restrict testing for stool cultures and O&P tests to patients with higher probabilities of infection.¹⁵ The algorithm directed clinicians to provide further information when attempting to order stool culture, O&P, or other specific stool tests. For patients hospitalized for more than 3 days, the system did not allow certain testing. For patients with worrisome features like severe symptoms or an immunocompromised state, the algorithm directed the clinician to place an infectious disease or gastroenterology consult rather than order stool tests. Decreased laboratory costs of all stool studies (including O&P) in adult inpatient locations led to the cost savings. Additionally, the study authors felt that they likely underestimated the cost savings because they did not account for other expenses in the analysis, such as nursing workload and supplies.¹⁵

Clinicians should evaluate patients on a case-by-case basis and determine the need to test based on the presence of high-risk features (Table).

When performing parasitic testing in patients without a recent travel history but with other high-risk features, test for the most prevalent parasites in the United States (ie, Giardia, Cryptosporidium, and Entamoeba histolytica) with DFA or EIA tests.³ DFA testing for Giardia is 99% sensitive.¹² In patients with symptoms lasting more than 7 days and recent travel, in addition to the above DFA/EIA tests, perform O&P testing with wet mount, modified acid-fast bacilli stain to detect rare parasites such as helminths, Strongyloides, Cyclospora, and Cystoisospora.³ In patients who live or travel to endemic areas (about 10% of traveler’s diarrhea is caused by parasitic infections), have unexplained eosinophilia, or are part of a community outbreak (eg, childcare institutions or drinking water/food outbreaks), test for Giardia, Cryptosporidium, Cyclospora, Cystoisospora, Entamoeba histolytica, and Isospora belli.⁹ In addition, among patients with AIDS or immunosuppression, testing should include assays for Microsporidia, Strongyloides, and Mycobacterium avium complex (Figure).^9,16 Newer tests, such as the multiplex real-time polymerase chain reaction assay, can also simultaneously detect Entamoeba histolytica, Giardia lamblia, and Cryptosporidium parvum. For more information on parasitic testing, we suggest reading the review article “Beyond O&P times three.”³ It is important to familiarize yourself with the parasitic tests available at your respective clinics/hospital so the optimal test can be used.

• Prescribe a trial of “wait and see” for patients without high-risk features for parasitic disease.
• Test patients with high-risk features for parasitic disease by utilizing targeted testing.
• For patients with high-risk features but no travel history, first perform DFA, EIA, or multiplex real-time polymerase chain reaction testing to evaluate for Giardia, Cryptosporidium, and Entamoeba histolytica.
• If DFA/EIA testing is negative, obtain O&P tests with and without stains, such as acid-fast bacilli, for detection of other rare parasites.

Hospitalists should risk-stratify patients to determine when O&P testing is appropriate. Employ more targeted testing, especially use of DFA/EIA tests when evaluating for parasites. Avoid parasitic testing if symptoms have lasted less than 7 days and the patient has no other high-risk features. Become familiar with the tests available at your institution and their sensitivities. As in our clinical scenario, most acute cases of diarrhea resolve without intervention and should be managed and treated conservatively.

Do you think this is a low-value practice? Is this truly a “Thing We Do for No Reason”? Share what you do in your practice and join in the conversation online by retweeting it on Twitter (#TWDFNR) and liking it on Facebook. We invite you to propose ideas for other “Things We Do for No Reason ^™ ” topics by emailing [email protected] .

The authors thank Dr Lenny Feldman for his assistance with editing the manuscript.

Inspired by the ABIM Foundation’s Choosing Wisely^® campaign, the “Things We Do for No Reason^™” (TWDFNR) series reviews practices that have become common parts of hospital care but may provide little value to our patients. Practices reviewed in the TWDFNR series do not represent clear-cut conclusions or clinical practice standards but are meant as a starting place for research and active discussions among hospitalists and patients. We invite you to be part of that discussion.

A 54-year-old immunocompetent man admitted to the hospital for non–ST-segment elevation myocardial infarction develops profuse watery diarrhea after his third day of admission. He denies prior episodes of diarrhea. He does not have any fevers, blood in the stool, recent travel, or antibiotic use. Vital signs include a blood pressure of 128/82 mm Hg, heart rate of 120 beats per minute, respiratory rate of 16 breaths per min, oxygen saturation of 100% on room air, and temperature of 36.9 °C. His physical examination is normal, without signs of abdominal tenderness, rebound, or guarding. Complete blood count is normal, without eosinophilia. The comprehensive metabolic panel shows mild hypokalemia of 3.3 mmol/L. The hospitalist resuscitates him with normal saline, provides oral potassium repletion, and orders a stool culture, Clostridioides difficile test, and an ova and parasite (O&P) test. Loperamide and time resolve his symptoms in 2 days. Results of his stool culture, C difficile, and O&P tests return negative in 3 days.

Acute diarrhea is a common complaint in both inpatient and outpatient settings. It is defined as the passage of three or more liquid or poorly formed stools in a 24-hour period lasting less than 14 days. Persistent diarrhea lasts from 14 to 29 days, while chronic diarrhea lasts longer than 30 days. There are 47.8 million cases of acute diarrhea per year in the United States, costing $150 million in US health expenditures.¹ Viral pathogens remain the most common cause of acute diarrhea in the United States.^1,2 Standard O&P testing consists of applying a stool sample to a slide with either saline or iodine (wet mount) and evaluating the specimen with a microscope.

Giardia and Cryptosporidium remain the most commonly implicated parasitic pathogens in acute diarrheal episodes in the United States.³Cryptosporidium cases in the United States range from 2.2 to 3.9 per 100,000 persons,⁴ and Giardia cases in the United States range from 5.8 to 6.4 per 100,000 persons.⁵ To avoid missing potentially treatable causes, providers often order O&P tests reflexively as part of a standard workup for acute diarrhea. From 2001 to 2007, Associated Regional and University Pathologists Laboratories experienced a 379% increase in O&P testing.⁶ Many providers ordering these tests assume that standard O&P testing covers most, if not all, parasites and that a negative test will rule out a parasitic cause of disease. Furthermore, providers are unaware that more sensitive tests to detect certain parasites have replaced standard O&P microscopy.³

Most hospitalized patients do not have a parasitic infection

In a review of 5,681 completed O&P tests from a tertiary care medical center in Canada over a 5-year period, only 1.4% of tests were positive.⁷ In a 3-year retrospective analysis of stool samples obtained after 3 days of hospitalization, positive results were found in only 1 of 191 stool cultures and in 0 of 90 O&P samples.⁸ Current practice guidelines suggest not testing patients with stool studies in cases of acute diarrhea lasting less than 7 days in the absence of significant risk factors for parasitic disease because it has been shown to be a rare event and most cases will self-resolve with supportive care only.^1,9

The stool O&P test has low sensitivity

Classically ordered stool O&P tests have low sensitivity for the detection of Giardia and Cryptosporidium, the two most common parasites in the United States.^6,10,11 O&P studies detect Giardia in only 66% to 79% of specimen samples and Cryptosporidium in less than 5% of specimens. Diagnostic yields can be improved with the use of special stains such as modified acid-fast stain (MAF).⁶ Despite use of MAF staining, though, sensitivity for Cryptosporidium detection has remained at only 55%.¹² Additionally, several studies have shown that physicians are generally unaware of the test characteristics of stool O&P tests and they do not know to order the newer more sensitive enzyme immunoassays (EIA) or direct fluorescent antibody (DFA) tests even in situations when testing for a parasitic infection is appropriate.^10,11,13,14 As stated earlier, a parasitic infection without significant risk factors is a rare event. A negative test with low or moderate sensitivity is not additive to such a low clinical suspicion because it does not significantly change posttest probability.

Testing can have adverse consequences

In addition to the low yield, O&P testing is technically complex, is time intensive, and requires an experienced technician’s interpretation. Inappropriate testing increases the cost of care and staff workload without much benefit.⁶ As such, some institutions have opted to send the O&P tests to labs with experienced technicians. Other institutions have adopted a restrictive stool O&P testing approach that reduces healthcare time and costs and improves the rate of positive tests.^13,15 A study at a single tertiary care medical center demonstrated an estimated cost savings of $21,931 annually by implementing a computer-based algorithm to restrict testing for stool cultures and O&P tests to patients with higher probabilities of infection.¹⁵ The algorithm directed clinicians to provide further information when attempting to order stool culture, O&P, or other specific stool tests. For patients hospitalized for more than 3 days, the system did not allow certain testing. For patients with worrisome features like severe symptoms or an immunocompromised state, the algorithm directed the clinician to place an infectious disease or gastroenterology consult rather than order stool tests. Decreased laboratory costs of all stool studies (including O&P) in adult inpatient locations led to the cost savings. Additionally, the study authors felt that they likely underestimated the cost savings because they did not account for other expenses in the analysis, such as nursing workload and supplies.¹⁵

Clinicians should evaluate patients on a case-by-case basis and determine the need to test based on the presence of high-risk features (Table).

When performing parasitic testing in patients without a recent travel history but with other high-risk features, test for the most prevalent parasites in the United States (ie, Giardia, Cryptosporidium, and Entamoeba histolytica) with DFA or EIA tests.³ DFA testing for Giardia is 99% sensitive.¹² In patients with symptoms lasting more than 7 days and recent travel, in addition to the above DFA/EIA tests, perform O&P testing with wet mount, modified acid-fast bacilli stain to detect rare parasites such as helminths, Strongyloides, Cyclospora, and Cystoisospora.³ In patients who live or travel to endemic areas (about 10% of traveler’s diarrhea is caused by parasitic infections), have unexplained eosinophilia, or are part of a community outbreak (eg, childcare institutions or drinking water/food outbreaks), test for Giardia, Cryptosporidium, Cyclospora, Cystoisospora, Entamoeba histolytica, and Isospora belli.⁹ In addition, among patients with AIDS or immunosuppression, testing should include assays for Microsporidia, Strongyloides, and Mycobacterium avium complex (Figure).^9,16 Newer tests, such as the multiplex real-time polymerase chain reaction assay, can also simultaneously detect Entamoeba histolytica, Giardia lamblia, and Cryptosporidium parvum. For more information on parasitic testing, we suggest reading the review article “Beyond O&P times three.”³ It is important to familiarize yourself with the parasitic tests available at your respective clinics/hospital so the optimal test can be used.

• Prescribe a trial of “wait and see” for patients without high-risk features for parasitic disease.
• Test patients with high-risk features for parasitic disease by utilizing targeted testing.
• For patients with high-risk features but no travel history, first perform DFA, EIA, or multiplex real-time polymerase chain reaction testing to evaluate for Giardia, Cryptosporidium, and Entamoeba histolytica.
• If DFA/EIA testing is negative, obtain O&P tests with and without stains, such as acid-fast bacilli, for detection of other rare parasites.

Hospitalists should risk-stratify patients to determine when O&P testing is appropriate. Employ more targeted testing, especially use of DFA/EIA tests when evaluating for parasites. Avoid parasitic testing if symptoms have lasted less than 7 days and the patient has no other high-risk features. Become familiar with the tests available at your institution and their sensitivities. As in our clinical scenario, most acute cases of diarrhea resolve without intervention and should be managed and treated conservatively.

Do you think this is a low-value practice? Is this truly a “Thing We Do for No Reason”? Share what you do in your practice and join in the conversation online by retweeting it on Twitter (#TWDFNR) and liking it on Facebook. We invite you to propose ideas for other “Things We Do for No Reason ^™ ” topics by emailing [email protected] .

The authors thank Dr Lenny Feldman for his assistance with editing the manuscript.

Despite the important clinical and public health implications of a COVID-19 diagnosis, respect for autonomy allows patients to decline testing without explanation and with impunity. Whether physicians believe a test is indicated for clinical care of an individual patient, prevention of nosocomial transmission, or the greater public health, patients may refuse. Such refusals may be increasing due to quarantine requirements, concerns regarding contact tracing, and the persistent absence of a curative treatment.^1,2 Mass screening of all healthcare workers (HCWs) is being considered to prevent hospital transmission,³ and universal screening in nursing homes has thwarted outbreaks while providing data to facilitate resource allocation.⁴ Given these circumstances, patients’ absolute right to refuse a noninvasive test with the potential for multifaceted downstream benefit is worthy of reconsideration, in favor of mandatory testing. Mandatory testing confers numerous benefits, including mitigating risk to other patients and HCWs, who play a central role in pandemic response. Because infected HCWs may transmit the virus to patients, they also should undergo mandatory testing,³ particularly in the presence of symptoms, since nasal secretions increase the diagnostic yield of testing.⁵ Although pretest probability (as an estimate of disease prevalence) typically determines the testing strategy for admitted patients, model-based analyses suggest that testing every 3 days for HCWs or continuously hospitalized patients would nearly eliminate infectivity.⁶

Tools for assisting frustrated HCWs navigating patients’ right to refuse testing have been developed that incorporate education, clear communication, and conflict resolution.⁷ Such approaches are, however, only moderately successful, making the use of personal protective equipment (PPE) based on a default assumption of COVID-19 positivity common.⁸ The burden and disheartening waste created by low-yield PPE use among patients unwilling to be tested becomes particularly evident in the context of shortages. Such vexing, stressful shortages, as well as the dual responsibilities of hospitals as stewards of both individual patient and population health, serve as reminders that efficient allocation of resources must be valued alongside the autonomous rights of patients.⁹ Moreover, recent reports suggest that test avoidance is a growing problem.^1,2 Refusal to accept testing may be rooted in anxiety, concerns about the consequences of a positive result (eg, inability to attend school or work), or a desire for self-determination.^1,2 The hesitancy that leads to refusal may also arise from misinformation, poor public health messaging, distrust in the establishment, and unproductive considerations related to conscientious objection without foundation.² Concepts of individual liberty that often underlie steadfast adherence to the principles of self-determination created opposition to masks that antagonized public health efforts to limit the spread of COVID-19. Although influencing inpatients’ behavior to benefit both the public and HCWs may be distinct from community settings, the attitudes that lead to test refusal and defiance of mask-related ordinances likely have substantial commonalities.

As a pillar of ethical decision-making, patient autonomy plays a powerful role in healthcare decisions in the United States. Whereas values such as beneficence, nonmaleficence, advocacy, and distributive justice impact certain decisions, patient autonomy has evolved into the dominant value. Although the beneficence model had historically guided medical decision-making, the bioethics community spearheaded the emergence of the autonomy model during the past several decades.¹⁰ Benevolent deception (ie, therapeutic privilege) and medical paternalism were central features of the beneficence model.¹¹ However, the cornerstone of the autonomy model is informed consent, which provides assurance that patients will be neither deceived nor coerced.¹⁰ Professionalism has always presupposed that the beneficence model would result in decisions directed at both improving patient health and minimizing individual patient harms. The public good and consequent positive externalities were acceptable considerations in decisions based on therapeutic privilege before the autonomy model became dominant. In keeping with the philosophical underpinnings of this approach, advocacy for the public health is still considered a justification for limiting informed consent and breaching confidentiality for disease reporting and contact tracing.⁹

In non-healthcare settings, the controversies surrounding vaccination and access to schools for unvaccinated children are perhaps the public and professional debate most analogous to COVID-19 testing refusal.¹² Although policymakers may distinguish between testing and vaccination, these interventions similarly hold the potential to limit disease incidence and mitigate health impact. To preserve public health, most states prevent (with varied exemptions) unvaccinated children from attending schools. COVID-19 testing may in the future become a requirement for participation in group social activities, athletic competitions, or physical presence in the workplace to facilitate quarantining and/or targeted use of PPE for transmission risk reduction. Given the dramatic mitigation benefits accruable on college campuses,¹³ required testing for in-person learning has become common.

There are also parallels, and therefore lessons, to be drawn from experience in testing for HIV, although HIV-related stigma and devalued status of the marginalized populations initially infected impacted the broader societal view of HIV compared with COVID-19. For example, antenatal HIV screening of pregnant women is strongly recommended to facilitate interventions that reduce the chance of vertical transmission.¹⁴ The limitations of purely elective testing are one justification for the current standard of opt-out screening. However, in this case, the health complications of refusal are largely the burden of the fetus, over whose future the mother holds a great deal of choice and responsibility, irrespective of HIV status. The public health implications of HIV test refusal are far less immediate than for COVID-19 infection because there is no effective curative therapy for COVID-19 and spread occurs through nonintimate, unintentional, and unpredictable exposure.

Translating societal attitudes and practices into the healthcare setting to consider mandated COVID-19 testing requires additional considerations related to both patients and providers: (1) HCWs have committed to a set of values and professional obligations that include tasks requiring risks¹⁵; (2) the public expects HCWs to perform their duties according to a social contract that has few restrictions¹⁶; (3) limiting patient access to hospital care due to COVID-19 testing refusal would contradict and create conflicts related to professional conceptions of hospitals and physicians as patient agents¹⁵; and (4) patients who conscientiously object to testing may seek healthcare less diligently, which may lead to health decrements. The associated postponement of essential care may unduly burden the healthcare system, particularly in situations such as ambulatory care–sensitive conditions.

The extent to which the public health justification for mandatory testing extends to hospitalized patients to protect HCWs is ambiguous. HCWs are of enormous instrumental value and are therefore essential for the pandemic response and health of the broader population. Their protection may therefore justify curtailing informed consent for diagnostic testing. Downstream effects on the supply of frontline HCWs may be realized. Poor control over working conditions may negatively impact motivation among HCWs. In addition, they may feel disenfranchised while obligatorily taking personal risks in caring for patients unwilling to commit to the common good through diagnostic test consent. Hospitalized patients who refuse testing may remain patients under investigation (PUIs), thus requiring special respiratory precautions (SRP) throughout their hospitalization, thereby placing a persistent burden on those with responsibilities requiring patient contact.¹⁷ Repeatedly donning and doffing PPE may remind at-risk HCWs that a myriad of benefits may accrue from frequent, ubiquitous testing. Their motivation may be tempered by the demoralizing requirement to care for patients who will not consent to a simple test, knowing that an opportunity to diminish the burdens of this communicable disease that has taken the lives of many HCWs is being relinquished.

Although HCWs could use SRP universally, their selective application in rooms of known COVID-19–positive patients and those with temporary PUI status has several advantages.¹⁷ First, we learned that HIV testing on patients was helpful in enabling surgeons to selectively implement special precautions among infected patients rather than universally applied intensive precautions. Even in the setting of high rates of HIV infection and educational interventions, HCWs do not reliably apply protective measures included in universal precautions.¹⁸ In keeping with these experiences, limiting the number of patients on SRP will minimize the “precautions fatigue” that drives nonadherent behavior among HCWs.¹⁷ As a result, minimizing the proportion of patients on SRP through testing (and liberation from unnecessary precautions in most cases) will improve uptake of crucial hand hygiene practices and adoption of vigilant PPE use. Second, definitive knowledge of COVID-19 status will increase patient access to care because, whether by personal choice or policy, many HCWs limit in-person contact with patients who are or may be COVID-19 positive. For example, many inpatient dialysis units do not accept patients without a negative COVID-19 nasal swab. Physical therapists may delay or avoid seeing a PUI, which will pose challenges for efficient determination of discharge disposition. Third, selective use of SRP will limit the environmental impact of disposed PPE, which is neither recyclable nor biodegradable. Infectious or regulated biomedical waste products are a significant source of environmental pollution, and the World Health Organization has recommended parsimonious, selective use of PPE to minimize the adverse environmental consequences of biomedical waste products.

In summary, there are substantial justifications for mandatory testing for COVID-19 in the hospital for HCWs and patients, as has been successfully piloted in selected long-term care facilities. Patients who refuse to allow testing may have to accept that their care may be compromised. For preservation of HCW supply and maintenance of HCW morale, hospital policies should make explicit, without punishment or coercion, that HCWs may modify the care they provide to patients who refuse to consent to COVID-19 testing.

Despite the important clinical and public health implications of a COVID-19 diagnosis, respect for autonomy allows patients to decline testing without explanation and with impunity. Whether physicians believe a test is indicated for clinical care of an individual patient, prevention of nosocomial transmission, or the greater public health, patients may refuse. Such refusals may be increasing due to quarantine requirements, concerns regarding contact tracing, and the persistent absence of a curative treatment.^1,2 Mass screening of all healthcare workers (HCWs) is being considered to prevent hospital transmission,³ and universal screening in nursing homes has thwarted outbreaks while providing data to facilitate resource allocation.⁴ Given these circumstances, patients’ absolute right to refuse a noninvasive test with the potential for multifaceted downstream benefit is worthy of reconsideration, in favor of mandatory testing. Mandatory testing confers numerous benefits, including mitigating risk to other patients and HCWs, who play a central role in pandemic response. Because infected HCWs may transmit the virus to patients, they also should undergo mandatory testing,³ particularly in the presence of symptoms, since nasal secretions increase the diagnostic yield of testing.⁵ Although pretest probability (as an estimate of disease prevalence) typically determines the testing strategy for admitted patients, model-based analyses suggest that testing every 3 days for HCWs or continuously hospitalized patients would nearly eliminate infectivity.⁶

Tools for assisting frustrated HCWs navigating patients’ right to refuse testing have been developed that incorporate education, clear communication, and conflict resolution.⁷ Such approaches are, however, only moderately successful, making the use of personal protective equipment (PPE) based on a default assumption of COVID-19 positivity common.⁸ The burden and disheartening waste created by low-yield PPE use among patients unwilling to be tested becomes particularly evident in the context of shortages. Such vexing, stressful shortages, as well as the dual responsibilities of hospitals as stewards of both individual patient and population health, serve as reminders that efficient allocation of resources must be valued alongside the autonomous rights of patients.⁹ Moreover, recent reports suggest that test avoidance is a growing problem.^1,2 Refusal to accept testing may be rooted in anxiety, concerns about the consequences of a positive result (eg, inability to attend school or work), or a desire for self-determination.^1,2 The hesitancy that leads to refusal may also arise from misinformation, poor public health messaging, distrust in the establishment, and unproductive considerations related to conscientious objection without foundation.² Concepts of individual liberty that often underlie steadfast adherence to the principles of self-determination created opposition to masks that antagonized public health efforts to limit the spread of COVID-19. Although influencing inpatients’ behavior to benefit both the public and HCWs may be distinct from community settings, the attitudes that lead to test refusal and defiance of mask-related ordinances likely have substantial commonalities.

As a pillar of ethical decision-making, patient autonomy plays a powerful role in healthcare decisions in the United States. Whereas values such as beneficence, nonmaleficence, advocacy, and distributive justice impact certain decisions, patient autonomy has evolved into the dominant value. Although the beneficence model had historically guided medical decision-making, the bioethics community spearheaded the emergence of the autonomy model during the past several decades.¹⁰ Benevolent deception (ie, therapeutic privilege) and medical paternalism were central features of the beneficence model.¹¹ However, the cornerstone of the autonomy model is informed consent, which provides assurance that patients will be neither deceived nor coerced.¹⁰ Professionalism has always presupposed that the beneficence model would result in decisions directed at both improving patient health and minimizing individual patient harms. The public good and consequent positive externalities were acceptable considerations in decisions based on therapeutic privilege before the autonomy model became dominant. In keeping with the philosophical underpinnings of this approach, advocacy for the public health is still considered a justification for limiting informed consent and breaching confidentiality for disease reporting and contact tracing.⁹

In non-healthcare settings, the controversies surrounding vaccination and access to schools for unvaccinated children are perhaps the public and professional debate most analogous to COVID-19 testing refusal.¹² Although policymakers may distinguish between testing and vaccination, these interventions similarly hold the potential to limit disease incidence and mitigate health impact. To preserve public health, most states prevent (with varied exemptions) unvaccinated children from attending schools. COVID-19 testing may in the future become a requirement for participation in group social activities, athletic competitions, or physical presence in the workplace to facilitate quarantining and/or targeted use of PPE for transmission risk reduction. Given the dramatic mitigation benefits accruable on college campuses,¹³ required testing for in-person learning has become common.

There are also parallels, and therefore lessons, to be drawn from experience in testing for HIV, although HIV-related stigma and devalued status of the marginalized populations initially infected impacted the broader societal view of HIV compared with COVID-19. For example, antenatal HIV screening of pregnant women is strongly recommended to facilitate interventions that reduce the chance of vertical transmission.¹⁴ The limitations of purely elective testing are one justification for the current standard of opt-out screening. However, in this case, the health complications of refusal are largely the burden of the fetus, over whose future the mother holds a great deal of choice and responsibility, irrespective of HIV status. The public health implications of HIV test refusal are far less immediate than for COVID-19 infection because there is no effective curative therapy for COVID-19 and spread occurs through nonintimate, unintentional, and unpredictable exposure.

Translating societal attitudes and practices into the healthcare setting to consider mandated COVID-19 testing requires additional considerations related to both patients and providers: (1) HCWs have committed to a set of values and professional obligations that include tasks requiring risks¹⁵; (2) the public expects HCWs to perform their duties according to a social contract that has few restrictions¹⁶; (3) limiting patient access to hospital care due to COVID-19 testing refusal would contradict and create conflicts related to professional conceptions of hospitals and physicians as patient agents¹⁵; and (4) patients who conscientiously object to testing may seek healthcare less diligently, which may lead to health decrements. The associated postponement of essential care may unduly burden the healthcare system, particularly in situations such as ambulatory care–sensitive conditions.

The extent to which the public health justification for mandatory testing extends to hospitalized patients to protect HCWs is ambiguous. HCWs are of enormous instrumental value and are therefore essential for the pandemic response and health of the broader population. Their protection may therefore justify curtailing informed consent for diagnostic testing. Downstream effects on the supply of frontline HCWs may be realized. Poor control over working conditions may negatively impact motivation among HCWs. In addition, they may feel disenfranchised while obligatorily taking personal risks in caring for patients unwilling to commit to the common good through diagnostic test consent. Hospitalized patients who refuse testing may remain patients under investigation (PUIs), thus requiring special respiratory precautions (SRP) throughout their hospitalization, thereby placing a persistent burden on those with responsibilities requiring patient contact.¹⁷ Repeatedly donning and doffing PPE may remind at-risk HCWs that a myriad of benefits may accrue from frequent, ubiquitous testing. Their motivation may be tempered by the demoralizing requirement to care for patients who will not consent to a simple test, knowing that an opportunity to diminish the burdens of this communicable disease that has taken the lives of many HCWs is being relinquished.

Although HCWs could use SRP universally, their selective application in rooms of known COVID-19–positive patients and those with temporary PUI status has several advantages.¹⁷ First, we learned that HIV testing on patients was helpful in enabling surgeons to selectively implement special precautions among infected patients rather than universally applied intensive precautions. Even in the setting of high rates of HIV infection and educational interventions, HCWs do not reliably apply protective measures included in universal precautions.¹⁸ In keeping with these experiences, limiting the number of patients on SRP will minimize the “precautions fatigue” that drives nonadherent behavior among HCWs.¹⁷ As a result, minimizing the proportion of patients on SRP through testing (and liberation from unnecessary precautions in most cases) will improve uptake of crucial hand hygiene practices and adoption of vigilant PPE use. Second, definitive knowledge of COVID-19 status will increase patient access to care because, whether by personal choice or policy, many HCWs limit in-person contact with patients who are or may be COVID-19 positive. For example, many inpatient dialysis units do not accept patients without a negative COVID-19 nasal swab. Physical therapists may delay or avoid seeing a PUI, which will pose challenges for efficient determination of discharge disposition. Third, selective use of SRP will limit the environmental impact of disposed PPE, which is neither recyclable nor biodegradable. Infectious or regulated biomedical waste products are a significant source of environmental pollution, and the World Health Organization has recommended parsimonious, selective use of PPE to minimize the adverse environmental consequences of biomedical waste products.

In summary, there are substantial justifications for mandatory testing for COVID-19 in the hospital for HCWs and patients, as has been successfully piloted in selected long-term care facilities. Patients who refuse to allow testing may have to accept that their care may be compromised. For preservation of HCW supply and maintenance of HCW morale, hospital policies should make explicit, without punishment or coercion, that HCWs may modify the care they provide to patients who refuse to consent to COVID-19 testing.

Despite the important clinical and public health implications of a COVID-19 diagnosis, respect for autonomy allows patients to decline testing without explanation and with impunity. Whether physicians believe a test is indicated for clinical care of an individual patient, prevention of nosocomial transmission, or the greater public health, patients may refuse. Such refusals may be increasing due to quarantine requirements, concerns regarding contact tracing, and the persistent absence of a curative treatment.^1,2 Mass screening of all healthcare workers (HCWs) is being considered to prevent hospital transmission,³ and universal screening in nursing homes has thwarted outbreaks while providing data to facilitate resource allocation.⁴ Given these circumstances, patients’ absolute right to refuse a noninvasive test with the potential for multifaceted downstream benefit is worthy of reconsideration, in favor of mandatory testing. Mandatory testing confers numerous benefits, including mitigating risk to other patients and HCWs, who play a central role in pandemic response. Because infected HCWs may transmit the virus to patients, they also should undergo mandatory testing,³ particularly in the presence of symptoms, since nasal secretions increase the diagnostic yield of testing.⁵ Although pretest probability (as an estimate of disease prevalence) typically determines the testing strategy for admitted patients, model-based analyses suggest that testing every 3 days for HCWs or continuously hospitalized patients would nearly eliminate infectivity.⁶

Tools for assisting frustrated HCWs navigating patients’ right to refuse testing have been developed that incorporate education, clear communication, and conflict resolution.⁷ Such approaches are, however, only moderately successful, making the use of personal protective equipment (PPE) based on a default assumption of COVID-19 positivity common.⁸ The burden and disheartening waste created by low-yield PPE use among patients unwilling to be tested becomes particularly evident in the context of shortages. Such vexing, stressful shortages, as well as the dual responsibilities of hospitals as stewards of both individual patient and population health, serve as reminders that efficient allocation of resources must be valued alongside the autonomous rights of patients.⁹ Moreover, recent reports suggest that test avoidance is a growing problem.^1,2 Refusal to accept testing may be rooted in anxiety, concerns about the consequences of a positive result (eg, inability to attend school or work), or a desire for self-determination.^1,2 The hesitancy that leads to refusal may also arise from misinformation, poor public health messaging, distrust in the establishment, and unproductive considerations related to conscientious objection without foundation.² Concepts of individual liberty that often underlie steadfast adherence to the principles of self-determination created opposition to masks that antagonized public health efforts to limit the spread of COVID-19. Although influencing inpatients’ behavior to benefit both the public and HCWs may be distinct from community settings, the attitudes that lead to test refusal and defiance of mask-related ordinances likely have substantial commonalities.

As a pillar of ethical decision-making, patient autonomy plays a powerful role in healthcare decisions in the United States. Whereas values such as beneficence, nonmaleficence, advocacy, and distributive justice impact certain decisions, patient autonomy has evolved into the dominant value. Although the beneficence model had historically guided medical decision-making, the bioethics community spearheaded the emergence of the autonomy model during the past several decades.¹⁰ Benevolent deception (ie, therapeutic privilege) and medical paternalism were central features of the beneficence model.¹¹ However, the cornerstone of the autonomy model is informed consent, which provides assurance that patients will be neither deceived nor coerced.¹⁰ Professionalism has always presupposed that the beneficence model would result in decisions directed at both improving patient health and minimizing individual patient harms. The public good and consequent positive externalities were acceptable considerations in decisions based on therapeutic privilege before the autonomy model became dominant. In keeping with the philosophical underpinnings of this approach, advocacy for the public health is still considered a justification for limiting informed consent and breaching confidentiality for disease reporting and contact tracing.⁹

In non-healthcare settings, the controversies surrounding vaccination and access to schools for unvaccinated children are perhaps the public and professional debate most analogous to COVID-19 testing refusal.¹² Although policymakers may distinguish between testing and vaccination, these interventions similarly hold the potential to limit disease incidence and mitigate health impact. To preserve public health, most states prevent (with varied exemptions) unvaccinated children from attending schools. COVID-19 testing may in the future become a requirement for participation in group social activities, athletic competitions, or physical presence in the workplace to facilitate quarantining and/or targeted use of PPE for transmission risk reduction. Given the dramatic mitigation benefits accruable on college campuses,¹³ required testing for in-person learning has become common.

There are also parallels, and therefore lessons, to be drawn from experience in testing for HIV, although HIV-related stigma and devalued status of the marginalized populations initially infected impacted the broader societal view of HIV compared with COVID-19. For example, antenatal HIV screening of pregnant women is strongly recommended to facilitate interventions that reduce the chance of vertical transmission.¹⁴ The limitations of purely elective testing are one justification for the current standard of opt-out screening. However, in this case, the health complications of refusal are largely the burden of the fetus, over whose future the mother holds a great deal of choice and responsibility, irrespective of HIV status. The public health implications of HIV test refusal are far less immediate than for COVID-19 infection because there is no effective curative therapy for COVID-19 and spread occurs through nonintimate, unintentional, and unpredictable exposure.

Translating societal attitudes and practices into the healthcare setting to consider mandated COVID-19 testing requires additional considerations related to both patients and providers: (1) HCWs have committed to a set of values and professional obligations that include tasks requiring risks¹⁵; (2) the public expects HCWs to perform their duties according to a social contract that has few restrictions¹⁶; (3) limiting patient access to hospital care due to COVID-19 testing refusal would contradict and create conflicts related to professional conceptions of hospitals and physicians as patient agents¹⁵; and (4) patients who conscientiously object to testing may seek healthcare less diligently, which may lead to health decrements. The associated postponement of essential care may unduly burden the healthcare system, particularly in situations such as ambulatory care–sensitive conditions.

The extent to which the public health justification for mandatory testing extends to hospitalized patients to protect HCWs is ambiguous. HCWs are of enormous instrumental value and are therefore essential for the pandemic response and health of the broader population. Their protection may therefore justify curtailing informed consent for diagnostic testing. Downstream effects on the supply of frontline HCWs may be realized. Poor control over working conditions may negatively impact motivation among HCWs. In addition, they may feel disenfranchised while obligatorily taking personal risks in caring for patients unwilling to commit to the common good through diagnostic test consent. Hospitalized patients who refuse testing may remain patients under investigation (PUIs), thus requiring special respiratory precautions (SRP) throughout their hospitalization, thereby placing a persistent burden on those with responsibilities requiring patient contact.¹⁷ Repeatedly donning and doffing PPE may remind at-risk HCWs that a myriad of benefits may accrue from frequent, ubiquitous testing. Their motivation may be tempered by the demoralizing requirement to care for patients who will not consent to a simple test, knowing that an opportunity to diminish the burdens of this communicable disease that has taken the lives of many HCWs is being relinquished.

Although HCWs could use SRP universally, their selective application in rooms of known COVID-19–positive patients and those with temporary PUI status has several advantages.¹⁷ First, we learned that HIV testing on patients was helpful in enabling surgeons to selectively implement special precautions among infected patients rather than universally applied intensive precautions. Even in the setting of high rates of HIV infection and educational interventions, HCWs do not reliably apply protective measures included in universal precautions.¹⁸ In keeping with these experiences, limiting the number of patients on SRP will minimize the “precautions fatigue” that drives nonadherent behavior among HCWs.¹⁷ As a result, minimizing the proportion of patients on SRP through testing (and liberation from unnecessary precautions in most cases) will improve uptake of crucial hand hygiene practices and adoption of vigilant PPE use. Second, definitive knowledge of COVID-19 status will increase patient access to care because, whether by personal choice or policy, many HCWs limit in-person contact with patients who are or may be COVID-19 positive. For example, many inpatient dialysis units do not accept patients without a negative COVID-19 nasal swab. Physical therapists may delay or avoid seeing a PUI, which will pose challenges for efficient determination of discharge disposition. Third, selective use of SRP will limit the environmental impact of disposed PPE, which is neither recyclable nor biodegradable. Infectious or regulated biomedical waste products are a significant source of environmental pollution, and the World Health Organization has recommended parsimonious, selective use of PPE to minimize the adverse environmental consequences of biomedical waste products.

In summary, there are substantial justifications for mandatory testing for COVID-19 in the hospital for HCWs and patients, as has been successfully piloted in selected long-term care facilities. Patients who refuse to allow testing may have to accept that their care may be compromised. For preservation of HCW supply and maintenance of HCW morale, hospital policies should make explicit, without punishment or coercion, that HCWs may modify the care they provide to patients who refuse to consent to COVID-19 testing.

In July 2020, the revision of The Pediatric Hospital Medicine Core Competencies was published, bringing to fruition three years of meticulous work.¹ The working group produced 66 chapters outlining the knowledge, skills, and attitudes needed for competent pediatric hospitalist practice. The arrival of these competencies is especially prescient given pediatric hospital medicine’s (PHM’s) relatively new standing as an American Board of Medical Specialties certified subspecialty, as the competencies can serve as a guide for improvement of fellowship curricula, assessment systems, and faculty development. The competencies also represent an opportunity for PHM to take a bold step forward in the world of graduate medical education (GME) by realizing a key tenet of competency-based medical education (CBME)—competency-based, time-variable training (CBTVT), in which learners train until competence is achieved rather than for a predetermined duration.^2,3 In this perspective, we describe how medical education in the United States adopted a time-based training paradigm (in which time-in-training is a surrogate for competence), how CBME has brought time-variable training to the fore, and how PHM has an opportunity to be on the leading edge of education innovation.

In the 1800s, during the time of the “Wild West,” medical education in the United States matched this moniker. There was little standardization across the multiple training pathways to become a practicing physician, including apprenticeships, lecture series, and university courses.⁴ Predictably, this led to significant heterogeneity in the quality of medical care that a patient of the day received. This problem became clearer as Americans traveled to Europe and witnessed more structured and rigorous training programs, only to return to the comparatively poor state of medical education back home.⁵ There was a clear need for curricular standardization.

In 1876, the American Medical College Association (which later became the Association of American Medical Colleges [AAMC]) was founded to meet this need, and in 1905 the Association adopted a set of minimum standards for medical training that included the now-familiar two years of basic sciences and two years of clinical training.⁶ Two subsequent national surveys in the United States were commissioned to evaluate whether medical schools met this new standard, with both surveys finding that roughly half of existing programs passed muster.^7,8 As a result, nearly half of US medical schools had closed by 1920 in a crusade to standardize curricula and produce competent physicians. By the time the American Medical Association established initial standards for internship (an archetype of GME),⁴ time-based medical training was the dominant paradigm. This historical perspective highlights the rationale for standardization of education processes and curricula, particularly in terms of accountability to the American public. But heralded by the 1978 landmark paper by McGaghie et al,⁹ the paradigm began to shift in the late twentieth century from a focus on the process of physician training to outcomes.

In contrast to the process-focused model of the early 1900s, CBME starts by identifying patient and healthcare system needs, defining competencies required to meet those needs, and then designing curricular and assessment processes to help learners achieve those competencies.² This outcomes-based approach grew as a response to calls for greater accountability to the public due to evidence that some graduates were unprepared for unsupervised practice, raising concerns that strictly time-based training was no longer defensible.¹⁰ CBME aims to mitigate these concerns by starting with desired outcomes of training and working backward to ensure those outcomes are met.

While many programs have attempted to implement CBME, most still rely heavily on time-in-training to determine competence. Learners participate in structured curricula and, unless they are extreme outliers, are deemed ready for unsupervised practice after a predetermined duration. This model presumes that competence and time are related in a fixed, predictable manner and that learners gain competence at a uniform rate. However, learners do not, in fact, progress uniformly. A study by Schumacher et al¹¹ involving 23 pediatric residency programs showed significant interlearner variability in rates of entrustment (used as a surrogate for competence), leading the authors to call for time-variable training in GME. Significant interlearner variation in rates of competence attainment have been shown in other specialties as well.¹² As more CBME studies on training outcomes emerge, the evidence is mounting that not all learners need the same duration of training to become competent providers. Time-in-training and competence attainment are not related in a fixed manner. As Dr Jason¹³ wrote in 1969, “By making time a constant, we make achievement a variable.” Variable achievement (competence, outcomes) was the very driver for medical education’s shift to a competency-based approach. If variable competence was not acceptable then, why should it be now? The goal of CBTVT is not shorter training, but rather flexible, individualized training both in terms of content and duration. While this also means some learners may need to extend their training, this should already be part of GME programs that are required to have remediation policies for learners who are not progressing as expected.

Time variability is an oft-cited tenet of CBME,^2,3 but one that is being piloted by relatively few programs in the United States, mostly in undergraduate medical education (UME).^14-16 The Education in Pediatrics Across the Continuum (EPAC), a consortium consisting of four institutions piloting CBTVT in UME,¹⁴ has shown early evidence of feasibility¹⁷ and that UME graduates from CBTVT programs enter residency with levels of competence similar to those of graduates of traditional time-based programs.¹⁸ We believe that PHM can take a step toward truly realizing CBME by implementing CBTVT in fellowship programs.

There are multiple reasons why this is an opportune time for PHM fellowships to consider CBTVT. First, PHM is a relatively new board-certified subspecialty with a recently revised set of core competencies¹ that are likely to catalyze programmatic innovation. A key step in change management is building on previous efforts to generate more change.¹⁹ Programs can leverage the momentum from current and impending change initiatives to innovate and implement CBTVT. Second, the revised PHM competencies provide the first crucial step in implementing a CBME program by defining desired training outcomes necessary to deliver high-quality patient care. With PHM competencies now well defined, programs can focus on developing programs of assessment and corresponding faculty development, which can help deliver valid, defensible decisions about fellow competence.

Finally, PHM has a workforce that can support CBTVT. A major barrier to time-variable training in GME is the need for trainees-as-workforce. In many GME programs, residents and fellows provide a relatively inexpensive, renewable workforce. Trainees’ clinical rotations are often scheduled up to 1 year in advance to ensure care teams are fully staffed, particularly in the inpatient setting, creating a system where flexibility in training is impossible without creating gaps in clinical coverage. However, many PHM fellowships do not completely rely on fellows to cover clinical service lines. PHM fellows spend 32 weeks over 2 years in core clinical rotations with faculty supervision, in accordance with the Accreditation Council for Graduate Medical Education program requirements, both for 2- and 3-year programs. Some CBME experts estimate (based on previous and ongoing CBTVT pilots) that training duration is likely to vary by roughly 20% from current time-based practices when CBTVT is initially implemented.²⁰ Thus, only a small number of clinical service weeks are likely to be affected. If a fellow were deemed ready for unsupervised practice before finishing 2 years of fellowship in a CBTVT program, the corresponding faculty supervisor could use the time previously assigned for supervision to pursue other priorities, such as education, scholarship, or quality improvement. Why provide supervision if a clinical competency committee has deemed a fellow ready for unsupervised practice? Some level of observation and formative feedback could continue, but full supervision would be redundant and unnecessary. CBTVT would allow for some fellows to experience the uncertainty that comes with unsupervised decision-making while still in an environment with trusted fellowship mentors and advisors.

PHM fellowship programs likely cannot flip a switch to “turn on” CBTVT immediately, but they can take steps toward making the transition. Validity, or defensibility of decisions, will be crucial for assessment in CBTVT systems. Programs will need to develop robust assessment systems that collect myriad data to answer the question, “When is this learner competent to deliver high-quality care without supervision?” Programs can align assessment instruments, faculty-development initiatives, and clinical competency committee (CCC) processes with the 2020 PHM competencies to provide a defensible answer. Program leaders should then seek validity evidence, either in existing literature or through novel scholarly initiatives, to support these summative decisions. Engaging all fellowship stakeholders in transitions to CBTVT will be important and should include fellows, program directors, CCC members, clinical leadership, and members from accrediting and credentialing bodies.

As fellowship programs review and revise curricula and assessment systems around the updated PHM core competencies, they should also consider what changes are necessary to implement CBTVT. Time variability is not a novelty but, rather, is a corollary to the outcomes-based approach of CBME. PHM fellowships should strike while the iron is hot and build on current change initiatives prompted by the growth of our specialty to be leaders in CBTVT.

In July 2020, the revision of The Pediatric Hospital Medicine Core Competencies was published, bringing to fruition three years of meticulous work.¹ The working group produced 66 chapters outlining the knowledge, skills, and attitudes needed for competent pediatric hospitalist practice. The arrival of these competencies is especially prescient given pediatric hospital medicine’s (PHM’s) relatively new standing as an American Board of Medical Specialties certified subspecialty, as the competencies can serve as a guide for improvement of fellowship curricula, assessment systems, and faculty development. The competencies also represent an opportunity for PHM to take a bold step forward in the world of graduate medical education (GME) by realizing a key tenet of competency-based medical education (CBME)—competency-based, time-variable training (CBTVT), in which learners train until competence is achieved rather than for a predetermined duration.^2,3 In this perspective, we describe how medical education in the United States adopted a time-based training paradigm (in which time-in-training is a surrogate for competence), how CBME has brought time-variable training to the fore, and how PHM has an opportunity to be on the leading edge of education innovation.

In the 1800s, during the time of the “Wild West,” medical education in the United States matched this moniker. There was little standardization across the multiple training pathways to become a practicing physician, including apprenticeships, lecture series, and university courses.⁴ Predictably, this led to significant heterogeneity in the quality of medical care that a patient of the day received. This problem became clearer as Americans traveled to Europe and witnessed more structured and rigorous training programs, only to return to the comparatively poor state of medical education back home.⁵ There was a clear need for curricular standardization.

In 1876, the American Medical College Association (which later became the Association of American Medical Colleges [AAMC]) was founded to meet this need, and in 1905 the Association adopted a set of minimum standards for medical training that included the now-familiar two years of basic sciences and two years of clinical training.⁶ Two subsequent national surveys in the United States were commissioned to evaluate whether medical schools met this new standard, with both surveys finding that roughly half of existing programs passed muster.^7,8 As a result, nearly half of US medical schools had closed by 1920 in a crusade to standardize curricula and produce competent physicians. By the time the American Medical Association established initial standards for internship (an archetype of GME),⁴ time-based medical training was the dominant paradigm. This historical perspective highlights the rationale for standardization of education processes and curricula, particularly in terms of accountability to the American public. But heralded by the 1978 landmark paper by McGaghie et al,⁹ the paradigm began to shift in the late twentieth century from a focus on the process of physician training to outcomes.

In contrast to the process-focused model of the early 1900s, CBME starts by identifying patient and healthcare system needs, defining competencies required to meet those needs, and then designing curricular and assessment processes to help learners achieve those competencies.² This outcomes-based approach grew as a response to calls for greater accountability to the public due to evidence that some graduates were unprepared for unsupervised practice, raising concerns that strictly time-based training was no longer defensible.¹⁰ CBME aims to mitigate these concerns by starting with desired outcomes of training and working backward to ensure those outcomes are met.

While many programs have attempted to implement CBME, most still rely heavily on time-in-training to determine competence. Learners participate in structured curricula and, unless they are extreme outliers, are deemed ready for unsupervised practice after a predetermined duration. This model presumes that competence and time are related in a fixed, predictable manner and that learners gain competence at a uniform rate. However, learners do not, in fact, progress uniformly. A study by Schumacher et al¹¹ involving 23 pediatric residency programs showed significant interlearner variability in rates of entrustment (used as a surrogate for competence), leading the authors to call for time-variable training in GME. Significant interlearner variation in rates of competence attainment have been shown in other specialties as well.¹² As more CBME studies on training outcomes emerge, the evidence is mounting that not all learners need the same duration of training to become competent providers. Time-in-training and competence attainment are not related in a fixed manner. As Dr Jason¹³ wrote in 1969, “By making time a constant, we make achievement a variable.” Variable achievement (competence, outcomes) was the very driver for medical education’s shift to a competency-based approach. If variable competence was not acceptable then, why should it be now? The goal of CBTVT is not shorter training, but rather flexible, individualized training both in terms of content and duration. While this also means some learners may need to extend their training, this should already be part of GME programs that are required to have remediation policies for learners who are not progressing as expected.

Time variability is an oft-cited tenet of CBME,^2,3 but one that is being piloted by relatively few programs in the United States, mostly in undergraduate medical education (UME).^14-16 The Education in Pediatrics Across the Continuum (EPAC), a consortium consisting of four institutions piloting CBTVT in UME,¹⁴ has shown early evidence of feasibility¹⁷ and that UME graduates from CBTVT programs enter residency with levels of competence similar to those of graduates of traditional time-based programs.¹⁸ We believe that PHM can take a step toward truly realizing CBME by implementing CBTVT in fellowship programs.

There are multiple reasons why this is an opportune time for PHM fellowships to consider CBTVT. First, PHM is a relatively new board-certified subspecialty with a recently revised set of core competencies¹ that are likely to catalyze programmatic innovation. A key step in change management is building on previous efforts to generate more change.¹⁹ Programs can leverage the momentum from current and impending change initiatives to innovate and implement CBTVT. Second, the revised PHM competencies provide the first crucial step in implementing a CBME program by defining desired training outcomes necessary to deliver high-quality patient care. With PHM competencies now well defined, programs can focus on developing programs of assessment and corresponding faculty development, which can help deliver valid, defensible decisions about fellow competence.

Finally, PHM has a workforce that can support CBTVT. A major barrier to time-variable training in GME is the need for trainees-as-workforce. In many GME programs, residents and fellows provide a relatively inexpensive, renewable workforce. Trainees’ clinical rotations are often scheduled up to 1 year in advance to ensure care teams are fully staffed, particularly in the inpatient setting, creating a system where flexibility in training is impossible without creating gaps in clinical coverage. However, many PHM fellowships do not completely rely on fellows to cover clinical service lines. PHM fellows spend 32 weeks over 2 years in core clinical rotations with faculty supervision, in accordance with the Accreditation Council for Graduate Medical Education program requirements, both for 2- and 3-year programs. Some CBME experts estimate (based on previous and ongoing CBTVT pilots) that training duration is likely to vary by roughly 20% from current time-based practices when CBTVT is initially implemented.²⁰ Thus, only a small number of clinical service weeks are likely to be affected. If a fellow were deemed ready for unsupervised practice before finishing 2 years of fellowship in a CBTVT program, the corresponding faculty supervisor could use the time previously assigned for supervision to pursue other priorities, such as education, scholarship, or quality improvement. Why provide supervision if a clinical competency committee has deemed a fellow ready for unsupervised practice? Some level of observation and formative feedback could continue, but full supervision would be redundant and unnecessary. CBTVT would allow for some fellows to experience the uncertainty that comes with unsupervised decision-making while still in an environment with trusted fellowship mentors and advisors.

PHM fellowship programs likely cannot flip a switch to “turn on” CBTVT immediately, but they can take steps toward making the transition. Validity, or defensibility of decisions, will be crucial for assessment in CBTVT systems. Programs will need to develop robust assessment systems that collect myriad data to answer the question, “When is this learner competent to deliver high-quality care without supervision?” Programs can align assessment instruments, faculty-development initiatives, and clinical competency committee (CCC) processes with the 2020 PHM competencies to provide a defensible answer. Program leaders should then seek validity evidence, either in existing literature or through novel scholarly initiatives, to support these summative decisions. Engaging all fellowship stakeholders in transitions to CBTVT will be important and should include fellows, program directors, CCC members, clinical leadership, and members from accrediting and credentialing bodies.

As fellowship programs review and revise curricula and assessment systems around the updated PHM core competencies, they should also consider what changes are necessary to implement CBTVT. Time variability is not a novelty but, rather, is a corollary to the outcomes-based approach of CBME. PHM fellowships should strike while the iron is hot and build on current change initiatives prompted by the growth of our specialty to be leaders in CBTVT.

In July 2020, the revision of The Pediatric Hospital Medicine Core Competencies was published, bringing to fruition three years of meticulous work.¹ The working group produced 66 chapters outlining the knowledge, skills, and attitudes needed for competent pediatric hospitalist practice. The arrival of these competencies is especially prescient given pediatric hospital medicine’s (PHM’s) relatively new standing as an American Board of Medical Specialties certified subspecialty, as the competencies can serve as a guide for improvement of fellowship curricula, assessment systems, and faculty development. The competencies also represent an opportunity for PHM to take a bold step forward in the world of graduate medical education (GME) by realizing a key tenet of competency-based medical education (CBME)—competency-based, time-variable training (CBTVT), in which learners train until competence is achieved rather than for a predetermined duration.^2,3 In this perspective, we describe how medical education in the United States adopted a time-based training paradigm (in which time-in-training is a surrogate for competence), how CBME has brought time-variable training to the fore, and how PHM has an opportunity to be on the leading edge of education innovation.

In the 1800s, during the time of the “Wild West,” medical education in the United States matched this moniker. There was little standardization across the multiple training pathways to become a practicing physician, including apprenticeships, lecture series, and university courses.⁴ Predictably, this led to significant heterogeneity in the quality of medical care that a patient of the day received. This problem became clearer as Americans traveled to Europe and witnessed more structured and rigorous training programs, only to return to the comparatively poor state of medical education back home.⁵ There was a clear need for curricular standardization.

In 1876, the American Medical College Association (which later became the Association of American Medical Colleges [AAMC]) was founded to meet this need, and in 1905 the Association adopted a set of minimum standards for medical training that included the now-familiar two years of basic sciences and two years of clinical training.⁶ Two subsequent national surveys in the United States were commissioned to evaluate whether medical schools met this new standard, with both surveys finding that roughly half of existing programs passed muster.^7,8 As a result, nearly half of US medical schools had closed by 1920 in a crusade to standardize curricula and produce competent physicians. By the time the American Medical Association established initial standards for internship (an archetype of GME),⁴ time-based medical training was the dominant paradigm. This historical perspective highlights the rationale for standardization of education processes and curricula, particularly in terms of accountability to the American public. But heralded by the 1978 landmark paper by McGaghie et al,⁹ the paradigm began to shift in the late twentieth century from a focus on the process of physician training to outcomes.

In contrast to the process-focused model of the early 1900s, CBME starts by identifying patient and healthcare system needs, defining competencies required to meet those needs, and then designing curricular and assessment processes to help learners achieve those competencies.² This outcomes-based approach grew as a response to calls for greater accountability to the public due to evidence that some graduates were unprepared for unsupervised practice, raising concerns that strictly time-based training was no longer defensible.¹⁰ CBME aims to mitigate these concerns by starting with desired outcomes of training and working backward to ensure those outcomes are met.

While many programs have attempted to implement CBME, most still rely heavily on time-in-training to determine competence. Learners participate in structured curricula and, unless they are extreme outliers, are deemed ready for unsupervised practice after a predetermined duration. This model presumes that competence and time are related in a fixed, predictable manner and that learners gain competence at a uniform rate. However, learners do not, in fact, progress uniformly. A study by Schumacher et al¹¹ involving 23 pediatric residency programs showed significant interlearner variability in rates of entrustment (used as a surrogate for competence), leading the authors to call for time-variable training in GME. Significant interlearner variation in rates of competence attainment have been shown in other specialties as well.¹² As more CBME studies on training outcomes emerge, the evidence is mounting that not all learners need the same duration of training to become competent providers. Time-in-training and competence attainment are not related in a fixed manner. As Dr Jason¹³ wrote in 1969, “By making time a constant, we make achievement a variable.” Variable achievement (competence, outcomes) was the very driver for medical education’s shift to a competency-based approach. If variable competence was not acceptable then, why should it be now? The goal of CBTVT is not shorter training, but rather flexible, individualized training both in terms of content and duration. While this also means some learners may need to extend their training, this should already be part of GME programs that are required to have remediation policies for learners who are not progressing as expected.

Time variability is an oft-cited tenet of CBME,^2,3 but one that is being piloted by relatively few programs in the United States, mostly in undergraduate medical education (UME).^14-16 The Education in Pediatrics Across the Continuum (EPAC), a consortium consisting of four institutions piloting CBTVT in UME,¹⁴ has shown early evidence of feasibility¹⁷ and that UME graduates from CBTVT programs enter residency with levels of competence similar to those of graduates of traditional time-based programs.¹⁸ We believe that PHM can take a step toward truly realizing CBME by implementing CBTVT in fellowship programs.

There are multiple reasons why this is an opportune time for PHM fellowships to consider CBTVT. First, PHM is a relatively new board-certified subspecialty with a recently revised set of core competencies¹ that are likely to catalyze programmatic innovation. A key step in change management is building on previous efforts to generate more change.¹⁹ Programs can leverage the momentum from current and impending change initiatives to innovate and implement CBTVT. Second, the revised PHM competencies provide the first crucial step in implementing a CBME program by defining desired training outcomes necessary to deliver high-quality patient care. With PHM competencies now well defined, programs can focus on developing programs of assessment and corresponding faculty development, which can help deliver valid, defensible decisions about fellow competence.

Finally, PHM has a workforce that can support CBTVT. A major barrier to time-variable training in GME is the need for trainees-as-workforce. In many GME programs, residents and fellows provide a relatively inexpensive, renewable workforce. Trainees’ clinical rotations are often scheduled up to 1 year in advance to ensure care teams are fully staffed, particularly in the inpatient setting, creating a system where flexibility in training is impossible without creating gaps in clinical coverage. However, many PHM fellowships do not completely rely on fellows to cover clinical service lines. PHM fellows spend 32 weeks over 2 years in core clinical rotations with faculty supervision, in accordance with the Accreditation Council for Graduate Medical Education program requirements, both for 2- and 3-year programs. Some CBME experts estimate (based on previous and ongoing CBTVT pilots) that training duration is likely to vary by roughly 20% from current time-based practices when CBTVT is initially implemented.²⁰ Thus, only a small number of clinical service weeks are likely to be affected. If a fellow were deemed ready for unsupervised practice before finishing 2 years of fellowship in a CBTVT program, the corresponding faculty supervisor could use the time previously assigned for supervision to pursue other priorities, such as education, scholarship, or quality improvement. Why provide supervision if a clinical competency committee has deemed a fellow ready for unsupervised practice? Some level of observation and formative feedback could continue, but full supervision would be redundant and unnecessary. CBTVT would allow for some fellows to experience the uncertainty that comes with unsupervised decision-making while still in an environment with trusted fellowship mentors and advisors.

PHM fellowship programs likely cannot flip a switch to “turn on” CBTVT immediately, but they can take steps toward making the transition. Validity, or defensibility of decisions, will be crucial for assessment in CBTVT systems. Programs will need to develop robust assessment systems that collect myriad data to answer the question, “When is this learner competent to deliver high-quality care without supervision?” Programs can align assessment instruments, faculty-development initiatives, and clinical competency committee (CCC) processes with the 2020 PHM competencies to provide a defensible answer. Program leaders should then seek validity evidence, either in existing literature or through novel scholarly initiatives, to support these summative decisions. Engaging all fellowship stakeholders in transitions to CBTVT will be important and should include fellows, program directors, CCC members, clinical leadership, and members from accrediting and credentialing bodies.

As fellowship programs review and revise curricula and assessment systems around the updated PHM core competencies, they should also consider what changes are necessary to implement CBTVT. Time variability is not a novelty but, rather, is a corollary to the outcomes-based approach of CBME. PHM fellowships should strike while the iron is hot and build on current change initiatives prompted by the growth of our specialty to be leaders in CBTVT.

The healthcare system in South Korea (Korea) is evolving. Korea began developing a national health insurance system for the entire population in 1989, and implementation took 12 years. The healthcare insurance premium was set in 1989 when the Korean gross domestic product (GDP) per capita was less than $5,000 USD. Since then, the incremental rise in healthcare insurance premiums, approximately 6% of the typical Korean income, has been relatively small considering the economic growth of Korea.^1-3 The success and rapid adoption of national health insurance in Korea revealed unanticipated problems in three specific areas: low individual contributions relative to costs of care, low levels of reimbursement to providers, and incomplete coverage of medical services, which then require out-of-pocket payments (typically 20% of the inpatient care fee).⁴ Additionally, while little attention has been paid to quality and safety in the past, the nation has come to recognize the importance of these considerations,⁵ particularly with regard to patient expectations for and consumption of medical care and the consequent demand for better services from the medical community and government.⁶

Healthcare constitutes about 7.5% of Korea’s GDP. In contrast, healthcare spending accounts for about 17.7% of US GDP and about 8.8% of the GDP of member nations of the Organisation for Economic Co-operation and Development (OECD) in aggregate. Additionally, Korea has a longer length of hospital stay (16.5 days vs 7.3 days for OECD countries) and fewer practicing physicians (1.1 per 1,000 persons vs 1.9 per 1,000 for OECD countries). Therefore, the average cumulative annual patient hospital days per physician is 2,394 days in Korea, three times higher than that in the OECD countries.⁷ Furthermore, the number of physicians providing hospital care in Korea has remained relatively low.⁸

Despite recent growth in the total number of practicing physicians, the number of hospital-based physicians remains insufficient to cover admitted patients. The pressure for hospital-based physicians to serve large numbers of patients to generate sufficient revenue has deterred growth of more physicians focused on inpatient care.⁶ In order to operate the hospital, doctors must see as many patients as possible because physicians are reimbursed primarily by an inpatient care fee rather than by type or extent of care provided. Therefore, this low fee schedule makes it difficult to provide good quality inpatient care while simultaneously trying to increase accessibility. The costs of testing and treatment are reimbursed through a separate fee schedule.

The Korean government enacted two new policies regarding medical school graduates and residents that led to implementation of a hospitalist system. The first policy created a quota of medical residents to match the total number of medical school graduates.⁹ Before the new regulation, the number of medical resident positions exceeded the number of medical school graduates by 20%, which led to a shortage of resident applicants in “unpopular” medical specialties or departments. These specialties (eg, general surgery, obstetrics and gynecology, urology) have a lower fee schedule, which leads to lower wages while having very high workloads. The decrease in the number of available medical resident positions results in greater workloads for physicians providing inpatient care. These changes, including the shortage of a resident workforce, led to a burgeoning hospital-based attending physician workforce to care for hospitalized patients.

The other policy, created in December 2015 and known as the Act on the Improvement of Training Conditions and Status of Medical Residents, was enacted to improve working conditions for residents by limiting their working hours to 80 or fewer per week.¹⁰ These work-hour restrictions made it nearly impossible to provide 24-hour inpatient care depending solely on residents.^10-12 This policy increased the need for hospitalists in Korea, similar to that of the US Accreditation Council for Graduate Medical Education in 2003, which limited resident work hours. Because of these changes, the hospitalist model was introduced to manage the growing volume of inpatients at teaching hospitals.¹³

The institutional implementation of the Korean hospitalist system was catalyzed by the need to solve current problems such as patient safety, healthcare quality, and residents’ well-being.^8,14 Therefore, the hospitalist system was designed and implemented to meet the needs of patients, medical professionals, and hospitals.^6,8,14 Furthermore, under the universal health insurance system in Korea, institutional implementation also meant creating a new set of fee schedules for hospitalists. The Korean hospitalist system reflects the nuances and challenges faced by the Korean healthcare system.⁵

A council was formed to implement a hospitalist model suitable for Korea. The council was composed of five groups: the Korean Association of Internal Medicine, Korean Surgical Society, Korean Medical Association, Korean Hospital Association, and Korean Academy of Medical Sciences. The Korean Association of Internal Medicine and the Korean Surgical Society were involved in creating a new medical discipline because their members provided a disproportionate amount of inpatient care and were most often responsible for hospital patient safety and quality care. Along with the support of the council, the Ministry of Health and Welfare began to realize the high demand on inpatient care and requested an official proposal to broaden the hospitalist model, with two conditions. First, the government requested a unified proposal from the medical community that reflected the collective voices of individual stakeholders, with an expectation that the new system would focus on patient safety and healthcare quality across all specialties. Second, the proposal had to be detailed and include a specific fee schedule for hospitalist services.

Before implementing a national hospitalist system, we conducted a pilot study supported by funding from the council. This first study, the Korean Hospitalist System Operation and Evaluation Research (September 2015 to August 2016)¹⁴, was designed to (a) determine hospitalist needs (eg, rotation schedule, salary, working conditions) for this new profession, (b) determine the necessary number of hospitalists and appropriate fee schedules to cover salary and benefits, and (c) provide Korean hospitalist models with operational methods to facilitate implementation at individual institutions. The council and research team selected four hospitals for the privately funded pilot study (Table). These hospitals already had an inpatient care system managed by specialists prior to the pilot study, so major changes in patient care and hospital operations were not required.

The pilot study demonstrated improvements in patient satisfaction, medical service quality, and patient safety.^8,14,15 As a result, fees billed from hospitalist services were now covered by national health insurance; previously, they were not covered by any inpatient care fee or bundled service fee. The next study (phase 2) was then conducted to evaluate the national implementation of the hospitalist system and its outcomes. The first phase defined criteria for monitoring and evaluating the institutionalized hospitalist system in Korea. The second phase, initiated in September 2016, evaluated how the implemented system could be expanded nationwide. The government was actively involved in the study; 31 of 344 (9%) general hospitals were selected and able to apply the hospitalist fee schedule for care of patients who were admitted to the hospitalist ward.

To investigate the effect of the hospitalist system, we measured the satisfaction of patients and medical providers (eg, specialists, residents, and nurses). The outcomes were number of doctors’ calls to hospitalists and to other specialties, duration of time required to address medical problems, number of contacts with the hospitalist (residents or other physicians in the control group), and time spent with hospitalized patients. In addition, we analyzed claims costs and the operating costs for implementing the system within each hospital (Table).

The new set of fee schedules was created specifically for hospitalist care services so hospitals could now claim separate inpatient care fees and hospitalist fees. The standard hospitalist fee schedule is applied only to patients who are on hospitalist-led wards. The fee schedule has two criteria: The hospitalist ward must have 50 beds which are assigned to hospitalists only, and each hospitalist ward has a team of five hospitalists. A maximum of 25 patients could be assigned to an individual hospitalist. However, fees can be adjusted based on individual hospital operation and management after government approval.

Under the national insurance system, the fee schedule is essential to obtain reimbursement for provided services. As a new medical profession, it is important to have the institutional implementation of the hospitalist system. In contrast to those in the United States, inpatients in Korea pay inpatient care fees only, which are charged per day. Therefore, reducing the length of stay without increasing patient volume does not financially benefit the hospital.

The definition of a Korean hospitalist was carefully developed, and a reimbursement system for hospitalists was created within the Korean healthcare system. All hospitalists are medical specialists who have completed advanced education and clinical training in their specialty area (internal medicine or surgery). Creating a definition of a hospitalist and a standard fee schedule was necessary for national incorporation of this new approach. The model must also be accepted by related parties, including healthcare professionals such as hospital executives, nonhospitalist doctors, and nurses. Therefore, the system has a minimum of two requirements to operate at the hospital level, and the remaining factors can be modified by individual hospitals to protect the new discipline within the healthcare system. First, hospitalist services are provided only to hospitalist patients. To establish the new system, limiting the patient range to a specific group was necessary to prevent abuse of human-based resources within the hospital. Second, hospitalists must be stationed near the hospitalist ward to enhance accessibility, patient safety, and healthcare quality. In other words, a hospitalist provides services to hospitalized patients who paid the hospitalist fee, and the hospitalist does not provide care beyond the hospitalist ward (eg, they cannot provide outpatient consultation or provide care on other wards).

Korean national health insurance is the universal health insurance under a single insurer (ie, the government). Therefore, all Korean citizens have national health insurance. Individuals can choose to have additional health insurance (private insurance) if they wish to pay an out-of-pocket fee with their additional private insurance. In Korea, the inpatient care fee includes all fees to provide care for the patient during hospital stays, such as the physician fee, facility fee, and consultation fee. The inpatient care fee schedule is charged per day including all the inpatient care composition. The claim and reimbursement system is different in Korea in that the hospital as a whole submits the claim for reimbursement. There is no separate claim from physicians, lab technicians, or facilities. Doctors provide care to patients and are then paid for the services in terms of salaries. Since the original inpatient care fee schedule was low and insufficient, it was difficult to provide high-quality care. Also, resources for spending on inpatient care management was limited. So a new system to improve inpatient care was needed. Implementing the hospitalist system in Korea led to the creation of a new fee schedule specifically for hospitalists so that hospitals could claim hospitalist fees on top of inpatient care fees when the patient was cared for by the hospitalist. The additional fees in the hospitalist fee schedule allow safe and high-quality care to be provided for patients during hospital stays.

The Korean hospitalist system has been implemented for 3 years with the government’s active involvement, with approximately 250 specialists working as hospitalists as of August 2020. Importantly, Korean hospitalists are not limited to internal medicine specialists; in fact, several different specialties practice as hospitalists, including surgeons and other medical specialists, who account for 20% and 30% of the hospitalist workforce, respectively. Since the system’s implementation, all inpatient care and management is now transferred to specialists from residents in hospitalist wards. This change has increased patient safety and care quality. At the beginning of the pilot study, the concept of a “hospitalist” was new in Korea and the public did not know who a hospitalist was nor what a hospitalist did. However, after 3 years, patients now seek hospitalist care.

National implementation of the hospitalist model represents a key paradigm shift in the Korean healthcare system. The Korean hospitalist system is the result of the hospitals’, doctors’, and patients’ desire for higher-quality care. We expect to see growth of hospitalists in Korea and provision of better, safer, and more efficient inpatient care across specialties, payers, and government. Since we are at the early stage of the system, further efforts to support implementation are required. We hope our implementation process of a new medical system could serve as a model for other countries who are seeking to adopt hospitalist systems within their current healthcare paradigms.

The authors thank the medical professionals, government officers, and other professionals who put great effort into the implementation of the Korean Hospitalist System.

The healthcare system in South Korea (Korea) is evolving. Korea began developing a national health insurance system for the entire population in 1989, and implementation took 12 years. The healthcare insurance premium was set in 1989 when the Korean gross domestic product (GDP) per capita was less than $5,000 USD. Since then, the incremental rise in healthcare insurance premiums, approximately 6% of the typical Korean income, has been relatively small considering the economic growth of Korea.^1-3 The success and rapid adoption of national health insurance in Korea revealed unanticipated problems in three specific areas: low individual contributions relative to costs of care, low levels of reimbursement to providers, and incomplete coverage of medical services, which then require out-of-pocket payments (typically 20% of the inpatient care fee).⁴ Additionally, while little attention has been paid to quality and safety in the past, the nation has come to recognize the importance of these considerations,⁵ particularly with regard to patient expectations for and consumption of medical care and the consequent demand for better services from the medical community and government.⁶

Healthcare constitutes about 7.5% of Korea’s GDP. In contrast, healthcare spending accounts for about 17.7% of US GDP and about 8.8% of the GDP of member nations of the Organisation for Economic Co-operation and Development (OECD) in aggregate. Additionally, Korea has a longer length of hospital stay (16.5 days vs 7.3 days for OECD countries) and fewer practicing physicians (1.1 per 1,000 persons vs 1.9 per 1,000 for OECD countries). Therefore, the average cumulative annual patient hospital days per physician is 2,394 days in Korea, three times higher than that in the OECD countries.⁷ Furthermore, the number of physicians providing hospital care in Korea has remained relatively low.⁸

Despite recent growth in the total number of practicing physicians, the number of hospital-based physicians remains insufficient to cover admitted patients. The pressure for hospital-based physicians to serve large numbers of patients to generate sufficient revenue has deterred growth of more physicians focused on inpatient care.⁶ In order to operate the hospital, doctors must see as many patients as possible because physicians are reimbursed primarily by an inpatient care fee rather than by type or extent of care provided. Therefore, this low fee schedule makes it difficult to provide good quality inpatient care while simultaneously trying to increase accessibility. The costs of testing and treatment are reimbursed through a separate fee schedule.

The Korean government enacted two new policies regarding medical school graduates and residents that led to implementation of a hospitalist system. The first policy created a quota of medical residents to match the total number of medical school graduates.⁹ Before the new regulation, the number of medical resident positions exceeded the number of medical school graduates by 20%, which led to a shortage of resident applicants in “unpopular” medical specialties or departments. These specialties (eg, general surgery, obstetrics and gynecology, urology) have a lower fee schedule, which leads to lower wages while having very high workloads. The decrease in the number of available medical resident positions results in greater workloads for physicians providing inpatient care. These changes, including the shortage of a resident workforce, led to a burgeoning hospital-based attending physician workforce to care for hospitalized patients.

The other policy, created in December 2015 and known as the Act on the Improvement of Training Conditions and Status of Medical Residents, was enacted to improve working conditions for residents by limiting their working hours to 80 or fewer per week.¹⁰ These work-hour restrictions made it nearly impossible to provide 24-hour inpatient care depending solely on residents.^10-12 This policy increased the need for hospitalists in Korea, similar to that of the US Accreditation Council for Graduate Medical Education in 2003, which limited resident work hours. Because of these changes, the hospitalist model was introduced to manage the growing volume of inpatients at teaching hospitals.¹³

The institutional implementation of the Korean hospitalist system was catalyzed by the need to solve current problems such as patient safety, healthcare quality, and residents’ well-being.^8,14 Therefore, the hospitalist system was designed and implemented to meet the needs of patients, medical professionals, and hospitals.^6,8,14 Furthermore, under the universal health insurance system in Korea, institutional implementation also meant creating a new set of fee schedules for hospitalists. The Korean hospitalist system reflects the nuances and challenges faced by the Korean healthcare system.⁵

A council was formed to implement a hospitalist model suitable for Korea. The council was composed of five groups: the Korean Association of Internal Medicine, Korean Surgical Society, Korean Medical Association, Korean Hospital Association, and Korean Academy of Medical Sciences. The Korean Association of Internal Medicine and the Korean Surgical Society were involved in creating a new medical discipline because their members provided a disproportionate amount of inpatient care and were most often responsible for hospital patient safety and quality care. Along with the support of the council, the Ministry of Health and Welfare began to realize the high demand on inpatient care and requested an official proposal to broaden the hospitalist model, with two conditions. First, the government requested a unified proposal from the medical community that reflected the collective voices of individual stakeholders, with an expectation that the new system would focus on patient safety and healthcare quality across all specialties. Second, the proposal had to be detailed and include a specific fee schedule for hospitalist services.

Before implementing a national hospitalist system, we conducted a pilot study supported by funding from the council. This first study, the Korean Hospitalist System Operation and Evaluation Research (September 2015 to August 2016)¹⁴, was designed to (a) determine hospitalist needs (eg, rotation schedule, salary, working conditions) for this new profession, (b) determine the necessary number of hospitalists and appropriate fee schedules to cover salary and benefits, and (c) provide Korean hospitalist models with operational methods to facilitate implementation at individual institutions. The council and research team selected four hospitals for the privately funded pilot study (Table). These hospitals already had an inpatient care system managed by specialists prior to the pilot study, so major changes in patient care and hospital operations were not required.

The pilot study demonstrated improvements in patient satisfaction, medical service quality, and patient safety.^8,14,15 As a result, fees billed from hospitalist services were now covered by national health insurance; previously, they were not covered by any inpatient care fee or bundled service fee. The next study (phase 2) was then conducted to evaluate the national implementation of the hospitalist system and its outcomes. The first phase defined criteria for monitoring and evaluating the institutionalized hospitalist system in Korea. The second phase, initiated in September 2016, evaluated how the implemented system could be expanded nationwide. The government was actively involved in the study; 31 of 344 (9%) general hospitals were selected and able to apply the hospitalist fee schedule for care of patients who were admitted to the hospitalist ward.

To investigate the effect of the hospitalist system, we measured the satisfaction of patients and medical providers (eg, specialists, residents, and nurses). The outcomes were number of doctors’ calls to hospitalists and to other specialties, duration of time required to address medical problems, number of contacts with the hospitalist (residents or other physicians in the control group), and time spent with hospitalized patients. In addition, we analyzed claims costs and the operating costs for implementing the system within each hospital (Table).

The new set of fee schedules was created specifically for hospitalist care services so hospitals could now claim separate inpatient care fees and hospitalist fees. The standard hospitalist fee schedule is applied only to patients who are on hospitalist-led wards. The fee schedule has two criteria: The hospitalist ward must have 50 beds which are assigned to hospitalists only, and each hospitalist ward has a team of five hospitalists. A maximum of 25 patients could be assigned to an individual hospitalist. However, fees can be adjusted based on individual hospital operation and management after government approval.

Under the national insurance system, the fee schedule is essential to obtain reimbursement for provided services. As a new medical profession, it is important to have the institutional implementation of the hospitalist system. In contrast to those in the United States, inpatients in Korea pay inpatient care fees only, which are charged per day. Therefore, reducing the length of stay without increasing patient volume does not financially benefit the hospital.

The definition of a Korean hospitalist was carefully developed, and a reimbursement system for hospitalists was created within the Korean healthcare system. All hospitalists are medical specialists who have completed advanced education and clinical training in their specialty area (internal medicine or surgery). Creating a definition of a hospitalist and a standard fee schedule was necessary for national incorporation of this new approach. The model must also be accepted by related parties, including healthcare professionals such as hospital executives, nonhospitalist doctors, and nurses. Therefore, the system has a minimum of two requirements to operate at the hospital level, and the remaining factors can be modified by individual hospitals to protect the new discipline within the healthcare system. First, hospitalist services are provided only to hospitalist patients. To establish the new system, limiting the patient range to a specific group was necessary to prevent abuse of human-based resources within the hospital. Second, hospitalists must be stationed near the hospitalist ward to enhance accessibility, patient safety, and healthcare quality. In other words, a hospitalist provides services to hospitalized patients who paid the hospitalist fee, and the hospitalist does not provide care beyond the hospitalist ward (eg, they cannot provide outpatient consultation or provide care on other wards).

Korean national health insurance is the universal health insurance under a single insurer (ie, the government). Therefore, all Korean citizens have national health insurance. Individuals can choose to have additional health insurance (private insurance) if they wish to pay an out-of-pocket fee with their additional private insurance. In Korea, the inpatient care fee includes all fees to provide care for the patient during hospital stays, such as the physician fee, facility fee, and consultation fee. The inpatient care fee schedule is charged per day including all the inpatient care composition. The claim and reimbursement system is different in Korea in that the hospital as a whole submits the claim for reimbursement. There is no separate claim from physicians, lab technicians, or facilities. Doctors provide care to patients and are then paid for the services in terms of salaries. Since the original inpatient care fee schedule was low and insufficient, it was difficult to provide high-quality care. Also, resources for spending on inpatient care management was limited. So a new system to improve inpatient care was needed. Implementing the hospitalist system in Korea led to the creation of a new fee schedule specifically for hospitalists so that hospitals could claim hospitalist fees on top of inpatient care fees when the patient was cared for by the hospitalist. The additional fees in the hospitalist fee schedule allow safe and high-quality care to be provided for patients during hospital stays.

The Korean hospitalist system has been implemented for 3 years with the government’s active involvement, with approximately 250 specialists working as hospitalists as of August 2020. Importantly, Korean hospitalists are not limited to internal medicine specialists; in fact, several different specialties practice as hospitalists, including surgeons and other medical specialists, who account for 20% and 30% of the hospitalist workforce, respectively. Since the system’s implementation, all inpatient care and management is now transferred to specialists from residents in hospitalist wards. This change has increased patient safety and care quality. At the beginning of the pilot study, the concept of a “hospitalist” was new in Korea and the public did not know who a hospitalist was nor what a hospitalist did. However, after 3 years, patients now seek hospitalist care.

National implementation of the hospitalist model represents a key paradigm shift in the Korean healthcare system. The Korean hospitalist system is the result of the hospitals’, doctors’, and patients’ desire for higher-quality care. We expect to see growth of hospitalists in Korea and provision of better, safer, and more efficient inpatient care across specialties, payers, and government. Since we are at the early stage of the system, further efforts to support implementation are required. We hope our implementation process of a new medical system could serve as a model for other countries who are seeking to adopt hospitalist systems within their current healthcare paradigms.

The authors thank the medical professionals, government officers, and other professionals who put great effort into the implementation of the Korean Hospitalist System.

The healthcare system in South Korea (Korea) is evolving. Korea began developing a national health insurance system for the entire population in 1989, and implementation took 12 years. The healthcare insurance premium was set in 1989 when the Korean gross domestic product (GDP) per capita was less than $5,000 USD. Since then, the incremental rise in healthcare insurance premiums, approximately 6% of the typical Korean income, has been relatively small considering the economic growth of Korea.^1-3 The success and rapid adoption of national health insurance in Korea revealed unanticipated problems in three specific areas: low individual contributions relative to costs of care, low levels of reimbursement to providers, and incomplete coverage of medical services, which then require out-of-pocket payments (typically 20% of the inpatient care fee).⁴ Additionally, while little attention has been paid to quality and safety in the past, the nation has come to recognize the importance of these considerations,⁵ particularly with regard to patient expectations for and consumption of medical care and the consequent demand for better services from the medical community and government.⁶

Healthcare constitutes about 7.5% of Korea’s GDP. In contrast, healthcare spending accounts for about 17.7% of US GDP and about 8.8% of the GDP of member nations of the Organisation for Economic Co-operation and Development (OECD) in aggregate. Additionally, Korea has a longer length of hospital stay (16.5 days vs 7.3 days for OECD countries) and fewer practicing physicians (1.1 per 1,000 persons vs 1.9 per 1,000 for OECD countries). Therefore, the average cumulative annual patient hospital days per physician is 2,394 days in Korea, three times higher than that in the OECD countries.⁷ Furthermore, the number of physicians providing hospital care in Korea has remained relatively low.⁸

Despite recent growth in the total number of practicing physicians, the number of hospital-based physicians remains insufficient to cover admitted patients. The pressure for hospital-based physicians to serve large numbers of patients to generate sufficient revenue has deterred growth of more physicians focused on inpatient care.⁶ In order to operate the hospital, doctors must see as many patients as possible because physicians are reimbursed primarily by an inpatient care fee rather than by type or extent of care provided. Therefore, this low fee schedule makes it difficult to provide good quality inpatient care while simultaneously trying to increase accessibility. The costs of testing and treatment are reimbursed through a separate fee schedule.

The Korean government enacted two new policies regarding medical school graduates and residents that led to implementation of a hospitalist system. The first policy created a quota of medical residents to match the total number of medical school graduates.⁹ Before the new regulation, the number of medical resident positions exceeded the number of medical school graduates by 20%, which led to a shortage of resident applicants in “unpopular” medical specialties or departments. These specialties (eg, general surgery, obstetrics and gynecology, urology) have a lower fee schedule, which leads to lower wages while having very high workloads. The decrease in the number of available medical resident positions results in greater workloads for physicians providing inpatient care. These changes, including the shortage of a resident workforce, led to a burgeoning hospital-based attending physician workforce to care for hospitalized patients.

The other policy, created in December 2015 and known as the Act on the Improvement of Training Conditions and Status of Medical Residents, was enacted to improve working conditions for residents by limiting their working hours to 80 or fewer per week.¹⁰ These work-hour restrictions made it nearly impossible to provide 24-hour inpatient care depending solely on residents.^10-12 This policy increased the need for hospitalists in Korea, similar to that of the US Accreditation Council for Graduate Medical Education in 2003, which limited resident work hours. Because of these changes, the hospitalist model was introduced to manage the growing volume of inpatients at teaching hospitals.¹³

The institutional implementation of the Korean hospitalist system was catalyzed by the need to solve current problems such as patient safety, healthcare quality, and residents’ well-being.^8,14 Therefore, the hospitalist system was designed and implemented to meet the needs of patients, medical professionals, and hospitals.^6,8,14 Furthermore, under the universal health insurance system in Korea, institutional implementation also meant creating a new set of fee schedules for hospitalists. The Korean hospitalist system reflects the nuances and challenges faced by the Korean healthcare system.⁵

A council was formed to implement a hospitalist model suitable for Korea. The council was composed of five groups: the Korean Association of Internal Medicine, Korean Surgical Society, Korean Medical Association, Korean Hospital Association, and Korean Academy of Medical Sciences. The Korean Association of Internal Medicine and the Korean Surgical Society were involved in creating a new medical discipline because their members provided a disproportionate amount of inpatient care and were most often responsible for hospital patient safety and quality care. Along with the support of the council, the Ministry of Health and Welfare began to realize the high demand on inpatient care and requested an official proposal to broaden the hospitalist model, with two conditions. First, the government requested a unified proposal from the medical community that reflected the collective voices of individual stakeholders, with an expectation that the new system would focus on patient safety and healthcare quality across all specialties. Second, the proposal had to be detailed and include a specific fee schedule for hospitalist services.

Before implementing a national hospitalist system, we conducted a pilot study supported by funding from the council. This first study, the Korean Hospitalist System Operation and Evaluation Research (September 2015 to August 2016)¹⁴, was designed to (a) determine hospitalist needs (eg, rotation schedule, salary, working conditions) for this new profession, (b) determine the necessary number of hospitalists and appropriate fee schedules to cover salary and benefits, and (c) provide Korean hospitalist models with operational methods to facilitate implementation at individual institutions. The council and research team selected four hospitals for the privately funded pilot study (Table). These hospitals already had an inpatient care system managed by specialists prior to the pilot study, so major changes in patient care and hospital operations were not required.

The pilot study demonstrated improvements in patient satisfaction, medical service quality, and patient safety.^8,14,15 As a result, fees billed from hospitalist services were now covered by national health insurance; previously, they were not covered by any inpatient care fee or bundled service fee. The next study (phase 2) was then conducted to evaluate the national implementation of the hospitalist system and its outcomes. The first phase defined criteria for monitoring and evaluating the institutionalized hospitalist system in Korea. The second phase, initiated in September 2016, evaluated how the implemented system could be expanded nationwide. The government was actively involved in the study; 31 of 344 (9%) general hospitals were selected and able to apply the hospitalist fee schedule for care of patients who were admitted to the hospitalist ward.

To investigate the effect of the hospitalist system, we measured the satisfaction of patients and medical providers (eg, specialists, residents, and nurses). The outcomes were number of doctors’ calls to hospitalists and to other specialties, duration of time required to address medical problems, number of contacts with the hospitalist (residents or other physicians in the control group), and time spent with hospitalized patients. In addition, we analyzed claims costs and the operating costs for implementing the system within each hospital (Table).

The new set of fee schedules was created specifically for hospitalist care services so hospitals could now claim separate inpatient care fees and hospitalist fees. The standard hospitalist fee schedule is applied only to patients who are on hospitalist-led wards. The fee schedule has two criteria: The hospitalist ward must have 50 beds which are assigned to hospitalists only, and each hospitalist ward has a team of five hospitalists. A maximum of 25 patients could be assigned to an individual hospitalist. However, fees can be adjusted based on individual hospital operation and management after government approval.

Under the national insurance system, the fee schedule is essential to obtain reimbursement for provided services. As a new medical profession, it is important to have the institutional implementation of the hospitalist system. In contrast to those in the United States, inpatients in Korea pay inpatient care fees only, which are charged per day. Therefore, reducing the length of stay without increasing patient volume does not financially benefit the hospital.

The definition of a Korean hospitalist was carefully developed, and a reimbursement system for hospitalists was created within the Korean healthcare system. All hospitalists are medical specialists who have completed advanced education and clinical training in their specialty area (internal medicine or surgery). Creating a definition of a hospitalist and a standard fee schedule was necessary for national incorporation of this new approach. The model must also be accepted by related parties, including healthcare professionals such as hospital executives, nonhospitalist doctors, and nurses. Therefore, the system has a minimum of two requirements to operate at the hospital level, and the remaining factors can be modified by individual hospitals to protect the new discipline within the healthcare system. First, hospitalist services are provided only to hospitalist patients. To establish the new system, limiting the patient range to a specific group was necessary to prevent abuse of human-based resources within the hospital. Second, hospitalists must be stationed near the hospitalist ward to enhance accessibility, patient safety, and healthcare quality. In other words, a hospitalist provides services to hospitalized patients who paid the hospitalist fee, and the hospitalist does not provide care beyond the hospitalist ward (eg, they cannot provide outpatient consultation or provide care on other wards).

Korean national health insurance is the universal health insurance under a single insurer (ie, the government). Therefore, all Korean citizens have national health insurance. Individuals can choose to have additional health insurance (private insurance) if they wish to pay an out-of-pocket fee with their additional private insurance. In Korea, the inpatient care fee includes all fees to provide care for the patient during hospital stays, such as the physician fee, facility fee, and consultation fee. The inpatient care fee schedule is charged per day including all the inpatient care composition. The claim and reimbursement system is different in Korea in that the hospital as a whole submits the claim for reimbursement. There is no separate claim from physicians, lab technicians, or facilities. Doctors provide care to patients and are then paid for the services in terms of salaries. Since the original inpatient care fee schedule was low and insufficient, it was difficult to provide high-quality care. Also, resources for spending on inpatient care management was limited. So a new system to improve inpatient care was needed. Implementing the hospitalist system in Korea led to the creation of a new fee schedule specifically for hospitalists so that hospitals could claim hospitalist fees on top of inpatient care fees when the patient was cared for by the hospitalist. The additional fees in the hospitalist fee schedule allow safe and high-quality care to be provided for patients during hospital stays.

The Korean hospitalist system has been implemented for 3 years with the government’s active involvement, with approximately 250 specialists working as hospitalists as of August 2020. Importantly, Korean hospitalists are not limited to internal medicine specialists; in fact, several different specialties practice as hospitalists, including surgeons and other medical specialists, who account for 20% and 30% of the hospitalist workforce, respectively. Since the system’s implementation, all inpatient care and management is now transferred to specialists from residents in hospitalist wards. This change has increased patient safety and care quality. At the beginning of the pilot study, the concept of a “hospitalist” was new in Korea and the public did not know who a hospitalist was nor what a hospitalist did. However, after 3 years, patients now seek hospitalist care.

National implementation of the hospitalist model represents a key paradigm shift in the Korean healthcare system. The Korean hospitalist system is the result of the hospitals’, doctors’, and patients’ desire for higher-quality care. We expect to see growth of hospitalists in Korea and provision of better, safer, and more efficient inpatient care across specialties, payers, and government. Since we are at the early stage of the system, further efforts to support implementation are required. We hope our implementation process of a new medical system could serve as a model for other countries who are seeking to adopt hospitalist systems within their current healthcare paradigms.

The authors thank the medical professionals, government officers, and other professionals who put great effort into the implementation of the Korean Hospitalist System.

The US Department of Veterans Affairs (VA) is the largest health care organization in the US, staffing more than 1,200 facilities and servicing about 9 million veterans.¹ Identifying VA practices that promote effective health care delivery has the potential to impact thousands of patients every day. The Surgical service at the Minneapolis VA Medical Center (MVAMC) in Minnesota often questioned colleagues whether many of the ordered tests, including blood cultures for patients with suspected infections, were clinically necessary. Despite recommendations for utilizing culture-driven results in choosing appropriate antimicrobials, it was debated whether these additional tests were simply drawn and ignored resulting only in increased costs and venipuncture discomfort for the patient. Thus, the purpose of this quality improvement study was to determine whether positive blood culture results actually influence clinical management at MVAMC.

Background

Accepted best practice when responding to positive blood culture results entails empiric treatment with broad-spectrum antibiotics that subsequently narrows in breadth of coverage once the pathogen has been identified.^2-4 This strategy has been labeled deescalation. Despite the acceptance of these standards, surveys of clinician attitudes towards antibiotics showed that 90% of physicians and residents stated they wanted more education on antimicrobials and 80% desired better schooling on antibiotic choices.^5,6 Additionally, in an online survey 18% of 402 inpatient and emergency department providers, including residents, fellows, intensive care unit (ICU) and emergency department attending physicians, hospitalists, physician assistants, and nurse practitioners, described a lack of confidence when deescalating antibiotic therapy and 45% reported that they had received training on antimicrobial prescribing that was not fully adequate.⁷

These surveys hint at a potential gap in provider education or confidence, which may serve as a barrier to ideal care, further confounding other individualized considerations taken into account when deescalating care. These considerations include patient renal toxicity profiles, the potential for missed pathogens not identified in culture results, unknown sources of infection, and the mindset of many providers to remain on broad therapy if the patient’s condition is improving.^8-10 A specific barrier to deescalation within the VA is the variance in antimicrobial stewardship practices between facilities. In a recent widespread survey of VA practices, Chou and colleagues identified that only 29 of 130 (22.3%) responding facilities had a formal policy to establish an antimicrobial stewardship program.¹¹

Overcoming these barriers to deescalation through effective stewardship practices can help to promote improved clinical outcomes. Most studies have demonstrated that outcomes of deescalation strategies have equivalent or improved mortalityand equivalent or even decreased length of ICU stay.^12-26 Although a 2014 study by Leone and colleagues reported longer overall ICU stay in deescalation treatment groups with equivalent mortality outcomes, newer data do not support these findings.^16,20,22

Furthermore, antibiotics can be expensive. Deescalation, particularly in response to positive blood culture results, has been associated with reduced antibiotic cost due to both a decrease in overall antibiotic usage and the utilization of less expensive choices.^22,24,26,27 The findings of these individual studies were corroborated in 2013 by a meta-analysis, including 89 additional studies.²⁸ Besides the direct costs of the drugs, the development of regional antibiotic resistance has been labeled as one of the most pressing concerns in public health, and major initiatives have been undertaken to stem its spread.^29,30 The majority of clinicians believe that deescalation of antibiotics would reduce antibiotic resistance. Thus, deescalation is widely cited as one of the primary goals in the management of resistance development.^{5,24,26,28,31,32}

Due to the proposed benefits and challenges of implementation, MVAMC instituted a program where the electronic health records (EHR) for all patients with positive blood culture results were reviewed by the on-call infectious disease attending physician to advise the primary care team on antibiotic administration. The MVAMC system for notification of positive blood culture results has 2 components. The first is phone notification to the on-call resident when the positive result of the pathogen identification is noted by the microbiology laboratory staff. Notably, this protocol of phone notification is only performed when identifying the pathogen and not for the subsequent sensitivity profile. The second component occurs each morning when the on-call infectious disease attending physician reviews all positive blood culture results and the current therapy. If the infectious disease attending physician feels some alterations in management are warranted, the physician calls the primary service. Additionally, the primary service may always request a formal consult with the infectious disease team. This quality improvement study was initiated to examine the success of this deescalation/stewardship program to determine whether positive blood culture results influenced clinical management.

Methods

From July 1, 2015 to June 30, 2016, 212 positive blood cultures at the MVAMC were analyzed. Four cases that did not have an antibiotic spectrum score were excluded, leaving 208 cases reviewed. Duplicate blood cultures were excluded from analysis. The microbiology laboratory used the BD Bactec automated blood culture system using the Plus aerobic and Lytic anaerobic media (Becton, Dickinson and Company).

Antibiotic alterations in response to culture results were classified as either deescalation or escalation, using a spectrum score developed by Madaras-Kelly and colleagues.³³ These investigators performed a 3-round modified Delphi survey of infectious disease staff of physicians and pharmacists. The resulting consensus spectrum score for each respective antibiotic reflected the relative susceptibilities of various pathogens to antibiotics and the intrinsic resistance of the pathogens. It is a nonlinear scale from 0 to 60 with a score of 0 indicating no antibacterial activity and a score of 60 indicating complete coverage of all critically identified pathogens. For example, a narrow-spectrum antibiotic such as metronidazole received a spectrum score of 4.0 and a broad-spectrum antibiotic such as piperacillin/tazobactam received a 42.3 score.

Data Recording and Analysis

Charts for review were flagged based on positive blood culture results from the microbiology laboratory. EHRs were manually reviewed to determine when antibiotics were started/stopped and when a member of the primary care team, usually a resident, was notified of culture results as documented by the microbiology laboratory personnel. Any alteration in antibiotics that fit the criteria of deescalation or escalation that occurred within 24 hours of notification of either critical laboratory value was recorded. The identity of infectious pathogens and the primary site of infection were not recorded as these data were not within the scope of the purpose of this study. We did not control for possible contaminants within positive blood cultures.

There were 3 time frames considered when determining culture driven alterations to the antibiotic regimen. The first 2 were changes within the 24 hours after notification of either (1) pathogen identification or (2) pathogen sensitivity. These were defined as culture-driven alterations in response to those particular laboratory findings. The third—whole case time frame—spanned from pathogen identification to 24 hours after sensitivity information was recorded. In cases where ≥ 1 antibiotic alteration was noted within a respective time frame, a classification of deescalation or escalation was still assigned. This was done by summing each change in spectrum score that occurred from antibiotic regimen alterations within the time frame, and classifying the net effect on the spectrum of coverage as either deescalation or escalation. Data were recorded in spreadsheet. RStudio 3.5.3 was used for statistical analysis.

Results

Of 208 cases assigned a spectrum score, 47 (22.6%) had the breadth of antibiotic coverage deescalated by the primary care team within 24 hours of pathogen identification with a mean (SD) physician response time of 8.0 (7.3) hours. Fourteen cases (6.7%) had the breadth of antibiotic coverage escalated from pathogen identification with a mean (SD) response time of 8.0 (7.4) hours. When taken together, within 24 hours of pathogen identification from positive blood cultures 61 cases (29.3%) had altered antibiotics, leaving 70.7% of cases unaltered (Tables 1 and 2). In this nonquantitative spectrum score method, deescalations typically involved larger changes in spectrum score than escalations.

Physician notification of pathogen sensitivities resulted in deescalation in 69 cases (33.2%) within 24 hours, with a mean (SD) response time of 10.4 (7) hours. The mean time to deescalation in response to pathogen identification was significantly shorter than the mean time to deescalation in response to sensitivities (P = .049). Broadening of coverage based on sensitivity information was reported for 17 cases (8.2%) within 24 hours, with a mean (SD) response time of 7.6 (6) hours (Table 3). In response to pathogen sensitivity results from positive blood cultures, 58.6% of cases had no antibiotic alterations. Deescalations involved notably larger changes in spectrum score than escalations.

More than half (58.6%) of cases resulted in an antibiotic alteration from empiric treatment when considering the time frame from empiric antibiotics to 24 hours after receiving sensitivity information. These were deemed the whole-case, culture-driven results. In addition to antibiotic alterations that occurred within 24 hours of either pathogen identification or sensitivity information, the whole-case category also considered antibiotic alterations that occurred more than 24 hours after pathogen identification was known and before sensitivity information was available, although this was rare. Some of these patients may have had their antibiotics altered twice, first after pathogen identification and later once sensitivities became available with the net effect recorded as the whole-case administration. Of those that had their antibiotics modified in response to laboratory results, by a ratio of 6.4:1, the change was a deescalation rather than an escalation.

Discussion

The strategy of the infectious disease team at MVAMC is one of deescalation. One challenge of quantifying deescalation was to make a reliable and agreed-upon definition of just what deescalation entails. In 2003, the pharmaceutical company Merck was granted a trademark for the phrase “De-Escalation Therapy” under the international class code 41 for educational and entertainment services. This seemed to correspond to marketing efforts for the antibiotic imipenem/cilastatin. Although the company trademarked the term, it was never defined. The usage of the phrase evolved from a reduction of the dosage of a specific antibiotic to a reduction in the number of antibiotics prescribed to that of monotherapy. The phrase continues to evolve and has now become associated with a change from combination therapy or broad-spectrum antibiotics to monotherapy, switching to an antibiotic that covers fewer pathogens, or even shortening the duration of antibiotic therapy.³⁴ The trademark expired at about the same time the imipenem/cilastatin patent expired. Notably, this drug had initially been marketed for use in empiric antibiotic therapy.³⁵

Barriers

The goal of the stewardship program was not to see a narrowing of the antibiotic spectrum in all patients. Some diseases such as diverticulitis or diabetic foot infections are usually associated with multiple pathogens where relatively broad-spectrum antibiotics seem to be preferred.^36,37 Heenen and colleagues reported that infectious disease specialists recommended deescalation in < 50% of cases they examined.³⁸

Comparing different institutions’ deescalation rates can be confusing due to varying definitions, differing patient populations, and health care provider behavior. Thus, the published rates of deescalation range widely from 10 to 70%.^2,39,40 In addition to the varied definitions of deescalation, it is challenging to directly compare the rate of deescalation between studies due to institutional variation in empirical broad-spectrum antibiotic usage. A hospital that uses broad-spectrum antibiotics at a higher rate than another has the potential to deescalate more often than one that has low rates of empirical broad-spectrum antibiotic use. Some studies use a conservative definition of deescalation such as narrowing the spectrum of coverage, while others use a more general definition, including both the narrowing of spectrum and/or the discontinuation of antibiotics from empirical therapy.^41-45 The more specific and validated definition of deescalation used in this study may allow for standardized comparisons. Another unique feature of this study is that all positive blood cultures were followed, not only those of a particular disease.

Clinical Response

This quality improvement study was initiated to determine whether positive blood culture results actually affect clinical management at MVAMC. The answer seems to be yes, with blood culture results altering antibiotic administration in about 60% of cases with the predominant change being deescalation. This overall rate of deescalation is toward the higher end of previously documented rates and coincides with the upper bound of the clinically advised deescalation rate described by Heenen and colleagues.³⁸

As noted, the spectrum score is not quantitative. Still, one may be able to contend that the values may provide some insight into the magnitude of the changes in antibiotic selection. Deescalations were on average much larger changes in spectrum than escalations. The larger magnitude of deescalations reflects that when already starting with a very broad spectrum of coverage, it is much easier to get narrower than even broader. Stated another way, when starting therapy using piperacillin/tazobactam at a spectrum score of 42.3 on a 60-point scale, there is much more room for deescalation to 0 than escalation to 60. Additionally, escalations were more likely with much smaller of a spectrum change due to accurate empirical judgment of the suspected pathogens with new findings only necessitating a minor expansion of the spectrum of coverage.

Future Study

Similar studies designed using the spectrum score methodology would allow for more meaningful interinstitutional comparison of antibiotic administration through the use of a unified definition of deescalation and escalation. Comparison of deescalation and escalation rates between hospital systems with similar patient populations with and without prompt infectious disease review and phone notification of blood culture results could further verify the value of such a protocol. It could also help determine which empiric antibiotics may be most effective in individual patient morbidity and mortality outcomes, length of stay, costs, and the development of antibiotic resistance. Chou and colleagues found that only 49 of 130 responding VA facilities had antimicrobial stewardship teams in place with even fewer (29) having a formal policy to establish an antimicrobial stewardship program.¹¹ This significant variation in the practices of VA facilities across the nation underscores the benefit to be gained from implementation of value-added protocols such as daily infectious disease case monitoring and microbiology laboratory phone notification of positive blood culture results as it occurs at MVAMC.

They also noted that systems of patient-level antibiotic review, and the presence of at least one full-time infectious disease physician were both associated with a statistically significant decrease in the use of antimicrobials, corroborating the results of this analysis.¹¹ Adapting the current system of infectious disease specialist review of positive blood culture results to use remote monitoring through the EHR could help to defer some of the cost of needing an in-house specialist while retaining the benefit of the oversite.

Another option for study would be a before and after design to determine whether the program of infectious disease specialist review led to increased use of deescalation strategies similar to studies investigating the efficacy of antimicrobial subcommittee implementation.^{13,20,23,24,26}

Conclusions

This analysis of empiric antibiotic use at the MVAMC indicates promising rates of deescalation. The results indicate that the medical service may be right and that positive blood culture results appear to affect clinical decision making in an appropriate and timely fashion. The VA is the largest health care organization in the US. Thus, identifying and propagating effective stewardship practices on a widespread basis can have a significant effect on the public health of the nation.

These data suggest that the program implemented at the MVAMC of phone notification to the primary care team along with daily infectious disease staff monitoring of blood culture information should be widely adopted at sister institutions using either in-house or remote specialist review.

The US Department of Veterans Affairs (VA) is the largest health care organization in the US, staffing more than 1,200 facilities and servicing about 9 million veterans.¹ Identifying VA practices that promote effective health care delivery has the potential to impact thousands of patients every day. The Surgical service at the Minneapolis VA Medical Center (MVAMC) in Minnesota often questioned colleagues whether many of the ordered tests, including blood cultures for patients with suspected infections, were clinically necessary. Despite recommendations for utilizing culture-driven results in choosing appropriate antimicrobials, it was debated whether these additional tests were simply drawn and ignored resulting only in increased costs and venipuncture discomfort for the patient. Thus, the purpose of this quality improvement study was to determine whether positive blood culture results actually influence clinical management at MVAMC.

Background

Accepted best practice when responding to positive blood culture results entails empiric treatment with broad-spectrum antibiotics that subsequently narrows in breadth of coverage once the pathogen has been identified.^2-4 This strategy has been labeled deescalation. Despite the acceptance of these standards, surveys of clinician attitudes towards antibiotics showed that 90% of physicians and residents stated they wanted more education on antimicrobials and 80% desired better schooling on antibiotic choices.^5,6 Additionally, in an online survey 18% of 402 inpatient and emergency department providers, including residents, fellows, intensive care unit (ICU) and emergency department attending physicians, hospitalists, physician assistants, and nurse practitioners, described a lack of confidence when deescalating antibiotic therapy and 45% reported that they had received training on antimicrobial prescribing that was not fully adequate.⁷

These surveys hint at a potential gap in provider education or confidence, which may serve as a barrier to ideal care, further confounding other individualized considerations taken into account when deescalating care. These considerations include patient renal toxicity profiles, the potential for missed pathogens not identified in culture results, unknown sources of infection, and the mindset of many providers to remain on broad therapy if the patient’s condition is improving.^8-10 A specific barrier to deescalation within the VA is the variance in antimicrobial stewardship practices between facilities. In a recent widespread survey of VA practices, Chou and colleagues identified that only 29 of 130 (22.3%) responding facilities had a formal policy to establish an antimicrobial stewardship program.¹¹

Overcoming these barriers to deescalation through effective stewardship practices can help to promote improved clinical outcomes. Most studies have demonstrated that outcomes of deescalation strategies have equivalent or improved mortalityand equivalent or even decreased length of ICU stay.^12-26 Although a 2014 study by Leone and colleagues reported longer overall ICU stay in deescalation treatment groups with equivalent mortality outcomes, newer data do not support these findings.^16,20,22

Furthermore, antibiotics can be expensive. Deescalation, particularly in response to positive blood culture results, has been associated with reduced antibiotic cost due to both a decrease in overall antibiotic usage and the utilization of less expensive choices.^22,24,26,27 The findings of these individual studies were corroborated in 2013 by a meta-analysis, including 89 additional studies.²⁸ Besides the direct costs of the drugs, the development of regional antibiotic resistance has been labeled as one of the most pressing concerns in public health, and major initiatives have been undertaken to stem its spread.^29,30 The majority of clinicians believe that deescalation of antibiotics would reduce antibiotic resistance. Thus, deescalation is widely cited as one of the primary goals in the management of resistance development.^{5,24,26,28,31,32}

Due to the proposed benefits and challenges of implementation, MVAMC instituted a program where the electronic health records (EHR) for all patients with positive blood culture results were reviewed by the on-call infectious disease attending physician to advise the primary care team on antibiotic administration. The MVAMC system for notification of positive blood culture results has 2 components. The first is phone notification to the on-call resident when the positive result of the pathogen identification is noted by the microbiology laboratory staff. Notably, this protocol of phone notification is only performed when identifying the pathogen and not for the subsequent sensitivity profile. The second component occurs each morning when the on-call infectious disease attending physician reviews all positive blood culture results and the current therapy. If the infectious disease attending physician feels some alterations in management are warranted, the physician calls the primary service. Additionally, the primary service may always request a formal consult with the infectious disease team. This quality improvement study was initiated to examine the success of this deescalation/stewardship program to determine whether positive blood culture results influenced clinical management.

Methods

From July 1, 2015 to June 30, 2016, 212 positive blood cultures at the MVAMC were analyzed. Four cases that did not have an antibiotic spectrum score were excluded, leaving 208 cases reviewed. Duplicate blood cultures were excluded from analysis. The microbiology laboratory used the BD Bactec automated blood culture system using the Plus aerobic and Lytic anaerobic media (Becton, Dickinson and Company).

Antibiotic alterations in response to culture results were classified as either deescalation or escalation, using a spectrum score developed by Madaras-Kelly and colleagues.³³ These investigators performed a 3-round modified Delphi survey of infectious disease staff of physicians and pharmacists. The resulting consensus spectrum score for each respective antibiotic reflected the relative susceptibilities of various pathogens to antibiotics and the intrinsic resistance of the pathogens. It is a nonlinear scale from 0 to 60 with a score of 0 indicating no antibacterial activity and a score of 60 indicating complete coverage of all critically identified pathogens. For example, a narrow-spectrum antibiotic such as metronidazole received a spectrum score of 4.0 and a broad-spectrum antibiotic such as piperacillin/tazobactam received a 42.3 score.

Data Recording and Analysis

Charts for review were flagged based on positive blood culture results from the microbiology laboratory. EHRs were manually reviewed to determine when antibiotics were started/stopped and when a member of the primary care team, usually a resident, was notified of culture results as documented by the microbiology laboratory personnel. Any alteration in antibiotics that fit the criteria of deescalation or escalation that occurred within 24 hours of notification of either critical laboratory value was recorded. The identity of infectious pathogens and the primary site of infection were not recorded as these data were not within the scope of the purpose of this study. We did not control for possible contaminants within positive blood cultures.

There were 3 time frames considered when determining culture driven alterations to the antibiotic regimen. The first 2 were changes within the 24 hours after notification of either (1) pathogen identification or (2) pathogen sensitivity. These were defined as culture-driven alterations in response to those particular laboratory findings. The third—whole case time frame—spanned from pathogen identification to 24 hours after sensitivity information was recorded. In cases where ≥ 1 antibiotic alteration was noted within a respective time frame, a classification of deescalation or escalation was still assigned. This was done by summing each change in spectrum score that occurred from antibiotic regimen alterations within the time frame, and classifying the net effect on the spectrum of coverage as either deescalation or escalation. Data were recorded in spreadsheet. RStudio 3.5.3 was used for statistical analysis.

Results

Of 208 cases assigned a spectrum score, 47 (22.6%) had the breadth of antibiotic coverage deescalated by the primary care team within 24 hours of pathogen identification with a mean (SD) physician response time of 8.0 (7.3) hours. Fourteen cases (6.7%) had the breadth of antibiotic coverage escalated from pathogen identification with a mean (SD) response time of 8.0 (7.4) hours. When taken together, within 24 hours of pathogen identification from positive blood cultures 61 cases (29.3%) had altered antibiotics, leaving 70.7% of cases unaltered (Tables 1 and 2). In this nonquantitative spectrum score method, deescalations typically involved larger changes in spectrum score than escalations.

Physician notification of pathogen sensitivities resulted in deescalation in 69 cases (33.2%) within 24 hours, with a mean (SD) response time of 10.4 (7) hours. The mean time to deescalation in response to pathogen identification was significantly shorter than the mean time to deescalation in response to sensitivities (P = .049). Broadening of coverage based on sensitivity information was reported for 17 cases (8.2%) within 24 hours, with a mean (SD) response time of 7.6 (6) hours (Table 3). In response to pathogen sensitivity results from positive blood cultures, 58.6% of cases had no antibiotic alterations. Deescalations involved notably larger changes in spectrum score than escalations.

More than half (58.6%) of cases resulted in an antibiotic alteration from empiric treatment when considering the time frame from empiric antibiotics to 24 hours after receiving sensitivity information. These were deemed the whole-case, culture-driven results. In addition to antibiotic alterations that occurred within 24 hours of either pathogen identification or sensitivity information, the whole-case category also considered antibiotic alterations that occurred more than 24 hours after pathogen identification was known and before sensitivity information was available, although this was rare. Some of these patients may have had their antibiotics altered twice, first after pathogen identification and later once sensitivities became available with the net effect recorded as the whole-case administration. Of those that had their antibiotics modified in response to laboratory results, by a ratio of 6.4:1, the change was a deescalation rather than an escalation.

Discussion

The strategy of the infectious disease team at MVAMC is one of deescalation. One challenge of quantifying deescalation was to make a reliable and agreed-upon definition of just what deescalation entails. In 2003, the pharmaceutical company Merck was granted a trademark for the phrase “De-Escalation Therapy” under the international class code 41 for educational and entertainment services. This seemed to correspond to marketing efforts for the antibiotic imipenem/cilastatin. Although the company trademarked the term, it was never defined. The usage of the phrase evolved from a reduction of the dosage of a specific antibiotic to a reduction in the number of antibiotics prescribed to that of monotherapy. The phrase continues to evolve and has now become associated with a change from combination therapy or broad-spectrum antibiotics to monotherapy, switching to an antibiotic that covers fewer pathogens, or even shortening the duration of antibiotic therapy.³⁴ The trademark expired at about the same time the imipenem/cilastatin patent expired. Notably, this drug had initially been marketed for use in empiric antibiotic therapy.³⁵

Barriers

The goal of the stewardship program was not to see a narrowing of the antibiotic spectrum in all patients. Some diseases such as diverticulitis or diabetic foot infections are usually associated with multiple pathogens where relatively broad-spectrum antibiotics seem to be preferred.^36,37 Heenen and colleagues reported that infectious disease specialists recommended deescalation in < 50% of cases they examined.³⁸

Comparing different institutions’ deescalation rates can be confusing due to varying definitions, differing patient populations, and health care provider behavior. Thus, the published rates of deescalation range widely from 10 to 70%.^2,39,40 In addition to the varied definitions of deescalation, it is challenging to directly compare the rate of deescalation between studies due to institutional variation in empirical broad-spectrum antibiotic usage. A hospital that uses broad-spectrum antibiotics at a higher rate than another has the potential to deescalate more often than one that has low rates of empirical broad-spectrum antibiotic use. Some studies use a conservative definition of deescalation such as narrowing the spectrum of coverage, while others use a more general definition, including both the narrowing of spectrum and/or the discontinuation of antibiotics from empirical therapy.^41-45 The more specific and validated definition of deescalation used in this study may allow for standardized comparisons. Another unique feature of this study is that all positive blood cultures were followed, not only those of a particular disease.

Clinical Response

This quality improvement study was initiated to determine whether positive blood culture results actually affect clinical management at MVAMC. The answer seems to be yes, with blood culture results altering antibiotic administration in about 60% of cases with the predominant change being deescalation. This overall rate of deescalation is toward the higher end of previously documented rates and coincides with the upper bound of the clinically advised deescalation rate described by Heenen and colleagues.³⁸

As noted, the spectrum score is not quantitative. Still, one may be able to contend that the values may provide some insight into the magnitude of the changes in antibiotic selection. Deescalations were on average much larger changes in spectrum than escalations. The larger magnitude of deescalations reflects that when already starting with a very broad spectrum of coverage, it is much easier to get narrower than even broader. Stated another way, when starting therapy using piperacillin/tazobactam at a spectrum score of 42.3 on a 60-point scale, there is much more room for deescalation to 0 than escalation to 60. Additionally, escalations were more likely with much smaller of a spectrum change due to accurate empirical judgment of the suspected pathogens with new findings only necessitating a minor expansion of the spectrum of coverage.

Future Study

Similar studies designed using the spectrum score methodology would allow for more meaningful interinstitutional comparison of antibiotic administration through the use of a unified definition of deescalation and escalation. Comparison of deescalation and escalation rates between hospital systems with similar patient populations with and without prompt infectious disease review and phone notification of blood culture results could further verify the value of such a protocol. It could also help determine which empiric antibiotics may be most effective in individual patient morbidity and mortality outcomes, length of stay, costs, and the development of antibiotic resistance. Chou and colleagues found that only 49 of 130 responding VA facilities had antimicrobial stewardship teams in place with even fewer (29) having a formal policy to establish an antimicrobial stewardship program.¹¹ This significant variation in the practices of VA facilities across the nation underscores the benefit to be gained from implementation of value-added protocols such as daily infectious disease case monitoring and microbiology laboratory phone notification of positive blood culture results as it occurs at MVAMC.

They also noted that systems of patient-level antibiotic review, and the presence of at least one full-time infectious disease physician were both associated with a statistically significant decrease in the use of antimicrobials, corroborating the results of this analysis.¹¹ Adapting the current system of infectious disease specialist review of positive blood culture results to use remote monitoring through the EHR could help to defer some of the cost of needing an in-house specialist while retaining the benefit of the oversite.

Another option for study would be a before and after design to determine whether the program of infectious disease specialist review led to increased use of deescalation strategies similar to studies investigating the efficacy of antimicrobial subcommittee implementation.^{13,20,23,24,26}

Conclusions

This analysis of empiric antibiotic use at the MVAMC indicates promising rates of deescalation. The results indicate that the medical service may be right and that positive blood culture results appear to affect clinical decision making in an appropriate and timely fashion. The VA is the largest health care organization in the US. Thus, identifying and propagating effective stewardship practices on a widespread basis can have a significant effect on the public health of the nation.

These data suggest that the program implemented at the MVAMC of phone notification to the primary care team along with daily infectious disease staff monitoring of blood culture information should be widely adopted at sister institutions using either in-house or remote specialist review.

The US Department of Veterans Affairs (VA) is the largest health care organization in the US, staffing more than 1,200 facilities and servicing about 9 million veterans.¹ Identifying VA practices that promote effective health care delivery has the potential to impact thousands of patients every day. The Surgical service at the Minneapolis VA Medical Center (MVAMC) in Minnesota often questioned colleagues whether many of the ordered tests, including blood cultures for patients with suspected infections, were clinically necessary. Despite recommendations for utilizing culture-driven results in choosing appropriate antimicrobials, it was debated whether these additional tests were simply drawn and ignored resulting only in increased costs and venipuncture discomfort for the patient. Thus, the purpose of this quality improvement study was to determine whether positive blood culture results actually influence clinical management at MVAMC.

Background

Accepted best practice when responding to positive blood culture results entails empiric treatment with broad-spectrum antibiotics that subsequently narrows in breadth of coverage once the pathogen has been identified.^2-4 This strategy has been labeled deescalation. Despite the acceptance of these standards, surveys of clinician attitudes towards antibiotics showed that 90% of physicians and residents stated they wanted more education on antimicrobials and 80% desired better schooling on antibiotic choices.^5,6 Additionally, in an online survey 18% of 402 inpatient and emergency department providers, including residents, fellows, intensive care unit (ICU) and emergency department attending physicians, hospitalists, physician assistants, and nurse practitioners, described a lack of confidence when deescalating antibiotic therapy and 45% reported that they had received training on antimicrobial prescribing that was not fully adequate.⁷

These surveys hint at a potential gap in provider education or confidence, which may serve as a barrier to ideal care, further confounding other individualized considerations taken into account when deescalating care. These considerations include patient renal toxicity profiles, the potential for missed pathogens not identified in culture results, unknown sources of infection, and the mindset of many providers to remain on broad therapy if the patient’s condition is improving.^8-10 A specific barrier to deescalation within the VA is the variance in antimicrobial stewardship practices between facilities. In a recent widespread survey of VA practices, Chou and colleagues identified that only 29 of 130 (22.3%) responding facilities had a formal policy to establish an antimicrobial stewardship program.¹¹

Overcoming these barriers to deescalation through effective stewardship practices can help to promote improved clinical outcomes. Most studies have demonstrated that outcomes of deescalation strategies have equivalent or improved mortalityand equivalent or even decreased length of ICU stay.^12-26 Although a 2014 study by Leone and colleagues reported longer overall ICU stay in deescalation treatment groups with equivalent mortality outcomes, newer data do not support these findings.^16,20,22

Furthermore, antibiotics can be expensive. Deescalation, particularly in response to positive blood culture results, has been associated with reduced antibiotic cost due to both a decrease in overall antibiotic usage and the utilization of less expensive choices.^22,24,26,27 The findings of these individual studies were corroborated in 2013 by a meta-analysis, including 89 additional studies.²⁸ Besides the direct costs of the drugs, the development of regional antibiotic resistance has been labeled as one of the most pressing concerns in public health, and major initiatives have been undertaken to stem its spread.^29,30 The majority of clinicians believe that deescalation of antibiotics would reduce antibiotic resistance. Thus, deescalation is widely cited as one of the primary goals in the management of resistance development.^{5,24,26,28,31,32}

Due to the proposed benefits and challenges of implementation, MVAMC instituted a program where the electronic health records (EHR) for all patients with positive blood culture results were reviewed by the on-call infectious disease attending physician to advise the primary care team on antibiotic administration. The MVAMC system for notification of positive blood culture results has 2 components. The first is phone notification to the on-call resident when the positive result of the pathogen identification is noted by the microbiology laboratory staff. Notably, this protocol of phone notification is only performed when identifying the pathogen and not for the subsequent sensitivity profile. The second component occurs each morning when the on-call infectious disease attending physician reviews all positive blood culture results and the current therapy. If the infectious disease attending physician feels some alterations in management are warranted, the physician calls the primary service. Additionally, the primary service may always request a formal consult with the infectious disease team. This quality improvement study was initiated to examine the success of this deescalation/stewardship program to determine whether positive blood culture results influenced clinical management.

Methods

From July 1, 2015 to June 30, 2016, 212 positive blood cultures at the MVAMC were analyzed. Four cases that did not have an antibiotic spectrum score were excluded, leaving 208 cases reviewed. Duplicate blood cultures were excluded from analysis. The microbiology laboratory used the BD Bactec automated blood culture system using the Plus aerobic and Lytic anaerobic media (Becton, Dickinson and Company).

Antibiotic alterations in response to culture results were classified as either deescalation or escalation, using a spectrum score developed by Madaras-Kelly and colleagues.³³ These investigators performed a 3-round modified Delphi survey of infectious disease staff of physicians and pharmacists. The resulting consensus spectrum score for each respective antibiotic reflected the relative susceptibilities of various pathogens to antibiotics and the intrinsic resistance of the pathogens. It is a nonlinear scale from 0 to 60 with a score of 0 indicating no antibacterial activity and a score of 60 indicating complete coverage of all critically identified pathogens. For example, a narrow-spectrum antibiotic such as metronidazole received a spectrum score of 4.0 and a broad-spectrum antibiotic such as piperacillin/tazobactam received a 42.3 score.

Data Recording and Analysis

Charts for review were flagged based on positive blood culture results from the microbiology laboratory. EHRs were manually reviewed to determine when antibiotics were started/stopped and when a member of the primary care team, usually a resident, was notified of culture results as documented by the microbiology laboratory personnel. Any alteration in antibiotics that fit the criteria of deescalation or escalation that occurred within 24 hours of notification of either critical laboratory value was recorded. The identity of infectious pathogens and the primary site of infection were not recorded as these data were not within the scope of the purpose of this study. We did not control for possible contaminants within positive blood cultures.

There were 3 time frames considered when determining culture driven alterations to the antibiotic regimen. The first 2 were changes within the 24 hours after notification of either (1) pathogen identification or (2) pathogen sensitivity. These were defined as culture-driven alterations in response to those particular laboratory findings. The third—whole case time frame—spanned from pathogen identification to 24 hours after sensitivity information was recorded. In cases where ≥ 1 antibiotic alteration was noted within a respective time frame, a classification of deescalation or escalation was still assigned. This was done by summing each change in spectrum score that occurred from antibiotic regimen alterations within the time frame, and classifying the net effect on the spectrum of coverage as either deescalation or escalation. Data were recorded in spreadsheet. RStudio 3.5.3 was used for statistical analysis.

Results

Of 208 cases assigned a spectrum score, 47 (22.6%) had the breadth of antibiotic coverage deescalated by the primary care team within 24 hours of pathogen identification with a mean (SD) physician response time of 8.0 (7.3) hours. Fourteen cases (6.7%) had the breadth of antibiotic coverage escalated from pathogen identification with a mean (SD) response time of 8.0 (7.4) hours. When taken together, within 24 hours of pathogen identification from positive blood cultures 61 cases (29.3%) had altered antibiotics, leaving 70.7% of cases unaltered (Tables 1 and 2). In this nonquantitative spectrum score method, deescalations typically involved larger changes in spectrum score than escalations.

Physician notification of pathogen sensitivities resulted in deescalation in 69 cases (33.2%) within 24 hours, with a mean (SD) response time of 10.4 (7) hours. The mean time to deescalation in response to pathogen identification was significantly shorter than the mean time to deescalation in response to sensitivities (P = .049). Broadening of coverage based on sensitivity information was reported for 17 cases (8.2%) within 24 hours, with a mean (SD) response time of 7.6 (6) hours (Table 3). In response to pathogen sensitivity results from positive blood cultures, 58.6% of cases had no antibiotic alterations. Deescalations involved notably larger changes in spectrum score than escalations.

More than half (58.6%) of cases resulted in an antibiotic alteration from empiric treatment when considering the time frame from empiric antibiotics to 24 hours after receiving sensitivity information. These were deemed the whole-case, culture-driven results. In addition to antibiotic alterations that occurred within 24 hours of either pathogen identification or sensitivity information, the whole-case category also considered antibiotic alterations that occurred more than 24 hours after pathogen identification was known and before sensitivity information was available, although this was rare. Some of these patients may have had their antibiotics altered twice, first after pathogen identification and later once sensitivities became available with the net effect recorded as the whole-case administration. Of those that had their antibiotics modified in response to laboratory results, by a ratio of 6.4:1, the change was a deescalation rather than an escalation.

Discussion

The strategy of the infectious disease team at MVAMC is one of deescalation. One challenge of quantifying deescalation was to make a reliable and agreed-upon definition of just what deescalation entails. In 2003, the pharmaceutical company Merck was granted a trademark for the phrase “De-Escalation Therapy” under the international class code 41 for educational and entertainment services. This seemed to correspond to marketing efforts for the antibiotic imipenem/cilastatin. Although the company trademarked the term, it was never defined. The usage of the phrase evolved from a reduction of the dosage of a specific antibiotic to a reduction in the number of antibiotics prescribed to that of monotherapy. The phrase continues to evolve and has now become associated with a change from combination therapy or broad-spectrum antibiotics to monotherapy, switching to an antibiotic that covers fewer pathogens, or even shortening the duration of antibiotic therapy.³⁴ The trademark expired at about the same time the imipenem/cilastatin patent expired. Notably, this drug had initially been marketed for use in empiric antibiotic therapy.³⁵

Barriers

The goal of the stewardship program was not to see a narrowing of the antibiotic spectrum in all patients. Some diseases such as diverticulitis or diabetic foot infections are usually associated with multiple pathogens where relatively broad-spectrum antibiotics seem to be preferred.^36,37 Heenen and colleagues reported that infectious disease specialists recommended deescalation in < 50% of cases they examined.³⁸

Comparing different institutions’ deescalation rates can be confusing due to varying definitions, differing patient populations, and health care provider behavior. Thus, the published rates of deescalation range widely from 10 to 70%.^2,39,40 In addition to the varied definitions of deescalation, it is challenging to directly compare the rate of deescalation between studies due to institutional variation in empirical broad-spectrum antibiotic usage. A hospital that uses broad-spectrum antibiotics at a higher rate than another has the potential to deescalate more often than one that has low rates of empirical broad-spectrum antibiotic use. Some studies use a conservative definition of deescalation such as narrowing the spectrum of coverage, while others use a more general definition, including both the narrowing of spectrum and/or the discontinuation of antibiotics from empirical therapy.^41-45 The more specific and validated definition of deescalation used in this study may allow for standardized comparisons. Another unique feature of this study is that all positive blood cultures were followed, not only those of a particular disease.

Clinical Response

This quality improvement study was initiated to determine whether positive blood culture results actually affect clinical management at MVAMC. The answer seems to be yes, with blood culture results altering antibiotic administration in about 60% of cases with the predominant change being deescalation. This overall rate of deescalation is toward the higher end of previously documented rates and coincides with the upper bound of the clinically advised deescalation rate described by Heenen and colleagues.³⁸

As noted, the spectrum score is not quantitative. Still, one may be able to contend that the values may provide some insight into the magnitude of the changes in antibiotic selection. Deescalations were on average much larger changes in spectrum than escalations. The larger magnitude of deescalations reflects that when already starting with a very broad spectrum of coverage, it is much easier to get narrower than even broader. Stated another way, when starting therapy using piperacillin/tazobactam at a spectrum score of 42.3 on a 60-point scale, there is much more room for deescalation to 0 than escalation to 60. Additionally, escalations were more likely with much smaller of a spectrum change due to accurate empirical judgment of the suspected pathogens with new findings only necessitating a minor expansion of the spectrum of coverage.

Future Study

Similar studies designed using the spectrum score methodology would allow for more meaningful interinstitutional comparison of antibiotic administration through the use of a unified definition of deescalation and escalation. Comparison of deescalation and escalation rates between hospital systems with similar patient populations with and without prompt infectious disease review and phone notification of blood culture results could further verify the value of such a protocol. It could also help determine which empiric antibiotics may be most effective in individual patient morbidity and mortality outcomes, length of stay, costs, and the development of antibiotic resistance. Chou and colleagues found that only 49 of 130 responding VA facilities had antimicrobial stewardship teams in place with even fewer (29) having a formal policy to establish an antimicrobial stewardship program.¹¹ This significant variation in the practices of VA facilities across the nation underscores the benefit to be gained from implementation of value-added protocols such as daily infectious disease case monitoring and microbiology laboratory phone notification of positive blood culture results as it occurs at MVAMC.

They also noted that systems of patient-level antibiotic review, and the presence of at least one full-time infectious disease physician were both associated with a statistically significant decrease in the use of antimicrobials, corroborating the results of this analysis.¹¹ Adapting the current system of infectious disease specialist review of positive blood culture results to use remote monitoring through the EHR could help to defer some of the cost of needing an in-house specialist while retaining the benefit of the oversite.

Another option for study would be a before and after design to determine whether the program of infectious disease specialist review led to increased use of deescalation strategies similar to studies investigating the efficacy of antimicrobial subcommittee implementation.^{13,20,23,24,26}

Conclusions

This analysis of empiric antibiotic use at the MVAMC indicates promising rates of deescalation. The results indicate that the medical service may be right and that positive blood culture results appear to affect clinical decision making in an appropriate and timely fashion. The VA is the largest health care organization in the US. Thus, identifying and propagating effective stewardship practices on a widespread basis can have a significant effect on the public health of the nation.

These data suggest that the program implemented at the MVAMC of phone notification to the primary care team along with daily infectious disease staff monitoring of blood culture information should be widely adopted at sister institutions using either in-house or remote specialist review.

In the United States, respiratory illnesses are the most common cause of emergency department (ED) visits and hospitalizations in children.¹ In response to the ongoing COVID-19 pandemic, several public health interventions, including school and business closures, stay-at-home orders, and mask mandates, were implemented to limit transmission of SARS-CoV-2.^2,3 Studies have shown that children can contribute to the spread of SARS-CoV-2 infections, especially within households.^4-6 Recent data suggest that COVID-19, and the associated public health measures enacted to slow its spread, may have affected the transmission of other respiratory pathogens.⁷ Similarly, the pandemic has likely affected healthcare utilization for nonrespiratory illnesses through adoption of social distancing recommendations, suspension and delays in nonemergent elective care, avoidance of healthcare settings, and the effect of decreased respiratory disease on exacerbation of chronic illness.⁸ The objective of this study was to examine associations between the COVID-19 pandemic and healthcare utilization for pediatric respiratory and nonrespiratory illnesses at US pediatric hospitals.

Study Design

This is a multicenter, cross-sectional study of encounters at 44 pediatric hospitals that reported data to the Pediatric Health Information System (PHIS) database maintained by the Children’s Hospital Association (Lenexa, Kansas).

Study Population

Children 2 months to 18 years of age discharged from ED or inpatient settings with a nonsurgical diagnosis from January 1 to September 30 over a 4-year period (2017-2020) were included.

Exposure

The primary exposure was the 2020 COVID-19 pandemic time, divided into three periods: pre-COVID-19 (January-February 2020, the period prior to the pandemic in the United States), early COVID-19 (March-April 2020, coinciding with the first reported US pediatric case of COVID-19 on March 2, 2020), and COVID-19 (May-September 2020, marked by the implementation of at least two of the following containment measures in every US state: stay-at-home/shelter orders, school closures, nonessential business closures, restaurant closures, or prohibition of gatherings of more than 10 people).²

Outcomes

Respiratory illness diagnoses were classified into mutually exclusive subgroups following a prespecified hierarchy: influenza, pneumonia, croup, bronchiolitis, asthma, unspecified influenza-like illness, and “other respiratory diagnoses” (Appendix Table 1). To assess the impact of COVID-19 after its International Classification of Diseases, Tenth Revision code was established on March 25, 2020, the “other respiratory” subgroup was divided into other respiratory illnesses with and without COVID-19. Nonrespiratory illness diagnoses were defined as all diagnoses not included in the respiratory illness cohort.

Statistical Analysis

Categorical variables were summarized using frequencies and percentages and compared using chi-square tests. Continuous variables were summarized as median and interquartile range (IQR) and compared using Wilcoxon rank sum tests. Weekly observed-to-expected (O:E) ratios were calculated for each hospital by dividing the number of observed respiratory illness and nonrespiratory illness encounters in a given week in 2020 (observed) by the average number of encounters for that same week during 2017-2019 (expected). O:E ratios were then aggregated over the three COVID-19 study periods, and 95% confidence intervals were established around mean O:E ratios across individual hospitals. Outcomes were then stratified by respiratory illness subgroups, geographic region, and age. Additional details can be found in the Supplemental Methods in the Appendix.

Study Population

A total of 9,051,980 encounters were included in the study, 6,811,799 with nonrespiratory illnesses and 2,240,181 with respiratory illnesses. Median age was 5 years (IQR, 1-11 years), and 52.7% of the population was male (Appendix Table 2 and Appendix Table 3).

Respiratory vs Nonrespiratory Illness During the COVID-19 Pandemic

Over the study period, fewer respiratory and nonrespiratory illness encounters were observed than expected, with a larger decrease in respiratory illness encounters (Table, Appendix Table 4).

Respiratory Subgroup Analyses

The O:E ratio decreased for all respiratory subgroups over the study period (Table, Appendix Table 4). There were significant differences in specific respiratory subgroups, including asthma, bronchiolitis, croup, influenza, and pneumonia (Appendix Figure 1A). Temporal trends in respiratory encounters were consistent across hospital settings, ages, and geographic regions (Appendix Figure 1B-D). When comparing the with and without COVID-19 subgroups in the “other respiratory illnesses” cohort, other respiratory illness without COVID-19 decreased and remained lower than expected over the rest of the study period, while other respiratory illness with COVID-19 increased markedly during the summer months and declined thereafter (Appendix Figure 2).

All age groups had reductions in respiratory illness encounters during the early COVID-19 and COVID-19 periods, although the decline was less pronounced in the 12- to 17-year-old group (Appendix Figure 1B). Similarly, while all age groups experienced increases in encounters for respiratory illnesses during the summer months, only children in the 12- to 17-year-old group experienced increases beyond pre-COVID-19 levels. Importantly, this increase in respiratory encounters was largely driven by COVID-19 diagnoses (Appendix Figure 3). The trend in nonrespiratory illness encounters stratified by age is shown in Appendix Figure 4.

When patients were stratified by hospital setting, there were no differences between those hospitalized and those discharged from the ED (Appendix Figure 1C). Patterns in respiratory illnesses by geographic location were qualitatively similar until the beginning of the summer 2020, after which geographical variation became more evident (Appendix Figure 1D).

In this large, multicenter study evaluating ED visits and hospitalizations for respiratory and nonrespiratory illnesses at US pediatric hospitals during the 2020 COVID-19 pandemic, we found a significant and substantial decrease in healthcare encounters for respiratory illnesses. A rapid and marked decline in encounters for respiratory illness in a relatively short period of time (March 12-April 2) was observed across all hospitals and US regions. Declines were consistent across common respiratory illnesses. More modest, yet still substantial, declines were also observed for nonrespiratory illnesses.

There are likely multiple underlying reasons for the observed reductions. Social distancing measures almost certainly played an important role in interrupting respiratory infection transmission. Rapid reduction in influenza transmission during the early COVID-19 period has been attributed to social distancing measures,³ and influenza transmission in children decreases with school closures.⁹ It is also possible that some families delayed seeking care at hospitals due to COVID-19, leading to less frequent encounters but more severe illness. The similar decrease in O:E ratio for ED visits and hospitalizations, however, is inconsistent with this explanation. It is also possible that nonurgent conditions cared for in the hospital settings were diverted to other care settings. For example, during this pandemic, telehealth and telephone visits for pediatric asthma increased by 61% and 19%, respectively, while ED and outpatient visits decreased concurrently.¹⁰Similar changes in location of care may also contribute to the decline in nonrespiratory illness encounters. Decreased use of hospital resources for nonurgent care diagnoses during the pandemic would suggest that, prior to COVID-19, there was overutilization of ambulatory services at children’s hospitals. Therefore, the pandemic may be driving care to more appropriate settings.

We also found relative differences in changes in encounters for respiratory illness by age. Adolescents’ levels of respiratory healthcare use declined less and recovered at a faster rate than those of younger children, returning to pre-COVID-19 levels by the end of the study period. The reason for this age differential is likely multifaceted. Infections, such as bronchiolitis and pneumonia, are more likely to be a source of respiratory illness in younger than in older children. It is also likely that disproportionate relaxation of social distancing measures among adolescents, who are known to have a stronger pattern of social interaction, contributed to the faster rise in respiratory illness–related encounters in this age group.¹¹ Adolescents have been reported to be more susceptible to, and more likely to transmit, SARS-CoV-2 compared to younger age groups.¹² More modest, albeit similar, age-based changes were observed in encounters for nonrespiratory illnesses. It is possible that pandemic-related stressors resulted in a subsequent increase in mental health encounters among this age group.¹³ While the reason for this also is likely multifactorial, adolescent behavior, as well as transmission of infectious illness that can exacerbate nonrespiratory conditions, may be a factor.

Emerging evidence suggests that school-age children may play an important role in SARS-CoV-2 transmission in the community.^4,14 Our finding that, compared to younger children, adolescents had significantly fewer reductions in respiratory illness encounters is concerning. These findings suggest that community-based efforts to help prevent respiratory illnesses, especially COVID-19, should focus on adolescents, who are most likely to maintain social interactions and transmit respiratory infections in the school setting and their households.

This study is limited by the inclusion of only tertiary care children’s hospitals, which may not be nationally representative, and the inability to assess the precise timing of when specific public health interventions were introduced. Moreover, previous studies suggest that social distancing behaviors may have changed even before formal recommendations were enacted.¹⁵ Future studies should investigate the local impact of state- and municipality-specific mandates on the burden of COVID-19 and other respiratory illnesses.

The COVID-19 pandemic was associated with substantial reductions in encounters for respiratory diseases, and also with more modest but still sizable reductions in encounters for nonrespiratory diseases. These reductions varied by age. Encounters among adolescents declined less and returned to previous levels faster compared with those of younger children.

This publication is dedicated to the memory of our coauthor, Dr. Michael Bendel-Stenzel. Dr. Bendel-Stenzel was dedicated to bettering the lives of children and advancing our knowledge of pediatrics through his research.

In the United States, respiratory illnesses are the most common cause of emergency department (ED) visits and hospitalizations in children.¹ In response to the ongoing COVID-19 pandemic, several public health interventions, including school and business closures, stay-at-home orders, and mask mandates, were implemented to limit transmission of SARS-CoV-2.^2,3 Studies have shown that children can contribute to the spread of SARS-CoV-2 infections, especially within households.^4-6 Recent data suggest that COVID-19, and the associated public health measures enacted to slow its spread, may have affected the transmission of other respiratory pathogens.⁷ Similarly, the pandemic has likely affected healthcare utilization for nonrespiratory illnesses through adoption of social distancing recommendations, suspension and delays in nonemergent elective care, avoidance of healthcare settings, and the effect of decreased respiratory disease on exacerbation of chronic illness.⁸ The objective of this study was to examine associations between the COVID-19 pandemic and healthcare utilization for pediatric respiratory and nonrespiratory illnesses at US pediatric hospitals.

Study Design

This is a multicenter, cross-sectional study of encounters at 44 pediatric hospitals that reported data to the Pediatric Health Information System (PHIS) database maintained by the Children’s Hospital Association (Lenexa, Kansas).

Study Population

Children 2 months to 18 years of age discharged from ED or inpatient settings with a nonsurgical diagnosis from January 1 to September 30 over a 4-year period (2017-2020) were included.

Exposure

The primary exposure was the 2020 COVID-19 pandemic time, divided into three periods: pre-COVID-19 (January-February 2020, the period prior to the pandemic in the United States), early COVID-19 (March-April 2020, coinciding with the first reported US pediatric case of COVID-19 on March 2, 2020), and COVID-19 (May-September 2020, marked by the implementation of at least two of the following containment measures in every US state: stay-at-home/shelter orders, school closures, nonessential business closures, restaurant closures, or prohibition of gatherings of more than 10 people).²

Outcomes

Respiratory illness diagnoses were classified into mutually exclusive subgroups following a prespecified hierarchy: influenza, pneumonia, croup, bronchiolitis, asthma, unspecified influenza-like illness, and “other respiratory diagnoses” (Appendix Table 1). To assess the impact of COVID-19 after its International Classification of Diseases, Tenth Revision code was established on March 25, 2020, the “other respiratory” subgroup was divided into other respiratory illnesses with and without COVID-19. Nonrespiratory illness diagnoses were defined as all diagnoses not included in the respiratory illness cohort.

Statistical Analysis

Categorical variables were summarized using frequencies and percentages and compared using chi-square tests. Continuous variables were summarized as median and interquartile range (IQR) and compared using Wilcoxon rank sum tests. Weekly observed-to-expected (O:E) ratios were calculated for each hospital by dividing the number of observed respiratory illness and nonrespiratory illness encounters in a given week in 2020 (observed) by the average number of encounters for that same week during 2017-2019 (expected). O:E ratios were then aggregated over the three COVID-19 study periods, and 95% confidence intervals were established around mean O:E ratios across individual hospitals. Outcomes were then stratified by respiratory illness subgroups, geographic region, and age. Additional details can be found in the Supplemental Methods in the Appendix.

Study Population

A total of 9,051,980 encounters were included in the study, 6,811,799 with nonrespiratory illnesses and 2,240,181 with respiratory illnesses. Median age was 5 years (IQR, 1-11 years), and 52.7% of the population was male (Appendix Table 2 and Appendix Table 3).

Respiratory vs Nonrespiratory Illness During the COVID-19 Pandemic

Over the study period, fewer respiratory and nonrespiratory illness encounters were observed than expected, with a larger decrease in respiratory illness encounters (Table, Appendix Table 4).

Respiratory Subgroup Analyses

The O:E ratio decreased for all respiratory subgroups over the study period (Table, Appendix Table 4). There were significant differences in specific respiratory subgroups, including asthma, bronchiolitis, croup, influenza, and pneumonia (Appendix Figure 1A). Temporal trends in respiratory encounters were consistent across hospital settings, ages, and geographic regions (Appendix Figure 1B-D). When comparing the with and without COVID-19 subgroups in the “other respiratory illnesses” cohort, other respiratory illness without COVID-19 decreased and remained lower than expected over the rest of the study period, while other respiratory illness with COVID-19 increased markedly during the summer months and declined thereafter (Appendix Figure 2).

All age groups had reductions in respiratory illness encounters during the early COVID-19 and COVID-19 periods, although the decline was less pronounced in the 12- to 17-year-old group (Appendix Figure 1B). Similarly, while all age groups experienced increases in encounters for respiratory illnesses during the summer months, only children in the 12- to 17-year-old group experienced increases beyond pre-COVID-19 levels. Importantly, this increase in respiratory encounters was largely driven by COVID-19 diagnoses (Appendix Figure 3). The trend in nonrespiratory illness encounters stratified by age is shown in Appendix Figure 4.

When patients were stratified by hospital setting, there were no differences between those hospitalized and those discharged from the ED (Appendix Figure 1C). Patterns in respiratory illnesses by geographic location were qualitatively similar until the beginning of the summer 2020, after which geographical variation became more evident (Appendix Figure 1D).

In this large, multicenter study evaluating ED visits and hospitalizations for respiratory and nonrespiratory illnesses at US pediatric hospitals during the 2020 COVID-19 pandemic, we found a significant and substantial decrease in healthcare encounters for respiratory illnesses. A rapid and marked decline in encounters for respiratory illness in a relatively short period of time (March 12-April 2) was observed across all hospitals and US regions. Declines were consistent across common respiratory illnesses. More modest, yet still substantial, declines were also observed for nonrespiratory illnesses.

There are likely multiple underlying reasons for the observed reductions. Social distancing measures almost certainly played an important role in interrupting respiratory infection transmission. Rapid reduction in influenza transmission during the early COVID-19 period has been attributed to social distancing measures,³ and influenza transmission in children decreases with school closures.⁹ It is also possible that some families delayed seeking care at hospitals due to COVID-19, leading to less frequent encounters but more severe illness. The similar decrease in O:E ratio for ED visits and hospitalizations, however, is inconsistent with this explanation. It is also possible that nonurgent conditions cared for in the hospital settings were diverted to other care settings. For example, during this pandemic, telehealth and telephone visits for pediatric asthma increased by 61% and 19%, respectively, while ED and outpatient visits decreased concurrently.¹⁰Similar changes in location of care may also contribute to the decline in nonrespiratory illness encounters. Decreased use of hospital resources for nonurgent care diagnoses during the pandemic would suggest that, prior to COVID-19, there was overutilization of ambulatory services at children’s hospitals. Therefore, the pandemic may be driving care to more appropriate settings.

We also found relative differences in changes in encounters for respiratory illness by age. Adolescents’ levels of respiratory healthcare use declined less and recovered at a faster rate than those of younger children, returning to pre-COVID-19 levels by the end of the study period. The reason for this age differential is likely multifaceted. Infections, such as bronchiolitis and pneumonia, are more likely to be a source of respiratory illness in younger than in older children. It is also likely that disproportionate relaxation of social distancing measures among adolescents, who are known to have a stronger pattern of social interaction, contributed to the faster rise in respiratory illness–related encounters in this age group.¹¹ Adolescents have been reported to be more susceptible to, and more likely to transmit, SARS-CoV-2 compared to younger age groups.¹² More modest, albeit similar, age-based changes were observed in encounters for nonrespiratory illnesses. It is possible that pandemic-related stressors resulted in a subsequent increase in mental health encounters among this age group.¹³ While the reason for this also is likely multifactorial, adolescent behavior, as well as transmission of infectious illness that can exacerbate nonrespiratory conditions, may be a factor.

Emerging evidence suggests that school-age children may play an important role in SARS-CoV-2 transmission in the community.^4,14 Our finding that, compared to younger children, adolescents had significantly fewer reductions in respiratory illness encounters is concerning. These findings suggest that community-based efforts to help prevent respiratory illnesses, especially COVID-19, should focus on adolescents, who are most likely to maintain social interactions and transmit respiratory infections in the school setting and their households.

This study is limited by the inclusion of only tertiary care children’s hospitals, which may not be nationally representative, and the inability to assess the precise timing of when specific public health interventions were introduced. Moreover, previous studies suggest that social distancing behaviors may have changed even before formal recommendations were enacted.¹⁵ Future studies should investigate the local impact of state- and municipality-specific mandates on the burden of COVID-19 and other respiratory illnesses.

The COVID-19 pandemic was associated with substantial reductions in encounters for respiratory diseases, and also with more modest but still sizable reductions in encounters for nonrespiratory diseases. These reductions varied by age. Encounters among adolescents declined less and returned to previous levels faster compared with those of younger children.

This publication is dedicated to the memory of our coauthor, Dr. Michael Bendel-Stenzel. Dr. Bendel-Stenzel was dedicated to bettering the lives of children and advancing our knowledge of pediatrics through his research.

In the United States, respiratory illnesses are the most common cause of emergency department (ED) visits and hospitalizations in children.¹ In response to the ongoing COVID-19 pandemic, several public health interventions, including school and business closures, stay-at-home orders, and mask mandates, were implemented to limit transmission of SARS-CoV-2.^2,3 Studies have shown that children can contribute to the spread of SARS-CoV-2 infections, especially within households.^4-6 Recent data suggest that COVID-19, and the associated public health measures enacted to slow its spread, may have affected the transmission of other respiratory pathogens.⁷ Similarly, the pandemic has likely affected healthcare utilization for nonrespiratory illnesses through adoption of social distancing recommendations, suspension and delays in nonemergent elective care, avoidance of healthcare settings, and the effect of decreased respiratory disease on exacerbation of chronic illness.⁸ The objective of this study was to examine associations between the COVID-19 pandemic and healthcare utilization for pediatric respiratory and nonrespiratory illnesses at US pediatric hospitals.

Study Design

This is a multicenter, cross-sectional study of encounters at 44 pediatric hospitals that reported data to the Pediatric Health Information System (PHIS) database maintained by the Children’s Hospital Association (Lenexa, Kansas).

Study Population

Children 2 months to 18 years of age discharged from ED or inpatient settings with a nonsurgical diagnosis from January 1 to September 30 over a 4-year period (2017-2020) were included.

Exposure

The primary exposure was the 2020 COVID-19 pandemic time, divided into three periods: pre-COVID-19 (January-February 2020, the period prior to the pandemic in the United States), early COVID-19 (March-April 2020, coinciding with the first reported US pediatric case of COVID-19 on March 2, 2020), and COVID-19 (May-September 2020, marked by the implementation of at least two of the following containment measures in every US state: stay-at-home/shelter orders, school closures, nonessential business closures, restaurant closures, or prohibition of gatherings of more than 10 people).²

Outcomes

Respiratory illness diagnoses were classified into mutually exclusive subgroups following a prespecified hierarchy: influenza, pneumonia, croup, bronchiolitis, asthma, unspecified influenza-like illness, and “other respiratory diagnoses” (Appendix Table 1). To assess the impact of COVID-19 after its International Classification of Diseases, Tenth Revision code was established on March 25, 2020, the “other respiratory” subgroup was divided into other respiratory illnesses with and without COVID-19. Nonrespiratory illness diagnoses were defined as all diagnoses not included in the respiratory illness cohort.

Statistical Analysis

Categorical variables were summarized using frequencies and percentages and compared using chi-square tests. Continuous variables were summarized as median and interquartile range (IQR) and compared using Wilcoxon rank sum tests. Weekly observed-to-expected (O:E) ratios were calculated for each hospital by dividing the number of observed respiratory illness and nonrespiratory illness encounters in a given week in 2020 (observed) by the average number of encounters for that same week during 2017-2019 (expected). O:E ratios were then aggregated over the three COVID-19 study periods, and 95% confidence intervals were established around mean O:E ratios across individual hospitals. Outcomes were then stratified by respiratory illness subgroups, geographic region, and age. Additional details can be found in the Supplemental Methods in the Appendix.

Study Population

A total of 9,051,980 encounters were included in the study, 6,811,799 with nonrespiratory illnesses and 2,240,181 with respiratory illnesses. Median age was 5 years (IQR, 1-11 years), and 52.7% of the population was male (Appendix Table 2 and Appendix Table 3).

Respiratory vs Nonrespiratory Illness During the COVID-19 Pandemic

Over the study period, fewer respiratory and nonrespiratory illness encounters were observed than expected, with a larger decrease in respiratory illness encounters (Table, Appendix Table 4).

Respiratory Subgroup Analyses

The O:E ratio decreased for all respiratory subgroups over the study period (Table, Appendix Table 4). There were significant differences in specific respiratory subgroups, including asthma, bronchiolitis, croup, influenza, and pneumonia (Appendix Figure 1A). Temporal trends in respiratory encounters were consistent across hospital settings, ages, and geographic regions (Appendix Figure 1B-D). When comparing the with and without COVID-19 subgroups in the “other respiratory illnesses” cohort, other respiratory illness without COVID-19 decreased and remained lower than expected over the rest of the study period, while other respiratory illness with COVID-19 increased markedly during the summer months and declined thereafter (Appendix Figure 2).

All age groups had reductions in respiratory illness encounters during the early COVID-19 and COVID-19 periods, although the decline was less pronounced in the 12- to 17-year-old group (Appendix Figure 1B). Similarly, while all age groups experienced increases in encounters for respiratory illnesses during the summer months, only children in the 12- to 17-year-old group experienced increases beyond pre-COVID-19 levels. Importantly, this increase in respiratory encounters was largely driven by COVID-19 diagnoses (Appendix Figure 3). The trend in nonrespiratory illness encounters stratified by age is shown in Appendix Figure 4.

When patients were stratified by hospital setting, there were no differences between those hospitalized and those discharged from the ED (Appendix Figure 1C). Patterns in respiratory illnesses by geographic location were qualitatively similar until the beginning of the summer 2020, after which geographical variation became more evident (Appendix Figure 1D).

In this large, multicenter study evaluating ED visits and hospitalizations for respiratory and nonrespiratory illnesses at US pediatric hospitals during the 2020 COVID-19 pandemic, we found a significant and substantial decrease in healthcare encounters for respiratory illnesses. A rapid and marked decline in encounters for respiratory illness in a relatively short period of time (March 12-April 2) was observed across all hospitals and US regions. Declines were consistent across common respiratory illnesses. More modest, yet still substantial, declines were also observed for nonrespiratory illnesses.

There are likely multiple underlying reasons for the observed reductions. Social distancing measures almost certainly played an important role in interrupting respiratory infection transmission. Rapid reduction in influenza transmission during the early COVID-19 period has been attributed to social distancing measures,³ and influenza transmission in children decreases with school closures.⁹ It is also possible that some families delayed seeking care at hospitals due to COVID-19, leading to less frequent encounters but more severe illness. The similar decrease in O:E ratio for ED visits and hospitalizations, however, is inconsistent with this explanation. It is also possible that nonurgent conditions cared for in the hospital settings were diverted to other care settings. For example, during this pandemic, telehealth and telephone visits for pediatric asthma increased by 61% and 19%, respectively, while ED and outpatient visits decreased concurrently.¹⁰Similar changes in location of care may also contribute to the decline in nonrespiratory illness encounters. Decreased use of hospital resources for nonurgent care diagnoses during the pandemic would suggest that, prior to COVID-19, there was overutilization of ambulatory services at children’s hospitals. Therefore, the pandemic may be driving care to more appropriate settings.

We also found relative differences in changes in encounters for respiratory illness by age. Adolescents’ levels of respiratory healthcare use declined less and recovered at a faster rate than those of younger children, returning to pre-COVID-19 levels by the end of the study period. The reason for this age differential is likely multifaceted. Infections, such as bronchiolitis and pneumonia, are more likely to be a source of respiratory illness in younger than in older children. It is also likely that disproportionate relaxation of social distancing measures among adolescents, who are known to have a stronger pattern of social interaction, contributed to the faster rise in respiratory illness–related encounters in this age group.¹¹ Adolescents have been reported to be more susceptible to, and more likely to transmit, SARS-CoV-2 compared to younger age groups.¹² More modest, albeit similar, age-based changes were observed in encounters for nonrespiratory illnesses. It is possible that pandemic-related stressors resulted in a subsequent increase in mental health encounters among this age group.¹³ While the reason for this also is likely multifactorial, adolescent behavior, as well as transmission of infectious illness that can exacerbate nonrespiratory conditions, may be a factor.

Emerging evidence suggests that school-age children may play an important role in SARS-CoV-2 transmission in the community.^4,14 Our finding that, compared to younger children, adolescents had significantly fewer reductions in respiratory illness encounters is concerning. These findings suggest that community-based efforts to help prevent respiratory illnesses, especially COVID-19, should focus on adolescents, who are most likely to maintain social interactions and transmit respiratory infections in the school setting and their households.

This study is limited by the inclusion of only tertiary care children’s hospitals, which may not be nationally representative, and the inability to assess the precise timing of when specific public health interventions were introduced. Moreover, previous studies suggest that social distancing behaviors may have changed even before formal recommendations were enacted.¹⁵ Future studies should investigate the local impact of state- and municipality-specific mandates on the burden of COVID-19 and other respiratory illnesses.

The COVID-19 pandemic was associated with substantial reductions in encounters for respiratory diseases, and also with more modest but still sizable reductions in encounters for nonrespiratory diseases. These reductions varied by age. Encounters among adolescents declined less and returned to previous levels faster compared with those of younger children.

This publication is dedicated to the memory of our coauthor, Dr. Michael Bendel-Stenzel. Dr. Bendel-Stenzel was dedicated to bettering the lives of children and advancing our knowledge of pediatrics through his research.

Common themes run through rural communities and their health needs, yet the rurality of our nation is quite diverse. Approximately 97% of the United States is rural, and yet there is no silver bullet to resolve the health disparities that exist between urban and rural America. Differences in economic condition, infrastructure, education, racial diversity, health habits, and job opportunities contribute to the health disparities seen in rural communities.

In this issue of the Journal of Hospital Medicine, Gutierrez and colleagues¹ evaluate the implementation and outcomes of a rural telehospitalist program. In a hub-and-spoke fashion, providers at a large tertiary care hospital utilized telemedicine to round on patients with an onsite advanced practice provider at a 10-bed critical access hospital. Outcomes were examined during pre- and postimplementation periods using quantitative metrics (length of stay [LOS], readmission rate, mortality, and satisfaction) and qualitative measures based on interviews with staff. LOS was reduced in the postimplementation period, with a lower but nonsignificant readmission rate and no difference in mortality. Overall satisfaction was high, although respondents noted significant communication and technology issues. This study helps broaden telemedicine research and opens the conversation on barriers in rural implementation. 

As we increasingly focus on the provision and financing of care aimed at the health of populations, the diverse issues facing rural America remain insufficiently addressed. Probst and colleagues² suggest that population-based health policies are biased toward large urban centers. Innovations to transcend this “structural urbanism” are still fraught with obstacles, as revealed during the 2020 public health emergency (PHE). Telemedicine, once thought the solution to rural health provider shortages, has not escaped structural urbanism. Prior to the PHE, Medicare did not cover many core services delivered via telehealth, including hospitalist service codes. Waivers during the PHE have temporarily opened up reimbursement pathways. Unlike most telehospitalist services, Gutierrez and colleagues did not face these payment barriers in their study. Without claims data to inform payors on the adequacy of such services, health systems remain unable to promote telemedicine as a solution to rural access and cost issues. For example, in 2018, Yu and colleagues³ described the state of telemedicine in Minnesota, again, without claims data to inform supply or demand for hospitalist services.

Beyond payment barriers, technology issues are a challenge to rural telemedicine. Access to affordable, simple, and reliable equipment and broadband internet is key to user satisfaction. In April 2020, the Federal Communications Commission estimated that 18 million Americans had insufficient access to broadband internet.⁴ This number points to the technological hurdles of rural telemedicine. 

Medicine, a highly relationship-based “team sport,” is another barrier to successful implementation. Whether serving as supervisor or primary attending (as noted in the evaluation by JaKa and colleagues⁵), the telehospitalist must be “on stage” for both patients and remote spoke colleagues. In our experience, telehospitalists also practice in-person daytime hospital medicine at spoke sites; this further enhances their relationship and connection with the spoke community and likely contributes to high satisfaction by hub hospitalists and spoke patients and nurses. 

These factors create a clear impetus for further evaluation of rural telemedicine programs. There is an extensive range of program structures to evaluate, from cross-coverage to a 24/7 virtual hospital. Satisfaction is also relative and must contextualize quantitative measurement to historic care models. The execution of a telehospitalist program should align with the goals and objectives of spoke hospitals, as satisfaction will reflect how well hub hospitalists are able to meet those needs. Thus, multicenter studies that examine commercial financial pressures and encompass a variety of patient populations are imperative.

A hub-and-spoke telemedicine program can be a crucial resource for rural hospitals. The foundation of this model revolves around key factors, including reliable financing, access to technology, seamless communication, and engendering satisfaction among providers, staff, and patients. Research must continue for these programs to overcome the financial and structural challenges to their success.

Common themes run through rural communities and their health needs, yet the rurality of our nation is quite diverse. Approximately 97% of the United States is rural, and yet there is no silver bullet to resolve the health disparities that exist between urban and rural America. Differences in economic condition, infrastructure, education, racial diversity, health habits, and job opportunities contribute to the health disparities seen in rural communities.

In this issue of the Journal of Hospital Medicine, Gutierrez and colleagues¹ evaluate the implementation and outcomes of a rural telehospitalist program. In a hub-and-spoke fashion, providers at a large tertiary care hospital utilized telemedicine to round on patients with an onsite advanced practice provider at a 10-bed critical access hospital. Outcomes were examined during pre- and postimplementation periods using quantitative metrics (length of stay [LOS], readmission rate, mortality, and satisfaction) and qualitative measures based on interviews with staff. LOS was reduced in the postimplementation period, with a lower but nonsignificant readmission rate and no difference in mortality. Overall satisfaction was high, although respondents noted significant communication and technology issues. This study helps broaden telemedicine research and opens the conversation on barriers in rural implementation. 

As we increasingly focus on the provision and financing of care aimed at the health of populations, the diverse issues facing rural America remain insufficiently addressed. Probst and colleagues² suggest that population-based health policies are biased toward large urban centers. Innovations to transcend this “structural urbanism” are still fraught with obstacles, as revealed during the 2020 public health emergency (PHE). Telemedicine, once thought the solution to rural health provider shortages, has not escaped structural urbanism. Prior to the PHE, Medicare did not cover many core services delivered via telehealth, including hospitalist service codes. Waivers during the PHE have temporarily opened up reimbursement pathways. Unlike most telehospitalist services, Gutierrez and colleagues did not face these payment barriers in their study. Without claims data to inform payors on the adequacy of such services, health systems remain unable to promote telemedicine as a solution to rural access and cost issues. For example, in 2018, Yu and colleagues³ described the state of telemedicine in Minnesota, again, without claims data to inform supply or demand for hospitalist services.

Beyond payment barriers, technology issues are a challenge to rural telemedicine. Access to affordable, simple, and reliable equipment and broadband internet is key to user satisfaction. In April 2020, the Federal Communications Commission estimated that 18 million Americans had insufficient access to broadband internet.⁴ This number points to the technological hurdles of rural telemedicine. 

Medicine, a highly relationship-based “team sport,” is another barrier to successful implementation. Whether serving as supervisor or primary attending (as noted in the evaluation by JaKa and colleagues⁵), the telehospitalist must be “on stage” for both patients and remote spoke colleagues. In our experience, telehospitalists also practice in-person daytime hospital medicine at spoke sites; this further enhances their relationship and connection with the spoke community and likely contributes to high satisfaction by hub hospitalists and spoke patients and nurses. 

These factors create a clear impetus for further evaluation of rural telemedicine programs. There is an extensive range of program structures to evaluate, from cross-coverage to a 24/7 virtual hospital. Satisfaction is also relative and must contextualize quantitative measurement to historic care models. The execution of a telehospitalist program should align with the goals and objectives of spoke hospitals, as satisfaction will reflect how well hub hospitalists are able to meet those needs. Thus, multicenter studies that examine commercial financial pressures and encompass a variety of patient populations are imperative.

A hub-and-spoke telemedicine program can be a crucial resource for rural hospitals. The foundation of this model revolves around key factors, including reliable financing, access to technology, seamless communication, and engendering satisfaction among providers, staff, and patients. Research must continue for these programs to overcome the financial and structural challenges to their success.

Common themes run through rural communities and their health needs, yet the rurality of our nation is quite diverse. Approximately 97% of the United States is rural, and yet there is no silver bullet to resolve the health disparities that exist between urban and rural America. Differences in economic condition, infrastructure, education, racial diversity, health habits, and job opportunities contribute to the health disparities seen in rural communities.

In this issue of the Journal of Hospital Medicine, Gutierrez and colleagues¹ evaluate the implementation and outcomes of a rural telehospitalist program. In a hub-and-spoke fashion, providers at a large tertiary care hospital utilized telemedicine to round on patients with an onsite advanced practice provider at a 10-bed critical access hospital. Outcomes were examined during pre- and postimplementation periods using quantitative metrics (length of stay [LOS], readmission rate, mortality, and satisfaction) and qualitative measures based on interviews with staff. LOS was reduced in the postimplementation period, with a lower but nonsignificant readmission rate and no difference in mortality. Overall satisfaction was high, although respondents noted significant communication and technology issues. This study helps broaden telemedicine research and opens the conversation on barriers in rural implementation. 

As we increasingly focus on the provision and financing of care aimed at the health of populations, the diverse issues facing rural America remain insufficiently addressed. Probst and colleagues² suggest that population-based health policies are biased toward large urban centers. Innovations to transcend this “structural urbanism” are still fraught with obstacles, as revealed during the 2020 public health emergency (PHE). Telemedicine, once thought the solution to rural health provider shortages, has not escaped structural urbanism. Prior to the PHE, Medicare did not cover many core services delivered via telehealth, including hospitalist service codes. Waivers during the PHE have temporarily opened up reimbursement pathways. Unlike most telehospitalist services, Gutierrez and colleagues did not face these payment barriers in their study. Without claims data to inform payors on the adequacy of such services, health systems remain unable to promote telemedicine as a solution to rural access and cost issues. For example, in 2018, Yu and colleagues³ described the state of telemedicine in Minnesota, again, without claims data to inform supply or demand for hospitalist services.

Beyond payment barriers, technology issues are a challenge to rural telemedicine. Access to affordable, simple, and reliable equipment and broadband internet is key to user satisfaction. In April 2020, the Federal Communications Commission estimated that 18 million Americans had insufficient access to broadband internet.⁴ This number points to the technological hurdles of rural telemedicine. 

Medicine, a highly relationship-based “team sport,” is another barrier to successful implementation. Whether serving as supervisor or primary attending (as noted in the evaluation by JaKa and colleagues⁵), the telehospitalist must be “on stage” for both patients and remote spoke colleagues. In our experience, telehospitalists also practice in-person daytime hospital medicine at spoke sites; this further enhances their relationship and connection with the spoke community and likely contributes to high satisfaction by hub hospitalists and spoke patients and nurses. 

These factors create a clear impetus for further evaluation of rural telemedicine programs. There is an extensive range of program structures to evaluate, from cross-coverage to a 24/7 virtual hospital. Satisfaction is also relative and must contextualize quantitative measurement to historic care models. The execution of a telehospitalist program should align with the goals and objectives of spoke hospitals, as satisfaction will reflect how well hub hospitalists are able to meet those needs. Thus, multicenter studies that examine commercial financial pressures and encompass a variety of patient populations are imperative.

A hub-and-spoke telemedicine program can be a crucial resource for rural hospitals. The foundation of this model revolves around key factors, including reliable financing, access to technology, seamless communication, and engendering satisfaction among providers, staff, and patients. Research must continue for these programs to overcome the financial and structural challenges to their success.

“One of the essential qualities of the clinician is interest in humanity, for the secret of the care of the patient is in caring for the patient.”

—Francis Peabody, 9 years after the pandemic of 1918

Two weeks after rounding on a coronavirus disease 2019 (COVID-19) unit at the start of the surge in Detroit, Michigan, I got infected. Simultaneously, my father was also fighting COVID-19, and we were mechanically ventilated at the same time. He was admitted a week before I was and, devastatingly, died about a week after I was discharged. I have three major takeaways from my experience that now inform how I practice.

I have always appreciated what a valuable member of the team nurses are, but to experience the caring reflected in their smiling eyes, magnified by a face shield, is an indelible image—compassion with passion. They risked their lives to save my life, but I am also grateful to the maintenance person who risked their life to fix the toilet in my room in the intensive care unit, the transportation aides and radiology techs who risked their lives so I could get a computed tomography scan, environmental services personnel, and too many to mention who put on personal protective equipment to care for my father and me. Lesson #1: Repeatedly thank everyone caring for patients. Now, when I am on rounds, I try to remember to say thank you to each member each day.

As my dyspnea worsened, a big concern (besides the “I might die” thing) was communicating with family. It turns out that my short cell phone cord could only stretch to just under my pillow, and reaching that far was exhausting when on high-flow oxygen. Nursing helped me to video chat with my family as we said potential goodbyes just prior to intubation and let me use their bagged personal cell phones when I woke up. Meanwhile, my siblings took over the calls from Dad’s team, and I was amazed at how much they had learned about ventilator settings while I was sleeping and how much the clinical team knew about my family. Although Dad’s delirium never allowed meaningful conversation or eye contact with us, every day the nurses would facilitate video chatting (again, with their phones) so we could give words of love and encouragement. Lesson #2: I have redoubled my efforts to keep families in the loop and carry a phone/tablet charger with a long cord to lend/give my patients, if needed.

While I was prone, paralyzed, and sedated, I had no awareness of my medical treatments, thanks to the skill of my caregivers. It is impossible to express my gratitude to those who sent good wishes that virtually always included the phrase, “We were praying for you”; I hope my prayers got through to Dad. In their call to more fully address religion and spirituality in medicine, Collier et al¹ remind us that approximately 90% of Americans have faith, and more than half state that religion is very important to them, yet I, like many, do not routinely incorporate this aspect into my discussions with patients or the care team. Lesson #3: Begin to more actively facilitate my patients’ spirituality into the team’s caring process.

“One of the essential qualities of the clinician is interest in humanity, for the secret of the care of the patient is in caring for the patient.”

—Francis Peabody, 9 years after the pandemic of 1918

Two weeks after rounding on a coronavirus disease 2019 (COVID-19) unit at the start of the surge in Detroit, Michigan, I got infected. Simultaneously, my father was also fighting COVID-19, and we were mechanically ventilated at the same time. He was admitted a week before I was and, devastatingly, died about a week after I was discharged. I have three major takeaways from my experience that now inform how I practice.

I have always appreciated what a valuable member of the team nurses are, but to experience the caring reflected in their smiling eyes, magnified by a face shield, is an indelible image—compassion with passion. They risked their lives to save my life, but I am also grateful to the maintenance person who risked their life to fix the toilet in my room in the intensive care unit, the transportation aides and radiology techs who risked their lives so I could get a computed tomography scan, environmental services personnel, and too many to mention who put on personal protective equipment to care for my father and me. Lesson #1: Repeatedly thank everyone caring for patients. Now, when I am on rounds, I try to remember to say thank you to each member each day.

As my dyspnea worsened, a big concern (besides the “I might die” thing) was communicating with family. It turns out that my short cell phone cord could only stretch to just under my pillow, and reaching that far was exhausting when on high-flow oxygen. Nursing helped me to video chat with my family as we said potential goodbyes just prior to intubation and let me use their bagged personal cell phones when I woke up. Meanwhile, my siblings took over the calls from Dad’s team, and I was amazed at how much they had learned about ventilator settings while I was sleeping and how much the clinical team knew about my family. Although Dad’s delirium never allowed meaningful conversation or eye contact with us, every day the nurses would facilitate video chatting (again, with their phones) so we could give words of love and encouragement. Lesson #2: I have redoubled my efforts to keep families in the loop and carry a phone/tablet charger with a long cord to lend/give my patients, if needed.

While I was prone, paralyzed, and sedated, I had no awareness of my medical treatments, thanks to the skill of my caregivers. It is impossible to express my gratitude to those who sent good wishes that virtually always included the phrase, “We were praying for you”; I hope my prayers got through to Dad. In their call to more fully address religion and spirituality in medicine, Collier et al¹ remind us that approximately 90% of Americans have faith, and more than half state that religion is very important to them, yet I, like many, do not routinely incorporate this aspect into my discussions with patients or the care team. Lesson #3: Begin to more actively facilitate my patients’ spirituality into the team’s caring process.

“One of the essential qualities of the clinician is interest in humanity, for the secret of the care of the patient is in caring for the patient.”

—Francis Peabody, 9 years after the pandemic of 1918

Two weeks after rounding on a coronavirus disease 2019 (COVID-19) unit at the start of the surge in Detroit, Michigan, I got infected. Simultaneously, my father was also fighting COVID-19, and we were mechanically ventilated at the same time. He was admitted a week before I was and, devastatingly, died about a week after I was discharged. I have three major takeaways from my experience that now inform how I practice.

I have always appreciated what a valuable member of the team nurses are, but to experience the caring reflected in their smiling eyes, magnified by a face shield, is an indelible image—compassion with passion. They risked their lives to save my life, but I am also grateful to the maintenance person who risked their life to fix the toilet in my room in the intensive care unit, the transportation aides and radiology techs who risked their lives so I could get a computed tomography scan, environmental services personnel, and too many to mention who put on personal protective equipment to care for my father and me. Lesson #1: Repeatedly thank everyone caring for patients. Now, when I am on rounds, I try to remember to say thank you to each member each day.

As my dyspnea worsened, a big concern (besides the “I might die” thing) was communicating with family. It turns out that my short cell phone cord could only stretch to just under my pillow, and reaching that far was exhausting when on high-flow oxygen. Nursing helped me to video chat with my family as we said potential goodbyes just prior to intubation and let me use their bagged personal cell phones when I woke up. Meanwhile, my siblings took over the calls from Dad’s team, and I was amazed at how much they had learned about ventilator settings while I was sleeping and how much the clinical team knew about my family. Although Dad’s delirium never allowed meaningful conversation or eye contact with us, every day the nurses would facilitate video chatting (again, with their phones) so we could give words of love and encouragement. Lesson #2: I have redoubled my efforts to keep families in the loop and carry a phone/tablet charger with a long cord to lend/give my patients, if needed.

While I was prone, paralyzed, and sedated, I had no awareness of my medical treatments, thanks to the skill of my caregivers. It is impossible to express my gratitude to those who sent good wishes that virtually always included the phrase, “We were praying for you”; I hope my prayers got through to Dad. In their call to more fully address religion and spirituality in medicine, Collier et al¹ remind us that approximately 90% of Americans have faith, and more than half state that religion is very important to them, yet I, like many, do not routinely incorporate this aspect into my discussions with patients or the care team. Lesson #3: Begin to more actively facilitate my patients’ spirituality into the team’s caring process.

Providing high-quality, efficient, and evidence-based healthcare is a complicated and complex process. The right approach or path forward is not always clear. In medicine, decision-making inherently involves uncertainty; evidence may be lacking, or values or context may differ, and thus, reasonable clinicians may choose to make different decisions based on the same data.

In this spirit of fostering education and healthy debate to improve understanding of challenges relevant to the field of hospital medicine, we are pleased to introduce our Point-Counterpoint series within the Perspectives in Hospital Medicine section of the journal. Point-Counterpoint Perspectives presents a debate by content experts. Each provides an interpretation of evidence regarding patient management or other controversial issues relating to hospital-based care. The format consists of an overview of the topic with an original point followed by a counterpoint response and, finally, a rebuttal (Table). We ask contributors to be as outspoken in their points and counterpoints as the evidence allows in order to fully elaborate the questions and uncertainties that may otherwise be familiar only to experts in the field or to those in other disciplines.

Our inaugural point-counterpoint articles address whether healthcare workers should receive priority for scarce drugs and therapies during the coronavirus disease 2019 (COVID-19) pandemic. The intermittent shortage of medical supplies and protective equipment has made it not only difficult but also at times dangerous for healthcare workers to care for infected patients.¹ The risks of developing COVID-19 and fear of transmitting it to loved ones has led to stress, fatigue, and burnout among healthcare workers, leading some to quit and even attempt suicide. The downstream effects of this stress may adversely affect patients and exacerbate staffing challenges in an already taxed healthcare system.² Do we have a special obligation to those on the front lines? We are grateful to Drs Kirk R Daffner, Armand Antommaria, and Ndidi I Unaka, for addressing this controversial topic.^3-5

Providing high-quality, efficient, and evidence-based healthcare is a complicated and complex process. The right approach or path forward is not always clear. In medicine, decision-making inherently involves uncertainty; evidence may be lacking, or values or context may differ, and thus, reasonable clinicians may choose to make different decisions based on the same data.

In this spirit of fostering education and healthy debate to improve understanding of challenges relevant to the field of hospital medicine, we are pleased to introduce our Point-Counterpoint series within the Perspectives in Hospital Medicine section of the journal. Point-Counterpoint Perspectives presents a debate by content experts. Each provides an interpretation of evidence regarding patient management or other controversial issues relating to hospital-based care. The format consists of an overview of the topic with an original point followed by a counterpoint response and, finally, a rebuttal (Table). We ask contributors to be as outspoken in their points and counterpoints as the evidence allows in order to fully elaborate the questions and uncertainties that may otherwise be familiar only to experts in the field or to those in other disciplines.

Our inaugural point-counterpoint articles address whether healthcare workers should receive priority for scarce drugs and therapies during the coronavirus disease 2019 (COVID-19) pandemic. The intermittent shortage of medical supplies and protective equipment has made it not only difficult but also at times dangerous for healthcare workers to care for infected patients.¹ The risks of developing COVID-19 and fear of transmitting it to loved ones has led to stress, fatigue, and burnout among healthcare workers, leading some to quit and even attempt suicide. The downstream effects of this stress may adversely affect patients and exacerbate staffing challenges in an already taxed healthcare system.² Do we have a special obligation to those on the front lines? We are grateful to Drs Kirk R Daffner, Armand Antommaria, and Ndidi I Unaka, for addressing this controversial topic.^3-5

Providing high-quality, efficient, and evidence-based healthcare is a complicated and complex process. The right approach or path forward is not always clear. In medicine, decision-making inherently involves uncertainty; evidence may be lacking, or values or context may differ, and thus, reasonable clinicians may choose to make different decisions based on the same data.

In this spirit of fostering education and healthy debate to improve understanding of challenges relevant to the field of hospital medicine, we are pleased to introduce our Point-Counterpoint series within the Perspectives in Hospital Medicine section of the journal. Point-Counterpoint Perspectives presents a debate by content experts. Each provides an interpretation of evidence regarding patient management or other controversial issues relating to hospital-based care. The format consists of an overview of the topic with an original point followed by a counterpoint response and, finally, a rebuttal (Table). We ask contributors to be as outspoken in their points and counterpoints as the evidence allows in order to fully elaborate the questions and uncertainties that may otherwise be familiar only to experts in the field or to those in other disciplines.

Our inaugural point-counterpoint articles address whether healthcare workers should receive priority for scarce drugs and therapies during the coronavirus disease 2019 (COVID-19) pandemic. The intermittent shortage of medical supplies and protective equipment has made it not only difficult but also at times dangerous for healthcare workers to care for infected patients.¹ The risks of developing COVID-19 and fear of transmitting it to loved ones has led to stress, fatigue, and burnout among healthcare workers, leading some to quit and even attempt suicide. The downstream effects of this stress may adversely affect patients and exacerbate staffing challenges in an already taxed healthcare system.² Do we have a special obligation to those on the front lines? We are grateful to Drs Kirk R Daffner, Armand Antommaria, and Ndidi I Unaka, for addressing this controversial topic.^3-5

Hospitalizations for infections are common in children, with respiratory illnesses, including pneumonia and bronchiolitis, among the most prevalent indications for hospitalization.^1,2 Infections are also among the most frequent indications for all-cause readmissions and for potentially preventable readmissions in children.^3-5 Beyond hospital resource use, infection hospitalizations and readmissions represent a considerable cause of life disruption for patients and their families.^6,7 While emerging evidence supports shortened durations of parenteral antibiotics before transitioning to oral therapy for some infections (eg, pyelonephritis, osteomyelitis),^8-10 other infections may require extended treatment courses for weeks. The risk of adverse outcomes (eg, complications of medical treatment, readmission risk) and burdens placed on patients and their families may therefore differ across infection types and extend well beyond the immediate hospitalization.

Although infections are common and pediatric providers are expected to have proficiency in managing infections, substantial variation in the management of common pediatric infections exists and is associated with adverse hospitalization outcomes, including increased readmission risk and healthcare costs.^11-18 Potentially avoidable resource use associated with hospital readmission from infection has led to adoption of hospital-level readmission metrics as indicators of the quality of healthcare delivery. For example, the Pediatric Quality Measures Program, established by the Children’s Health Insurance Program Reauthorization Act of 2009, has prioritized measurement of readmissions following hospitalization for lower respiratory tract infection.¹⁹ With government agencies increasingly using readmission metrics to assess quality of healthcare delivery, developing metrics that focus on these resource-intensive conditions is essential.

Because infections are a common and costly indication for hospital resource use and because substantial variation in management has been observed, promoting a broader understanding of infection-specific readmission rates is important for prioritizing readmission-reduction opportunities in children. This study’s objectives were the following: (1) to describe the prevalence and characteristics of infection hospitalizations in children and their associated readmissions and (2) to estimate the number of readmissions avoided and costs saved if all hospitals achieved the 10th percentile of the hospitals’ risk-adjusted readmission rate (ie, readmission benchmark).

Study Design and Data Source

We performed a retrospective cohort analysis using the 2014 Agency for Healthcare Research and Quality (AHRQ) Nationwide Readmissions Database (NRD).²⁰ The 2014 NRD is an administrative database that contains information on inpatient stays from January 1, 2014, to December 31, 2014, for all payers and allows for weighted national estimates of readmissions for all US individuals. Data within NRD are aggregated from 22 geographically diverse states representing approximately one-half of the US population. NRD contains deidentified patient-level data with unique verified patient identifiers to track individuals within and across hospitals in a state. However, AHRQ guidelines specify that NRD cannot be used for reporting hospital-specific readmission rates. Thus, for the current study, the Inpatient Essentials (Children’s Hospital Association), or IE, database was used to measure hospital-level readmission rates and to distinguish benchmark readmission rates for individual infection diagnoses.²¹ The IE database is composed of 90 children’s hospitals distributed throughout all regions of the United States. The inclusion of free-standing children’s hospitals and children’s hospitals within adult hospitals allows for comparisons and benchmarking across hospitals on multiple metrics, including readmissions.

Study Population

Children 0 to 17 years of age with a primary diagnosis at the index admission for infection between January 1, 2014, and November 30, 2014, were included. The end date of November 30, 2014, allowed for a full 30-day readmission window for all index admissions. We excluded index admissions that resulted in transfer to another acute care hospital or in-hospital mortality. Additionally, we excluded index admissions of children who had hematologic or immunologic conditions, malignancy, or history of bone marrow and solid-organ transplant, using the classification system for complex chronic conditions (CCCs) from Feudtner et al.²² Due to the high likelihood of immunosuppression in patients with these conditions, children may have nuanced experiences with illness severity, trajectory, and treatment associated with infection that place them at increased risk for nonpreventable readmission.

Main Exposure

The main exposure was infection type during the index admission. Condition-specific index admissions were identified using AHRQ’s Clinical Classifications Software (CCS) categories.²³ CCS is a classification schema that categorizes the greater than 14,000 International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis codes and 3,900 ICD-9-CM procedure codes into clinically meaningful categories of 295 diagnosis (including mental health codes and E-codes) and 231 procedural groupings. Twenty-two groupings indicative of infection were distinguished and used for the current study. Examples of infections included aspiration pneumonia, pneumonia, bronchiolitis, and sexually transmitted infection. We combined related CCS categories when possible for ease of interpretation and presentation of data (Appendix Table 1).

Main Outcome Measure

The main outcome measure was 30-day hospital readmission. Readmission was defined as all-cause, unplanned admission within 30 days following discharge from a preceding hospitalization. Planned hospital readmissions were identified and excluded using methods from AHRQ’s Pediatric All-Condition Readmission Measure.²⁴ We defined a same-cause return as a return with the same CCS infection category as the index admission. Costs associated with readmissions were estimated from charges using hospital-specific cost-to-charge ratios provided with NRD.

Patient Demographic and Clinical Characteristics

Patient demographic characteristics included age at index admission (<1 year, 1-5 years, 6-9 years, 10-14 years, and 15-18 years), sex, payer (ie, government, private, other), and discharge disposition (ie, routine, home health, other). We assessed all patients for medical complexity, as defined by the presence of at least one CCC, and we reported the categories of CCCs by organ system involved.²² Otherwise, patients were identified as without medical complexity.

Statistical Analysis

We summarized continuous variables with medians and interquartile ranges and categorical variables with frequencies and percentages. To develop benchmark readmission rates for each infection type, we used generalized linear mixed models with random intercepts for each hospital in the IE database. For each infection type, the benchmark readmission rate was defined as the 10th percentile of hospitals’ risk-adjusted readmission rates. The 10th percentile was chosen to identify the best performing 10% of hospitals (ie, hospitals with the lowest readmission rates). Because children with medical complexity account for a large proportion of hospital resource use and are at high risk for readmission,^4,25 we developed benchmarks stratified by presence/absence of a CCC (ie, with complexity vs without complexity). Models were adjusted for severity of illness using the Hospitalization Resource Intensity Score for Kids (H-RISK),²⁶ a scoring system that assigns relative weights for each All Patient Refined Diagnosis-Related Group (3M Corporation) and severity of illness level, and each hospital’s risk-adjusted readmission rate was determined.

With use of weights to achieve national estimates of index admissions and readmissions, we determined the number of potentially avoidable readmissions by calculating the number of readmissions observed in the NRD that would not occur if all hospitals achieved readmission rates equal to the 10th percentile. Avoidable costs were estimated by multiplying the number of potentially avoidable readmissions by the mean cost of a readmission for infections of that type. Estimates of avoidable readmissions and costs were stratified by medical complexity. In addition to describing estimates at the 10th percentile benchmark, we similarly developed estimates of potentially avoidable readmissions and avoidable costs for the 5th and 25th percentiles, which are presented within Appendix Table 2 (children without complexity) and Appendix Table 3 (children with complexity).

All statistical analyses were performed using SAS version 9.4 (SAS Institute), and P values <.001 were considered statistically significant due to the large sample size. The Office of Research Integrity at Children’s Mercy Hospital deemed this study exempt from institutional board review.

Characteristics of the Study Population

The study included 380,067 index admissions for infection and an accompanying 18,469 unplanned all-cause readmissions over the 30 days following discharge (readmission rate, 4.9%; Table 1). Children ages 1 to 5 years accounted for the largest percentage (32.9%) of index hospitalizations, followed by infants younger than 1 year (30.3%). The readmission rate by age group was highest for infants younger than 1 year, compared with rates among all other age groups (5.6% among infants < 1 year vs 4.4%-4.7% for other age groups; P < .001). In the overall cohort, 16.2% of admissions included patients with a CCC. Children with medical complexity had higher readmission rates than those without medical complexity (no CCC, 3.2%; 1 CCC, 9.2%; 2+ CCCs, 18.9%). A greater percentage of children experiencing a readmission had government insurance (63.0% vs 59.2%; P < .001) and received home health nursing (10.1% vs 2.7%; P < .001) following the index encounter.

Children Without Complexity

Index Admissions and 30-day Readmissions

Among patients without medical complexity, index admissions occurred most frequently for pneumonia (n = 54,717), bronchiolitis (n = 53,959), and appendicitis (n = 45,036) (Figure 1). The median length of stay (LOS) for index admissions ranged from 1 to 5 days (Table 2), with septic arthritis and osteomyelitis having the longest median LOS at 5 (IQR, 3-7) days.

Thirty-day readmission rates varied substantially by infection at the index admission (range, 1.5% for sexually transmitted infection to 8.6% for peritonitis) (Figure 1). The median LOS for 30-day readmissions varied from 2 to 7 days (Table 2), while the median number of days to readmission varied substantially by infection type (range, 4 days for bacterial infection [site unspecified] to 24 days for sexually transmitted infections). Among the top five indications for admission for children without complexity, 14.9% to 52.8% of readmissions were for the same cause as the index admission; however, many additional returns were likely related to the index admission (Appendix Table 4). For example, among other return reasons, an additional 992 (61.7%) readmissions following appendicitis hospitalizations were for complications of surgical procedures or medical care, peritonitis, intestinal obstruction, and abdominal pain.

Impact of Achieving Readmission Benchmarks

Among children without complexity, readmission benchmarks (ie, the 10th percentile of readmission rates across hospitals) ranged from 0% to 26.7% (Figure 2). An estimated 54.7% of readmissions (n = 5,507) could potentially be reduced if hospitals achieved infection-specific benchmark readmission rates, which could result in an estimated $44.5 million in savings. Pneumonia, bronchiolitis, gastroenteritis, and upper respiratory tract infections were among conditions with the greatest potential reductions in readmissions and costs if a 10th percentile benchmark was achieved.

Children With Medical Complexity

Index Admissions and 30-day Readmissions

Among patients with complexity, index admissions occurred most frequently for pneumonia (n = 14,344), bronchiolitis (n = 8,618), and upper respiratory tract infection (n = 6,407) (Figure 1). The median LOS for index admissions ranged from 1 to 9 days (Table 2), with septicemia and CNS infections having the longest median LOS at 9 days.

Thirty-day readmission rates varied substantially by the type of infection at the index admission (range, 0% for sexually transmitted infection to 21.6% for aspiration pneumonia) (Figure 1). The median LOS for 30-day readmissions varied from 2 to 14 days (Table 2), and the median number of days to readmission varied substantially by infection type (range, 6 days for tonsillitis to 23 days for other infection). Among the top five indications for admission for medically complex children, 8% to 40.4% of readmissions were for the same cause as the index admission (Appendix Table 4). As with the children without complexity, additional returns were likely related to the index admission.

Impact of Achieving Readmission Benchmarks

Among children with medical complexity, readmission benchmarks ranged from 0% to 30.3% (Figure 2). An estimated 42.6% of readmissions (n = 3,576) could potentially be reduced if hospitals achieved infection-specific benchmark readmission rates, which could result in an estimated $70.8 million in savings. Pneumonia, bronchiolitis, septicemia, and upper respiratory tract infections were among conditions with the greatest potential reductions in readmissions and costs if the benchmarks were achieved.

The current study uncovered new findings regarding unplanned readmissions following index infection hospitalizations for children. In particular, readmission rates and time to readmission varied substantially by infection subtype. The study also identified priority infections and unique considerations for children with CCCs, all of which may help maximize the value of readmission metrics aimed at advancing hospital quality and reducing costs for infection hospitalizations in children. If all hospitals achieved the readmission rates of the best performing hospitals, 42.6% to 54.7% fewer readmissions could be realized with associated cost savings.

Nationally, studies have reported 30-day, all-cause unplanned readmission rates of 6.2% to 10.3%.^5,27 In our current study we observed an overall readmission rate of 4.9% across all infectious conditions; however, readmission rates varied substantially by condition, with upper and lower respiratory tract infections, septicemia, and gastroenteritis among infections with the greatest number of potentially avoidable readmissions based on achievement of readmission benchmarks. Currently, pediatric-specific all-cause and lower respiratory tract infection readmission metrics have been developed with the aim of improving quality of care and reducing healthcare expenditures.²⁸ Future readmission studies and metrics may prioritize conditions such as septicemia, gastroenteritis, and other respiratory tract infections. Our current study demonstrated that many readmissions following infection hospitalizations were associated with the same CCS category or within a related CCS category (eg, complications of surgical procedures or medical care, appendicitis, peritonitis, intestinal obstruction, and abdominal pain constituted the top five indications for readmission for appendicitis, whereas respiratory illnesses constituted the top five indications for readmissions for pneumonia). While this current study cannot clarify this relationship further, and the “avoidability” of unplanned readmissions is debated,^29-31 our findings suggest that future investigations might focus on identifying whether condition-specific interventions during the index admission could mitigate some readmissions.

While the LOS of the index admission and the readmission were similar for most infection subtypes, we observed substantial variability in the temporal risk for readmission by infection subtype. Our observations complement studies exploring the timing of readmissions for other pediatric conditions.^32-34 In particular, our findings further highlight that the composition and interaction of factors influencing infection readmissions may vary by condition. Infections represent a diverse group of conditions, with treatment courses that vary in need for parenteral antibiotics, ability to tailor treatment based on organism and susceptibilities, and length of treatment. While treatment for some infections may be accomplished, or nearly accomplished, prior to discharge, other infections (eg, osteomyelitis) may require prolonged treatment, shifting the burden of management and monitoring to patients and their families, which along with the timeliness and adequacy of outpatient follow-up, may modify an individual’s readmission risk. Consequently, a “one-size fits all” approach to discharge counseling may not be successful across all conditions. Our study lays the groundwork for how these temporal relationships may be used by clinicians to counsel families regarding the likely readmission timeframe, based on infection, and to establish follow-up appointments ahead of the infection-specific “readmission window,” which may allow outpatient providers to intervene when readmission risk is greatest.

Consistent with prior literature, children with medical complexity in our study had increased frequency of 30-day, all-cause unplanned readmissions and LOS, compared with peers who did not have complexity.⁵ Readmission rates following hospitalizations for aspiration pneumonia were comparable to those reported by Thompson et al in their study examining rates of pneumonia in children with neurologic impairment.³⁵ In our current study, nearly 96% of readmissions following aspiration pneumonia hospitalizations were for children with medical complexity, and more than 58% of these readmissions were for aspiration pneumonia or respiratory illness. Future investigations should seek to explore factors contributing to readmissions in children with medical complexity and to evaluate whether interventions such as postdischarge coaching or discharge bundles could assist with reductions in healthcare resource use.^36,37

Despite a lack of clear association between readmissions and quality of care for children,³⁸ readmissions rates continue to be used as a quality measure for hospitalized patients. Within our present study, we found that achieving benchmark readmission rates for infection hospitalizations could lead to substantial reductions in readmissions; however, these reductions were seen across this relatively similar group of infection diagnoses, and hospitals may face greater challenges when attempting to achieve a 10th percentile readmission benchmark across a more expansive set of diagnoses. Despite these challenges, understanding the intricacies of readmissions may lead to improved readmission metrics and the systematic identification of avoidable readmissions, the goal of which is to enhance the quality of healthcare for hospitalized children.

Our findings should be interpreted in the context of several limitations. We defined our readmission benchmark at the 10th percentile using the IE database. Because an estimated 70% of hospitalizations for children occur within general hospitals,³⁹ we believe that our use of the IE database allowed for increased generalizability, though the broadening of our findings to nonacademic hospital settings may be less reliable. While we describe the 10th percentile readmission benchmark here, alternative benchmarks would result in different estimates of avoidable readmissions and associated readmission costs (Appendix Tables 2 and 3). The IE and NRD databases do not distinguish intensive care use. We tried to address this limitation through model adjustments using H-RISK, which is particularly helpful for adjusting for NICU admissions through use of the 27 All Patient Refined Diagnosis-Related Groups for neonatal conditions. Additionally, the NRD uses state-level data to derive national estimates and is not equipped to measure readmissions to hospitals in a different state, distinguish patient deaths occurring after discharge, or to examine the specific indication for readmission (ie, unable to assess if the readmission is related to a complication of the treatment plan, progression of the illness course, or for an unrelated reason). While sociodemographic and socioeconomic factors may affect readmissions, the NRD does not contain information on patients’ race/ethnicity, family/social attributes, or postdischarge follow-up health services, which are known to influence the need for readmission.

Despite these limitations, this study highlights future areas for research and potential opportunities for reducing readmission following infection hospitalizations. First, identifying hospital- and systems-based factors that contribute to readmission reductions at the best-performing hospitals may identify opportunities for hospitals with the highest readmission rates to achieve the rates of the best-performing hospitals. Second, conditions such as upper and lower respiratory tract infections, along with septicemia and gastroenteritis, may serve as prime targets for future investigation based on the estimated number of avoidable readmissions and potential cost savings associated with these conditions. Finally, future investigations that explore discharge counseling and follow-up tailored to the infection-specific temporal risk and patient complexity may identify opportunities for further readmission reductions.

Our study provides a broad look at readmissions following infection hospitalizations and highlights substantial variation in readmissions based on infection type. To improve hospital resource use for infections, future preventive measures could prioritize children with complex chronic conditions and those with specific diagnoses (eg, upper and lower respiratory tract infections).

This information or content and conclusions are those of the authors and should not be construed as the official position or policy of, nor should any endorsements be inferred by, NIH or the US government.

Hospitalizations for infections are common in children, with respiratory illnesses, including pneumonia and bronchiolitis, among the most prevalent indications for hospitalization.^1,2 Infections are also among the most frequent indications for all-cause readmissions and for potentially preventable readmissions in children.^3-5 Beyond hospital resource use, infection hospitalizations and readmissions represent a considerable cause of life disruption for patients and their families.^6,7 While emerging evidence supports shortened durations of parenteral antibiotics before transitioning to oral therapy for some infections (eg, pyelonephritis, osteomyelitis),^8-10 other infections may require extended treatment courses for weeks. The risk of adverse outcomes (eg, complications of medical treatment, readmission risk) and burdens placed on patients and their families may therefore differ across infection types and extend well beyond the immediate hospitalization.

Although infections are common and pediatric providers are expected to have proficiency in managing infections, substantial variation in the management of common pediatric infections exists and is associated with adverse hospitalization outcomes, including increased readmission risk and healthcare costs.^11-18 Potentially avoidable resource use associated with hospital readmission from infection has led to adoption of hospital-level readmission metrics as indicators of the quality of healthcare delivery. For example, the Pediatric Quality Measures Program, established by the Children’s Health Insurance Program Reauthorization Act of 2009, has prioritized measurement of readmissions following hospitalization for lower respiratory tract infection.¹⁹ With government agencies increasingly using readmission metrics to assess quality of healthcare delivery, developing metrics that focus on these resource-intensive conditions is essential.

Because infections are a common and costly indication for hospital resource use and because substantial variation in management has been observed, promoting a broader understanding of infection-specific readmission rates is important for prioritizing readmission-reduction opportunities in children. This study’s objectives were the following: (1) to describe the prevalence and characteristics of infection hospitalizations in children and their associated readmissions and (2) to estimate the number of readmissions avoided and costs saved if all hospitals achieved the 10th percentile of the hospitals’ risk-adjusted readmission rate (ie, readmission benchmark).

Study Design and Data Source

We performed a retrospective cohort analysis using the 2014 Agency for Healthcare Research and Quality (AHRQ) Nationwide Readmissions Database (NRD).²⁰ The 2014 NRD is an administrative database that contains information on inpatient stays from January 1, 2014, to December 31, 2014, for all payers and allows for weighted national estimates of readmissions for all US individuals. Data within NRD are aggregated from 22 geographically diverse states representing approximately one-half of the US population. NRD contains deidentified patient-level data with unique verified patient identifiers to track individuals within and across hospitals in a state. However, AHRQ guidelines specify that NRD cannot be used for reporting hospital-specific readmission rates. Thus, for the current study, the Inpatient Essentials (Children’s Hospital Association), or IE, database was used to measure hospital-level readmission rates and to distinguish benchmark readmission rates for individual infection diagnoses.²¹ The IE database is composed of 90 children’s hospitals distributed throughout all regions of the United States. The inclusion of free-standing children’s hospitals and children’s hospitals within adult hospitals allows for comparisons and benchmarking across hospitals on multiple metrics, including readmissions.

Study Population

Children 0 to 17 years of age with a primary diagnosis at the index admission for infection between January 1, 2014, and November 30, 2014, were included. The end date of November 30, 2014, allowed for a full 30-day readmission window for all index admissions. We excluded index admissions that resulted in transfer to another acute care hospital or in-hospital mortality. Additionally, we excluded index admissions of children who had hematologic or immunologic conditions, malignancy, or history of bone marrow and solid-organ transplant, using the classification system for complex chronic conditions (CCCs) from Feudtner et al.²² Due to the high likelihood of immunosuppression in patients with these conditions, children may have nuanced experiences with illness severity, trajectory, and treatment associated with infection that place them at increased risk for nonpreventable readmission.

Main Exposure

The main exposure was infection type during the index admission. Condition-specific index admissions were identified using AHRQ’s Clinical Classifications Software (CCS) categories.²³ CCS is a classification schema that categorizes the greater than 14,000 International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis codes and 3,900 ICD-9-CM procedure codes into clinically meaningful categories of 295 diagnosis (including mental health codes and E-codes) and 231 procedural groupings. Twenty-two groupings indicative of infection were distinguished and used for the current study. Examples of infections included aspiration pneumonia, pneumonia, bronchiolitis, and sexually transmitted infection. We combined related CCS categories when possible for ease of interpretation and presentation of data (Appendix Table 1).

Main Outcome Measure

The main outcome measure was 30-day hospital readmission. Readmission was defined as all-cause, unplanned admission within 30 days following discharge from a preceding hospitalization. Planned hospital readmissions were identified and excluded using methods from AHRQ’s Pediatric All-Condition Readmission Measure.²⁴ We defined a same-cause return as a return with the same CCS infection category as the index admission. Costs associated with readmissions were estimated from charges using hospital-specific cost-to-charge ratios provided with NRD.

Patient Demographic and Clinical Characteristics

Patient demographic characteristics included age at index admission (<1 year, 1-5 years, 6-9 years, 10-14 years, and 15-18 years), sex, payer (ie, government, private, other), and discharge disposition (ie, routine, home health, other). We assessed all patients for medical complexity, as defined by the presence of at least one CCC, and we reported the categories of CCCs by organ system involved.²² Otherwise, patients were identified as without medical complexity.

Statistical Analysis

We summarized continuous variables with medians and interquartile ranges and categorical variables with frequencies and percentages. To develop benchmark readmission rates for each infection type, we used generalized linear mixed models with random intercepts for each hospital in the IE database. For each infection type, the benchmark readmission rate was defined as the 10th percentile of hospitals’ risk-adjusted readmission rates. The 10th percentile was chosen to identify the best performing 10% of hospitals (ie, hospitals with the lowest readmission rates). Because children with medical complexity account for a large proportion of hospital resource use and are at high risk for readmission,^4,25 we developed benchmarks stratified by presence/absence of a CCC (ie, with complexity vs without complexity). Models were adjusted for severity of illness using the Hospitalization Resource Intensity Score for Kids (H-RISK),²⁶ a scoring system that assigns relative weights for each All Patient Refined Diagnosis-Related Group (3M Corporation) and severity of illness level, and each hospital’s risk-adjusted readmission rate was determined.

With use of weights to achieve national estimates of index admissions and readmissions, we determined the number of potentially avoidable readmissions by calculating the number of readmissions observed in the NRD that would not occur if all hospitals achieved readmission rates equal to the 10th percentile. Avoidable costs were estimated by multiplying the number of potentially avoidable readmissions by the mean cost of a readmission for infections of that type. Estimates of avoidable readmissions and costs were stratified by medical complexity. In addition to describing estimates at the 10th percentile benchmark, we similarly developed estimates of potentially avoidable readmissions and avoidable costs for the 5th and 25th percentiles, which are presented within Appendix Table 2 (children without complexity) and Appendix Table 3 (children with complexity).

All statistical analyses were performed using SAS version 9.4 (SAS Institute), and P values <.001 were considered statistically significant due to the large sample size. The Office of Research Integrity at Children’s Mercy Hospital deemed this study exempt from institutional board review.

Characteristics of the Study Population

The study included 380,067 index admissions for infection and an accompanying 18,469 unplanned all-cause readmissions over the 30 days following discharge (readmission rate, 4.9%; Table 1). Children ages 1 to 5 years accounted for the largest percentage (32.9%) of index hospitalizations, followed by infants younger than 1 year (30.3%). The readmission rate by age group was highest for infants younger than 1 year, compared with rates among all other age groups (5.6% among infants < 1 year vs 4.4%-4.7% for other age groups; P < .001). In the overall cohort, 16.2% of admissions included patients with a CCC. Children with medical complexity had higher readmission rates than those without medical complexity (no CCC, 3.2%; 1 CCC, 9.2%; 2+ CCCs, 18.9%). A greater percentage of children experiencing a readmission had government insurance (63.0% vs 59.2%; P < .001) and received home health nursing (10.1% vs 2.7%; P < .001) following the index encounter.

Children Without Complexity

Index Admissions and 30-day Readmissions

Among patients without medical complexity, index admissions occurred most frequently for pneumonia (n = 54,717), bronchiolitis (n = 53,959), and appendicitis (n = 45,036) (Figure 1). The median length of stay (LOS) for index admissions ranged from 1 to 5 days (Table 2), with septic arthritis and osteomyelitis having the longest median LOS at 5 (IQR, 3-7) days.

Thirty-day readmission rates varied substantially by infection at the index admission (range, 1.5% for sexually transmitted infection to 8.6% for peritonitis) (Figure 1). The median LOS for 30-day readmissions varied from 2 to 7 days (Table 2), while the median number of days to readmission varied substantially by infection type (range, 4 days for bacterial infection [site unspecified] to 24 days for sexually transmitted infections). Among the top five indications for admission for children without complexity, 14.9% to 52.8% of readmissions were for the same cause as the index admission; however, many additional returns were likely related to the index admission (Appendix Table 4). For example, among other return reasons, an additional 992 (61.7%) readmissions following appendicitis hospitalizations were for complications of surgical procedures or medical care, peritonitis, intestinal obstruction, and abdominal pain.

Impact of Achieving Readmission Benchmarks

Among children without complexity, readmission benchmarks (ie, the 10th percentile of readmission rates across hospitals) ranged from 0% to 26.7% (Figure 2). An estimated 54.7% of readmissions (n = 5,507) could potentially be reduced if hospitals achieved infection-specific benchmark readmission rates, which could result in an estimated $44.5 million in savings. Pneumonia, bronchiolitis, gastroenteritis, and upper respiratory tract infections were among conditions with the greatest potential reductions in readmissions and costs if a 10th percentile benchmark was achieved.

Children With Medical Complexity

Index Admissions and 30-day Readmissions

Among patients with complexity, index admissions occurred most frequently for pneumonia (n = 14,344), bronchiolitis (n = 8,618), and upper respiratory tract infection (n = 6,407) (Figure 1). The median LOS for index admissions ranged from 1 to 9 days (Table 2), with septicemia and CNS infections having the longest median LOS at 9 days.

Thirty-day readmission rates varied substantially by the type of infection at the index admission (range, 0% for sexually transmitted infection to 21.6% for aspiration pneumonia) (Figure 1). The median LOS for 30-day readmissions varied from 2 to 14 days (Table 2), and the median number of days to readmission varied substantially by infection type (range, 6 days for tonsillitis to 23 days for other infection). Among the top five indications for admission for medically complex children, 8% to 40.4% of readmissions were for the same cause as the index admission (Appendix Table 4). As with the children without complexity, additional returns were likely related to the index admission.

Impact of Achieving Readmission Benchmarks

Among children with medical complexity, readmission benchmarks ranged from 0% to 30.3% (Figure 2). An estimated 42.6% of readmissions (n = 3,576) could potentially be reduced if hospitals achieved infection-specific benchmark readmission rates, which could result in an estimated $70.8 million in savings. Pneumonia, bronchiolitis, septicemia, and upper respiratory tract infections were among conditions with the greatest potential reductions in readmissions and costs if the benchmarks were achieved.

The current study uncovered new findings regarding unplanned readmissions following index infection hospitalizations for children. In particular, readmission rates and time to readmission varied substantially by infection subtype. The study also identified priority infections and unique considerations for children with CCCs, all of which may help maximize the value of readmission metrics aimed at advancing hospital quality and reducing costs for infection hospitalizations in children. If all hospitals achieved the readmission rates of the best performing hospitals, 42.6% to 54.7% fewer readmissions could be realized with associated cost savings.

Nationally, studies have reported 30-day, all-cause unplanned readmission rates of 6.2% to 10.3%.^5,27 In our current study we observed an overall readmission rate of 4.9% across all infectious conditions; however, readmission rates varied substantially by condition, with upper and lower respiratory tract infections, septicemia, and gastroenteritis among infections with the greatest number of potentially avoidable readmissions based on achievement of readmission benchmarks. Currently, pediatric-specific all-cause and lower respiratory tract infection readmission metrics have been developed with the aim of improving quality of care and reducing healthcare expenditures.²⁸ Future readmission studies and metrics may prioritize conditions such as septicemia, gastroenteritis, and other respiratory tract infections. Our current study demonstrated that many readmissions following infection hospitalizations were associated with the same CCS category or within a related CCS category (eg, complications of surgical procedures or medical care, appendicitis, peritonitis, intestinal obstruction, and abdominal pain constituted the top five indications for readmission for appendicitis, whereas respiratory illnesses constituted the top five indications for readmissions for pneumonia). While this current study cannot clarify this relationship further, and the “avoidability” of unplanned readmissions is debated,^29-31 our findings suggest that future investigations might focus on identifying whether condition-specific interventions during the index admission could mitigate some readmissions.

While the LOS of the index admission and the readmission were similar for most infection subtypes, we observed substantial variability in the temporal risk for readmission by infection subtype. Our observations complement studies exploring the timing of readmissions for other pediatric conditions.^32-34 In particular, our findings further highlight that the composition and interaction of factors influencing infection readmissions may vary by condition. Infections represent a diverse group of conditions, with treatment courses that vary in need for parenteral antibiotics, ability to tailor treatment based on organism and susceptibilities, and length of treatment. While treatment for some infections may be accomplished, or nearly accomplished, prior to discharge, other infections (eg, osteomyelitis) may require prolonged treatment, shifting the burden of management and monitoring to patients and their families, which along with the timeliness and adequacy of outpatient follow-up, may modify an individual’s readmission risk. Consequently, a “one-size fits all” approach to discharge counseling may not be successful across all conditions. Our study lays the groundwork for how these temporal relationships may be used by clinicians to counsel families regarding the likely readmission timeframe, based on infection, and to establish follow-up appointments ahead of the infection-specific “readmission window,” which may allow outpatient providers to intervene when readmission risk is greatest.

Consistent with prior literature, children with medical complexity in our study had increased frequency of 30-day, all-cause unplanned readmissions and LOS, compared with peers who did not have complexity.⁵ Readmission rates following hospitalizations for aspiration pneumonia were comparable to those reported by Thompson et al in their study examining rates of pneumonia in children with neurologic impairment.³⁵ In our current study, nearly 96% of readmissions following aspiration pneumonia hospitalizations were for children with medical complexity, and more than 58% of these readmissions were for aspiration pneumonia or respiratory illness. Future investigations should seek to explore factors contributing to readmissions in children with medical complexity and to evaluate whether interventions such as postdischarge coaching or discharge bundles could assist with reductions in healthcare resource use.^36,37

Despite a lack of clear association between readmissions and quality of care for children,³⁸ readmissions rates continue to be used as a quality measure for hospitalized patients. Within our present study, we found that achieving benchmark readmission rates for infection hospitalizations could lead to substantial reductions in readmissions; however, these reductions were seen across this relatively similar group of infection diagnoses, and hospitals may face greater challenges when attempting to achieve a 10th percentile readmission benchmark across a more expansive set of diagnoses. Despite these challenges, understanding the intricacies of readmissions may lead to improved readmission metrics and the systematic identification of avoidable readmissions, the goal of which is to enhance the quality of healthcare for hospitalized children.

Our findings should be interpreted in the context of several limitations. We defined our readmission benchmark at the 10th percentile using the IE database. Because an estimated 70% of hospitalizations for children occur within general hospitals,³⁹ we believe that our use of the IE database allowed for increased generalizability, though the broadening of our findings to nonacademic hospital settings may be less reliable. While we describe the 10th percentile readmission benchmark here, alternative benchmarks would result in different estimates of avoidable readmissions and associated readmission costs (Appendix Tables 2 and 3). The IE and NRD databases do not distinguish intensive care use. We tried to address this limitation through model adjustments using H-RISK, which is particularly helpful for adjusting for NICU admissions through use of the 27 All Patient Refined Diagnosis-Related Groups for neonatal conditions. Additionally, the NRD uses state-level data to derive national estimates and is not equipped to measure readmissions to hospitals in a different state, distinguish patient deaths occurring after discharge, or to examine the specific indication for readmission (ie, unable to assess if the readmission is related to a complication of the treatment plan, progression of the illness course, or for an unrelated reason). While sociodemographic and socioeconomic factors may affect readmissions, the NRD does not contain information on patients’ race/ethnicity, family/social attributes, or postdischarge follow-up health services, which are known to influence the need for readmission.

Despite these limitations, this study highlights future areas for research and potential opportunities for reducing readmission following infection hospitalizations. First, identifying hospital- and systems-based factors that contribute to readmission reductions at the best-performing hospitals may identify opportunities for hospitals with the highest readmission rates to achieve the rates of the best-performing hospitals. Second, conditions such as upper and lower respiratory tract infections, along with septicemia and gastroenteritis, may serve as prime targets for future investigation based on the estimated number of avoidable readmissions and potential cost savings associated with these conditions. Finally, future investigations that explore discharge counseling and follow-up tailored to the infection-specific temporal risk and patient complexity may identify opportunities for further readmission reductions.

Our study provides a broad look at readmissions following infection hospitalizations and highlights substantial variation in readmissions based on infection type. To improve hospital resource use for infections, future preventive measures could prioritize children with complex chronic conditions and those with specific diagnoses (eg, upper and lower respiratory tract infections).

This information or content and conclusions are those of the authors and should not be construed as the official position or policy of, nor should any endorsements be inferred by, NIH or the US government.

Hospitalizations for infections are common in children, with respiratory illnesses, including pneumonia and bronchiolitis, among the most prevalent indications for hospitalization.^1,2 Infections are also among the most frequent indications for all-cause readmissions and for potentially preventable readmissions in children.^3-5 Beyond hospital resource use, infection hospitalizations and readmissions represent a considerable cause of life disruption for patients and their families.^6,7 While emerging evidence supports shortened durations of parenteral antibiotics before transitioning to oral therapy for some infections (eg, pyelonephritis, osteomyelitis),^8-10 other infections may require extended treatment courses for weeks. The risk of adverse outcomes (eg, complications of medical treatment, readmission risk) and burdens placed on patients and their families may therefore differ across infection types and extend well beyond the immediate hospitalization.

Although infections are common and pediatric providers are expected to have proficiency in managing infections, substantial variation in the management of common pediatric infections exists and is associated with adverse hospitalization outcomes, including increased readmission risk and healthcare costs.^11-18 Potentially avoidable resource use associated with hospital readmission from infection has led to adoption of hospital-level readmission metrics as indicators of the quality of healthcare delivery. For example, the Pediatric Quality Measures Program, established by the Children’s Health Insurance Program Reauthorization Act of 2009, has prioritized measurement of readmissions following hospitalization for lower respiratory tract infection.¹⁹ With government agencies increasingly using readmission metrics to assess quality of healthcare delivery, developing metrics that focus on these resource-intensive conditions is essential.

Because infections are a common and costly indication for hospital resource use and because substantial variation in management has been observed, promoting a broader understanding of infection-specific readmission rates is important for prioritizing readmission-reduction opportunities in children. This study’s objectives were the following: (1) to describe the prevalence and characteristics of infection hospitalizations in children and their associated readmissions and (2) to estimate the number of readmissions avoided and costs saved if all hospitals achieved the 10th percentile of the hospitals’ risk-adjusted readmission rate (ie, readmission benchmark).

Study Design and Data Source

We performed a retrospective cohort analysis using the 2014 Agency for Healthcare Research and Quality (AHRQ) Nationwide Readmissions Database (NRD).²⁰ The 2014 NRD is an administrative database that contains information on inpatient stays from January 1, 2014, to December 31, 2014, for all payers and allows for weighted national estimates of readmissions for all US individuals. Data within NRD are aggregated from 22 geographically diverse states representing approximately one-half of the US population. NRD contains deidentified patient-level data with unique verified patient identifiers to track individuals within and across hospitals in a state. However, AHRQ guidelines specify that NRD cannot be used for reporting hospital-specific readmission rates. Thus, for the current study, the Inpatient Essentials (Children’s Hospital Association), or IE, database was used to measure hospital-level readmission rates and to distinguish benchmark readmission rates for individual infection diagnoses.²¹ The IE database is composed of 90 children’s hospitals distributed throughout all regions of the United States. The inclusion of free-standing children’s hospitals and children’s hospitals within adult hospitals allows for comparisons and benchmarking across hospitals on multiple metrics, including readmissions.

Study Population

Children 0 to 17 years of age with a primary diagnosis at the index admission for infection between January 1, 2014, and November 30, 2014, were included. The end date of November 30, 2014, allowed for a full 30-day readmission window for all index admissions. We excluded index admissions that resulted in transfer to another acute care hospital or in-hospital mortality. Additionally, we excluded index admissions of children who had hematologic or immunologic conditions, malignancy, or history of bone marrow and solid-organ transplant, using the classification system for complex chronic conditions (CCCs) from Feudtner et al.²² Due to the high likelihood of immunosuppression in patients with these conditions, children may have nuanced experiences with illness severity, trajectory, and treatment associated with infection that place them at increased risk for nonpreventable readmission.

Main Exposure

The main exposure was infection type during the index admission. Condition-specific index admissions were identified using AHRQ’s Clinical Classifications Software (CCS) categories.²³ CCS is a classification schema that categorizes the greater than 14,000 International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis codes and 3,900 ICD-9-CM procedure codes into clinically meaningful categories of 295 diagnosis (including mental health codes and E-codes) and 231 procedural groupings. Twenty-two groupings indicative of infection were distinguished and used for the current study. Examples of infections included aspiration pneumonia, pneumonia, bronchiolitis, and sexually transmitted infection. We combined related CCS categories when possible for ease of interpretation and presentation of data (Appendix Table 1).

Main Outcome Measure

The main outcome measure was 30-day hospital readmission. Readmission was defined as all-cause, unplanned admission within 30 days following discharge from a preceding hospitalization. Planned hospital readmissions were identified and excluded using methods from AHRQ’s Pediatric All-Condition Readmission Measure.²⁴ We defined a same-cause return as a return with the same CCS infection category as the index admission. Costs associated with readmissions were estimated from charges using hospital-specific cost-to-charge ratios provided with NRD.

Patient Demographic and Clinical Characteristics

Patient demographic characteristics included age at index admission (<1 year, 1-5 years, 6-9 years, 10-14 years, and 15-18 years), sex, payer (ie, government, private, other), and discharge disposition (ie, routine, home health, other). We assessed all patients for medical complexity, as defined by the presence of at least one CCC, and we reported the categories of CCCs by organ system involved.²² Otherwise, patients were identified as without medical complexity.

Statistical Analysis

We summarized continuous variables with medians and interquartile ranges and categorical variables with frequencies and percentages. To develop benchmark readmission rates for each infection type, we used generalized linear mixed models with random intercepts for each hospital in the IE database. For each infection type, the benchmark readmission rate was defined as the 10th percentile of hospitals’ risk-adjusted readmission rates. The 10th percentile was chosen to identify the best performing 10% of hospitals (ie, hospitals with the lowest readmission rates). Because children with medical complexity account for a large proportion of hospital resource use and are at high risk for readmission,^4,25 we developed benchmarks stratified by presence/absence of a CCC (ie, with complexity vs without complexity). Models were adjusted for severity of illness using the Hospitalization Resource Intensity Score for Kids (H-RISK),²⁶ a scoring system that assigns relative weights for each All Patient Refined Diagnosis-Related Group (3M Corporation) and severity of illness level, and each hospital’s risk-adjusted readmission rate was determined.

With use of weights to achieve national estimates of index admissions and readmissions, we determined the number of potentially avoidable readmissions by calculating the number of readmissions observed in the NRD that would not occur if all hospitals achieved readmission rates equal to the 10th percentile. Avoidable costs were estimated by multiplying the number of potentially avoidable readmissions by the mean cost of a readmission for infections of that type. Estimates of avoidable readmissions and costs were stratified by medical complexity. In addition to describing estimates at the 10th percentile benchmark, we similarly developed estimates of potentially avoidable readmissions and avoidable costs for the 5th and 25th percentiles, which are presented within Appendix Table 2 (children without complexity) and Appendix Table 3 (children with complexity).

All statistical analyses were performed using SAS version 9.4 (SAS Institute), and P values <.001 were considered statistically significant due to the large sample size. The Office of Research Integrity at Children’s Mercy Hospital deemed this study exempt from institutional board review.

Characteristics of the Study Population

The study included 380,067 index admissions for infection and an accompanying 18,469 unplanned all-cause readmissions over the 30 days following discharge (readmission rate, 4.9%; Table 1). Children ages 1 to 5 years accounted for the largest percentage (32.9%) of index hospitalizations, followed by infants younger than 1 year (30.3%). The readmission rate by age group was highest for infants younger than 1 year, compared with rates among all other age groups (5.6% among infants < 1 year vs 4.4%-4.7% for other age groups; P < .001). In the overall cohort, 16.2% of admissions included patients with a CCC. Children with medical complexity had higher readmission rates than those without medical complexity (no CCC, 3.2%; 1 CCC, 9.2%; 2+ CCCs, 18.9%). A greater percentage of children experiencing a readmission had government insurance (63.0% vs 59.2%; P < .001) and received home health nursing (10.1% vs 2.7%; P < .001) following the index encounter.

Children Without Complexity

Index Admissions and 30-day Readmissions

Among patients without medical complexity, index admissions occurred most frequently for pneumonia (n = 54,717), bronchiolitis (n = 53,959), and appendicitis (n = 45,036) (Figure 1). The median length of stay (LOS) for index admissions ranged from 1 to 5 days (Table 2), with septic arthritis and osteomyelitis having the longest median LOS at 5 (IQR, 3-7) days.

Thirty-day readmission rates varied substantially by infection at the index admission (range, 1.5% for sexually transmitted infection to 8.6% for peritonitis) (Figure 1). The median LOS for 30-day readmissions varied from 2 to 7 days (Table 2), while the median number of days to readmission varied substantially by infection type (range, 4 days for bacterial infection [site unspecified] to 24 days for sexually transmitted infections). Among the top five indications for admission for children without complexity, 14.9% to 52.8% of readmissions were for the same cause as the index admission; however, many additional returns were likely related to the index admission (Appendix Table 4). For example, among other return reasons, an additional 992 (61.7%) readmissions following appendicitis hospitalizations were for complications of surgical procedures or medical care, peritonitis, intestinal obstruction, and abdominal pain.

Impact of Achieving Readmission Benchmarks

Among children without complexity, readmission benchmarks (ie, the 10th percentile of readmission rates across hospitals) ranged from 0% to 26.7% (Figure 2). An estimated 54.7% of readmissions (n = 5,507) could potentially be reduced if hospitals achieved infection-specific benchmark readmission rates, which could result in an estimated $44.5 million in savings. Pneumonia, bronchiolitis, gastroenteritis, and upper respiratory tract infections were among conditions with the greatest potential reductions in readmissions and costs if a 10th percentile benchmark was achieved.

Children With Medical Complexity

Index Admissions and 30-day Readmissions

Among patients with complexity, index admissions occurred most frequently for pneumonia (n = 14,344), bronchiolitis (n = 8,618), and upper respiratory tract infection (n = 6,407) (Figure 1). The median LOS for index admissions ranged from 1 to 9 days (Table 2), with septicemia and CNS infections having the longest median LOS at 9 days.

Thirty-day readmission rates varied substantially by the type of infection at the index admission (range, 0% for sexually transmitted infection to 21.6% for aspiration pneumonia) (Figure 1). The median LOS for 30-day readmissions varied from 2 to 14 days (Table 2), and the median number of days to readmission varied substantially by infection type (range, 6 days for tonsillitis to 23 days for other infection). Among the top five indications for admission for medically complex children, 8% to 40.4% of readmissions were for the same cause as the index admission (Appendix Table 4). As with the children without complexity, additional returns were likely related to the index admission.

Impact of Achieving Readmission Benchmarks

Among children with medical complexity, readmission benchmarks ranged from 0% to 30.3% (Figure 2). An estimated 42.6% of readmissions (n = 3,576) could potentially be reduced if hospitals achieved infection-specific benchmark readmission rates, which could result in an estimated $70.8 million in savings. Pneumonia, bronchiolitis, septicemia, and upper respiratory tract infections were among conditions with the greatest potential reductions in readmissions and costs if the benchmarks were achieved.

The current study uncovered new findings regarding unplanned readmissions following index infection hospitalizations for children. In particular, readmission rates and time to readmission varied substantially by infection subtype. The study also identified priority infections and unique considerations for children with CCCs, all of which may help maximize the value of readmission metrics aimed at advancing hospital quality and reducing costs for infection hospitalizations in children. If all hospitals achieved the readmission rates of the best performing hospitals, 42.6% to 54.7% fewer readmissions could be realized with associated cost savings.

Nationally, studies have reported 30-day, all-cause unplanned readmission rates of 6.2% to 10.3%.^5,27 In our current study we observed an overall readmission rate of 4.9% across all infectious conditions; however, readmission rates varied substantially by condition, with upper and lower respiratory tract infections, septicemia, and gastroenteritis among infections with the greatest number of potentially avoidable readmissions based on achievement of readmission benchmarks. Currently, pediatric-specific all-cause and lower respiratory tract infection readmission metrics have been developed with the aim of improving quality of care and reducing healthcare expenditures.²⁸ Future readmission studies and metrics may prioritize conditions such as septicemia, gastroenteritis, and other respiratory tract infections. Our current study demonstrated that many readmissions following infection hospitalizations were associated with the same CCS category or within a related CCS category (eg, complications of surgical procedures or medical care, appendicitis, peritonitis, intestinal obstruction, and abdominal pain constituted the top five indications for readmission for appendicitis, whereas respiratory illnesses constituted the top five indications for readmissions for pneumonia). While this current study cannot clarify this relationship further, and the “avoidability” of unplanned readmissions is debated,^29-31 our findings suggest that future investigations might focus on identifying whether condition-specific interventions during the index admission could mitigate some readmissions.

While the LOS of the index admission and the readmission were similar for most infection subtypes, we observed substantial variability in the temporal risk for readmission by infection subtype. Our observations complement studies exploring the timing of readmissions for other pediatric conditions.^32-34 In particular, our findings further highlight that the composition and interaction of factors influencing infection readmissions may vary by condition. Infections represent a diverse group of conditions, with treatment courses that vary in need for parenteral antibiotics, ability to tailor treatment based on organism and susceptibilities, and length of treatment. While treatment for some infections may be accomplished, or nearly accomplished, prior to discharge, other infections (eg, osteomyelitis) may require prolonged treatment, shifting the burden of management and monitoring to patients and their families, which along with the timeliness and adequacy of outpatient follow-up, may modify an individual’s readmission risk. Consequently, a “one-size fits all” approach to discharge counseling may not be successful across all conditions. Our study lays the groundwork for how these temporal relationships may be used by clinicians to counsel families regarding the likely readmission timeframe, based on infection, and to establish follow-up appointments ahead of the infection-specific “readmission window,” which may allow outpatient providers to intervene when readmission risk is greatest.

Consistent with prior literature, children with medical complexity in our study had increased frequency of 30-day, all-cause unplanned readmissions and LOS, compared with peers who did not have complexity.⁵ Readmission rates following hospitalizations for aspiration pneumonia were comparable to those reported by Thompson et al in their study examining rates of pneumonia in children with neurologic impairment.³⁵ In our current study, nearly 96% of readmissions following aspiration pneumonia hospitalizations were for children with medical complexity, and more than 58% of these readmissions were for aspiration pneumonia or respiratory illness. Future investigations should seek to explore factors contributing to readmissions in children with medical complexity and to evaluate whether interventions such as postdischarge coaching or discharge bundles could assist with reductions in healthcare resource use.^36,37

Despite a lack of clear association between readmissions and quality of care for children,³⁸ readmissions rates continue to be used as a quality measure for hospitalized patients. Within our present study, we found that achieving benchmark readmission rates for infection hospitalizations could lead to substantial reductions in readmissions; however, these reductions were seen across this relatively similar group of infection diagnoses, and hospitals may face greater challenges when attempting to achieve a 10th percentile readmission benchmark across a more expansive set of diagnoses. Despite these challenges, understanding the intricacies of readmissions may lead to improved readmission metrics and the systematic identification of avoidable readmissions, the goal of which is to enhance the quality of healthcare for hospitalized children.

Our findings should be interpreted in the context of several limitations. We defined our readmission benchmark at the 10th percentile using the IE database. Because an estimated 70% of hospitalizations for children occur within general hospitals,³⁹ we believe that our use of the IE database allowed for increased generalizability, though the broadening of our findings to nonacademic hospital settings may be less reliable. While we describe the 10th percentile readmission benchmark here, alternative benchmarks would result in different estimates of avoidable readmissions and associated readmission costs (Appendix Tables 2 and 3). The IE and NRD databases do not distinguish intensive care use. We tried to address this limitation through model adjustments using H-RISK, which is particularly helpful for adjusting for NICU admissions through use of the 27 All Patient Refined Diagnosis-Related Groups for neonatal conditions. Additionally, the NRD uses state-level data to derive national estimates and is not equipped to measure readmissions to hospitals in a different state, distinguish patient deaths occurring after discharge, or to examine the specific indication for readmission (ie, unable to assess if the readmission is related to a complication of the treatment plan, progression of the illness course, or for an unrelated reason). While sociodemographic and socioeconomic factors may affect readmissions, the NRD does not contain information on patients’ race/ethnicity, family/social attributes, or postdischarge follow-up health services, which are known to influence the need for readmission.

Despite these limitations, this study highlights future areas for research and potential opportunities for reducing readmission following infection hospitalizations. First, identifying hospital- and systems-based factors that contribute to readmission reductions at the best-performing hospitals may identify opportunities for hospitals with the highest readmission rates to achieve the rates of the best-performing hospitals. Second, conditions such as upper and lower respiratory tract infections, along with septicemia and gastroenteritis, may serve as prime targets for future investigation based on the estimated number of avoidable readmissions and potential cost savings associated with these conditions. Finally, future investigations that explore discharge counseling and follow-up tailored to the infection-specific temporal risk and patient complexity may identify opportunities for further readmission reductions.

Our study provides a broad look at readmissions following infection hospitalizations and highlights substantial variation in readmissions based on infection type. To improve hospital resource use for infections, future preventive measures could prioritize children with complex chronic conditions and those with specific diagnoses (eg, upper and lower respiratory tract infections).

This information or content and conclusions are those of the authors and should not be construed as the official position or policy of, nor should any endorsements be inferred by, NIH or the US government.