Overutilization of resources is a significant, yet underappreciated, problem in medicine. Many interventions target underutilization (eg, immunizations) or misuse (eg, antibiotic prescribing for viral pharyngitis), yet overutilization remains as a significant contributor to healthcare waste.[1] In an effort to reduce waste, the Choosing Wisely campaign created a work group to highlight areas of overutilization, specifically noting both diagnostic tests and therapies for common pediatric conditions with no proven benefit and possible harm to the patient.[2] Respiratory illnesses have been a target of many quality‐improvement efforts, and pneumonia represents a common diagnosis in pediatrics.[3] The use of diagnostic testing for pneumonia is an area where care can be optimized and aligned with evidence.

Laboratory testing and diagnostic imaging are routinely used for the management of children with community‐acquired pneumonia (CAP). Several studies have documented substantial variability in the use of these resources for pneumonia management, with higher resource use associated with a higher chance of hospitalization after emergency department (ED) evaluation and a longer length of stay among those requiring hospitalization.[4, 5] This variation in diagnostic resource utilization has been attributed, at least in part, to a lack of consensus on the management of pneumonia. There is wide variability in diagnostic testing, and due to potential consequences for patients presenting with pneumonia, efforts to standardize care offer an opportunity to improve healthcare value.

In August 2011, the first national, evidence‐based consensus guidelines for the management of childhood CAP were published jointly by the Pediatric Infectious Diseases Society (PIDS) and the Infectious Diseases Society of America (IDSA).[6] A primary focus of these guidelines was the recommendation for the use of narrow spectrum antibiotics for the management of uncomplicated pneumonia. Previous studies have assessed the impact of the publication of the PIDS/IDSA guidelines on empiric antibiotic selection for the management of pneumonia.[7, 8] In addition, the guidelines provided recommendations regarding diagnostic test utilization, in particular discouraging blood tests (eg, complete blood counts) and radiologic studies for nontoxic, fully immunized children treated as outpatients, as well as repeat testing for children hospitalized with CAP who are improving.

Although single centers have demonstrated changes in utilization patterns based on clinical practice guidelines,[9, 10, 11, 12] whether these guidelines have impacted diagnostic test utilization among US children with CAP in a larger scale remains unknown. Therefore, we sought to determine the impact of the PIDS/IDSA guidelines on the use of diagnostic testing among children with CAP using a national sample of US children's hospitals. Because the guidelines discourage repeat diagnostic testing in patients who are improving, we also evaluated the association between repeat diagnostic studies and severity of illness.

METHODS

This retrospective cohort study used data from the Pediatric Health Information System (PHIS) (Children's Hospital Association, Overland Park, KS). The PHIS database contains deidentified administrative data, detailing demographic, diagnostic, procedure, and billing data from 47 freestanding, tertiary care children's hospitals. This database accounts for approximately 20% of all annual pediatric hospitalizations in the United States. Data quality is ensured through a joint effort between the Children's Hospital Association and participating hospitals.

RESULTS

There were 275,288 hospital admissions meeting study inclusion criteria of 1 to 18 years of age with a diagnosis of pneumonia from 2008 to 2014. Of these, 54,749 met exclusion criteria (1874 had pleural drainage procedure on day 0 or 1, 51,306 had complex chronic conditions, 1569 were hospitalized with pneumonia in the last 30 days). Characteristics of the remaining 220,539 patients in the final sample are shown in Table 1. The median age was 4 years (IQR, 27 years); a majority of the children were male (53%) and had public insurance (58%). There were 128,855 patients in the preguideline period (January 1, 2008 to July 31, 2011) and 91,684 in the post guideline period (August 1, 2011June 30, 2014).

Discharged From the ED

Throughout the study, utilization of CBC, blood cultures, and CRP was <20%, whereas CXR use was >75%. In segmented regression analysis, CRP utilization was relatively stable before the guidelines publication. However, by the end of the study period, the projected estimate of CRP utilization without guidelines (expected) was 2.9% compared with 4.8% with the guidelines (observed) (P < 0.05) (Figure 1). A similar pattern of higher rates of diagnostic utilization after the guidelines compared with projected estimates without the guidelines was also seen in the ED utilization of CBC, blood cultures, and CXR (Figure 1); however, these trends did not achieve statistical significance. Table 2 provides specific values. Using a standard cost of $19.52 for CRP testing, annual costs across all hospitals increased $11,783 for ED evaluation of CAP.

Table 2.Utilization Rates Over Study Period

	Baseline (%)	Preguideline Trend	Level Change at Guideline	Change in Trend After Guideline	Estimates at End of Study*
					Without Guideline (%)	With Guideline (%)	P
NOTE: Abbreviations: CBC, complete blood count; CRP, C‐reactive protein; ED, emergency department; NS, not significant (P > 0.05). *Estimates are based on pre and post guideline trends.
ED‐only encounters
Blood culture	14.6	0.1	0.8	0.1	5.5	8.6	NS
CBC	19.2	0.1	0.4	0.1	10.7	14.0	NS
CRP	5.4	0.0	0.6	0.1	2.9	4.8	<0.05
Chest x‐ray	85.4	0.1	0.1	0.0	80.9	81.1	NS
Inpatient encounters
Blood culture	50.6	0.0	1.7	0.2	49.2	41.4	<0.05
Repeat blood culture	6.5	0.0	1.0	0.1	8.9	5.8	NS
CBC	65.2	0.0	3.1	0.0	65.0	62.2	NS
Repeat CBC	23.4	0.0	4.2	0.0	20.8	16.0	NS
CRP	25.7	0.0	1.1	0.0	23.8	23.5	NS
Repeat CRP	12.5	0.1	2.2	0.1	7.1	7.3	NS
Chest x‐ray	89.4	0.1	0.7	0.0	85.4	83.9	NS
Repeat chest x‐ray	25.5	0.0	2.0	0.1	24.1	17.7	<0.05

Figure 1

Inpatient Encounters

In the segmented regression analysis of children hospitalized with CAP, guideline publication was associated with changes in the monthly use of some diagnostic tests. For example, by the end of the study period, the use of blood culture was 41.4% (observed), whereas the projected estimated use in the absence of the guidelines was 49.2% (expected) (P < 0.05) (Figure 2). Table 2 includes the data for the other tests, CBC, CRP, and CXR, in which similar patterns are noted with lower utilization rates after the guidelines, compared with expected utilization rates without the guidelines; however, these trends did not achieve statistical significance. Evaluating the utilization of repeat testing for inpatients, only repeat CXR achieved statistical significance (P < 0.05), with utilization rates of 17.7% with the guidelines (actual) compared with 24.1% without the guidelines (predicted).

Figure 2

To better understand the use of repeat testing, a comparison of severity outcomesLOS, ICU transfer, and pleural drainage procedureswas performed between patients with no repeat testing (70%) and patients with 1 or more repeat tests (30%). Patients with repeat testing had longer LOS (no repeat testing LOS 1 [IQR, 12]) versus 1 repeat test LOS 3 ([IQR, 24] vs 2+ repeat tests LOS 5 [IQR, 38]), higher rate of ICU transfer (no repeat testing 4.6% vs 1 repeat test 14.6% vs 2+ repeat test 35.6%), and higher rate of pleural drainage (no repeat testing 0% vs 1 repeat test 0.1% vs 2+ repeat test 5.9%] (all P < 0.001).

Using standard costs of $37.57 for blood cultures and $73.28 for CXR, annual costs for children with CAP across all hospitals decreased by $91,512 due to decreased utilization of blood cultures, and by $146,840 due to decreased utilization of CXR.

Hospital‐Level Variation in the Impact of the National Guideline

Figure 3 is a visual representation (heat map) of the impact of the guidelines at the hospital level at the end of the study from the individual interrupted time series. Based on this heat map (Figure 3), there was wide variability between hospitals in the impact of the guideline on each test in different settings (ED or inpatient). By diagnostic testing, 7 hospitals significantly decreased utilization of blood cultures for inpatients, and 5 hospitals significantly decreased utilization for repeat blood cultures and repeat CXR. Correlation between the number of positive improvements at a hospital and region (P = 0.974), number of CAP cases (P = 0.731), or percentage of public insurance (P = 0.241) were all nonsignificant.

Figure 3

DISCUSSION

This study complements previous assessments by evaluating the impact of the 2011 IDSA/PIDS consensus guidelines on the management of children with CAP cared for at US children's hospitals. Prior studies have shown increased use of narrow‐spectrum antibiotics for children with CAP after the publication of these guidelines.[7] The current study focused on diagnostic testing for CAP before and after the publication of the 2011 guidelines. In the ED setting, use of some diagnostic tests (blood culture, CBC, CXR, CRP) was declining prior to guideline publication, but appeared to plateau and/or increase after 2011. Among children admitted with CAP, use of diagnostic testing was relatively stable prior to 2011, and use of these tests (blood culture, CBC, CXR, CRP) declined after guideline publication. Overall, changes in diagnostic resource utilization 3 years after publication were modest, with few changes achieving statistical significance. There was a large variability in the impact of guidelines on test use between hospitals.

For outpatients, including those managed in the ED, the PIDS/IDSA guidelines recommend limited laboratory testing in nontoxic, fully immunized patients. The guidelines discourage the use of diagnostic testing among outpatients because of their low yield (eg, blood culture), and because test results may not impact management (eg, CBC).[6] In the years prior to guideline publication, there was already a declining trend in testing rates, including blood cultures, CBC, and CRP, for patients in the ED. After guideline publication, the rate of blood cultures, CBC, and CRP increased, but only the increase in CRP utilization achieved statistical significance. We would not expect utilization for common diagnostic tests (eg, CBC for outpatients with CAP) to be at or close to 0% because of the complexity of clinical decision making regarding admission that factors in aspects of patient history, exam findings, and underlying risk.[15] ED utilization of blood cultures was <10%, CBC <15%, and CRP <5% after guideline publication, which may represent the lowest testing limit that could be achieved.

CXRs obtained in the ED did not decrease over the entire study period. The rates of CXR use (close to 80%) seen in our study are similar to prior ED studies.[5, 16] Management of children with CAP in the ED might be different than outpatient primary care management because (1) unlike primary care providers, ED providers do not have an established relationship with their patients and do not have the opportunity for follow‐up and serial exams, making them less likely to tolerate diagnostic uncertainty; and (2) ED providers may see sicker patients. However, use of CXR in the ED does represent an opportunity for further study to understand if decreased utilization is feasible without adversely impacting clinical outcomes.

The CAP guidelines provide a strong recommendation to obtain blood culture in moderate to severe pneumonia. Despite this, blood culture utilization declined after guideline publication. Less than 10% of children hospitalized with uncomplicated CAP have positive blood cultures, which calls into question the utility of blood cultures for all admitted patients.[17, 18, 19] The recent EPIC (Epidemiology of Pneumonia in the Community) study showed that a majority of children hospitalized with pneumonia do not have growth of bacteria in culture, but there may be a role for blood cultures in patients with a strong suspicion of complicated CAP or in the patient with moderate to severe disease.[20] In addition to blood cultures, the guidelines also recommend CBC and CXR in moderate to severely ill children. This observed decline in testing in CBC and CXR may be related to individual physician assessments of which patients are moderately to severely ill, as the guidelines do not recommend testing for children with less severe disease. Our exclusion of patients requiring intensive care management or pleural drainage on admission might have selected children with a milder course of illness, although still requiring admission.

The guidelines discourage repeat diagnostic testing among children hospitalized with CAP who are improving. In this study, repeat CXR and CBC occurred in approximately 20% of patients, but repeat blood culture and CRP was much lower. As with initial diagnostic testing for inpatients with CAP, the rates of some repeat testing decreased with the guidelines. However, those with repeat testing had longer LOS and were more likely to require ICU transfer or a pleural drainage procedure compared to children without repeat testing. This suggests that repeat testing is used more often in children with a severe presentation or a worsening clinical course, and not done routinely on hospitalized patients.

The financial impact of decreased testing is modest, because the tests themselves are relatively inexpensive. However, the lack of substantial cost savings should not preclude efforts to continue to improve adherence to the guidelines. Not only is increased testing associated with higher hospitalization rates,[5] potentially yielding higher costs and family stress, increased testing may also lead to patient discomfort and possibly increased radiation exposure through chest radiography.

Many of the diagnostic testing recommendations in the CAP guidelines are based on weak evidence, which may contribute to the lack of substantial adoption. Nevertheless, adherence to guideline recommendations requires sustained effort on the part of individual physicians that should be encouraged through institutional support.[21] Continuous education and clinical decision support, as well as reminders in the electronic medical record, would make guideline recommendations more visible and may help overcome the inertia of previous practice.[15] The hospital‐level heat map (Figure 3) included in this study demonstrates that the impact of the guidelines was variable across sites. Although a few sites had decreased diagnostic testing in many areas with no increased testing in any category, there were several sites that had no improvement in any diagnostic testing category. In addition, hospital‐level factors like size, geography, and insurance status were not associated with number of improvements. To better understand drivers of change at individual hospitals, future studies should evaluate specific strategies utilized by the rapid guideline adopters.

This study is subject to several limitations. The use of ICD‐9 codes to identify patients with CAP may not capture all patients with this diagnosis; however, these codes have been previously validated.[13] Additionally, because patients were identified using ICD‐9 coding assigned at the time of discharge, testing performed in the ED setting may not reflect care for a child with known pneumonia, but rather may reflect testing for a child with fever or other signs of infection. PHIS collects data from freestanding children's hospitals, which care for a majority of children with CAP in the US, but our findings may not be generalizable to other hospitals. In addition, we did not examine drivers of trends within individual institutions. We did not have detailed information to examine whether the PHIS hospitals in our study had actively worked to adopt the CAP guidelines. We were also unable to assess physician's familiarity with guidelines or the level of disagreement with the recommendations. Furthermore, the PHIS database does not permit detailed correlation of diagnostic testing with clinical parameters. In contrast to the diagnostic testing evaluated in this study, which is primarily discouraged by the IDSA/PIDS guidelines, respiratory viral testing for children with CAP is recommended but could not be evaluated, as data on such testing are not readily available in PHIS.

CONCLUSION

Publication of the IDSA/PIDS evidence‐based guidelines for the management of CAP was associated with modest, variable changes in use of diagnostic testing. Further adoption of the CAP guidelines should reduce variation in care and decrease unnecessary resource utilization in the management of CAP. Our study demonstrates that efforts to promote decreased resource utilization should target specific situations (eg, repeat testing for inpatients who are improving). Adherence to guidelines may be improved by the adoption of local practices that integrate and improve daily workflow, like order sets and clinical decision support tools.

Disclosure: Nothing to report.

Overutilization of resources is a significant, yet underappreciated, problem in medicine. Many interventions target underutilization (eg, immunizations) or misuse (eg, antibiotic prescribing for viral pharyngitis), yet overutilization remains as a significant contributor to healthcare waste.[1] In an effort to reduce waste, the Choosing Wisely campaign created a work group to highlight areas of overutilization, specifically noting both diagnostic tests and therapies for common pediatric conditions with no proven benefit and possible harm to the patient.[2] Respiratory illnesses have been a target of many quality‐improvement efforts, and pneumonia represents a common diagnosis in pediatrics.[3] The use of diagnostic testing for pneumonia is an area where care can be optimized and aligned with evidence.

Laboratory testing and diagnostic imaging are routinely used for the management of children with community‐acquired pneumonia (CAP). Several studies have documented substantial variability in the use of these resources for pneumonia management, with higher resource use associated with a higher chance of hospitalization after emergency department (ED) evaluation and a longer length of stay among those requiring hospitalization.[4, 5] This variation in diagnostic resource utilization has been attributed, at least in part, to a lack of consensus on the management of pneumonia. There is wide variability in diagnostic testing, and due to potential consequences for patients presenting with pneumonia, efforts to standardize care offer an opportunity to improve healthcare value.

In August 2011, the first national, evidence‐based consensus guidelines for the management of childhood CAP were published jointly by the Pediatric Infectious Diseases Society (PIDS) and the Infectious Diseases Society of America (IDSA).[6] A primary focus of these guidelines was the recommendation for the use of narrow spectrum antibiotics for the management of uncomplicated pneumonia. Previous studies have assessed the impact of the publication of the PIDS/IDSA guidelines on empiric antibiotic selection for the management of pneumonia.[7, 8] In addition, the guidelines provided recommendations regarding diagnostic test utilization, in particular discouraging blood tests (eg, complete blood counts) and radiologic studies for nontoxic, fully immunized children treated as outpatients, as well as repeat testing for children hospitalized with CAP who are improving.

Although single centers have demonstrated changes in utilization patterns based on clinical practice guidelines,[9, 10, 11, 12] whether these guidelines have impacted diagnostic test utilization among US children with CAP in a larger scale remains unknown. Therefore, we sought to determine the impact of the PIDS/IDSA guidelines on the use of diagnostic testing among children with CAP using a national sample of US children's hospitals. Because the guidelines discourage repeat diagnostic testing in patients who are improving, we also evaluated the association between repeat diagnostic studies and severity of illness.

METHODS

This retrospective cohort study used data from the Pediatric Health Information System (PHIS) (Children's Hospital Association, Overland Park, KS). The PHIS database contains deidentified administrative data, detailing demographic, diagnostic, procedure, and billing data from 47 freestanding, tertiary care children's hospitals. This database accounts for approximately 20% of all annual pediatric hospitalizations in the United States. Data quality is ensured through a joint effort between the Children's Hospital Association and participating hospitals.

RESULTS

There were 275,288 hospital admissions meeting study inclusion criteria of 1 to 18 years of age with a diagnosis of pneumonia from 2008 to 2014. Of these, 54,749 met exclusion criteria (1874 had pleural drainage procedure on day 0 or 1, 51,306 had complex chronic conditions, 1569 were hospitalized with pneumonia in the last 30 days). Characteristics of the remaining 220,539 patients in the final sample are shown in Table 1. The median age was 4 years (IQR, 27 years); a majority of the children were male (53%) and had public insurance (58%). There were 128,855 patients in the preguideline period (January 1, 2008 to July 31, 2011) and 91,684 in the post guideline period (August 1, 2011June 30, 2014).

Discharged From the ED

Throughout the study, utilization of CBC, blood cultures, and CRP was <20%, whereas CXR use was >75%. In segmented regression analysis, CRP utilization was relatively stable before the guidelines publication. However, by the end of the study period, the projected estimate of CRP utilization without guidelines (expected) was 2.9% compared with 4.8% with the guidelines (observed) (P < 0.05) (Figure 1). A similar pattern of higher rates of diagnostic utilization after the guidelines compared with projected estimates without the guidelines was also seen in the ED utilization of CBC, blood cultures, and CXR (Figure 1); however, these trends did not achieve statistical significance. Table 2 provides specific values. Using a standard cost of $19.52 for CRP testing, annual costs across all hospitals increased $11,783 for ED evaluation of CAP.

Table 2.Utilization Rates Over Study Period

	Baseline (%)	Preguideline Trend	Level Change at Guideline	Change in Trend After Guideline	Estimates at End of Study*
					Without Guideline (%)	With Guideline (%)	P
NOTE: Abbreviations: CBC, complete blood count; CRP, C‐reactive protein; ED, emergency department; NS, not significant (P > 0.05). *Estimates are based on pre and post guideline trends.
ED‐only encounters
Blood culture	14.6	0.1	0.8	0.1	5.5	8.6	NS
CBC	19.2	0.1	0.4	0.1	10.7	14.0	NS
CRP	5.4	0.0	0.6	0.1	2.9	4.8	<0.05
Chest x‐ray	85.4	0.1	0.1	0.0	80.9	81.1	NS
Inpatient encounters
Blood culture	50.6	0.0	1.7	0.2	49.2	41.4	<0.05
Repeat blood culture	6.5	0.0	1.0	0.1	8.9	5.8	NS
CBC	65.2	0.0	3.1	0.0	65.0	62.2	NS
Repeat CBC	23.4	0.0	4.2	0.0	20.8	16.0	NS
CRP	25.7	0.0	1.1	0.0	23.8	23.5	NS
Repeat CRP	12.5	0.1	2.2	0.1	7.1	7.3	NS
Chest x‐ray	89.4	0.1	0.7	0.0	85.4	83.9	NS
Repeat chest x‐ray	25.5	0.0	2.0	0.1	24.1	17.7	<0.05

Figure 1

Inpatient Encounters

In the segmented regression analysis of children hospitalized with CAP, guideline publication was associated with changes in the monthly use of some diagnostic tests. For example, by the end of the study period, the use of blood culture was 41.4% (observed), whereas the projected estimated use in the absence of the guidelines was 49.2% (expected) (P < 0.05) (Figure 2). Table 2 includes the data for the other tests, CBC, CRP, and CXR, in which similar patterns are noted with lower utilization rates after the guidelines, compared with expected utilization rates without the guidelines; however, these trends did not achieve statistical significance. Evaluating the utilization of repeat testing for inpatients, only repeat CXR achieved statistical significance (P < 0.05), with utilization rates of 17.7% with the guidelines (actual) compared with 24.1% without the guidelines (predicted).

Figure 2

To better understand the use of repeat testing, a comparison of severity outcomesLOS, ICU transfer, and pleural drainage procedureswas performed between patients with no repeat testing (70%) and patients with 1 or more repeat tests (30%). Patients with repeat testing had longer LOS (no repeat testing LOS 1 [IQR, 12]) versus 1 repeat test LOS 3 ([IQR, 24] vs 2+ repeat tests LOS 5 [IQR, 38]), higher rate of ICU transfer (no repeat testing 4.6% vs 1 repeat test 14.6% vs 2+ repeat test 35.6%), and higher rate of pleural drainage (no repeat testing 0% vs 1 repeat test 0.1% vs 2+ repeat test 5.9%] (all P < 0.001).

Using standard costs of $37.57 for blood cultures and $73.28 for CXR, annual costs for children with CAP across all hospitals decreased by $91,512 due to decreased utilization of blood cultures, and by $146,840 due to decreased utilization of CXR.

Hospital‐Level Variation in the Impact of the National Guideline

Figure 3 is a visual representation (heat map) of the impact of the guidelines at the hospital level at the end of the study from the individual interrupted time series. Based on this heat map (Figure 3), there was wide variability between hospitals in the impact of the guideline on each test in different settings (ED or inpatient). By diagnostic testing, 7 hospitals significantly decreased utilization of blood cultures for inpatients, and 5 hospitals significantly decreased utilization for repeat blood cultures and repeat CXR. Correlation between the number of positive improvements at a hospital and region (P = 0.974), number of CAP cases (P = 0.731), or percentage of public insurance (P = 0.241) were all nonsignificant.

Figure 3

DISCUSSION

This study complements previous assessments by evaluating the impact of the 2011 IDSA/PIDS consensus guidelines on the management of children with CAP cared for at US children's hospitals. Prior studies have shown increased use of narrow‐spectrum antibiotics for children with CAP after the publication of these guidelines.[7] The current study focused on diagnostic testing for CAP before and after the publication of the 2011 guidelines. In the ED setting, use of some diagnostic tests (blood culture, CBC, CXR, CRP) was declining prior to guideline publication, but appeared to plateau and/or increase after 2011. Among children admitted with CAP, use of diagnostic testing was relatively stable prior to 2011, and use of these tests (blood culture, CBC, CXR, CRP) declined after guideline publication. Overall, changes in diagnostic resource utilization 3 years after publication were modest, with few changes achieving statistical significance. There was a large variability in the impact of guidelines on test use between hospitals.

For outpatients, including those managed in the ED, the PIDS/IDSA guidelines recommend limited laboratory testing in nontoxic, fully immunized patients. The guidelines discourage the use of diagnostic testing among outpatients because of their low yield (eg, blood culture), and because test results may not impact management (eg, CBC).[6] In the years prior to guideline publication, there was already a declining trend in testing rates, including blood cultures, CBC, and CRP, for patients in the ED. After guideline publication, the rate of blood cultures, CBC, and CRP increased, but only the increase in CRP utilization achieved statistical significance. We would not expect utilization for common diagnostic tests (eg, CBC for outpatients with CAP) to be at or close to 0% because of the complexity of clinical decision making regarding admission that factors in aspects of patient history, exam findings, and underlying risk.[15] ED utilization of blood cultures was <10%, CBC <15%, and CRP <5% after guideline publication, which may represent the lowest testing limit that could be achieved.

CXRs obtained in the ED did not decrease over the entire study period. The rates of CXR use (close to 80%) seen in our study are similar to prior ED studies.[5, 16] Management of children with CAP in the ED might be different than outpatient primary care management because (1) unlike primary care providers, ED providers do not have an established relationship with their patients and do not have the opportunity for follow‐up and serial exams, making them less likely to tolerate diagnostic uncertainty; and (2) ED providers may see sicker patients. However, use of CXR in the ED does represent an opportunity for further study to understand if decreased utilization is feasible without adversely impacting clinical outcomes.

The CAP guidelines provide a strong recommendation to obtain blood culture in moderate to severe pneumonia. Despite this, blood culture utilization declined after guideline publication. Less than 10% of children hospitalized with uncomplicated CAP have positive blood cultures, which calls into question the utility of blood cultures for all admitted patients.[17, 18, 19] The recent EPIC (Epidemiology of Pneumonia in the Community) study showed that a majority of children hospitalized with pneumonia do not have growth of bacteria in culture, but there may be a role for blood cultures in patients with a strong suspicion of complicated CAP or in the patient with moderate to severe disease.[20] In addition to blood cultures, the guidelines also recommend CBC and CXR in moderate to severely ill children. This observed decline in testing in CBC and CXR may be related to individual physician assessments of which patients are moderately to severely ill, as the guidelines do not recommend testing for children with less severe disease. Our exclusion of patients requiring intensive care management or pleural drainage on admission might have selected children with a milder course of illness, although still requiring admission.

The guidelines discourage repeat diagnostic testing among children hospitalized with CAP who are improving. In this study, repeat CXR and CBC occurred in approximately 20% of patients, but repeat blood culture and CRP was much lower. As with initial diagnostic testing for inpatients with CAP, the rates of some repeat testing decreased with the guidelines. However, those with repeat testing had longer LOS and were more likely to require ICU transfer or a pleural drainage procedure compared to children without repeat testing. This suggests that repeat testing is used more often in children with a severe presentation or a worsening clinical course, and not done routinely on hospitalized patients.

The financial impact of decreased testing is modest, because the tests themselves are relatively inexpensive. However, the lack of substantial cost savings should not preclude efforts to continue to improve adherence to the guidelines. Not only is increased testing associated with higher hospitalization rates,[5] potentially yielding higher costs and family stress, increased testing may also lead to patient discomfort and possibly increased radiation exposure through chest radiography.

Many of the diagnostic testing recommendations in the CAP guidelines are based on weak evidence, which may contribute to the lack of substantial adoption. Nevertheless, adherence to guideline recommendations requires sustained effort on the part of individual physicians that should be encouraged through institutional support.[21] Continuous education and clinical decision support, as well as reminders in the electronic medical record, would make guideline recommendations more visible and may help overcome the inertia of previous practice.[15] The hospital‐level heat map (Figure 3) included in this study demonstrates that the impact of the guidelines was variable across sites. Although a few sites had decreased diagnostic testing in many areas with no increased testing in any category, there were several sites that had no improvement in any diagnostic testing category. In addition, hospital‐level factors like size, geography, and insurance status were not associated with number of improvements. To better understand drivers of change at individual hospitals, future studies should evaluate specific strategies utilized by the rapid guideline adopters.

This study is subject to several limitations. The use of ICD‐9 codes to identify patients with CAP may not capture all patients with this diagnosis; however, these codes have been previously validated.[13] Additionally, because patients were identified using ICD‐9 coding assigned at the time of discharge, testing performed in the ED setting may not reflect care for a child with known pneumonia, but rather may reflect testing for a child with fever or other signs of infection. PHIS collects data from freestanding children's hospitals, which care for a majority of children with CAP in the US, but our findings may not be generalizable to other hospitals. In addition, we did not examine drivers of trends within individual institutions. We did not have detailed information to examine whether the PHIS hospitals in our study had actively worked to adopt the CAP guidelines. We were also unable to assess physician's familiarity with guidelines or the level of disagreement with the recommendations. Furthermore, the PHIS database does not permit detailed correlation of diagnostic testing with clinical parameters. In contrast to the diagnostic testing evaluated in this study, which is primarily discouraged by the IDSA/PIDS guidelines, respiratory viral testing for children with CAP is recommended but could not be evaluated, as data on such testing are not readily available in PHIS.

CONCLUSION

Publication of the IDSA/PIDS evidence‐based guidelines for the management of CAP was associated with modest, variable changes in use of diagnostic testing. Further adoption of the CAP guidelines should reduce variation in care and decrease unnecessary resource utilization in the management of CAP. Our study demonstrates that efforts to promote decreased resource utilization should target specific situations (eg, repeat testing for inpatients who are improving). Adherence to guidelines may be improved by the adoption of local practices that integrate and improve daily workflow, like order sets and clinical decision support tools.

Disclosure: Nothing to report.

Overutilization of resources is a significant, yet underappreciated, problem in medicine. Many interventions target underutilization (eg, immunizations) or misuse (eg, antibiotic prescribing for viral pharyngitis), yet overutilization remains as a significant contributor to healthcare waste.[1] In an effort to reduce waste, the Choosing Wisely campaign created a work group to highlight areas of overutilization, specifically noting both diagnostic tests and therapies for common pediatric conditions with no proven benefit and possible harm to the patient.[2] Respiratory illnesses have been a target of many quality‐improvement efforts, and pneumonia represents a common diagnosis in pediatrics.[3] The use of diagnostic testing for pneumonia is an area where care can be optimized and aligned with evidence.

Laboratory testing and diagnostic imaging are routinely used for the management of children with community‐acquired pneumonia (CAP). Several studies have documented substantial variability in the use of these resources for pneumonia management, with higher resource use associated with a higher chance of hospitalization after emergency department (ED) evaluation and a longer length of stay among those requiring hospitalization.[4, 5] This variation in diagnostic resource utilization has been attributed, at least in part, to a lack of consensus on the management of pneumonia. There is wide variability in diagnostic testing, and due to potential consequences for patients presenting with pneumonia, efforts to standardize care offer an opportunity to improve healthcare value.

In August 2011, the first national, evidence‐based consensus guidelines for the management of childhood CAP were published jointly by the Pediatric Infectious Diseases Society (PIDS) and the Infectious Diseases Society of America (IDSA).[6] A primary focus of these guidelines was the recommendation for the use of narrow spectrum antibiotics for the management of uncomplicated pneumonia. Previous studies have assessed the impact of the publication of the PIDS/IDSA guidelines on empiric antibiotic selection for the management of pneumonia.[7, 8] In addition, the guidelines provided recommendations regarding diagnostic test utilization, in particular discouraging blood tests (eg, complete blood counts) and radiologic studies for nontoxic, fully immunized children treated as outpatients, as well as repeat testing for children hospitalized with CAP who are improving.

Although single centers have demonstrated changes in utilization patterns based on clinical practice guidelines,[9, 10, 11, 12] whether these guidelines have impacted diagnostic test utilization among US children with CAP in a larger scale remains unknown. Therefore, we sought to determine the impact of the PIDS/IDSA guidelines on the use of diagnostic testing among children with CAP using a national sample of US children's hospitals. Because the guidelines discourage repeat diagnostic testing in patients who are improving, we also evaluated the association between repeat diagnostic studies and severity of illness.

METHODS

This retrospective cohort study used data from the Pediatric Health Information System (PHIS) (Children's Hospital Association, Overland Park, KS). The PHIS database contains deidentified administrative data, detailing demographic, diagnostic, procedure, and billing data from 47 freestanding, tertiary care children's hospitals. This database accounts for approximately 20% of all annual pediatric hospitalizations in the United States. Data quality is ensured through a joint effort between the Children's Hospital Association and participating hospitals.

RESULTS

There were 275,288 hospital admissions meeting study inclusion criteria of 1 to 18 years of age with a diagnosis of pneumonia from 2008 to 2014. Of these, 54,749 met exclusion criteria (1874 had pleural drainage procedure on day 0 or 1, 51,306 had complex chronic conditions, 1569 were hospitalized with pneumonia in the last 30 days). Characteristics of the remaining 220,539 patients in the final sample are shown in Table 1. The median age was 4 years (IQR, 27 years); a majority of the children were male (53%) and had public insurance (58%). There were 128,855 patients in the preguideline period (January 1, 2008 to July 31, 2011) and 91,684 in the post guideline period (August 1, 2011June 30, 2014).

Discharged From the ED

Throughout the study, utilization of CBC, blood cultures, and CRP was <20%, whereas CXR use was >75%. In segmented regression analysis, CRP utilization was relatively stable before the guidelines publication. However, by the end of the study period, the projected estimate of CRP utilization without guidelines (expected) was 2.9% compared with 4.8% with the guidelines (observed) (P < 0.05) (Figure 1). A similar pattern of higher rates of diagnostic utilization after the guidelines compared with projected estimates without the guidelines was also seen in the ED utilization of CBC, blood cultures, and CXR (Figure 1); however, these trends did not achieve statistical significance. Table 2 provides specific values. Using a standard cost of $19.52 for CRP testing, annual costs across all hospitals increased $11,783 for ED evaluation of CAP.

Table 2.Utilization Rates Over Study Period

	Baseline (%)	Preguideline Trend	Level Change at Guideline	Change in Trend After Guideline	Estimates at End of Study*
					Without Guideline (%)	With Guideline (%)	P
NOTE: Abbreviations: CBC, complete blood count; CRP, C‐reactive protein; ED, emergency department; NS, not significant (P > 0.05). *Estimates are based on pre and post guideline trends.
ED‐only encounters
Blood culture	14.6	0.1	0.8	0.1	5.5	8.6	NS
CBC	19.2	0.1	0.4	0.1	10.7	14.0	NS
CRP	5.4	0.0	0.6	0.1	2.9	4.8	<0.05
Chest x‐ray	85.4	0.1	0.1	0.0	80.9	81.1	NS
Inpatient encounters
Blood culture	50.6	0.0	1.7	0.2	49.2	41.4	<0.05
Repeat blood culture	6.5	0.0	1.0	0.1	8.9	5.8	NS
CBC	65.2	0.0	3.1	0.0	65.0	62.2	NS
Repeat CBC	23.4	0.0	4.2	0.0	20.8	16.0	NS
CRP	25.7	0.0	1.1	0.0	23.8	23.5	NS
Repeat CRP	12.5	0.1	2.2	0.1	7.1	7.3	NS
Chest x‐ray	89.4	0.1	0.7	0.0	85.4	83.9	NS
Repeat chest x‐ray	25.5	0.0	2.0	0.1	24.1	17.7	<0.05

Figure 1

Inpatient Encounters

In the segmented regression analysis of children hospitalized with CAP, guideline publication was associated with changes in the monthly use of some diagnostic tests. For example, by the end of the study period, the use of blood culture was 41.4% (observed), whereas the projected estimated use in the absence of the guidelines was 49.2% (expected) (P < 0.05) (Figure 2). Table 2 includes the data for the other tests, CBC, CRP, and CXR, in which similar patterns are noted with lower utilization rates after the guidelines, compared with expected utilization rates without the guidelines; however, these trends did not achieve statistical significance. Evaluating the utilization of repeat testing for inpatients, only repeat CXR achieved statistical significance (P < 0.05), with utilization rates of 17.7% with the guidelines (actual) compared with 24.1% without the guidelines (predicted).

Figure 2

To better understand the use of repeat testing, a comparison of severity outcomesLOS, ICU transfer, and pleural drainage procedureswas performed between patients with no repeat testing (70%) and patients with 1 or more repeat tests (30%). Patients with repeat testing had longer LOS (no repeat testing LOS 1 [IQR, 12]) versus 1 repeat test LOS 3 ([IQR, 24] vs 2+ repeat tests LOS 5 [IQR, 38]), higher rate of ICU transfer (no repeat testing 4.6% vs 1 repeat test 14.6% vs 2+ repeat test 35.6%), and higher rate of pleural drainage (no repeat testing 0% vs 1 repeat test 0.1% vs 2+ repeat test 5.9%] (all P < 0.001).

Using standard costs of $37.57 for blood cultures and $73.28 for CXR, annual costs for children with CAP across all hospitals decreased by $91,512 due to decreased utilization of blood cultures, and by $146,840 due to decreased utilization of CXR.

Hospital‐Level Variation in the Impact of the National Guideline

Figure 3 is a visual representation (heat map) of the impact of the guidelines at the hospital level at the end of the study from the individual interrupted time series. Based on this heat map (Figure 3), there was wide variability between hospitals in the impact of the guideline on each test in different settings (ED or inpatient). By diagnostic testing, 7 hospitals significantly decreased utilization of blood cultures for inpatients, and 5 hospitals significantly decreased utilization for repeat blood cultures and repeat CXR. Correlation between the number of positive improvements at a hospital and region (P = 0.974), number of CAP cases (P = 0.731), or percentage of public insurance (P = 0.241) were all nonsignificant.

Figure 3

DISCUSSION

This study complements previous assessments by evaluating the impact of the 2011 IDSA/PIDS consensus guidelines on the management of children with CAP cared for at US children's hospitals. Prior studies have shown increased use of narrow‐spectrum antibiotics for children with CAP after the publication of these guidelines.[7] The current study focused on diagnostic testing for CAP before and after the publication of the 2011 guidelines. In the ED setting, use of some diagnostic tests (blood culture, CBC, CXR, CRP) was declining prior to guideline publication, but appeared to plateau and/or increase after 2011. Among children admitted with CAP, use of diagnostic testing was relatively stable prior to 2011, and use of these tests (blood culture, CBC, CXR, CRP) declined after guideline publication. Overall, changes in diagnostic resource utilization 3 years after publication were modest, with few changes achieving statistical significance. There was a large variability in the impact of guidelines on test use between hospitals.

For outpatients, including those managed in the ED, the PIDS/IDSA guidelines recommend limited laboratory testing in nontoxic, fully immunized patients. The guidelines discourage the use of diagnostic testing among outpatients because of their low yield (eg, blood culture), and because test results may not impact management (eg, CBC).[6] In the years prior to guideline publication, there was already a declining trend in testing rates, including blood cultures, CBC, and CRP, for patients in the ED. After guideline publication, the rate of blood cultures, CBC, and CRP increased, but only the increase in CRP utilization achieved statistical significance. We would not expect utilization for common diagnostic tests (eg, CBC for outpatients with CAP) to be at or close to 0% because of the complexity of clinical decision making regarding admission that factors in aspects of patient history, exam findings, and underlying risk.[15] ED utilization of blood cultures was <10%, CBC <15%, and CRP <5% after guideline publication, which may represent the lowest testing limit that could be achieved.

CXRs obtained in the ED did not decrease over the entire study period. The rates of CXR use (close to 80%) seen in our study are similar to prior ED studies.[5, 16] Management of children with CAP in the ED might be different than outpatient primary care management because (1) unlike primary care providers, ED providers do not have an established relationship with their patients and do not have the opportunity for follow‐up and serial exams, making them less likely to tolerate diagnostic uncertainty; and (2) ED providers may see sicker patients. However, use of CXR in the ED does represent an opportunity for further study to understand if decreased utilization is feasible without adversely impacting clinical outcomes.

The CAP guidelines provide a strong recommendation to obtain blood culture in moderate to severe pneumonia. Despite this, blood culture utilization declined after guideline publication. Less than 10% of children hospitalized with uncomplicated CAP have positive blood cultures, which calls into question the utility of blood cultures for all admitted patients.[17, 18, 19] The recent EPIC (Epidemiology of Pneumonia in the Community) study showed that a majority of children hospitalized with pneumonia do not have growth of bacteria in culture, but there may be a role for blood cultures in patients with a strong suspicion of complicated CAP or in the patient with moderate to severe disease.[20] In addition to blood cultures, the guidelines also recommend CBC and CXR in moderate to severely ill children. This observed decline in testing in CBC and CXR may be related to individual physician assessments of which patients are moderately to severely ill, as the guidelines do not recommend testing for children with less severe disease. Our exclusion of patients requiring intensive care management or pleural drainage on admission might have selected children with a milder course of illness, although still requiring admission.

The guidelines discourage repeat diagnostic testing among children hospitalized with CAP who are improving. In this study, repeat CXR and CBC occurred in approximately 20% of patients, but repeat blood culture and CRP was much lower. As with initial diagnostic testing for inpatients with CAP, the rates of some repeat testing decreased with the guidelines. However, those with repeat testing had longer LOS and were more likely to require ICU transfer or a pleural drainage procedure compared to children without repeat testing. This suggests that repeat testing is used more often in children with a severe presentation or a worsening clinical course, and not done routinely on hospitalized patients.

The financial impact of decreased testing is modest, because the tests themselves are relatively inexpensive. However, the lack of substantial cost savings should not preclude efforts to continue to improve adherence to the guidelines. Not only is increased testing associated with higher hospitalization rates,[5] potentially yielding higher costs and family stress, increased testing may also lead to patient discomfort and possibly increased radiation exposure through chest radiography.

Many of the diagnostic testing recommendations in the CAP guidelines are based on weak evidence, which may contribute to the lack of substantial adoption. Nevertheless, adherence to guideline recommendations requires sustained effort on the part of individual physicians that should be encouraged through institutional support.[21] Continuous education and clinical decision support, as well as reminders in the electronic medical record, would make guideline recommendations more visible and may help overcome the inertia of previous practice.[15] The hospital‐level heat map (Figure 3) included in this study demonstrates that the impact of the guidelines was variable across sites. Although a few sites had decreased diagnostic testing in many areas with no increased testing in any category, there were several sites that had no improvement in any diagnostic testing category. In addition, hospital‐level factors like size, geography, and insurance status were not associated with number of improvements. To better understand drivers of change at individual hospitals, future studies should evaluate specific strategies utilized by the rapid guideline adopters.

This study is subject to several limitations. The use of ICD‐9 codes to identify patients with CAP may not capture all patients with this diagnosis; however, these codes have been previously validated.[13] Additionally, because patients were identified using ICD‐9 coding assigned at the time of discharge, testing performed in the ED setting may not reflect care for a child with known pneumonia, but rather may reflect testing for a child with fever or other signs of infection. PHIS collects data from freestanding children's hospitals, which care for a majority of children with CAP in the US, but our findings may not be generalizable to other hospitals. In addition, we did not examine drivers of trends within individual institutions. We did not have detailed information to examine whether the PHIS hospitals in our study had actively worked to adopt the CAP guidelines. We were also unable to assess physician's familiarity with guidelines or the level of disagreement with the recommendations. Furthermore, the PHIS database does not permit detailed correlation of diagnostic testing with clinical parameters. In contrast to the diagnostic testing evaluated in this study, which is primarily discouraged by the IDSA/PIDS guidelines, respiratory viral testing for children with CAP is recommended but could not be evaluated, as data on such testing are not readily available in PHIS.

CONCLUSION

Publication of the IDSA/PIDS evidence‐based guidelines for the management of CAP was associated with modest, variable changes in use of diagnostic testing. Further adoption of the CAP guidelines should reduce variation in care and decrease unnecessary resource utilization in the management of CAP. Our study demonstrates that efforts to promote decreased resource utilization should target specific situations (eg, repeat testing for inpatients who are improving). Adherence to guidelines may be improved by the adoption of local practices that integrate and improve daily workflow, like order sets and clinical decision support tools.

Disclosure: Nothing to report.

Hyperkalemia occurs in as many as 10% of all hospitalized patients,[1] leading to potentially fatal arrhythmias or cardiac arrest that results from ionic imbalance within the resting membrane potential of myocardial tissue.[2] Acute instances may be stabilized with insulin to stimulate intracellular uptake of potassium, but this increases the risk of hypoglycemia.[2] Centers for Medicare and Medicaid Services quality measures require hospitals to minimize hypoglycemic events, particularly serious events with blood glucose (BG) <40 mg/dL,[3] due to an association with an increase in mortality in the hospital setting.[4] Previous research at our tertiary care hospital found that 8.7% of patients had suffered a hypoglycemic event following insulin administration pursuant to acute hyperkalemia treatment, and that patients with a lower body weight are at increased risk of hypoglycemia, particularly severe hypoglycemia (BG <40 mg/dL).[5] Increasing the total dose of dextrose provided around the time of insulin administration is suggested to reduce this concern.[5]

Patients at our institution receive 50 g of dextrose in conjunction with intravenous (IV) insulin for hyperkalemia treatment. To further reduce the potential for hypoglycemia, our institution amended the acute hyperkalemia order set to provide prescribers an alternative dosing strategy to the standard 10 U of IV insulin traditionally used for this purpose. Beginning November 10, 2013, our computer prescriber order entry (CPOE) system automatically prepopulated a dose of 0.1 U/kg of body weight for any patients weighing <95 kg (doses rounded to the nearest whole unit) when the acute hyperkalemia order set was utilized. The maximum dose allowed continued to be 10 U. The revised order set also changed nursing orders to require BG monitoring as frequently as every hour following the administration of insulin and dextrose for the treatment of hyperkalemia.

The purpose of this study is to investigate whether weight‐based insulin dosing (0.1 U/kg) for patients weighing <95 kg, rather than a standard 10‐U insulin dose, resulted in fewer hypoglycemic episodes and patients affected. Secondarily, this study sought to determine the impact of weight‐based insulin dosing on potassium‐lowering effects of therapy and to detect any risk factors for development of hypoglycemia among this patient population.

METHODS

This institutional review boardapproved, single‐center, retrospective chart review examined patients for whom the physician order entry set for hyperkalemia therapy was utilized, including patients who weighed less than 95 kg and received regular insulin via weight‐based dosing (0.1 U/kg of body weight up to a maximum of 10 U) during the period November 10, 2013 to May 31, 2014, versus those who received fixed insulin dosing (10 U regardless of body weight) during the period May 1, 2013 to November 9, 2013. During each of these periods, the CPOE system autopopulated the recommended insulin dose, with the possibility for physician manual dose entry. Data collection was limited to the first use of insulin for hyperkalemia treatment per patient in each period.

Patients weighing <95 kg were the focus of this study because they received <10 U of insulin under the weight‐based dosing strategy. Patients were excluded from the study if they had a body weight >95 kg or no weight recorded, were not administered insulin as ordered, received greater than the CPOE‐specified insulin dose, or had no BG readings recorded within 24 hours of insulin administration. The first 66 patients within each group meeting all inclusion and exclusion criteria were randomly selected for analysis. This recruitment target was developed to provide enough patients for a meaningful analysis of hypoglycemia events based on previous reports from our institution.[5]

Hypoglycemia was defined as a recorded BG level <70 mg/dL within 24 hours after insulin administration; severe hypoglycemia was defined as a recorded BG <40 mg/dL within 24 hours. Individual episodes of hypoglycemia and severe hypoglycemia were recorded for each instance of such event separated by at least 1 hour from the time of the first recorded event. In addition, episodes of hypoglycemia or severe hypoglycemia and number of patients affected were assessed at within 6 hours, 6 to 12 hours, and 12 to 24 hours after insulin administration as separate subsets for statistical analysis.

For the purpose of assessing the potassium‐lowering efficacy of weight‐based versus traditional dosing of insulin, maximum serum potassium levels were examined in the 12‐hour interval before the hyperkalemia order set was implemented and compared with minimum potassium levels in the 12 hours after insulin was administered. A comparison of the treatment groups assessed differences between the mean decrease in serum potassium from baseline, the mean minimum potassium achieved, the number of patients achieving minimum potassium below 5.0 mEq/L, and the number of patients who subsequently received repeat treatment for hyperkalemia within 24 hours of treatment with insulin.

Statistical analysis was conducted utilizing ² and Fisher exact tests for nominal data and Student t test for continuous data to detect statistically significant differences between the groups. Binomial logistic multivariable analysis using a backward stepwise approach was used to determine factors for development of hypoglycemia, analyzed on a per‐patient basis to prevent characteristics from being over‐represented when events occurred multiple times to a single patient. All analyses were completed by using SPSS version 18 (SPSS Inc., Chicago, IL).

RESULTS

In total, 1734 entries were available for the acute hyperkalemia order set with insulin during the 2 periods investigated. Only 464 patients were eligible for manual chart review once weight‐based exclusions were identified by electronic database, with additional exclusion criteria later extracted from patient charts. Patients in both treatment groups were fairly well balanced, with a slightly lower body weight in the 10‐U insulin group recorded (Table 1). Patients in the weight‐based dosing group received between 4 and 9 U of insulin, depending on body weight.

A reduction in the number of hypoglycemic episodes was detected in the weight‐based dosing group of 56% within 24 hours, from 18 to 8 events (P = 0.05) (Table 2). The number of hypoglycemic events in every subset of time intervals was likewise reduced by at least 50% using weight‐based dosing (from 7 to 3 events within 6 hours, from 5 to 2 events in 612 hours, from 6 to 3 events in 1224 hours). The number of patients who experienced hypoglycemia within 24 hours after receiving insulin also was reduced in the weight‐based dosing group by 46% (P = 0.22).

Potassium lowering was comparable across both dosing strategies in every measure assessed (Table 2). Multivariate analysis revealed that baseline BG <140 mg/dL (adjusted odds ratio: 4.3, 95% confidence interval [CI]: 1.4‐13.7, P = 0.01) and female gender (adjusted odds ratio: 3.2, 95% CI: 1.1‐9.1, P = 0.03) were associated with an increased risk of hypoglycemia. Other factors, including administration of insulin beyond that for hyperkalemia treatment and use of additional hypoglycemic agents, were not associated with the development of hypoglycemia, which is consistent with previous reports.[6]

CONCLUSIONS

Our findings indicate that using a weight‐based approach to insulin dosing when treating hyperkalemia may lead to a reduction in hypoglycemia without sacrificing the efficacy of potassium lowering. Females and patients with glucose values <140 mg/dL were at increased risk of hypoglycemia in this cohort. Based on the results of this research, a weight‐based dosing strategy of 0.1 U/kg IV insulin up to a maximum of 10 U should be considered, with further research desirable to validate these results.

This study was strengthened by the inclusion of all patients regardless of baseline glucose, baseline potassium, administration of other insulins, level of renal impairment, or symptomatic display of hypoglycemia or cardiac dysfunction, thus providing a broad representation of patients treated for acute hyperkalemia. This pilot study was limited in its scope by data collection for only 66 randomized patients per group rather than the entire patient population. In addition, the study utilized patient information from a single site, with few ethnicities represented. Validation of this research using a larger sample size should include greater variation in the patients served. Our inclusion of a hypoglycemia definition up to 24 hours after treatment may also be criticized. However, this is similar to previous reports and allows for a liberal time period for follow‐up glucose monitoring to be recorded.[7]

Because of its small sample size and the low event rate, this study was unable to draw conclusions about the ability of weight‐based insulin dosing to affect severe hypoglycemic events (<40 mg/dL). A study of more than 400 patients would be necessary to find statistically significant differences in the risk of severe hypoglycemia. Furthermore, because we did not examine the results from all patients in this cohort, we cannot conclusively determine the impact of treatment. The retrospective nature of this study limited our ability to capture hypoglycemic episodes during periods in which BG levels were not recorded. Additionally, changes to the post‐treatment glucose monitoring protocol may have also affected the incidence of hypoglycemia in 2 potential ways. First, early and unrecorded interventions may have occurred in patients with a trend toward hypoglycemia. Second, the longer time to follow‐up in the nonweight‐based group may have led to additional hypoglycemic episodes being missed. A prospective trial design could provide more comprehensive information about patient response to weight‐based versus traditional dosing of IV insulin for hyperkalemia. Further investigations on reducing adverse effects of insulin when treating hyperkalemia should focus on female patients and those with lower baseline BG values. Additionally, as newer agents to treat hyperkalemia are developed and tested, the approach to management should be revisited.[8, 9, 10]

Disclosures: Garry S. Tobin, MD, lectures or is on the speakers bureau for Eli Lilly, Jansen, Boehringher Ingelheim, and Novo Nordisk, and performs data safety monitoring for Novo Nordisk. The authors report no other potential conflicts of interest.

Hyperkalemia occurs in as many as 10% of all hospitalized patients,[1] leading to potentially fatal arrhythmias or cardiac arrest that results from ionic imbalance within the resting membrane potential of myocardial tissue.[2] Acute instances may be stabilized with insulin to stimulate intracellular uptake of potassium, but this increases the risk of hypoglycemia.[2] Centers for Medicare and Medicaid Services quality measures require hospitals to minimize hypoglycemic events, particularly serious events with blood glucose (BG) <40 mg/dL,[3] due to an association with an increase in mortality in the hospital setting.[4] Previous research at our tertiary care hospital found that 8.7% of patients had suffered a hypoglycemic event following insulin administration pursuant to acute hyperkalemia treatment, and that patients with a lower body weight are at increased risk of hypoglycemia, particularly severe hypoglycemia (BG <40 mg/dL).[5] Increasing the total dose of dextrose provided around the time of insulin administration is suggested to reduce this concern.[5]

Patients at our institution receive 50 g of dextrose in conjunction with intravenous (IV) insulin for hyperkalemia treatment. To further reduce the potential for hypoglycemia, our institution amended the acute hyperkalemia order set to provide prescribers an alternative dosing strategy to the standard 10 U of IV insulin traditionally used for this purpose. Beginning November 10, 2013, our computer prescriber order entry (CPOE) system automatically prepopulated a dose of 0.1 U/kg of body weight for any patients weighing <95 kg (doses rounded to the nearest whole unit) when the acute hyperkalemia order set was utilized. The maximum dose allowed continued to be 10 U. The revised order set also changed nursing orders to require BG monitoring as frequently as every hour following the administration of insulin and dextrose for the treatment of hyperkalemia.

The purpose of this study is to investigate whether weight‐based insulin dosing (0.1 U/kg) for patients weighing <95 kg, rather than a standard 10‐U insulin dose, resulted in fewer hypoglycemic episodes and patients affected. Secondarily, this study sought to determine the impact of weight‐based insulin dosing on potassium‐lowering effects of therapy and to detect any risk factors for development of hypoglycemia among this patient population.

METHODS

This institutional review boardapproved, single‐center, retrospective chart review examined patients for whom the physician order entry set for hyperkalemia therapy was utilized, including patients who weighed less than 95 kg and received regular insulin via weight‐based dosing (0.1 U/kg of body weight up to a maximum of 10 U) during the period November 10, 2013 to May 31, 2014, versus those who received fixed insulin dosing (10 U regardless of body weight) during the period May 1, 2013 to November 9, 2013. During each of these periods, the CPOE system autopopulated the recommended insulin dose, with the possibility for physician manual dose entry. Data collection was limited to the first use of insulin for hyperkalemia treatment per patient in each period.

Patients weighing <95 kg were the focus of this study because they received <10 U of insulin under the weight‐based dosing strategy. Patients were excluded from the study if they had a body weight >95 kg or no weight recorded, were not administered insulin as ordered, received greater than the CPOE‐specified insulin dose, or had no BG readings recorded within 24 hours of insulin administration. The first 66 patients within each group meeting all inclusion and exclusion criteria were randomly selected for analysis. This recruitment target was developed to provide enough patients for a meaningful analysis of hypoglycemia events based on previous reports from our institution.[5]

Hypoglycemia was defined as a recorded BG level <70 mg/dL within 24 hours after insulin administration; severe hypoglycemia was defined as a recorded BG <40 mg/dL within 24 hours. Individual episodes of hypoglycemia and severe hypoglycemia were recorded for each instance of such event separated by at least 1 hour from the time of the first recorded event. In addition, episodes of hypoglycemia or severe hypoglycemia and number of patients affected were assessed at within 6 hours, 6 to 12 hours, and 12 to 24 hours after insulin administration as separate subsets for statistical analysis.

For the purpose of assessing the potassium‐lowering efficacy of weight‐based versus traditional dosing of insulin, maximum serum potassium levels were examined in the 12‐hour interval before the hyperkalemia order set was implemented and compared with minimum potassium levels in the 12 hours after insulin was administered. A comparison of the treatment groups assessed differences between the mean decrease in serum potassium from baseline, the mean minimum potassium achieved, the number of patients achieving minimum potassium below 5.0 mEq/L, and the number of patients who subsequently received repeat treatment for hyperkalemia within 24 hours of treatment with insulin.

Statistical analysis was conducted utilizing ² and Fisher exact tests for nominal data and Student t test for continuous data to detect statistically significant differences between the groups. Binomial logistic multivariable analysis using a backward stepwise approach was used to determine factors for development of hypoglycemia, analyzed on a per‐patient basis to prevent characteristics from being over‐represented when events occurred multiple times to a single patient. All analyses were completed by using SPSS version 18 (SPSS Inc., Chicago, IL).

RESULTS

In total, 1734 entries were available for the acute hyperkalemia order set with insulin during the 2 periods investigated. Only 464 patients were eligible for manual chart review once weight‐based exclusions were identified by electronic database, with additional exclusion criteria later extracted from patient charts. Patients in both treatment groups were fairly well balanced, with a slightly lower body weight in the 10‐U insulin group recorded (Table 1). Patients in the weight‐based dosing group received between 4 and 9 U of insulin, depending on body weight.

A reduction in the number of hypoglycemic episodes was detected in the weight‐based dosing group of 56% within 24 hours, from 18 to 8 events (P = 0.05) (Table 2). The number of hypoglycemic events in every subset of time intervals was likewise reduced by at least 50% using weight‐based dosing (from 7 to 3 events within 6 hours, from 5 to 2 events in 612 hours, from 6 to 3 events in 1224 hours). The number of patients who experienced hypoglycemia within 24 hours after receiving insulin also was reduced in the weight‐based dosing group by 46% (P = 0.22).

Potassium lowering was comparable across both dosing strategies in every measure assessed (Table 2). Multivariate analysis revealed that baseline BG <140 mg/dL (adjusted odds ratio: 4.3, 95% confidence interval [CI]: 1.4‐13.7, P = 0.01) and female gender (adjusted odds ratio: 3.2, 95% CI: 1.1‐9.1, P = 0.03) were associated with an increased risk of hypoglycemia. Other factors, including administration of insulin beyond that for hyperkalemia treatment and use of additional hypoglycemic agents, were not associated with the development of hypoglycemia, which is consistent with previous reports.[6]

CONCLUSIONS

Our findings indicate that using a weight‐based approach to insulin dosing when treating hyperkalemia may lead to a reduction in hypoglycemia without sacrificing the efficacy of potassium lowering. Females and patients with glucose values <140 mg/dL were at increased risk of hypoglycemia in this cohort. Based on the results of this research, a weight‐based dosing strategy of 0.1 U/kg IV insulin up to a maximum of 10 U should be considered, with further research desirable to validate these results.

This study was strengthened by the inclusion of all patients regardless of baseline glucose, baseline potassium, administration of other insulins, level of renal impairment, or symptomatic display of hypoglycemia or cardiac dysfunction, thus providing a broad representation of patients treated for acute hyperkalemia. This pilot study was limited in its scope by data collection for only 66 randomized patients per group rather than the entire patient population. In addition, the study utilized patient information from a single site, with few ethnicities represented. Validation of this research using a larger sample size should include greater variation in the patients served. Our inclusion of a hypoglycemia definition up to 24 hours after treatment may also be criticized. However, this is similar to previous reports and allows for a liberal time period for follow‐up glucose monitoring to be recorded.[7]

Because of its small sample size and the low event rate, this study was unable to draw conclusions about the ability of weight‐based insulin dosing to affect severe hypoglycemic events (<40 mg/dL). A study of more than 400 patients would be necessary to find statistically significant differences in the risk of severe hypoglycemia. Furthermore, because we did not examine the results from all patients in this cohort, we cannot conclusively determine the impact of treatment. The retrospective nature of this study limited our ability to capture hypoglycemic episodes during periods in which BG levels were not recorded. Additionally, changes to the post‐treatment glucose monitoring protocol may have also affected the incidence of hypoglycemia in 2 potential ways. First, early and unrecorded interventions may have occurred in patients with a trend toward hypoglycemia. Second, the longer time to follow‐up in the nonweight‐based group may have led to additional hypoglycemic episodes being missed. A prospective trial design could provide more comprehensive information about patient response to weight‐based versus traditional dosing of IV insulin for hyperkalemia. Further investigations on reducing adverse effects of insulin when treating hyperkalemia should focus on female patients and those with lower baseline BG values. Additionally, as newer agents to treat hyperkalemia are developed and tested, the approach to management should be revisited.[8, 9, 10]

Disclosures: Garry S. Tobin, MD, lectures or is on the speakers bureau for Eli Lilly, Jansen, Boehringher Ingelheim, and Novo Nordisk, and performs data safety monitoring for Novo Nordisk. The authors report no other potential conflicts of interest.

Hyperkalemia occurs in as many as 10% of all hospitalized patients,[1] leading to potentially fatal arrhythmias or cardiac arrest that results from ionic imbalance within the resting membrane potential of myocardial tissue.[2] Acute instances may be stabilized with insulin to stimulate intracellular uptake of potassium, but this increases the risk of hypoglycemia.[2] Centers for Medicare and Medicaid Services quality measures require hospitals to minimize hypoglycemic events, particularly serious events with blood glucose (BG) <40 mg/dL,[3] due to an association with an increase in mortality in the hospital setting.[4] Previous research at our tertiary care hospital found that 8.7% of patients had suffered a hypoglycemic event following insulin administration pursuant to acute hyperkalemia treatment, and that patients with a lower body weight are at increased risk of hypoglycemia, particularly severe hypoglycemia (BG <40 mg/dL).[5] Increasing the total dose of dextrose provided around the time of insulin administration is suggested to reduce this concern.[5]

Patients at our institution receive 50 g of dextrose in conjunction with intravenous (IV) insulin for hyperkalemia treatment. To further reduce the potential for hypoglycemia, our institution amended the acute hyperkalemia order set to provide prescribers an alternative dosing strategy to the standard 10 U of IV insulin traditionally used for this purpose. Beginning November 10, 2013, our computer prescriber order entry (CPOE) system automatically prepopulated a dose of 0.1 U/kg of body weight for any patients weighing <95 kg (doses rounded to the nearest whole unit) when the acute hyperkalemia order set was utilized. The maximum dose allowed continued to be 10 U. The revised order set also changed nursing orders to require BG monitoring as frequently as every hour following the administration of insulin and dextrose for the treatment of hyperkalemia.

The purpose of this study is to investigate whether weight‐based insulin dosing (0.1 U/kg) for patients weighing <95 kg, rather than a standard 10‐U insulin dose, resulted in fewer hypoglycemic episodes and patients affected. Secondarily, this study sought to determine the impact of weight‐based insulin dosing on potassium‐lowering effects of therapy and to detect any risk factors for development of hypoglycemia among this patient population.

METHODS

This institutional review boardapproved, single‐center, retrospective chart review examined patients for whom the physician order entry set for hyperkalemia therapy was utilized, including patients who weighed less than 95 kg and received regular insulin via weight‐based dosing (0.1 U/kg of body weight up to a maximum of 10 U) during the period November 10, 2013 to May 31, 2014, versus those who received fixed insulin dosing (10 U regardless of body weight) during the period May 1, 2013 to November 9, 2013. During each of these periods, the CPOE system autopopulated the recommended insulin dose, with the possibility for physician manual dose entry. Data collection was limited to the first use of insulin for hyperkalemia treatment per patient in each period.

Patients weighing <95 kg were the focus of this study because they received <10 U of insulin under the weight‐based dosing strategy. Patients were excluded from the study if they had a body weight >95 kg or no weight recorded, were not administered insulin as ordered, received greater than the CPOE‐specified insulin dose, or had no BG readings recorded within 24 hours of insulin administration. The first 66 patients within each group meeting all inclusion and exclusion criteria were randomly selected for analysis. This recruitment target was developed to provide enough patients for a meaningful analysis of hypoglycemia events based on previous reports from our institution.[5]

Hypoglycemia was defined as a recorded BG level <70 mg/dL within 24 hours after insulin administration; severe hypoglycemia was defined as a recorded BG <40 mg/dL within 24 hours. Individual episodes of hypoglycemia and severe hypoglycemia were recorded for each instance of such event separated by at least 1 hour from the time of the first recorded event. In addition, episodes of hypoglycemia or severe hypoglycemia and number of patients affected were assessed at within 6 hours, 6 to 12 hours, and 12 to 24 hours after insulin administration as separate subsets for statistical analysis.

For the purpose of assessing the potassium‐lowering efficacy of weight‐based versus traditional dosing of insulin, maximum serum potassium levels were examined in the 12‐hour interval before the hyperkalemia order set was implemented and compared with minimum potassium levels in the 12 hours after insulin was administered. A comparison of the treatment groups assessed differences between the mean decrease in serum potassium from baseline, the mean minimum potassium achieved, the number of patients achieving minimum potassium below 5.0 mEq/L, and the number of patients who subsequently received repeat treatment for hyperkalemia within 24 hours of treatment with insulin.

Statistical analysis was conducted utilizing ² and Fisher exact tests for nominal data and Student t test for continuous data to detect statistically significant differences between the groups. Binomial logistic multivariable analysis using a backward stepwise approach was used to determine factors for development of hypoglycemia, analyzed on a per‐patient basis to prevent characteristics from being over‐represented when events occurred multiple times to a single patient. All analyses were completed by using SPSS version 18 (SPSS Inc., Chicago, IL).

RESULTS

In total, 1734 entries were available for the acute hyperkalemia order set with insulin during the 2 periods investigated. Only 464 patients were eligible for manual chart review once weight‐based exclusions were identified by electronic database, with additional exclusion criteria later extracted from patient charts. Patients in both treatment groups were fairly well balanced, with a slightly lower body weight in the 10‐U insulin group recorded (Table 1). Patients in the weight‐based dosing group received between 4 and 9 U of insulin, depending on body weight.

A reduction in the number of hypoglycemic episodes was detected in the weight‐based dosing group of 56% within 24 hours, from 18 to 8 events (P = 0.05) (Table 2). The number of hypoglycemic events in every subset of time intervals was likewise reduced by at least 50% using weight‐based dosing (from 7 to 3 events within 6 hours, from 5 to 2 events in 612 hours, from 6 to 3 events in 1224 hours). The number of patients who experienced hypoglycemia within 24 hours after receiving insulin also was reduced in the weight‐based dosing group by 46% (P = 0.22).

Potassium lowering was comparable across both dosing strategies in every measure assessed (Table 2). Multivariate analysis revealed that baseline BG <140 mg/dL (adjusted odds ratio: 4.3, 95% confidence interval [CI]: 1.4‐13.7, P = 0.01) and female gender (adjusted odds ratio: 3.2, 95% CI: 1.1‐9.1, P = 0.03) were associated with an increased risk of hypoglycemia. Other factors, including administration of insulin beyond that for hyperkalemia treatment and use of additional hypoglycemic agents, were not associated with the development of hypoglycemia, which is consistent with previous reports.[6]

CONCLUSIONS

Our findings indicate that using a weight‐based approach to insulin dosing when treating hyperkalemia may lead to a reduction in hypoglycemia without sacrificing the efficacy of potassium lowering. Females and patients with glucose values <140 mg/dL were at increased risk of hypoglycemia in this cohort. Based on the results of this research, a weight‐based dosing strategy of 0.1 U/kg IV insulin up to a maximum of 10 U should be considered, with further research desirable to validate these results.

This study was strengthened by the inclusion of all patients regardless of baseline glucose, baseline potassium, administration of other insulins, level of renal impairment, or symptomatic display of hypoglycemia or cardiac dysfunction, thus providing a broad representation of patients treated for acute hyperkalemia. This pilot study was limited in its scope by data collection for only 66 randomized patients per group rather than the entire patient population. In addition, the study utilized patient information from a single site, with few ethnicities represented. Validation of this research using a larger sample size should include greater variation in the patients served. Our inclusion of a hypoglycemia definition up to 24 hours after treatment may also be criticized. However, this is similar to previous reports and allows for a liberal time period for follow‐up glucose monitoring to be recorded.[7]

Because of its small sample size and the low event rate, this study was unable to draw conclusions about the ability of weight‐based insulin dosing to affect severe hypoglycemic events (<40 mg/dL). A study of more than 400 patients would be necessary to find statistically significant differences in the risk of severe hypoglycemia. Furthermore, because we did not examine the results from all patients in this cohort, we cannot conclusively determine the impact of treatment. The retrospective nature of this study limited our ability to capture hypoglycemic episodes during periods in which BG levels were not recorded. Additionally, changes to the post‐treatment glucose monitoring protocol may have also affected the incidence of hypoglycemia in 2 potential ways. First, early and unrecorded interventions may have occurred in patients with a trend toward hypoglycemia. Second, the longer time to follow‐up in the nonweight‐based group may have led to additional hypoglycemic episodes being missed. A prospective trial design could provide more comprehensive information about patient response to weight‐based versus traditional dosing of IV insulin for hyperkalemia. Further investigations on reducing adverse effects of insulin when treating hyperkalemia should focus on female patients and those with lower baseline BG values. Additionally, as newer agents to treat hyperkalemia are developed and tested, the approach to management should be revisited.[8, 9, 10]

Disclosures: Garry S. Tobin, MD, lectures or is on the speakers bureau for Eli Lilly, Jansen, Boehringher Ingelheim, and Novo Nordisk, and performs data safety monitoring for Novo Nordisk. The authors report no other potential conflicts of interest.

Patient‐centered communication is a strategy that is used to promote shared understanding of the plan of care among providers and patients.[1, 2, 3] Caring for hospitalized patients is a collaborative effort that requires seamless patient‐centered communication among a rapidly changing care team to safely progress a patient from admission through discharge. Yet, hospitals continue to struggle with improving the complex and increasingly electronic conversation patterns among care team members and patients to achieve effective patient‐centered communication.[4, 5] When members of the care team operate in this environment, patients often receive conflicting information regarding their plan of care, medications, and test results. Ineffective communication can lead to a suboptimal patient experience, additional costs, medical errors, and preventable adverse events.[6, 7, 8, 9, 10]

A critical first step to improving patient‐centered communication is identifying the care team.[11, 12] Accurate and reliable identification of all care team members is a pressing information need; it is fundamental to efficiently conveying information about the plan of care to those who know the patient the best, must make timely decisions, or will assume care once the patient leaves the hospital.[13] Furthermore, it has implications for engaging patients more meaningfully in their care.[14, 15, 16, 17] Ideally, the process of identifying an individual caring for the patient in a specific role is quickly and reliably determined from the electronic health record, the single source of truth where any provider can quickly identify other team members. This source of truth can be updated manually when individual members assign and remove themselves from the care team, or automatically when accessing the patient's record, writing a note, placing an order, or adding a patient to a coverage list. When providers correctly identify other team members in this way, hospital paging directories and secure messaging tools that link to the electronic health record become more effective at supporting care team communication.[18]

In general, the process of identifying care teams is difficult,[19] and maintaining role assignments in the electronic health record is equally challenging. Vawdrey et al. previously reported that care team lists are inaccurate and cannot be used to reliably identify other members at any given moment.[18] The inability to identify team members often leads to incorrectly routed pages, e‐mail messages, and phone calls.[20] Consequently, the potential to reliably manage the care team and improve electronic communication remains untapped, rendering team collaboration and care coordination less effective.[18, 21, 22]

In recent years, the trend toward restructuring inpatient teamsgeographical localization, structured communication interventions, teamwork training, and interdisciplinary roundswould seem to diminish the need for electronic care team identification, as those efforts have already made a positive impact with regard to interprofessional communication and collaboration, team satisfaction, and adverse events.[23, 24, 25, 26] Nonetheless, interdisciplinary teamwork, though critically important for patient‐centered communication, does not completely obviate the need for accurate and reliable care team identification.[26] Although care teams are statically located on units, the plan of care is dynamic; it evolves when the patient's status changes, when new information becomes available, and when key longitudinal providers (eg, primary care physician, subspecialty consultant) make recommendations. Thus, information conveyed as a team on rounds quickly becomes out of date, requiring additional forms of communication. Furthermore, due to frequent ad hoc coverage among team members, the identity of providers covering the patient at any given moment is often not clear.[27] This is particularly problematic for nonunit‐based providers who try to communicate with unit‐based care team members. These providers, in particular, have valuable knowledge and insight that can aid the primary team in decision making.[28, 29] However, they typically do not participate in rounds, often waste time identifying responsible providers,[20] and may communicate their recommendations directly with the patient without discussing with the primary team. These factors in part explain why geographic localization has shown limited improvement in shared understanding of the plan of care.[23]

From the perspective of patients and caregivers, identification of the providers entering and leaving their room is also challenging; only 11% to 51% of patients identify their providers correctly.[30] This adds to confusion regarding who is responsible for which aspects of the patient's care and can negatively affect the perception of the quality of care received.[31] Use of whiteboards has been shown to improve the proportion of patients who could identify key providers,[32] but these are not reliably updated and generally cannot accommodate all team members. When face cards are used, patients and caregivers report that they are more likely to identify their providers correctly.[14, 33, 34] However, potential confusion may ensue when another provider assumes care of the patient in the same role. Finally, use of technology to display team members at the bedside is typically a feature that patients like and can improve identification of care team members.[14, 15, 16] Yet, patient engagement technologies are not readily available in the hospital setting,[35] and ideally should be linked to the electronic health record, which again must be reliably updated.[11, 12, 15, 16]

If care team identification is so critical for delivering effective patient‐centered communication, why is maintaining role assignment problematic? At the individual level, reasons include discontinuity of the care team due to changing clinical rotations and intrateam coverage, shift‐based schedules, and lack of awareness and underutilization of functionality. Additionally, clinicians may have different ways to maintain lists of patients. At the institutional level, functionality to enforce role assignment when accessing patient records may be disabled (to avoid perceived burdens on clinical staff or nonclinical personnel who require access for administrative functions). Finally, electronic health record vendors currently have no incentive to adopt functionality that supports more effective care coordination across settings.[22]

However, more than technical solutions and policy changes are required; care team identification in the electronic health record requires a change in institutional culture. Maintaining an accurate relationship to each patient requires work without tangible benefitsthe benefits accrue only when everyone else identifies their role on the teama tragedy of the commons. This can be illustrated by our own experience. We conducted a quality improvement initiative (Table 1) as a part of 2 concurrent research initiatives that serve to promote patient‐centered communication:[12] PCORI (Patient‐Centered Outcomes Research Institute Transitions), the goal of which is to improve care transitions within the Partners' Pioneer Accountable Care Organization; and the PROSPECT (Promoting Respect and Ongoing Safety Through Patient‐Centeredness, Engagement, Communication, and Technology) project, an initiative funded by the Gordon and Betty Moore Foundation to eliminate preventable harms in the acute care environment.[29] Our goal was to electronically manage the care team with a high degree of fidelity. We enhanced a home‐grown application, which was developed to improve management of team lists for inpatient providers, accessible from our electronic health record, to facilitate role assignment. Specifically, we leveraged existing care processes (eg, nursing log‐on to the electronic medication administration system) to automatically assign certain providers to the care team at change of shift, added functionality to make it easy to assign a provider to all patients on a list for a defined period of time, and encouraged providers to assign their role by demonstrating benefits including quick access to patient‐specific group e‐mail and secure messaging tools (Table 1, Key Facets). The initiative was well‐received by most disciplines, but uptake was suboptimal. Our research assistants routinely assigned residents and others to the care team because our proactive attempts at advertising and reinforcing use of the application failed to reach a critical mass. Most did not see immediate benefits because it was an added step to their busy day, had other methods of managing team lists, and only saw benefit if everyone else participated. Key facets of our care team identification initiative, successes, and challenges are outlined in Table 1.

There were a few glimmers of hope, however. On several PROSPECT units, we displayed team members on a tablet‐based patient portal so that patients would recognize their providers.[11, 17, 36] Similar to recent work by O'Leary et al.,[14] patients on PROSPECT units were able to correctly identify several care team members, but regularly asked why other providers (eg, consulting fellow) were not listed. Those providers asked the same question, and some eventually learned to assign their role via the application. As part of PROSEPCT, we visited other institutions and learned of an effort to display team members on high‐definition televisions in the patient's room. Several providers, wondering why they were not listed, learned to assign their role and their picture then appeared. Social pressure was the driving force.

Coincidently, we recently implemented a new electronic health record at our institution. Anecdotally, although no formal policy was established, many providers (eg, attendings, first responders, nurses, care coordinators, and other unit‐based providers) appear to be assigning their roles. Other providers (eg, dieticians, physical therapists, residents) also assign their role, but often fail to end role assignments upon completing their rotation or when the patient transfers to another service. Finally, even when actively involved, most subspecialists still do not designate their role. Despite these gaps and inconsistencies, we have made progress toward improving care team identification. The reasons for this progress are straightforward; during required training for the new electronic health record, all inpatient providers were taught to assign their role on the treatment team when assuming care of patients and now have 1 option for managing team lists. However, most providers were not trained to end their role assignments, and many have learned that role assignment is not required to access the patient's record; functionality to enforce this was disabled. Based on lessons learned from our experience,[12] we offer several strategies that hospitalists can employ to improve care team identification in the electronic health record (Table 2).

In the future, care team identification in the electronic health record can be automated by integrating directly with electronic workflows, online scheduling applications, and provider directories. Hospitals could then leverage care team lists to facilitate patient‐centered communication via secure web‐based and mobile messaging applications configured to simultaneously update all team members (eg, group messaging apps, microblogs).[11, 37, 38] By synchronizing with the electronic health record, role assignments can be automatically updated via these applications, further increasing fidelity of care team identification.[12] Finally, as hospitals implement acute care patient portals, team lists can be leveraged to display all care team members correctly so that patients and caregivers can communicate more easily with providers.[17] The potential ramifications for patient‐centered communication are tremendous.

Patient‐centered communication is a strategy that is used to promote shared understanding of the plan of care among providers and patients.[1, 2, 3] Caring for hospitalized patients is a collaborative effort that requires seamless patient‐centered communication among a rapidly changing care team to safely progress a patient from admission through discharge. Yet, hospitals continue to struggle with improving the complex and increasingly electronic conversation patterns among care team members and patients to achieve effective patient‐centered communication.[4, 5] When members of the care team operate in this environment, patients often receive conflicting information regarding their plan of care, medications, and test results. Ineffective communication can lead to a suboptimal patient experience, additional costs, medical errors, and preventable adverse events.[6, 7, 8, 9, 10]

A critical first step to improving patient‐centered communication is identifying the care team.[11, 12] Accurate and reliable identification of all care team members is a pressing information need; it is fundamental to efficiently conveying information about the plan of care to those who know the patient the best, must make timely decisions, or will assume care once the patient leaves the hospital.[13] Furthermore, it has implications for engaging patients more meaningfully in their care.[14, 15, 16, 17] Ideally, the process of identifying an individual caring for the patient in a specific role is quickly and reliably determined from the electronic health record, the single source of truth where any provider can quickly identify other team members. This source of truth can be updated manually when individual members assign and remove themselves from the care team, or automatically when accessing the patient's record, writing a note, placing an order, or adding a patient to a coverage list. When providers correctly identify other team members in this way, hospital paging directories and secure messaging tools that link to the electronic health record become more effective at supporting care team communication.[18]

In general, the process of identifying care teams is difficult,[19] and maintaining role assignments in the electronic health record is equally challenging. Vawdrey et al. previously reported that care team lists are inaccurate and cannot be used to reliably identify other members at any given moment.[18] The inability to identify team members often leads to incorrectly routed pages, e‐mail messages, and phone calls.[20] Consequently, the potential to reliably manage the care team and improve electronic communication remains untapped, rendering team collaboration and care coordination less effective.[18, 21, 22]

In recent years, the trend toward restructuring inpatient teamsgeographical localization, structured communication interventions, teamwork training, and interdisciplinary roundswould seem to diminish the need for electronic care team identification, as those efforts have already made a positive impact with regard to interprofessional communication and collaboration, team satisfaction, and adverse events.[23, 24, 25, 26] Nonetheless, interdisciplinary teamwork, though critically important for patient‐centered communication, does not completely obviate the need for accurate and reliable care team identification.[26] Although care teams are statically located on units, the plan of care is dynamic; it evolves when the patient's status changes, when new information becomes available, and when key longitudinal providers (eg, primary care physician, subspecialty consultant) make recommendations. Thus, information conveyed as a team on rounds quickly becomes out of date, requiring additional forms of communication. Furthermore, due to frequent ad hoc coverage among team members, the identity of providers covering the patient at any given moment is often not clear.[27] This is particularly problematic for nonunit‐based providers who try to communicate with unit‐based care team members. These providers, in particular, have valuable knowledge and insight that can aid the primary team in decision making.[28, 29] However, they typically do not participate in rounds, often waste time identifying responsible providers,[20] and may communicate their recommendations directly with the patient without discussing with the primary team. These factors in part explain why geographic localization has shown limited improvement in shared understanding of the plan of care.[23]

From the perspective of patients and caregivers, identification of the providers entering and leaving their room is also challenging; only 11% to 51% of patients identify their providers correctly.[30] This adds to confusion regarding who is responsible for which aspects of the patient's care and can negatively affect the perception of the quality of care received.[31] Use of whiteboards has been shown to improve the proportion of patients who could identify key providers,[32] but these are not reliably updated and generally cannot accommodate all team members. When face cards are used, patients and caregivers report that they are more likely to identify their providers correctly.[14, 33, 34] However, potential confusion may ensue when another provider assumes care of the patient in the same role. Finally, use of technology to display team members at the bedside is typically a feature that patients like and can improve identification of care team members.[14, 15, 16] Yet, patient engagement technologies are not readily available in the hospital setting,[35] and ideally should be linked to the electronic health record, which again must be reliably updated.[11, 12, 15, 16]

If care team identification is so critical for delivering effective patient‐centered communication, why is maintaining role assignment problematic? At the individual level, reasons include discontinuity of the care team due to changing clinical rotations and intrateam coverage, shift‐based schedules, and lack of awareness and underutilization of functionality. Additionally, clinicians may have different ways to maintain lists of patients. At the institutional level, functionality to enforce role assignment when accessing patient records may be disabled (to avoid perceived burdens on clinical staff or nonclinical personnel who require access for administrative functions). Finally, electronic health record vendors currently have no incentive to adopt functionality that supports more effective care coordination across settings.[22]

However, more than technical solutions and policy changes are required; care team identification in the electronic health record requires a change in institutional culture. Maintaining an accurate relationship to each patient requires work without tangible benefitsthe benefits accrue only when everyone else identifies their role on the teama tragedy of the commons. This can be illustrated by our own experience. We conducted a quality improvement initiative (Table 1) as a part of 2 concurrent research initiatives that serve to promote patient‐centered communication:[12] PCORI (Patient‐Centered Outcomes Research Institute Transitions), the goal of which is to improve care transitions within the Partners' Pioneer Accountable Care Organization; and the PROSPECT (Promoting Respect and Ongoing Safety Through Patient‐Centeredness, Engagement, Communication, and Technology) project, an initiative funded by the Gordon and Betty Moore Foundation to eliminate preventable harms in the acute care environment.[29] Our goal was to electronically manage the care team with a high degree of fidelity. We enhanced a home‐grown application, which was developed to improve management of team lists for inpatient providers, accessible from our electronic health record, to facilitate role assignment. Specifically, we leveraged existing care processes (eg, nursing log‐on to the electronic medication administration system) to automatically assign certain providers to the care team at change of shift, added functionality to make it easy to assign a provider to all patients on a list for a defined period of time, and encouraged providers to assign their role by demonstrating benefits including quick access to patient‐specific group e‐mail and secure messaging tools (Table 1, Key Facets). The initiative was well‐received by most disciplines, but uptake was suboptimal. Our research assistants routinely assigned residents and others to the care team because our proactive attempts at advertising and reinforcing use of the application failed to reach a critical mass. Most did not see immediate benefits because it was an added step to their busy day, had other methods of managing team lists, and only saw benefit if everyone else participated. Key facets of our care team identification initiative, successes, and challenges are outlined in Table 1.

There were a few glimmers of hope, however. On several PROSPECT units, we displayed team members on a tablet‐based patient portal so that patients would recognize their providers.[11, 17, 36] Similar to recent work by O'Leary et al.,[14] patients on PROSPECT units were able to correctly identify several care team members, but regularly asked why other providers (eg, consulting fellow) were not listed. Those providers asked the same question, and some eventually learned to assign their role via the application. As part of PROSEPCT, we visited other institutions and learned of an effort to display team members on high‐definition televisions in the patient's room. Several providers, wondering why they were not listed, learned to assign their role and their picture then appeared. Social pressure was the driving force.

Coincidently, we recently implemented a new electronic health record at our institution. Anecdotally, although no formal policy was established, many providers (eg, attendings, first responders, nurses, care coordinators, and other unit‐based providers) appear to be assigning their roles. Other providers (eg, dieticians, physical therapists, residents) also assign their role, but often fail to end role assignments upon completing their rotation or when the patient transfers to another service. Finally, even when actively involved, most subspecialists still do not designate their role. Despite these gaps and inconsistencies, we have made progress toward improving care team identification. The reasons for this progress are straightforward; during required training for the new electronic health record, all inpatient providers were taught to assign their role on the treatment team when assuming care of patients and now have 1 option for managing team lists. However, most providers were not trained to end their role assignments, and many have learned that role assignment is not required to access the patient's record; functionality to enforce this was disabled. Based on lessons learned from our experience,[12] we offer several strategies that hospitalists can employ to improve care team identification in the electronic health record (Table 2).

In the future, care team identification in the electronic health record can be automated by integrating directly with electronic workflows, online scheduling applications, and provider directories. Hospitals could then leverage care team lists to facilitate patient‐centered communication via secure web‐based and mobile messaging applications configured to simultaneously update all team members (eg, group messaging apps, microblogs).[11, 37, 38] By synchronizing with the electronic health record, role assignments can be automatically updated via these applications, further increasing fidelity of care team identification.[12] Finally, as hospitals implement acute care patient portals, team lists can be leveraged to display all care team members correctly so that patients and caregivers can communicate more easily with providers.[17] The potential ramifications for patient‐centered communication are tremendous.

Patient‐centered communication is a strategy that is used to promote shared understanding of the plan of care among providers and patients.[1, 2, 3] Caring for hospitalized patients is a collaborative effort that requires seamless patient‐centered communication among a rapidly changing care team to safely progress a patient from admission through discharge. Yet, hospitals continue to struggle with improving the complex and increasingly electronic conversation patterns among care team members and patients to achieve effective patient‐centered communication.[4, 5] When members of the care team operate in this environment, patients often receive conflicting information regarding their plan of care, medications, and test results. Ineffective communication can lead to a suboptimal patient experience, additional costs, medical errors, and preventable adverse events.[6, 7, 8, 9, 10]

A critical first step to improving patient‐centered communication is identifying the care team.[11, 12] Accurate and reliable identification of all care team members is a pressing information need; it is fundamental to efficiently conveying information about the plan of care to those who know the patient the best, must make timely decisions, or will assume care once the patient leaves the hospital.[13] Furthermore, it has implications for engaging patients more meaningfully in their care.[14, 15, 16, 17] Ideally, the process of identifying an individual caring for the patient in a specific role is quickly and reliably determined from the electronic health record, the single source of truth where any provider can quickly identify other team members. This source of truth can be updated manually when individual members assign and remove themselves from the care team, or automatically when accessing the patient's record, writing a note, placing an order, or adding a patient to a coverage list. When providers correctly identify other team members in this way, hospital paging directories and secure messaging tools that link to the electronic health record become more effective at supporting care team communication.[18]

In general, the process of identifying care teams is difficult,[19] and maintaining role assignments in the electronic health record is equally challenging. Vawdrey et al. previously reported that care team lists are inaccurate and cannot be used to reliably identify other members at any given moment.[18] The inability to identify team members often leads to incorrectly routed pages, e‐mail messages, and phone calls.[20] Consequently, the potential to reliably manage the care team and improve electronic communication remains untapped, rendering team collaboration and care coordination less effective.[18, 21, 22]

In recent years, the trend toward restructuring inpatient teamsgeographical localization, structured communication interventions, teamwork training, and interdisciplinary roundswould seem to diminish the need for electronic care team identification, as those efforts have already made a positive impact with regard to interprofessional communication and collaboration, team satisfaction, and adverse events.[23, 24, 25, 26] Nonetheless, interdisciplinary teamwork, though critically important for patient‐centered communication, does not completely obviate the need for accurate and reliable care team identification.[26] Although care teams are statically located on units, the plan of care is dynamic; it evolves when the patient's status changes, when new information becomes available, and when key longitudinal providers (eg, primary care physician, subspecialty consultant) make recommendations. Thus, information conveyed as a team on rounds quickly becomes out of date, requiring additional forms of communication. Furthermore, due to frequent ad hoc coverage among team members, the identity of providers covering the patient at any given moment is often not clear.[27] This is particularly problematic for nonunit‐based providers who try to communicate with unit‐based care team members. These providers, in particular, have valuable knowledge and insight that can aid the primary team in decision making.[28, 29] However, they typically do not participate in rounds, often waste time identifying responsible providers,[20] and may communicate their recommendations directly with the patient without discussing with the primary team. These factors in part explain why geographic localization has shown limited improvement in shared understanding of the plan of care.[23]

From the perspective of patients and caregivers, identification of the providers entering and leaving their room is also challenging; only 11% to 51% of patients identify their providers correctly.[30] This adds to confusion regarding who is responsible for which aspects of the patient's care and can negatively affect the perception of the quality of care received.[31] Use of whiteboards has been shown to improve the proportion of patients who could identify key providers,[32] but these are not reliably updated and generally cannot accommodate all team members. When face cards are used, patients and caregivers report that they are more likely to identify their providers correctly.[14, 33, 34] However, potential confusion may ensue when another provider assumes care of the patient in the same role. Finally, use of technology to display team members at the bedside is typically a feature that patients like and can improve identification of care team members.[14, 15, 16] Yet, patient engagement technologies are not readily available in the hospital setting,[35] and ideally should be linked to the electronic health record, which again must be reliably updated.[11, 12, 15, 16]

If care team identification is so critical for delivering effective patient‐centered communication, why is maintaining role assignment problematic? At the individual level, reasons include discontinuity of the care team due to changing clinical rotations and intrateam coverage, shift‐based schedules, and lack of awareness and underutilization of functionality. Additionally, clinicians may have different ways to maintain lists of patients. At the institutional level, functionality to enforce role assignment when accessing patient records may be disabled (to avoid perceived burdens on clinical staff or nonclinical personnel who require access for administrative functions). Finally, electronic health record vendors currently have no incentive to adopt functionality that supports more effective care coordination across settings.[22]

However, more than technical solutions and policy changes are required; care team identification in the electronic health record requires a change in institutional culture. Maintaining an accurate relationship to each patient requires work without tangible benefitsthe benefits accrue only when everyone else identifies their role on the teama tragedy of the commons. This can be illustrated by our own experience. We conducted a quality improvement initiative (Table 1) as a part of 2 concurrent research initiatives that serve to promote patient‐centered communication:[12] PCORI (Patient‐Centered Outcomes Research Institute Transitions), the goal of which is to improve care transitions within the Partners' Pioneer Accountable Care Organization; and the PROSPECT (Promoting Respect and Ongoing Safety Through Patient‐Centeredness, Engagement, Communication, and Technology) project, an initiative funded by the Gordon and Betty Moore Foundation to eliminate preventable harms in the acute care environment.[29] Our goal was to electronically manage the care team with a high degree of fidelity. We enhanced a home‐grown application, which was developed to improve management of team lists for inpatient providers, accessible from our electronic health record, to facilitate role assignment. Specifically, we leveraged existing care processes (eg, nursing log‐on to the electronic medication administration system) to automatically assign certain providers to the care team at change of shift, added functionality to make it easy to assign a provider to all patients on a list for a defined period of time, and encouraged providers to assign their role by demonstrating benefits including quick access to patient‐specific group e‐mail and secure messaging tools (Table 1, Key Facets). The initiative was well‐received by most disciplines, but uptake was suboptimal. Our research assistants routinely assigned residents and others to the care team because our proactive attempts at advertising and reinforcing use of the application failed to reach a critical mass. Most did not see immediate benefits because it was an added step to their busy day, had other methods of managing team lists, and only saw benefit if everyone else participated. Key facets of our care team identification initiative, successes, and challenges are outlined in Table 1.

There were a few glimmers of hope, however. On several PROSPECT units, we displayed team members on a tablet‐based patient portal so that patients would recognize their providers.[11, 17, 36] Similar to recent work by O'Leary et al.,[14] patients on PROSPECT units were able to correctly identify several care team members, but regularly asked why other providers (eg, consulting fellow) were not listed. Those providers asked the same question, and some eventually learned to assign their role via the application. As part of PROSEPCT, we visited other institutions and learned of an effort to display team members on high‐definition televisions in the patient's room. Several providers, wondering why they were not listed, learned to assign their role and their picture then appeared. Social pressure was the driving force.

Coincidently, we recently implemented a new electronic health record at our institution. Anecdotally, although no formal policy was established, many providers (eg, attendings, first responders, nurses, care coordinators, and other unit‐based providers) appear to be assigning their roles. Other providers (eg, dieticians, physical therapists, residents) also assign their role, but often fail to end role assignments upon completing their rotation or when the patient transfers to another service. Finally, even when actively involved, most subspecialists still do not designate their role. Despite these gaps and inconsistencies, we have made progress toward improving care team identification. The reasons for this progress are straightforward; during required training for the new electronic health record, all inpatient providers were taught to assign their role on the treatment team when assuming care of patients and now have 1 option for managing team lists. However, most providers were not trained to end their role assignments, and many have learned that role assignment is not required to access the patient's record; functionality to enforce this was disabled. Based on lessons learned from our experience,[12] we offer several strategies that hospitalists can employ to improve care team identification in the electronic health record (Table 2).

In the future, care team identification in the electronic health record can be automated by integrating directly with electronic workflows, online scheduling applications, and provider directories. Hospitals could then leverage care team lists to facilitate patient‐centered communication via secure web‐based and mobile messaging applications configured to simultaneously update all team members (eg, group messaging apps, microblogs).[11, 37, 38] By synchronizing with the electronic health record, role assignments can be automatically updated via these applications, further increasing fidelity of care team identification.[12] Finally, as hospitals implement acute care patient portals, team lists can be leveraged to display all care team members correctly so that patients and caregivers can communicate more easily with providers.[17] The potential ramifications for patient‐centered communication are tremendous.

Blood cultures are the gold standard test for the diagnosis of bloodstream infections (BSI). Given the high mortality associated with BSI,[1, 2, 3] physicians have a low threshold to obtain blood cultures.[4, 5] Unfortunately, physicians are poor at predicting which hospitalized patients have BSI,[6, 7] and published guidelines do not provide clear indications for the use of blood cultures.[8] As a result, current practice follows a culture if spikes paradigm, whereby inpatient providers often obtain blood cultures in the setting of any fever. This is the most common anticipatory guidance communicated between providers, involving up to 75% of written sign‐out instructions.[9] The result is a low rate of true positive blood cultures (5%10%)[10, 11, 12] with only a slightly lower rate of false positive blood cultures (contaminants).[12, 13, 14] False positive blood cultures often lead to repeat blood cultures, unnecessary antibiotic use, and increased hospital cost and length of stay.[13]

Over the last several years, there has been an increased emphasis on practicing high‐value care by avoiding unnecessary and duplicate testing. In 2012, the American Board of Internal Medicine introduced the Choosing Wisely campaign, with specific initiatives to reduce medical waste and overuse. Given the low yield of blood cultures, guidance on patients in whom blood cultures are most appropriate would be welcome. Studies assessing risk factors for bacteremia have led to the development of multiple stratification systems without overall consensus.[10, 15, 16, 17, 18, 19, 20] Furthermore, much of the current literature on blood culture utilization includes cultures drawn in the emergency department (ED) or intensive care unit setting (ICU).[10, 18, 19, 20] Less is known regarding the rates of positivity and utility for blood cultures drawn on patients hospitalized on an acute care medical ward.

Our study had 3 main objectives: (1) determine the rates of true positive and false positive blood cultures among hospitalized medical patients, (2) determine the ability of physician‐selected indications and patient characteristics to predict BSI, and (3) identify populations in which blood cultures may not be necessary.

PATIENTS AND METHODS

RESULTS

DISCUSSION

In this prospective study, we determined the diagnostic yield of blood cultures ordered on hospitalized medical patients to be low, with just 3.6% of orders identifying a true BSI. This was coupled with a similar false positive rate of 2.3%. Our study found rates of true positive blood cultures much lower in hospitalized medical patients than in rates previously described when ED and ICU patients were included.[11, 16]

Although ordering blood cultures is a routine clinical behavior when there is concern for an infection, a clinician's ability to subjectively predict who has a BSI only improves the likelihood 2‐fold.[6] Despite the availability of multiple scoring systems to aid the clinicians,[10, 21, 22] our study found that over 50% of cultures were ordered in the setting of fever or leukocytosis, potentially demonstrating a triggered response to an event, rather than a decision based on probabilities. This common clinician instinct to culture if spikes is an ineffective practice if not coupled with additional clinical information. In fact, in 1 retrospective study, there was no association between fever spike and blood culture positivity.[23]

Our study suggests that objective and easily obtainable clinical characteristics may be effective in helping determine the probability of blood cultures revealing a BSI. Although more robust prediction models have value, they often require multiple inputs, limiting their utility to the bedside clinician. Stratifying patients by either antibiotic exposure or working diagnosis may provide the most benefit for the hospitalized medical patient. For those on antibiotics, the yield of true positive blood cultures is so low that they are unlikely to provide clinically useful information. In fact, although nearly two‐thirds of cultures were obtained after antibiotic exposure, only 1 (0.2%) of these patients had a culture that provided additional information regarding a BSI. Bacteremia had already been established for the other 4 patients. These results are similar to a prior study, which concluded that physicians should wait 72 hours from time of preantibiotic cultures before considering additional blood cultures given the lack of additional information provided.[24]

The working diagnosis also drives the probability of a positive blood culture. As has been shown with other studies, blood cultures are unlikely to diagnose a BSI for patients being treated for either cellulitis or pneumonia.[25, 26, 27] In our study, the working diagnosis prompting the most blood cultures was pneumonia, with the false positive rate exceeding the true positive rate, a finding consistent with previous literature. This situation may lead to the addition of unnecessary antibiotics while waiting for a positive culture to be confirmed as a false positive (eg, vancomycin for a preliminary culture showing gram‐positive cocci in clusters).

There are a number of limitations to our study. Physician‐chosen indication may not correlate with the actual clinical picture and/or may not represent the full set of variables involved in the clinical decision to order a blood culture. However, the subjective clinical indication and the objective clinical criteria found in the chart provided similar LRs. Our study did not evaluate the potential harm of not ordering a blood culture. We also did not assess the value of a true negative culture particularly in patients with endovascular infections where additional cultures are often required to document clearance of bacteremia. Lastly, our study applies to patients on a hospitalized medical service and was performed at a VA hospital with a specific population of elderly male patients, which may limit the generalizability of our results.

Despite these limitations, this study benefits from its prospective design, along with the fact that >90% of blood culture orders placed included a corresponding indication. This provides insight into physician clinical reasoning at the time the blood culture was ordered. In addition, our ability to calculate likelihood ratios provides bedside physicians with an easy and powerful way of modifying the probability of BSI prior to ordering blood cultures, aiding them in providing high‐value clinical care while potentially reducing testing overuse.

The acceptability of not obtaining blood cultures may vary by clinical experience and by specialty. Physicians must weigh the low true positive rate against the consequences of missing a BSI. Although not a substitute for clinical judgement, the LRs in this study can provide a framework to aid in clinical decision making. For example, assuming a pretest probability of 3.6% (the rate of true positive for our entire cohort), blood cultures may not be equally as compelling in 2 similar patients with fever. The first is not on antibiotics and also has a leukocytosis. The second is being treated for pneumonia and is already on antibiotics. For the first patient, using a LR+ of 5.6 (for the fever and leukocytosis in the absence of antibiotics) modifies the patient's probability of a true positive blood culture to 17.3%. Blood cultures should be ordered. In contrast, for the second patient, using a LR+ of 0.4 (for the presence of antibiotics) decreases the patient's probability of a true positive blood culture to 1.5%. Armed with these data, the bedside clinician can now decide whether this rate of true positivity warrants blood cultures. For some, this rate will be comfortably low. For others, this rate will not assuage them; only the negative culture will. Our data are not meant to make this decision, but may aid in making it a probability‐based decision.

Blood cultures are the gold standard test for the diagnosis of bloodstream infections (BSI). Given the high mortality associated with BSI,[1, 2, 3] physicians have a low threshold to obtain blood cultures.[4, 5] Unfortunately, physicians are poor at predicting which hospitalized patients have BSI,[6, 7] and published guidelines do not provide clear indications for the use of blood cultures.[8] As a result, current practice follows a culture if spikes paradigm, whereby inpatient providers often obtain blood cultures in the setting of any fever. This is the most common anticipatory guidance communicated between providers, involving up to 75% of written sign‐out instructions.[9] The result is a low rate of true positive blood cultures (5%10%)[10, 11, 12] with only a slightly lower rate of false positive blood cultures (contaminants).[12, 13, 14] False positive blood cultures often lead to repeat blood cultures, unnecessary antibiotic use, and increased hospital cost and length of stay.[13]

Over the last several years, there has been an increased emphasis on practicing high‐value care by avoiding unnecessary and duplicate testing. In 2012, the American Board of Internal Medicine introduced the Choosing Wisely campaign, with specific initiatives to reduce medical waste and overuse. Given the low yield of blood cultures, guidance on patients in whom blood cultures are most appropriate would be welcome. Studies assessing risk factors for bacteremia have led to the development of multiple stratification systems without overall consensus.[10, 15, 16, 17, 18, 19, 20] Furthermore, much of the current literature on blood culture utilization includes cultures drawn in the emergency department (ED) or intensive care unit setting (ICU).[10, 18, 19, 20] Less is known regarding the rates of positivity and utility for blood cultures drawn on patients hospitalized on an acute care medical ward.

Our study had 3 main objectives: (1) determine the rates of true positive and false positive blood cultures among hospitalized medical patients, (2) determine the ability of physician‐selected indications and patient characteristics to predict BSI, and (3) identify populations in which blood cultures may not be necessary.

PATIENTS AND METHODS

RESULTS

DISCUSSION

In this prospective study, we determined the diagnostic yield of blood cultures ordered on hospitalized medical patients to be low, with just 3.6% of orders identifying a true BSI. This was coupled with a similar false positive rate of 2.3%. Our study found rates of true positive blood cultures much lower in hospitalized medical patients than in rates previously described when ED and ICU patients were included.[11, 16]

Although ordering blood cultures is a routine clinical behavior when there is concern for an infection, a clinician's ability to subjectively predict who has a BSI only improves the likelihood 2‐fold.[6] Despite the availability of multiple scoring systems to aid the clinicians,[10, 21, 22] our study found that over 50% of cultures were ordered in the setting of fever or leukocytosis, potentially demonstrating a triggered response to an event, rather than a decision based on probabilities. This common clinician instinct to culture if spikes is an ineffective practice if not coupled with additional clinical information. In fact, in 1 retrospective study, there was no association between fever spike and blood culture positivity.[23]

Our study suggests that objective and easily obtainable clinical characteristics may be effective in helping determine the probability of blood cultures revealing a BSI. Although more robust prediction models have value, they often require multiple inputs, limiting their utility to the bedside clinician. Stratifying patients by either antibiotic exposure or working diagnosis may provide the most benefit for the hospitalized medical patient. For those on antibiotics, the yield of true positive blood cultures is so low that they are unlikely to provide clinically useful information. In fact, although nearly two‐thirds of cultures were obtained after antibiotic exposure, only 1 (0.2%) of these patients had a culture that provided additional information regarding a BSI. Bacteremia had already been established for the other 4 patients. These results are similar to a prior study, which concluded that physicians should wait 72 hours from time of preantibiotic cultures before considering additional blood cultures given the lack of additional information provided.[24]

The working diagnosis also drives the probability of a positive blood culture. As has been shown with other studies, blood cultures are unlikely to diagnose a BSI for patients being treated for either cellulitis or pneumonia.[25, 26, 27] In our study, the working diagnosis prompting the most blood cultures was pneumonia, with the false positive rate exceeding the true positive rate, a finding consistent with previous literature. This situation may lead to the addition of unnecessary antibiotics while waiting for a positive culture to be confirmed as a false positive (eg, vancomycin for a preliminary culture showing gram‐positive cocci in clusters).

There are a number of limitations to our study. Physician‐chosen indication may not correlate with the actual clinical picture and/or may not represent the full set of variables involved in the clinical decision to order a blood culture. However, the subjective clinical indication and the objective clinical criteria found in the chart provided similar LRs. Our study did not evaluate the potential harm of not ordering a blood culture. We also did not assess the value of a true negative culture particularly in patients with endovascular infections where additional cultures are often required to document clearance of bacteremia. Lastly, our study applies to patients on a hospitalized medical service and was performed at a VA hospital with a specific population of elderly male patients, which may limit the generalizability of our results.

Despite these limitations, this study benefits from its prospective design, along with the fact that >90% of blood culture orders placed included a corresponding indication. This provides insight into physician clinical reasoning at the time the blood culture was ordered. In addition, our ability to calculate likelihood ratios provides bedside physicians with an easy and powerful way of modifying the probability of BSI prior to ordering blood cultures, aiding them in providing high‐value clinical care while potentially reducing testing overuse.

The acceptability of not obtaining blood cultures may vary by clinical experience and by specialty. Physicians must weigh the low true positive rate against the consequences of missing a BSI. Although not a substitute for clinical judgement, the LRs in this study can provide a framework to aid in clinical decision making. For example, assuming a pretest probability of 3.6% (the rate of true positive for our entire cohort), blood cultures may not be equally as compelling in 2 similar patients with fever. The first is not on antibiotics and also has a leukocytosis. The second is being treated for pneumonia and is already on antibiotics. For the first patient, using a LR+ of 5.6 (for the fever and leukocytosis in the absence of antibiotics) modifies the patient's probability of a true positive blood culture to 17.3%. Blood cultures should be ordered. In contrast, for the second patient, using a LR+ of 0.4 (for the presence of antibiotics) decreases the patient's probability of a true positive blood culture to 1.5%. Armed with these data, the bedside clinician can now decide whether this rate of true positivity warrants blood cultures. For some, this rate will be comfortably low. For others, this rate will not assuage them; only the negative culture will. Our data are not meant to make this decision, but may aid in making it a probability‐based decision.

Blood cultures are the gold standard test for the diagnosis of bloodstream infections (BSI). Given the high mortality associated with BSI,[1, 2, 3] physicians have a low threshold to obtain blood cultures.[4, 5] Unfortunately, physicians are poor at predicting which hospitalized patients have BSI,[6, 7] and published guidelines do not provide clear indications for the use of blood cultures.[8] As a result, current practice follows a culture if spikes paradigm, whereby inpatient providers often obtain blood cultures in the setting of any fever. This is the most common anticipatory guidance communicated between providers, involving up to 75% of written sign‐out instructions.[9] The result is a low rate of true positive blood cultures (5%10%)[10, 11, 12] with only a slightly lower rate of false positive blood cultures (contaminants).[12, 13, 14] False positive blood cultures often lead to repeat blood cultures, unnecessary antibiotic use, and increased hospital cost and length of stay.[13]

Over the last several years, there has been an increased emphasis on practicing high‐value care by avoiding unnecessary and duplicate testing. In 2012, the American Board of Internal Medicine introduced the Choosing Wisely campaign, with specific initiatives to reduce medical waste and overuse. Given the low yield of blood cultures, guidance on patients in whom blood cultures are most appropriate would be welcome. Studies assessing risk factors for bacteremia have led to the development of multiple stratification systems without overall consensus.[10, 15, 16, 17, 18, 19, 20] Furthermore, much of the current literature on blood culture utilization includes cultures drawn in the emergency department (ED) or intensive care unit setting (ICU).[10, 18, 19, 20] Less is known regarding the rates of positivity and utility for blood cultures drawn on patients hospitalized on an acute care medical ward.

Our study had 3 main objectives: (1) determine the rates of true positive and false positive blood cultures among hospitalized medical patients, (2) determine the ability of physician‐selected indications and patient characteristics to predict BSI, and (3) identify populations in which blood cultures may not be necessary.

PATIENTS AND METHODS

RESULTS