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Regional Variation in Standardized Costs of Care at Children’s Hospitals
With some areas of the country spending close to 3 times more on healthcare than others, regional variation in healthcare spending has been the focus of national attention.1-7 Since 1973, the Dartmouth Institute has studied regional variation in healthcare utilization and spending and concluded that variation is “unwarranted” because it is driven by providers’ practice patterns rather than differences in medical need, patient preferences, or evidence-based medicine.8-11 However, critics of the Dartmouth Institute’s findings argue that their approach does not adequately adjust for community-level income, and that higher costs in some areas reflect greater patient needs that are not reflected in illness acuity alone.12-14
While Medicare data have made it possible to study variations in spending for the senior population, fragmentation of insurance coverage and nonstandardized data structures make studying the pediatric population more difficult. However, the Children’s Hospital Association’s (CHA) Pediatric Health Information System (PHIS) has made large-scale comparisons more feasible. To overcome challenges associated with using charges and nonuniform cost data, PHIS-derived standardized costs provide new opportunities for comparisons.15,16 Initial analyses using PHIS data showed significant interhospital variations in costs of care,15 but they did not adjust for differences in populations and assess the drivers of variation. A more recent study that controlled for payer status, comorbidities, and illness severity found that intensive care unit (ICU) utilization varied significantly for children hospitalized for asthma, suggesting that hospital practice patterns drive differences in cost.17
This study uses PHIS data to analyze regional variations in standardized costs of care for 3 conditions for which children are hospitalized. To assess potential drivers of variation, the study investigates the effects of patient-level demographic and illness-severity variables as well as encounter-level variables on costs of care. It also estimates cost savings from reducing variation.
METHODS
Data Source
This retrospective cohort study uses the PHIS database (CHA, Overland Park, KS), which includes 48 freestanding children’s hospitals located in noncompeting markets across the United States and accounts for approximately 20% of pediatric hospitalizations. PHIS includes patient demographics, International Classification of Diseases, 9th Revision (ICD-9) diagnosis and procedure codes, as well as hospital charges. In addition to total charges, PHIS reports imaging, laboratory, pharmacy, and “other” charges. The “other” category aggregates clinical, supply, room, and nursing charges (including facility fees and ancillary staff services).
Inclusion Criteria
Inpatient- and observation-status hospitalizations for asthma, diabetic ketoacidosis (DKA), and acute gastroenteritis (AGE) at 46 PHIS hospitals from October 2014 to September 2015 were included. Two hospitals were excluded because of missing data. Hospitalizations for patients >18 years were excluded.
Hospitalizations were categorized by using All Patient Refined-Diagnosis Related Groups (APR-DRGs) version 24 (3M Health Information Systems, St. Paul, MN)18 based on the ICD-9 diagnosis and procedure codes assigned during the episode of care. Analyses included APR-DRG 141 (asthma), primary diagnosis ICD-9 codes 250.11 and 250.13 (DKA), and APR-DRG 249 (AGE). ICD-9 codes were used for DKA for increased specificity.19 These conditions were chosen to represent 3 clinical scenarios: (1) a diagnosis for which hospitals differ on whether certain aspects of care are provided in the ICU (asthma), (2) a diagnosis that frequently includes care in an ICU (DKA), and (3) a diagnosis that typically does not include ICU care (AGE).19
Study Design
To focus the analysis on variation in resource utilization across hospitals rather than variations in hospital item charges, each billed resource was assigned a standardized cost.15,16 For each clinical transaction code (CTC), the median unit cost was calculated for each hospital. The median of the hospital medians was defined as the standardized unit cost for that CTC.
The primary outcome variable was the total standardized cost for the hospitalization adjusted for patient-level demographic and illness-severity variables. Patient demographic and illness-severity covariates included age, race, gender, ZIP code-based median annual household income (HHI), rural-urban location, distance from home ZIP code to the hospital, chronic condition indicator (CCI), and severity-of-illness (SOI). When assessing drivers of variation, encounter-level covariates were added, including length of stay (LOS) in hours, ICU utilization, and 7-day readmission (an imprecise measure to account for quality of care during the index visit). The contribution of imaging, laboratory, pharmacy, and “other” costs was also considered.
Median annual HHI for patients’ home ZIP code was obtained from 2010 US Census data. Community-level HHI, a proxy for socioeconomic status (SES),20,21 was classified into categories based on the 2015 US federal poverty level (FPL) for a family of 422: HHI-1 = ≤ 1.5 × FPL; HHI-2 = 1.5 to 2 × FPL; HHI-3 = 2 to 3 × FPL; HHI-4 = ≥ 3 × FPL. Rural-urban commuting area (RUCA) codes were used to determine the rural-urban classification of the patient’s home.23 The distance from home ZIP code to the hospital was included as an additional control for illness severity because patients traveling longer distances are often more sick and require more resources.24
The Agency for Healthcare Research and Quality CCI classification system was used to identify the presence of a chronic condition.25 For asthma, CCI was flagged if the patient had a chronic condition other than asthma; for DKA, CCI was flagged if the patient had a chronic condition other than DKA; and for AGE, CCI was flagged if the patient had any chronic condition.
The APR-DRG system provides a 4-level SOI score with each APR-DRG category. Patient factors, such as comorbid diagnoses, are considered in severity scores generated through 3M’s proprietary algorithms.18
For the first analysis, the 46 hospitals were categorized into 7 geographic regions based on 2010 US Census Divisions.26 To overcome small hospital sample sizes, Mountain and Pacific were combined into West, and Middle Atlantic and New England were combined into North East. Because PHIS hospitals are located in noncompeting geographic regions, for the second analysis, we examined hospital-level variation (considering each hospital as its own region).
Data Analysis
To focus the analysis on “typical” patients and produce more robust estimates of central tendencies, the top and bottom 5% of hospitalizations with the most extreme standardized costs by condition were trimmed.27 Standardized costs were log-transformed because of their nonnormal distribution and analyzed by using linear mixed models. Covariates were added stepwise to assess the proportion of the variance explained by each predictor. Post-hoc tests with conservative single-step stepwise mutation model corrections for multiple testing were used to compare adjusted costs. Statistical analyses were performed using SAS version 9.3 (SAS Institute, Cary, NC). P values < 0.05 were considered significant. The Children’s Hospital of Philadelphia Institutional Review Board did not classify this study as human subjects research.
RESULTS
During the study period, there were 26,430 hospitalizations for asthma, 5056 for DKA, and 16,274 for AGE (Table 1).
Variation Across Census Regions
After adjusting for patient-level demographic and illness-severity variables, differences in adjusted total standardized costs remained between regions (P < 0.001). Although no region was an outlier compared to the overall mean for any of the conditions, regions were statistically different in pairwise comparison. The East North Central, South Atlantic, and West South Central regions had the highest adjusted total standardized costs for each of the conditions. The East South Central and West North Central regions had the lowest costs for each of the conditions. Adjusted total standardized costs were 120% higher for asthma ($1920 vs $4227), 46% higher for DKA ($7429 vs $10,881), and 150% higher for AGE ($3316 vs $8292) in the highest-cost region compared with the lowest-cost region (Table 2A).
Variation Within Census Regions
After controlling for patient-level demographic and illness-severity variables, standardized costs were different across hospitals in the same region (P < 0.001; panel A in Figure). This was true for all conditions in each region. Differences between the lowest- and highest-cost hospitals within the same region ranged from 111% to 420% for asthma, 101% to 398% for DKA, and 166% to 787% for AGE (Table 3).
Variation Across Hospitals (Each Hospital as Its Own Region)
One hospital had the highest adjusted standardized costs for all 3 conditions ($9087 for asthma, $28,564 for DKA, and $23,387 for AGE) and was outside of the 95% confidence interval compared with the overall means. The second highest-cost hospitals for asthma ($5977) and AGE ($18,780) were also outside of the 95% confidence interval. After removing these outliers, the difference between the highest- and lowest-cost hospitals was 549% for asthma ($721 vs $4678), 491% for DKA ($2738 vs $16,192), and 681% for AGE ($1317 vs $10,281; Table 2B).
Drivers of Variation Across Census Regions
Patient-level demographic and illness-severity variables explained very little of the variation in standardized costs across regions. For each of the conditions, age, race, gender, community-level HHI, RUCA, and distance from home to the hospital each accounted for <1.5% of variation, while SOI and CCI each accounted for <5%. Overall, patient-level variables explained 5.5%, 3.7%, and 6.7% of variation for asthma, DKA, and AGE.
Encounter-level variables explained a much larger percentage of the variation in costs. LOS accounted for 17.8% of the variation for asthma, 9.8% for DKA, and 8.7% for AGE. ICU utilization explained 6.9% of the variation for asthma and 12.5% for DKA; ICU use was not a major driver for AGE. Seven-day readmissions accounted for <0.5% for each of the conditions. The combination of patient-level and encounter-level variables explained 27%, 24%, and 15% of the variation for asthma, DKA, and AGE.
Drivers of Variation Across Hospitals
For each of the conditions, patient-level demographic variables each accounted for <2% of variation in costs between hospitals. SOI accounted for 4.5% of the variation for asthma and CCI accounted for 5.2% for AGE. Overall, patient-level variables explained 6.9%, 5.3%, and 7.3% of variation for asthma, DKA, and AGE.
Encounter-level variables accounted for a much larger percentage of the variation in cost. LOS explained 25.4% for asthma, 13.3% for DKA, and 14.2% for AGE. ICU utilization accounted for 13.4% for asthma and 21.9% for DKA; ICU use was not a major driver for AGE. Seven-day readmissions accounted for <0.5% for each of the conditions. Together, patient-level and encounter-level variables explained 40%, 36%, and 22% of variation for asthma, DKA, and AGE.
Imaging, Laboratory, Pharmacy, and “Other” Costs
The largest contributor to total costs adjusted for patient-level factors for all conditions was “other,” which aggregates room, nursing, clinical, and supply charges (panel B in Figure). When considering drivers of variation, this category explained >50% for each of the conditions. The next largest contributor to total costs was laboratory charges, which accounted for 15% of the variation across regions for asthma and 11% for DKA. Differences in imaging accounted for 18% of the variation for DKA and 15% for AGE. Differences in pharmacy charges accounted for <4% of the variation for each of the conditions. Adding the 4 cost components to the other patient- and encounter-level covariates, the model explained 81%, 78%, and 72% of the variation across census regions for asthma, DKA, and AGE.
For the hospital-level analysis, differences in “other” remained the largest driver of cost variation. For asthma, “other” explained 61% of variation, while pharmacy, laboratory, and imaging each accounted for <8%. For DKA, differences in imaging accounted for 18% of the variation and laboratory charges accounted for 12%. For AGE, imaging accounted for 15% of the variation. Adding the 4 cost components to the other patient- and encounter-level covariates, the model explained 81%, 72%, and 67% of the variation for asthma, DKA, and AGE.
Cost Savings
If all hospitals in this cohort with adjusted standardized costs above the national PHIS average achieved costs equal to the national PHIS average, estimated annual savings in adjusted standardized costs for these 3 conditions would be $69.1 million. If each hospital with adjusted costs above the average within its census region achieved costs equal to its regional average, estimated annual savings in adjusted standardized costs for these conditions would be $25.2 million.
DISCUSSION
This study reported on the regional variation in costs of care for 3 conditions treated at 46 children’s hospitals across 7 geographic regions, and it demonstrated that variations in costs of care exist in pediatrics. This study used standardized costs to compare utilization patterns across hospitals and adjusted for several patient-level demographic and illness-severity factors, and it found that differences in costs of care for children hospitalized with asthma, DKA, and AGE remained both between and within regions.
These variations are noteworthy, as hospitals strive to improve the value of healthcare. If the higher-cost hospitals in this cohort could achieve costs equal to the national PHIS averages, estimated annual savings in adjusted standardized costs for these conditions alone would equal $69.1 million. If higher-cost hospitals relative to the average in their own region reduced costs to their regional averages, annual standardized cost savings could equal $25.2 million for these conditions.
The differences observed are also significant in that they provide a foundation for exploring whether lower-cost regions or lower-cost hospitals achieve comparable quality outcomes.28 If so, studying what those hospitals do to achieve outcomes more efficiently can serve as the basis for the establishment of best practices.29 Standardizing best practices through protocols, pathways, and care-model redesign can reduce potentially unnecessary spending.30
Our findings showed that patient-level demographic and illness-severity covariates, including community-level HHI and SOI, did not consistently explain cost differences. Instead, LOS and ICU utilization were associated with higher costs.17,19 When considering the effect of the 4 cost components on the variation in total standardized costs between regions and between hospitals, the fact that the “other” category accounted for the largest percent of the variation is not surprising, because the cost of room occupancy and nursing services increases with longer LOS and more time in the ICU. Other individual cost components that were major drivers of variation were laboratory utilization for asthma and imaging for DKA and AGE31 (though they accounted for a much smaller proportion of total adjusted costs).19
To determine if these factors are modifiable, more information is needed to explain why practices differ. Many factors may contribute to varying utilization patterns, including differences in capabilities and resources (in the hospital and in the community) and patient volumes. For example, some hospitals provide continuous albuterol for status asthmaticus only in ICUs, while others provide it on regular units.32 But if certain hospitals do not have adequate resources or volumes to effectively care for certain populations outside of the ICU, their higher-value approach (considering quality and cost) may be to utilize ICU beds, even if some other hospitals care for those patients on non-ICU floors. Another possibility is that family preferences about care delivery (such as how long children stay in the hospital) may vary across regions.33
Other evidence suggests that physician practice and spending patterns are strongly influenced by the practices of the region where they trained.34 Because physicians often practice close to where they trained,35,36 this may partially explain how regional patterns are reinforced.
Even considering all mentioned covariates, our model did not fully explain variation in standardized costs. After adding the cost components as covariates, between one-third and one-fifth of the variation remained unexplained. It is possible that this unexplained variation stemmed from unmeasured patient-level factors.
In addition, while proxies for SES, including community-level HHI, did not significantly predict differences in costs across regions, it is possible that SES affected LOS differently in different regions. Previous studies have suggested that lower SES is associated with longer LOS.37 If this effect is more pronounced in certain regions (potentially because of differences in social service infrastructures), SES may be contributing to variations in cost through LOS.
Our findings were subject to limitations. First, this study only examined 3 diagnoses and did not include surgical or less common conditions. Second, while PHIS includes tertiary care, academic, and freestanding children’s hospitals, it does not include general hospitals, which is where most pediatric patients receive care.38 Third, we used ZIP code-based median annual HHI to account for SES, and we used ZIP codes to determine the distance to the hospital and rural-urban location of patients’ homes. These approximations lack precision because SES and distances vary within ZIP codes.39 Fourth, while adjusted standardized costs allow for comparisons between hospitals, they do not represent actual costs to patients or individual hospitals. Additionally, when determining whether variation remained after controlling for patient-level variables, we included SOI as a reflection of illness-severity at presentation. However, in practice, SOI scores may be assigned partially based on factors determined during the hospitalization.18 Finally, the use of other regional boundaries or the selection of different hospitals may yield different results.
CONCLUSION
This study reveals regional variations in costs of care for 3 inpatient pediatric conditions. Future studies should explore whether lower-cost regions or lower-cost hospitals achieve comparable quality outcomes. To the extent that variation is driven by modifiable factors and lower spending does not compromise outcomes, these data may prompt reviews of care models to reduce unwarranted variation and improve the value of care delivery at local, regional, and national levels.
Disclosure
Internal funds from the CHA and The Children’s Hospital of Philadelphia supported the conduct of this work. The authors have no financial interests, relationships, or affiliations relevant to the subject matter or materials discussed in the manuscript to disclose. The authors have no potential conflicts of interest relevant to the subject matter or materials discussed in the manuscript to disclose
1. Fisher E, Skinner J. Making Sense of Geographic Variations in Health Care: The New IOM Report. 2013; http://healthaffairs.org/blog/2013/07/24/making-sense-of-geographic-variations-in-health-care-the-new-iom-report/. Accessed on April 11, 2014.
With some areas of the country spending close to 3 times more on healthcare than others, regional variation in healthcare spending has been the focus of national attention.1-7 Since 1973, the Dartmouth Institute has studied regional variation in healthcare utilization and spending and concluded that variation is “unwarranted” because it is driven by providers’ practice patterns rather than differences in medical need, patient preferences, or evidence-based medicine.8-11 However, critics of the Dartmouth Institute’s findings argue that their approach does not adequately adjust for community-level income, and that higher costs in some areas reflect greater patient needs that are not reflected in illness acuity alone.12-14
While Medicare data have made it possible to study variations in spending for the senior population, fragmentation of insurance coverage and nonstandardized data structures make studying the pediatric population more difficult. However, the Children’s Hospital Association’s (CHA) Pediatric Health Information System (PHIS) has made large-scale comparisons more feasible. To overcome challenges associated with using charges and nonuniform cost data, PHIS-derived standardized costs provide new opportunities for comparisons.15,16 Initial analyses using PHIS data showed significant interhospital variations in costs of care,15 but they did not adjust for differences in populations and assess the drivers of variation. A more recent study that controlled for payer status, comorbidities, and illness severity found that intensive care unit (ICU) utilization varied significantly for children hospitalized for asthma, suggesting that hospital practice patterns drive differences in cost.17
This study uses PHIS data to analyze regional variations in standardized costs of care for 3 conditions for which children are hospitalized. To assess potential drivers of variation, the study investigates the effects of patient-level demographic and illness-severity variables as well as encounter-level variables on costs of care. It also estimates cost savings from reducing variation.
METHODS
Data Source
This retrospective cohort study uses the PHIS database (CHA, Overland Park, KS), which includes 48 freestanding children’s hospitals located in noncompeting markets across the United States and accounts for approximately 20% of pediatric hospitalizations. PHIS includes patient demographics, International Classification of Diseases, 9th Revision (ICD-9) diagnosis and procedure codes, as well as hospital charges. In addition to total charges, PHIS reports imaging, laboratory, pharmacy, and “other” charges. The “other” category aggregates clinical, supply, room, and nursing charges (including facility fees and ancillary staff services).
Inclusion Criteria
Inpatient- and observation-status hospitalizations for asthma, diabetic ketoacidosis (DKA), and acute gastroenteritis (AGE) at 46 PHIS hospitals from October 2014 to September 2015 were included. Two hospitals were excluded because of missing data. Hospitalizations for patients >18 years were excluded.
Hospitalizations were categorized by using All Patient Refined-Diagnosis Related Groups (APR-DRGs) version 24 (3M Health Information Systems, St. Paul, MN)18 based on the ICD-9 diagnosis and procedure codes assigned during the episode of care. Analyses included APR-DRG 141 (asthma), primary diagnosis ICD-9 codes 250.11 and 250.13 (DKA), and APR-DRG 249 (AGE). ICD-9 codes were used for DKA for increased specificity.19 These conditions were chosen to represent 3 clinical scenarios: (1) a diagnosis for which hospitals differ on whether certain aspects of care are provided in the ICU (asthma), (2) a diagnosis that frequently includes care in an ICU (DKA), and (3) a diagnosis that typically does not include ICU care (AGE).19
Study Design
To focus the analysis on variation in resource utilization across hospitals rather than variations in hospital item charges, each billed resource was assigned a standardized cost.15,16 For each clinical transaction code (CTC), the median unit cost was calculated for each hospital. The median of the hospital medians was defined as the standardized unit cost for that CTC.
The primary outcome variable was the total standardized cost for the hospitalization adjusted for patient-level demographic and illness-severity variables. Patient demographic and illness-severity covariates included age, race, gender, ZIP code-based median annual household income (HHI), rural-urban location, distance from home ZIP code to the hospital, chronic condition indicator (CCI), and severity-of-illness (SOI). When assessing drivers of variation, encounter-level covariates were added, including length of stay (LOS) in hours, ICU utilization, and 7-day readmission (an imprecise measure to account for quality of care during the index visit). The contribution of imaging, laboratory, pharmacy, and “other” costs was also considered.
Median annual HHI for patients’ home ZIP code was obtained from 2010 US Census data. Community-level HHI, a proxy for socioeconomic status (SES),20,21 was classified into categories based on the 2015 US federal poverty level (FPL) for a family of 422: HHI-1 = ≤ 1.5 × FPL; HHI-2 = 1.5 to 2 × FPL; HHI-3 = 2 to 3 × FPL; HHI-4 = ≥ 3 × FPL. Rural-urban commuting area (RUCA) codes were used to determine the rural-urban classification of the patient’s home.23 The distance from home ZIP code to the hospital was included as an additional control for illness severity because patients traveling longer distances are often more sick and require more resources.24
The Agency for Healthcare Research and Quality CCI classification system was used to identify the presence of a chronic condition.25 For asthma, CCI was flagged if the patient had a chronic condition other than asthma; for DKA, CCI was flagged if the patient had a chronic condition other than DKA; and for AGE, CCI was flagged if the patient had any chronic condition.
The APR-DRG system provides a 4-level SOI score with each APR-DRG category. Patient factors, such as comorbid diagnoses, are considered in severity scores generated through 3M’s proprietary algorithms.18
For the first analysis, the 46 hospitals were categorized into 7 geographic regions based on 2010 US Census Divisions.26 To overcome small hospital sample sizes, Mountain and Pacific were combined into West, and Middle Atlantic and New England were combined into North East. Because PHIS hospitals are located in noncompeting geographic regions, for the second analysis, we examined hospital-level variation (considering each hospital as its own region).
Data Analysis
To focus the analysis on “typical” patients and produce more robust estimates of central tendencies, the top and bottom 5% of hospitalizations with the most extreme standardized costs by condition were trimmed.27 Standardized costs were log-transformed because of their nonnormal distribution and analyzed by using linear mixed models. Covariates were added stepwise to assess the proportion of the variance explained by each predictor. Post-hoc tests with conservative single-step stepwise mutation model corrections for multiple testing were used to compare adjusted costs. Statistical analyses were performed using SAS version 9.3 (SAS Institute, Cary, NC). P values < 0.05 were considered significant. The Children’s Hospital of Philadelphia Institutional Review Board did not classify this study as human subjects research.
RESULTS
During the study period, there were 26,430 hospitalizations for asthma, 5056 for DKA, and 16,274 for AGE (Table 1).
Variation Across Census Regions
After adjusting for patient-level demographic and illness-severity variables, differences in adjusted total standardized costs remained between regions (P < 0.001). Although no region was an outlier compared to the overall mean for any of the conditions, regions were statistically different in pairwise comparison. The East North Central, South Atlantic, and West South Central regions had the highest adjusted total standardized costs for each of the conditions. The East South Central and West North Central regions had the lowest costs for each of the conditions. Adjusted total standardized costs were 120% higher for asthma ($1920 vs $4227), 46% higher for DKA ($7429 vs $10,881), and 150% higher for AGE ($3316 vs $8292) in the highest-cost region compared with the lowest-cost region (Table 2A).
Variation Within Census Regions
After controlling for patient-level demographic and illness-severity variables, standardized costs were different across hospitals in the same region (P < 0.001; panel A in Figure). This was true for all conditions in each region. Differences between the lowest- and highest-cost hospitals within the same region ranged from 111% to 420% for asthma, 101% to 398% for DKA, and 166% to 787% for AGE (Table 3).
Variation Across Hospitals (Each Hospital as Its Own Region)
One hospital had the highest adjusted standardized costs for all 3 conditions ($9087 for asthma, $28,564 for DKA, and $23,387 for AGE) and was outside of the 95% confidence interval compared with the overall means. The second highest-cost hospitals for asthma ($5977) and AGE ($18,780) were also outside of the 95% confidence interval. After removing these outliers, the difference between the highest- and lowest-cost hospitals was 549% for asthma ($721 vs $4678), 491% for DKA ($2738 vs $16,192), and 681% for AGE ($1317 vs $10,281; Table 2B).
Drivers of Variation Across Census Regions
Patient-level demographic and illness-severity variables explained very little of the variation in standardized costs across regions. For each of the conditions, age, race, gender, community-level HHI, RUCA, and distance from home to the hospital each accounted for <1.5% of variation, while SOI and CCI each accounted for <5%. Overall, patient-level variables explained 5.5%, 3.7%, and 6.7% of variation for asthma, DKA, and AGE.
Encounter-level variables explained a much larger percentage of the variation in costs. LOS accounted for 17.8% of the variation for asthma, 9.8% for DKA, and 8.7% for AGE. ICU utilization explained 6.9% of the variation for asthma and 12.5% for DKA; ICU use was not a major driver for AGE. Seven-day readmissions accounted for <0.5% for each of the conditions. The combination of patient-level and encounter-level variables explained 27%, 24%, and 15% of the variation for asthma, DKA, and AGE.
Drivers of Variation Across Hospitals
For each of the conditions, patient-level demographic variables each accounted for <2% of variation in costs between hospitals. SOI accounted for 4.5% of the variation for asthma and CCI accounted for 5.2% for AGE. Overall, patient-level variables explained 6.9%, 5.3%, and 7.3% of variation for asthma, DKA, and AGE.
Encounter-level variables accounted for a much larger percentage of the variation in cost. LOS explained 25.4% for asthma, 13.3% for DKA, and 14.2% for AGE. ICU utilization accounted for 13.4% for asthma and 21.9% for DKA; ICU use was not a major driver for AGE. Seven-day readmissions accounted for <0.5% for each of the conditions. Together, patient-level and encounter-level variables explained 40%, 36%, and 22% of variation for asthma, DKA, and AGE.
Imaging, Laboratory, Pharmacy, and “Other” Costs
The largest contributor to total costs adjusted for patient-level factors for all conditions was “other,” which aggregates room, nursing, clinical, and supply charges (panel B in Figure). When considering drivers of variation, this category explained >50% for each of the conditions. The next largest contributor to total costs was laboratory charges, which accounted for 15% of the variation across regions for asthma and 11% for DKA. Differences in imaging accounted for 18% of the variation for DKA and 15% for AGE. Differences in pharmacy charges accounted for <4% of the variation for each of the conditions. Adding the 4 cost components to the other patient- and encounter-level covariates, the model explained 81%, 78%, and 72% of the variation across census regions for asthma, DKA, and AGE.
For the hospital-level analysis, differences in “other” remained the largest driver of cost variation. For asthma, “other” explained 61% of variation, while pharmacy, laboratory, and imaging each accounted for <8%. For DKA, differences in imaging accounted for 18% of the variation and laboratory charges accounted for 12%. For AGE, imaging accounted for 15% of the variation. Adding the 4 cost components to the other patient- and encounter-level covariates, the model explained 81%, 72%, and 67% of the variation for asthma, DKA, and AGE.
Cost Savings
If all hospitals in this cohort with adjusted standardized costs above the national PHIS average achieved costs equal to the national PHIS average, estimated annual savings in adjusted standardized costs for these 3 conditions would be $69.1 million. If each hospital with adjusted costs above the average within its census region achieved costs equal to its regional average, estimated annual savings in adjusted standardized costs for these conditions would be $25.2 million.
DISCUSSION
This study reported on the regional variation in costs of care for 3 conditions treated at 46 children’s hospitals across 7 geographic regions, and it demonstrated that variations in costs of care exist in pediatrics. This study used standardized costs to compare utilization patterns across hospitals and adjusted for several patient-level demographic and illness-severity factors, and it found that differences in costs of care for children hospitalized with asthma, DKA, and AGE remained both between and within regions.
These variations are noteworthy, as hospitals strive to improve the value of healthcare. If the higher-cost hospitals in this cohort could achieve costs equal to the national PHIS averages, estimated annual savings in adjusted standardized costs for these conditions alone would equal $69.1 million. If higher-cost hospitals relative to the average in their own region reduced costs to their regional averages, annual standardized cost savings could equal $25.2 million for these conditions.
The differences observed are also significant in that they provide a foundation for exploring whether lower-cost regions or lower-cost hospitals achieve comparable quality outcomes.28 If so, studying what those hospitals do to achieve outcomes more efficiently can serve as the basis for the establishment of best practices.29 Standardizing best practices through protocols, pathways, and care-model redesign can reduce potentially unnecessary spending.30
Our findings showed that patient-level demographic and illness-severity covariates, including community-level HHI and SOI, did not consistently explain cost differences. Instead, LOS and ICU utilization were associated with higher costs.17,19 When considering the effect of the 4 cost components on the variation in total standardized costs between regions and between hospitals, the fact that the “other” category accounted for the largest percent of the variation is not surprising, because the cost of room occupancy and nursing services increases with longer LOS and more time in the ICU. Other individual cost components that were major drivers of variation were laboratory utilization for asthma and imaging for DKA and AGE31 (though they accounted for a much smaller proportion of total adjusted costs).19
To determine if these factors are modifiable, more information is needed to explain why practices differ. Many factors may contribute to varying utilization patterns, including differences in capabilities and resources (in the hospital and in the community) and patient volumes. For example, some hospitals provide continuous albuterol for status asthmaticus only in ICUs, while others provide it on regular units.32 But if certain hospitals do not have adequate resources or volumes to effectively care for certain populations outside of the ICU, their higher-value approach (considering quality and cost) may be to utilize ICU beds, even if some other hospitals care for those patients on non-ICU floors. Another possibility is that family preferences about care delivery (such as how long children stay in the hospital) may vary across regions.33
Other evidence suggests that physician practice and spending patterns are strongly influenced by the practices of the region where they trained.34 Because physicians often practice close to where they trained,35,36 this may partially explain how regional patterns are reinforced.
Even considering all mentioned covariates, our model did not fully explain variation in standardized costs. After adding the cost components as covariates, between one-third and one-fifth of the variation remained unexplained. It is possible that this unexplained variation stemmed from unmeasured patient-level factors.
In addition, while proxies for SES, including community-level HHI, did not significantly predict differences in costs across regions, it is possible that SES affected LOS differently in different regions. Previous studies have suggested that lower SES is associated with longer LOS.37 If this effect is more pronounced in certain regions (potentially because of differences in social service infrastructures), SES may be contributing to variations in cost through LOS.
Our findings were subject to limitations. First, this study only examined 3 diagnoses and did not include surgical or less common conditions. Second, while PHIS includes tertiary care, academic, and freestanding children’s hospitals, it does not include general hospitals, which is where most pediatric patients receive care.38 Third, we used ZIP code-based median annual HHI to account for SES, and we used ZIP codes to determine the distance to the hospital and rural-urban location of patients’ homes. These approximations lack precision because SES and distances vary within ZIP codes.39 Fourth, while adjusted standardized costs allow for comparisons between hospitals, they do not represent actual costs to patients or individual hospitals. Additionally, when determining whether variation remained after controlling for patient-level variables, we included SOI as a reflection of illness-severity at presentation. However, in practice, SOI scores may be assigned partially based on factors determined during the hospitalization.18 Finally, the use of other regional boundaries or the selection of different hospitals may yield different results.
CONCLUSION
This study reveals regional variations in costs of care for 3 inpatient pediatric conditions. Future studies should explore whether lower-cost regions or lower-cost hospitals achieve comparable quality outcomes. To the extent that variation is driven by modifiable factors and lower spending does not compromise outcomes, these data may prompt reviews of care models to reduce unwarranted variation and improve the value of care delivery at local, regional, and national levels.
Disclosure
Internal funds from the CHA and The Children’s Hospital of Philadelphia supported the conduct of this work. The authors have no financial interests, relationships, or affiliations relevant to the subject matter or materials discussed in the manuscript to disclose. The authors have no potential conflicts of interest relevant to the subject matter or materials discussed in the manuscript to disclose
With some areas of the country spending close to 3 times more on healthcare than others, regional variation in healthcare spending has been the focus of national attention.1-7 Since 1973, the Dartmouth Institute has studied regional variation in healthcare utilization and spending and concluded that variation is “unwarranted” because it is driven by providers’ practice patterns rather than differences in medical need, patient preferences, or evidence-based medicine.8-11 However, critics of the Dartmouth Institute’s findings argue that their approach does not adequately adjust for community-level income, and that higher costs in some areas reflect greater patient needs that are not reflected in illness acuity alone.12-14
While Medicare data have made it possible to study variations in spending for the senior population, fragmentation of insurance coverage and nonstandardized data structures make studying the pediatric population more difficult. However, the Children’s Hospital Association’s (CHA) Pediatric Health Information System (PHIS) has made large-scale comparisons more feasible. To overcome challenges associated with using charges and nonuniform cost data, PHIS-derived standardized costs provide new opportunities for comparisons.15,16 Initial analyses using PHIS data showed significant interhospital variations in costs of care,15 but they did not adjust for differences in populations and assess the drivers of variation. A more recent study that controlled for payer status, comorbidities, and illness severity found that intensive care unit (ICU) utilization varied significantly for children hospitalized for asthma, suggesting that hospital practice patterns drive differences in cost.17
This study uses PHIS data to analyze regional variations in standardized costs of care for 3 conditions for which children are hospitalized. To assess potential drivers of variation, the study investigates the effects of patient-level demographic and illness-severity variables as well as encounter-level variables on costs of care. It also estimates cost savings from reducing variation.
METHODS
Data Source
This retrospective cohort study uses the PHIS database (CHA, Overland Park, KS), which includes 48 freestanding children’s hospitals located in noncompeting markets across the United States and accounts for approximately 20% of pediatric hospitalizations. PHIS includes patient demographics, International Classification of Diseases, 9th Revision (ICD-9) diagnosis and procedure codes, as well as hospital charges. In addition to total charges, PHIS reports imaging, laboratory, pharmacy, and “other” charges. The “other” category aggregates clinical, supply, room, and nursing charges (including facility fees and ancillary staff services).
Inclusion Criteria
Inpatient- and observation-status hospitalizations for asthma, diabetic ketoacidosis (DKA), and acute gastroenteritis (AGE) at 46 PHIS hospitals from October 2014 to September 2015 were included. Two hospitals were excluded because of missing data. Hospitalizations for patients >18 years were excluded.
Hospitalizations were categorized by using All Patient Refined-Diagnosis Related Groups (APR-DRGs) version 24 (3M Health Information Systems, St. Paul, MN)18 based on the ICD-9 diagnosis and procedure codes assigned during the episode of care. Analyses included APR-DRG 141 (asthma), primary diagnosis ICD-9 codes 250.11 and 250.13 (DKA), and APR-DRG 249 (AGE). ICD-9 codes were used for DKA for increased specificity.19 These conditions were chosen to represent 3 clinical scenarios: (1) a diagnosis for which hospitals differ on whether certain aspects of care are provided in the ICU (asthma), (2) a diagnosis that frequently includes care in an ICU (DKA), and (3) a diagnosis that typically does not include ICU care (AGE).19
Study Design
To focus the analysis on variation in resource utilization across hospitals rather than variations in hospital item charges, each billed resource was assigned a standardized cost.15,16 For each clinical transaction code (CTC), the median unit cost was calculated for each hospital. The median of the hospital medians was defined as the standardized unit cost for that CTC.
The primary outcome variable was the total standardized cost for the hospitalization adjusted for patient-level demographic and illness-severity variables. Patient demographic and illness-severity covariates included age, race, gender, ZIP code-based median annual household income (HHI), rural-urban location, distance from home ZIP code to the hospital, chronic condition indicator (CCI), and severity-of-illness (SOI). When assessing drivers of variation, encounter-level covariates were added, including length of stay (LOS) in hours, ICU utilization, and 7-day readmission (an imprecise measure to account for quality of care during the index visit). The contribution of imaging, laboratory, pharmacy, and “other” costs was also considered.
Median annual HHI for patients’ home ZIP code was obtained from 2010 US Census data. Community-level HHI, a proxy for socioeconomic status (SES),20,21 was classified into categories based on the 2015 US federal poverty level (FPL) for a family of 422: HHI-1 = ≤ 1.5 × FPL; HHI-2 = 1.5 to 2 × FPL; HHI-3 = 2 to 3 × FPL; HHI-4 = ≥ 3 × FPL. Rural-urban commuting area (RUCA) codes were used to determine the rural-urban classification of the patient’s home.23 The distance from home ZIP code to the hospital was included as an additional control for illness severity because patients traveling longer distances are often more sick and require more resources.24
The Agency for Healthcare Research and Quality CCI classification system was used to identify the presence of a chronic condition.25 For asthma, CCI was flagged if the patient had a chronic condition other than asthma; for DKA, CCI was flagged if the patient had a chronic condition other than DKA; and for AGE, CCI was flagged if the patient had any chronic condition.
The APR-DRG system provides a 4-level SOI score with each APR-DRG category. Patient factors, such as comorbid diagnoses, are considered in severity scores generated through 3M’s proprietary algorithms.18
For the first analysis, the 46 hospitals were categorized into 7 geographic regions based on 2010 US Census Divisions.26 To overcome small hospital sample sizes, Mountain and Pacific were combined into West, and Middle Atlantic and New England were combined into North East. Because PHIS hospitals are located in noncompeting geographic regions, for the second analysis, we examined hospital-level variation (considering each hospital as its own region).
Data Analysis
To focus the analysis on “typical” patients and produce more robust estimates of central tendencies, the top and bottom 5% of hospitalizations with the most extreme standardized costs by condition were trimmed.27 Standardized costs were log-transformed because of their nonnormal distribution and analyzed by using linear mixed models. Covariates were added stepwise to assess the proportion of the variance explained by each predictor. Post-hoc tests with conservative single-step stepwise mutation model corrections for multiple testing were used to compare adjusted costs. Statistical analyses were performed using SAS version 9.3 (SAS Institute, Cary, NC). P values < 0.05 were considered significant. The Children’s Hospital of Philadelphia Institutional Review Board did not classify this study as human subjects research.
RESULTS
During the study period, there were 26,430 hospitalizations for asthma, 5056 for DKA, and 16,274 for AGE (Table 1).
Variation Across Census Regions
After adjusting for patient-level demographic and illness-severity variables, differences in adjusted total standardized costs remained between regions (P < 0.001). Although no region was an outlier compared to the overall mean for any of the conditions, regions were statistically different in pairwise comparison. The East North Central, South Atlantic, and West South Central regions had the highest adjusted total standardized costs for each of the conditions. The East South Central and West North Central regions had the lowest costs for each of the conditions. Adjusted total standardized costs were 120% higher for asthma ($1920 vs $4227), 46% higher for DKA ($7429 vs $10,881), and 150% higher for AGE ($3316 vs $8292) in the highest-cost region compared with the lowest-cost region (Table 2A).
Variation Within Census Regions
After controlling for patient-level demographic and illness-severity variables, standardized costs were different across hospitals in the same region (P < 0.001; panel A in Figure). This was true for all conditions in each region. Differences between the lowest- and highest-cost hospitals within the same region ranged from 111% to 420% for asthma, 101% to 398% for DKA, and 166% to 787% for AGE (Table 3).
Variation Across Hospitals (Each Hospital as Its Own Region)
One hospital had the highest adjusted standardized costs for all 3 conditions ($9087 for asthma, $28,564 for DKA, and $23,387 for AGE) and was outside of the 95% confidence interval compared with the overall means. The second highest-cost hospitals for asthma ($5977) and AGE ($18,780) were also outside of the 95% confidence interval. After removing these outliers, the difference between the highest- and lowest-cost hospitals was 549% for asthma ($721 vs $4678), 491% for DKA ($2738 vs $16,192), and 681% for AGE ($1317 vs $10,281; Table 2B).
Drivers of Variation Across Census Regions
Patient-level demographic and illness-severity variables explained very little of the variation in standardized costs across regions. For each of the conditions, age, race, gender, community-level HHI, RUCA, and distance from home to the hospital each accounted for <1.5% of variation, while SOI and CCI each accounted for <5%. Overall, patient-level variables explained 5.5%, 3.7%, and 6.7% of variation for asthma, DKA, and AGE.
Encounter-level variables explained a much larger percentage of the variation in costs. LOS accounted for 17.8% of the variation for asthma, 9.8% for DKA, and 8.7% for AGE. ICU utilization explained 6.9% of the variation for asthma and 12.5% for DKA; ICU use was not a major driver for AGE. Seven-day readmissions accounted for <0.5% for each of the conditions. The combination of patient-level and encounter-level variables explained 27%, 24%, and 15% of the variation for asthma, DKA, and AGE.
Drivers of Variation Across Hospitals
For each of the conditions, patient-level demographic variables each accounted for <2% of variation in costs between hospitals. SOI accounted for 4.5% of the variation for asthma and CCI accounted for 5.2% for AGE. Overall, patient-level variables explained 6.9%, 5.3%, and 7.3% of variation for asthma, DKA, and AGE.
Encounter-level variables accounted for a much larger percentage of the variation in cost. LOS explained 25.4% for asthma, 13.3% for DKA, and 14.2% for AGE. ICU utilization accounted for 13.4% for asthma and 21.9% for DKA; ICU use was not a major driver for AGE. Seven-day readmissions accounted for <0.5% for each of the conditions. Together, patient-level and encounter-level variables explained 40%, 36%, and 22% of variation for asthma, DKA, and AGE.
Imaging, Laboratory, Pharmacy, and “Other” Costs
The largest contributor to total costs adjusted for patient-level factors for all conditions was “other,” which aggregates room, nursing, clinical, and supply charges (panel B in Figure). When considering drivers of variation, this category explained >50% for each of the conditions. The next largest contributor to total costs was laboratory charges, which accounted for 15% of the variation across regions for asthma and 11% for DKA. Differences in imaging accounted for 18% of the variation for DKA and 15% for AGE. Differences in pharmacy charges accounted for <4% of the variation for each of the conditions. Adding the 4 cost components to the other patient- and encounter-level covariates, the model explained 81%, 78%, and 72% of the variation across census regions for asthma, DKA, and AGE.
For the hospital-level analysis, differences in “other” remained the largest driver of cost variation. For asthma, “other” explained 61% of variation, while pharmacy, laboratory, and imaging each accounted for <8%. For DKA, differences in imaging accounted for 18% of the variation and laboratory charges accounted for 12%. For AGE, imaging accounted for 15% of the variation. Adding the 4 cost components to the other patient- and encounter-level covariates, the model explained 81%, 72%, and 67% of the variation for asthma, DKA, and AGE.
Cost Savings
If all hospitals in this cohort with adjusted standardized costs above the national PHIS average achieved costs equal to the national PHIS average, estimated annual savings in adjusted standardized costs for these 3 conditions would be $69.1 million. If each hospital with adjusted costs above the average within its census region achieved costs equal to its regional average, estimated annual savings in adjusted standardized costs for these conditions would be $25.2 million.
DISCUSSION
This study reported on the regional variation in costs of care for 3 conditions treated at 46 children’s hospitals across 7 geographic regions, and it demonstrated that variations in costs of care exist in pediatrics. This study used standardized costs to compare utilization patterns across hospitals and adjusted for several patient-level demographic and illness-severity factors, and it found that differences in costs of care for children hospitalized with asthma, DKA, and AGE remained both between and within regions.
These variations are noteworthy, as hospitals strive to improve the value of healthcare. If the higher-cost hospitals in this cohort could achieve costs equal to the national PHIS averages, estimated annual savings in adjusted standardized costs for these conditions alone would equal $69.1 million. If higher-cost hospitals relative to the average in their own region reduced costs to their regional averages, annual standardized cost savings could equal $25.2 million for these conditions.
The differences observed are also significant in that they provide a foundation for exploring whether lower-cost regions or lower-cost hospitals achieve comparable quality outcomes.28 If so, studying what those hospitals do to achieve outcomes more efficiently can serve as the basis for the establishment of best practices.29 Standardizing best practices through protocols, pathways, and care-model redesign can reduce potentially unnecessary spending.30
Our findings showed that patient-level demographic and illness-severity covariates, including community-level HHI and SOI, did not consistently explain cost differences. Instead, LOS and ICU utilization were associated with higher costs.17,19 When considering the effect of the 4 cost components on the variation in total standardized costs between regions and between hospitals, the fact that the “other” category accounted for the largest percent of the variation is not surprising, because the cost of room occupancy and nursing services increases with longer LOS and more time in the ICU. Other individual cost components that were major drivers of variation were laboratory utilization for asthma and imaging for DKA and AGE31 (though they accounted for a much smaller proportion of total adjusted costs).19
To determine if these factors are modifiable, more information is needed to explain why practices differ. Many factors may contribute to varying utilization patterns, including differences in capabilities and resources (in the hospital and in the community) and patient volumes. For example, some hospitals provide continuous albuterol for status asthmaticus only in ICUs, while others provide it on regular units.32 But if certain hospitals do not have adequate resources or volumes to effectively care for certain populations outside of the ICU, their higher-value approach (considering quality and cost) may be to utilize ICU beds, even if some other hospitals care for those patients on non-ICU floors. Another possibility is that family preferences about care delivery (such as how long children stay in the hospital) may vary across regions.33
Other evidence suggests that physician practice and spending patterns are strongly influenced by the practices of the region where they trained.34 Because physicians often practice close to where they trained,35,36 this may partially explain how regional patterns are reinforced.
Even considering all mentioned covariates, our model did not fully explain variation in standardized costs. After adding the cost components as covariates, between one-third and one-fifth of the variation remained unexplained. It is possible that this unexplained variation stemmed from unmeasured patient-level factors.
In addition, while proxies for SES, including community-level HHI, did not significantly predict differences in costs across regions, it is possible that SES affected LOS differently in different regions. Previous studies have suggested that lower SES is associated with longer LOS.37 If this effect is more pronounced in certain regions (potentially because of differences in social service infrastructures), SES may be contributing to variations in cost through LOS.
Our findings were subject to limitations. First, this study only examined 3 diagnoses and did not include surgical or less common conditions. Second, while PHIS includes tertiary care, academic, and freestanding children’s hospitals, it does not include general hospitals, which is where most pediatric patients receive care.38 Third, we used ZIP code-based median annual HHI to account for SES, and we used ZIP codes to determine the distance to the hospital and rural-urban location of patients’ homes. These approximations lack precision because SES and distances vary within ZIP codes.39 Fourth, while adjusted standardized costs allow for comparisons between hospitals, they do not represent actual costs to patients or individual hospitals. Additionally, when determining whether variation remained after controlling for patient-level variables, we included SOI as a reflection of illness-severity at presentation. However, in practice, SOI scores may be assigned partially based on factors determined during the hospitalization.18 Finally, the use of other regional boundaries or the selection of different hospitals may yield different results.
CONCLUSION
This study reveals regional variations in costs of care for 3 inpatient pediatric conditions. Future studies should explore whether lower-cost regions or lower-cost hospitals achieve comparable quality outcomes. To the extent that variation is driven by modifiable factors and lower spending does not compromise outcomes, these data may prompt reviews of care models to reduce unwarranted variation and improve the value of care delivery at local, regional, and national levels.
Disclosure
Internal funds from the CHA and The Children’s Hospital of Philadelphia supported the conduct of this work. The authors have no financial interests, relationships, or affiliations relevant to the subject matter or materials discussed in the manuscript to disclose. The authors have no potential conflicts of interest relevant to the subject matter or materials discussed in the manuscript to disclose
1. Fisher E, Skinner J. Making Sense of Geographic Variations in Health Care: The New IOM Report. 2013; http://healthaffairs.org/blog/2013/07/24/making-sense-of-geographic-variations-in-health-care-the-new-iom-report/. Accessed on April 11, 2014.
1. Fisher E, Skinner J. Making Sense of Geographic Variations in Health Care: The New IOM Report. 2013; http://healthaffairs.org/blog/2013/07/24/making-sense-of-geographic-variations-in-health-care-the-new-iom-report/. Accessed on April 11, 2014.
© 2017 Society of Hospital Medicine
Return Visits to Pediatric EDs
Returns to the hospital following recent encounters, such as an admission to the inpatient unit or evaluation in an emergency department (ED), may reflect the natural progression of a disease, the quality of care received during the initial admission or visit, or the quality of the underlying healthcare system.[1, 2, 3, 4, 5, 6, 7, 8, 9, 10] Although national attention has focused on hospital readmissions,[3, 4, 5, 6, 7, 11, 12] ED revisits are a source of concern to emergency physicians.[8, 9] Some ED revisits are medically necessary, but revisits that may be managed in the primary care setting contribute to ED crowding, can be stressful to patients and providers, and increase healthcare costs.[10, 11, 12] Approximately 27 million annual ED visits are made by children, accounting for over one‐quarter of all ED visits in the United States, with a reported ED revisit rate of 2.5% to 5.2%.[2, 13, 14, 15, 16, 17, 18, 19, 20] Improved understanding of the patient‐level or visit‐level factors associated with ED revisits may provide an opportunity to enhance disposition decision making at the index visit and optimize site of and communication around follow‐up care.
Previous studies on ED revisits have largely been conducted in single centers and have used variable visit intervals ranging between 48 hours and 30 days.[2, 13, 16, 18, 21, 22, 23, 24, 25] Two national studies used the National Hospital Ambulatory Medical Care Survey, which includes data from both general and pediatric EDs.[13, 14] Factors identified to be associated with increased odds of returning were: young age, higher acuity, chronic conditions, and public insurance. One national study identified some diagnoses associated with higher likelihood of returning,[13] whereas the other focused primarily on infectious diseaserelated diagnoses.[14]
The purpose of this study was to describe the prevalence of return visits specifically to pediatric EDs and to investigate patient‐level, visit‐level, and healthcare systemrelated factors that may be associated with return visits and hospitalization at return.
METHODS
Study Design and Data Source
This retrospective cohort study used data from the Pediatric Health Information System (PHIS), an administrative database with data from 44 tertiary care pediatric hospitals in 27 US states and the District of Columbia. This database contains patient demographics, diagnoses, and procedures as well as medications, diagnostic imaging, laboratory, and supply charges for each patient. Data are deidentified prior to inclusion; encrypted medical record numbers allow for the identification of individual patients across all ED visits and hospitalizations to the same hospital. The Children's Hospital Association (Overland Park, KS) and participating hospitals jointly assure the quality and integrity of the data. This study was approved by the institutional review board at Boston Children's Hospital with a waiver for informed consent granted.
Study Population and Protocol
To standardize comparisons across the hospitals, we included data from 23 of the 44 hospitals in PHIS; 7 were excluded for not including ED‐specific data. For institutions that collect information from multiple hospitals within their healthcare system, we included only records from the main campus or children's hospital when possible, leading to the exclusion of 9 hospitals where the data were not able to be segregated. As an additional level of data validation, we compared the hospital‐level ED volume and admission rates as reported in the PHIS to those reported to a separate database (the Pediatric Analysis and Comparison Tool). We further excluded 5 hospitals whose volume differed by >10% between these 2 data sources.
Patients <18 years of age who were discharged from these EDs following their index visit in 2012 formed the eligible cohort.
Key Outcome Measures
The primary outcomes were return visits within 72 hours of discharge from the ED, and return visits resulting in hospitalization, including observation status. We defined an ED revisit as a return within 72 hours of ED discharge regardless of whether the patient was subsequently discharged from the ED on the return visit or hospitalized. We assessed revisits within 72 hours of an index ED discharge, because return visits within this time frame are likely to be related to the index visit.[2, 13, 16, 21, 22, 24, 25, 26]
Factors Associated With ED Revisits
A priori, we chose to adjust for the following patient‐level factors: age (<30 days, 30 days<1 year, 14 years, 511 years, 1217 years), gender, and socioeconomic status (SES) measured as the zip codebased median household income, obtained from the 2010 US Census, with respect to the federal poverty level (FPL) (<1.5 FPL, 1.52 FPL, 23 FPL, and >3 FPL).[27] We also adjusted for insurance type (commercial, government, or other), proximity of patient's home zip code to hospital (modeled as the natural log of the geographical distance to patient's home address from the hospital), ED diagnosis‐based severity classification system score (1=low severity, 5=high severity),[28] presence of a complex chronic condition at the index or prior visits using a validated classification scheme,[15, 29, 30, 31] and primary care physician (PCP) density per 100,000 in the patient's residential area (modeled as quartiles: very low, <57.2; low, 57.267.9; medium, 68.078.7; high, >78.8). PCP density, defined by the Dartmouth Atlas of Health Care,[32, 33, 34] is the number of primary care physicians per 100,000 residents (PCP count) in federal health service areas (HSA). Patients were assigned to a corresponding HSA based on their home zip code.
Visit‐level factors included arrival time of index visit (8:01 am 4:00 pm, 4:01 pm12:00 am, 12:01 am8 am representing day, evening, and overnight arrival, respectively), day of the week, season, length of stay (LOS) in the ED during the index visit, and ED crowding (calculated as the average daily LOS/yearly average LOS for the individual ED).[35] We categorized the ED primary diagnosis for each visit using the major diagnosis groupings of a previously described pediatric ED‐specific classification scheme.[36] Using International Classification of Diseases, Ninth Revision (ICD‐9) codes, we identified the conditions with the highest ED revisit rates.
Statistical Analyses
Categorical variables describing the study cohort were summarized using frequencies and percentages. Continuous variables were summarized using mean, median, and interquartile range values, where appropriate. We used 2 different hierarchical logistic regression models to assess revisit rates by patient‐ and visit‐level characteristics. The initial model included all patients discharged from the ED following the index visit and assessed for the outcome of a revisit within 72 hours. The second model considered only patients who returned within 72 hours of an index visit and assessed for hospitalization on that return visit. We used generalized linear mixed effects models, with hospital as a random effect to account for the presence of correlated data (within hospitals), nonconstant variability (across hospitals), and binary responses. Adjusted odds ratios with 95% confidence intervals were used as summary measures of the effect of the individual adjusters. Adjusters were missing in fewer than 5% of patients across participating hospitals. Statistical analyses were performed using SAS version 9.3 (SAS Institute Inc., Cary, NC); 2‐sided P values <0.004 were considered statistically significant to account for multiple comparisons (Bonferroni‐adjusted level of significance=0.0038).
RESULTS
Patients
A total of 1,610,201 patients <18 years of age evaluated across the 23 PHIS EDs in 2012 were included in the study. Twenty‐one of the 23 EDs have academic affiliations; 10 are located in the South, 6 in the Midwest, 5 in the West, and 2 in the Northeast region of the United States. The annual ED volume for these EDs ranged from 25,090 to 136,160 (median, 65,075; interquartile range, 45,28085,206). Of the total patients, 1,415,721 (87.9%) were discharged following the index visit and comprised our study cohort. Of these patients, 47,294 (revisit rate: 3.3%) had an ED revisit within 72 hours. There were 4015 patients (0.3%) who returned more than once within 72 hours, and the largest proportion of these returned with infection‐related conditions. Of those returning, 37,999 (80.3%) were discharged again, whereas 9295 (19.7%) were admitted to the hospital (Figure 1). The demographic and clinical characteristics of study participants are displayed in Table 1.
| Index Visit, n=1,415,721, n (%) | Return Visits Within 72 Hours of Discharge, n=47,294, 3.3% | ||
|---|---|---|---|
| Return to Discharge, n (%) | Return to Admission, n (%) | ||
| |||
| Gender, female | 659,417 (46.6) | 17,665 (46.5) | 4,304 (46.3) |
| Payor | |||
| Commercial | 379,403 (26.8) | 8,388 (22.1) | 3,214 (34.6) |
| Government | 925,147 (65.4) | 26,880 (70.7) | 5,786 (62.3) |
| Other | 111,171 (7.9) | 2,731 (7.2) | 295 (3.2) |
| Age | |||
| <30 days | 19,217 (1.4) | 488 (1.3) | 253 (2.7) |
| 30 days to <1 year | 216,967 (15.3) | 8,280 (21.8) | 2,372 (25.5) |
| 1 year to 4 years | 547,083 (38.6) | 15,542 (40.9) | 3,187 (34.3) |
| 5 years to 11 years | 409,463 (28.9) | 8,906 (23.4) | 1,964 (21.1) |
| 12 years to 17 years | 222,991 (15.8) | 4,783 (12.6) | 1,519 (16.3) |
| Socioeconomic statusa | |||
| <1.5 times FPL | 493,770 (34.9) | 13,851 (36.5) | 2,879 (31.0) |
| 1.5 to 2 times FPL | 455,490 (32.2) | 12,364 (32.5) | 2,904 (31.2) |
| 2 to 3 times FPL | 367,557 (26.0) | 9,560 (25.2) | 2,714 (29.2) |
| >3 times FPL | 98,904 (7.0) | 2,224 (5.9) | 798 (8.6) |
| Primary care physician density per 100,000 patients | |||
| Very low | 351,798 (24.9) | 8,727 (23.0) | 2,628 (28.3) |
| Low | 357,099 (25.2) | 9,810 (25.8) | 2,067 (22.2) |
| Medium | 347,995 (24.6) | 10,186 (26.8) | 2,035 (21.9) |
| High | 358,829 (25.4) | 9,276 (24.4) | 2,565 (27.6) |
| CCC present, yes | 125,774 (8.9) | 4,446 (11.7) | 2,825 (30.4) |
| Severity score | |||
| Low severity (0,1,2) | 721,061 (50.9) | 17,310 (45.6) | 2,955 (31.8) |
| High severity (3,4,5) | 694,660 (49.1) | 20,689 (54.5) | 6,340 (68.2) |
| Time of arrival | |||
| Day | 533,328 (37.7) | 13,449 (35.4) | 3,396 (36.5) |
| Evening | 684,873 (48.4) | 18,417 (48.5) | 4,378 (47.1) |
| Overnight | 197,520 (14.0) | 6,133 (16.1) | 1,521 (16.4) |
| Season | |||
| Winter | 384,957 (27.2) | 10,603 (27.9) | 2,844 (30.6) |
| Spring | 367,434 (26.0) | 9,923 (26.1) | 2,311 (24.9) |
| Summer | 303,872 (21.5) | 8,308 (21.9) | 1,875 (20.2) |
| Fall | 359,458 (25.4) | 9,165 (24.1) | 2,265 (24.4) |
| Weekday/weekend | |||
| Monday | 217,774 (15.4) | 5,646 (14.9) | 1,394 (15) |
| Tuesday | 198,220 (14.0) | 5,054 (13.3) | 1,316 (14.2) |
| Wednesday | 194,295 (13.7) | 4,985 (13.1) | 1,333 (14.3) |
| Thursday | 191,950 (13.6) | 5,123 (13.5) | 1,234 (13.3) |
| Friday | 190,022 (13.4) | 5,449 (14.3) | 1,228 (13.2) |
| Saturday | 202,247 (14.3) | 5,766 (15.2) | 1,364 (14.7) |
| Sunday | 221,213 (15.6) | 5,976 (15.7) | 1,426 (15.3) |
| Distance from hospital in miles, median (IQR) | 8.3 (4.614.9) | 9.2 (4.917.4) | 8.3 (4.614.9) |
| ED crowding score at index visit, median (IQR) | 1.0 (0.91.1) | 1.0 (0.91.1) | 1.0 (0.91.1) |
| ED LOS in hours at index visit, median (IQR) | 2.0 (1.03.0) | 3.0 (2.05.0) | 2.0 (1.03.0) |
ED Revisit Rates and Revisits Resulting in Admission
In multivariate analyses, compared to patients who did not return to the ED, patients who returned within 72 hours of discharge had higher odds of revisit if they had the following characteristics: a chronic condition, were <1 year old, a higher severity score, and public insurance. Visit‐level factors associated with higher odds of revisits included arrival for the index visit during the evening or overnight shift or on a Friday or Saturday, index visit during times of lower ED crowding, and living closer to the hospital. On return, patients were more likely to be hospitalized if they had a higher severity score, a chronic condition, private insurance, or were <30 days old. Visit‐level factors associated with higher odds of hospitalization at revisit included an index visit during the evening and overnight shift and living further from the hospital. Although the median SES and PCP density of a patient's area of residence were not associated with greater likelihood of returning, when they returned, patients residing in an area with a lower SES and higher PCP densities (>78.8 PCPs/100,000) had lower odds of being admitted to the hospital. Patients whose index visit was on a Sunday also had lower odds of being hospitalized upon return (Table 2).
| Characteristic | Adjusted OR of 72‐Hour Revisit (95% CI), n=1,380,723 | P Value | Adjusted OR of 72‐Hour Revisit Admissions (95% CI), n=46,364 | P Value |
|---|---|---|---|---|
| ||||
| Gender | ||||
| Male | 0.99 (0.971.01) | 0.2809 | 1.02 (0.971.07) | 0.5179 |
| Female | Reference | Reference | ||
| Payor | ||||
| Government | 1.14 (1.111.17) | <0.0001 | 0.68 (0.640.72) | <0.0001 |
| Other | 0.97 (0.921.01) | 0.1148 | 0.33 (0.280.39) | <0.0001 |
| Private | Reference | Reference | ||
| Age group | ||||
| 30 days to <1 year | 1.32 (1.221.42) | <0.0001 | 0.58 (0.490.69) | <0.0001 |
| 1 year to 5 years | 0.89 (0.830.96) | 0.003 | 0.41 (0.340.48) | <0.0001 |
| 5 years to 11 years | 0.69 (0.640.74) | <0.0001 | 0.40 (0.330.48) | <0.0001 |
| 12 years to 17 years | 0.72 (0.660.77) | <0.0001 | 0.50 (0.420.60) | <0.0001 |
| <30 days | Reference | Reference | ||
| Socioeconomic statusa | ||||
| % <1.5 times FPL | 0.96 (0.921.01) | 0.0992 | 0.82 (0.740.92) | 0.0005 |
| % 1.5 to 2 times FPL | 0.98 (0.941.02) | 0.2992 | 0.83 (0.750.92) | 0.0005 |
| % 2 to 3 times FPL | 1.02 (0.981.07) | 0.292 | 0.88 (0.790.97) | 0.01 |
| % >3 times FPL | Reference | Reference | ||
| Severity score | ||||
| High severity, 4, 5, 6 | 1.43 (1.401.45) | <0.0001 | 3.42 (3.233.62) | <0.0001 |
| Low severity, 1, 2, 3 | Reference | Reference | ||
| Presence of any CCC | ||||
| Yes | 1.90 (1.861.96) | <0.0001 | 2.92 (2.753.10) | <0.0001 |
| No | Reference | Reference | ||
| Time of arrival | ||||
| Evening | 1.05 (1.031.08) | <0.0001 | 1.37 (1.291.44) | <0.0001 |
| Overnight | 1.19 (1.151.22) | <0.0001 | 1.84 (1.711.97) | <0.0001 |
| Day | Reference | Reference | ||
| Season | ||||
| Winter | 1.09 (1.061.11) | <0.0001 | 1.06 (0.991.14) | 0.0722 |
| Spring | 1.07 (1.041.10) | <0.0001 | 0.98 (0.911.046) | 0.4763 |
| Summer | 1.05 (1.021.08) | 0.0011 | 0.93 (0.871.01) | 0.0729 |
| Fall | Reference | Reference | ||
| Weekday/weekend | ||||
| Thursday | 1.02 (0.9821.055) | 0.3297 | 0.983 (0.8971.078) | 0.7185 |
| Friday | 1.08 (1.041.11) | <0.0001 | 1.03 (0.941.13) | 0.5832 |
| Saturday | 1.08 (1.041.12) | <0.0001 | 0.89 (0.810.97) | 0.0112 |
| Sunday | 1.02 (0.991.06) | 0.2054 | 0.81 (0.740.89) | <0.0001 |
| Monday | 1.00 (0.961.03) | 0.8928 | 0.98 (0.901.07) | 0.6647 |
| Tuesday | 0.99 (0.951.03) | 0.5342 | 0.93 (0.851.02) | 0.1417 |
| Wednesday | Reference | Reference | ||
| PCP ratio per 100,000 patients | ||||
| 57.267.9 | 1.00 (0.961.04) | 0.8844 | 0.93 (0.841.03) | 0.1669 |
| 68.078.7 | 1.00 (0.951.04) | 0.8156 | 0.86 (0.770.96) | 0.0066 |
| >78.8 | 1.00 (0.951.04) | 0.6883 | 0.82 (0.730.92) | 0.001 |
| <57.2 | Reference | Reference | ||
| ED crowding score at index visitb | ||||
| 2 | 0.92 (0.900.95) | <0.0001 | 0.96 (0.881.05) | 0.3435 |
| 1 | Reference | Reference | ||
| Distance from hospitalc | ||||
| 3.168, 23.6 miles | 0.95 (0.940.96) | <0.0001 | 1.16 (1.121.19) | <0.0001 |
| 2.168, 8.7 miles | Reference | Reference | ||
| ED LOS at index visitb | ||||
| 3.7 hours | 1.003 (1.0011.005) | 0.0052 | NA | |
| 2.7 hours | Reference | |||
Diagnoses Associated With Return Visits
Patients with index visit diagnoses of sickle cell disease and leukemia had the highest proportion of return visits (10.7% and 7.3%, respectively). Other conditions with high revisit rates included infectious diseases such as cellulitis, bronchiolitis, and gastroenteritis. Patients with other chronic diseases such as diabetes and with devices, such as gastrostomy tubes, also had high rates of return visits. At return, the rate of hospitalization for these conditions ranged from a 1‐in‐6 chance of hospitalization for the diagnoses of a fever to a 1‐in‐2 chance of hospitalization for patients with sickle cell anemia (Table 3).
| Major Diagnostic Subgroup | No. of Index ED Visit Dischargesa | 72‐Hour Revisit, % (95% CI) | Admitted on Return, % (95% CI) |
|---|---|---|---|
| |||
| Sickle cell anemia | 2,531 | 10.7 (9.511.9) | 49.6 (43.755.6) |
| Neoplastic diseases, cancer | 536 | 7.3 (5.19.5) | 36 (2151) |
| Infectious gastrointestinal diseases | 802 | 7.2 (5.49.0) | 21 (1031) |
| Devices and complications of the circulatory systemb | 1,033 | 6.9 (5.38.4) | 45 (3457) |
| Other hematologic diseasesb | 1,538 | 6.1 (4.97.3) | 33 (2443) |
| Fever | 80,626 | 5.9 (5.76.0) | 16.3 (15.217.3) |
| Dehydration | 7,362 | 5.4 (5.25.5) | 34.6 (30.139) |
| Infectious respiratory diseases | 72,652 | 5.4 (5.25.5) | 28.6 (27.230) |
| Seizures | 17,637 | 5.3 (4.95.6) | 33.3 (30.336.4) |
| Other devices and complicationsb | 1,896 | 5.3 (4.36.3) | 39.0 (29.448.6) |
| Infectious skin, dermatologic and soft tissue diseases | 40,272 | 4.7 (4.55) | 20.0 (18.221.8) |
| Devices and complications of the gastrointestinal systemb | 4,692 | 4.6 (4.05.2) | 24.7 (18.930.4) |
| Vomiting | 44,730 | 4.4 (4.24.6) | 23.7 (21.825.6) |
| Infectious urinary tract diseases | 17,020 | 4.4 (4.14.7) | 25.9 (22.729) |
| Headache | 19,016 | 4.3 (4.14.6) | 28.2 (25.131.3) |
| Diabetes mellitus | 1,531 | 4.5 (3.35.3) | 29 (1840) |
| Abdominal pain | 39,594 | 4.2 (44.4) | 24.8 (22.726.8) |
| Other infectious diseasesb | 647 | 4.2 (2.65.7) | 33 (1651) |
| Gastroenteritis | 55,613 | 4.0 (3.84.1) | 20.6 (18.922.3) |
DISCUSSION
In this nationally representative sample of free‐standing children's hospitals, 3.3% of patients discharged from the ED returned to the same ED within 72 hours. This rate is similar to rates previously published in studies of general EDs.[11, 15] Of the returning children, over 80% were discharged again, and 19.7% were hospitalized, which is two‐thirds more than the admission rate at index visit (12%). In accordance with previous studies,[14, 16, 25] we found higher disease severity, presence of a chronic condition, and younger age were strongly associated with both the odds of patients returning to the ED and of being hospitalized at return. Patients who were hospitalized lived further away from the hospital and were of a higher SES. In this study, we show that visit‐level and access‐related factors are also associated with increased risk of return, although to a lesser degree. Patients seen on a weekend (Friday or Saturday) were found to have higher odds of returning, whereas those seen initially on a Sunday had lower odds of hospitalization at return. In this study, we also found that patients seen on the evening or night shifts at the index presentation had a significant association with return visits and hospitalization at return. Additionally, we found that although PCP density was not associated with the odds of returning to the ED, patients from areas with a higher PCP density were less likely to be admitted at return. In addition, by evaluating the diagnoses of patients who returned, we found that many infectious conditions commonly seen in the ED also had high return rates.
As previously shown,[23] we found that patients with complex and chronic diseases were at risk for ED revisits, especially patients with sickle cell anemia and cancer (mainly acute leukemia). In addition, patients with a chronic condition were 3 times more likely to be hospitalized when they returned. These findings may indicate an opportunity for improved discharge planning and coordination of care with subspecialty care providers for particularly at‐risk populations, or stronger consideration of admission at the index visit. However, admission for these patients at revisit may be unavoidable.
Excluding patients with chronic and complex conditions, the majority of conditions with high revisit rates were acute infectious conditions. One national study showed that >70% of ED revisits by patients with infectious conditions had planned ED follow‐up.[13] Although this study was unable to assess the reasons for return or admission at return, children with infectious diseases often worsen over time (eg, those with bronchiolitis). The relatively low admission rates at return for these conditions, despite evidence that providers may have a lower threshold for admission when a patient returns to the ED shortly after discharge,[24] may reflect the potential for improving follow‐up at the PCP office. However, although some revisits may be prevented,[37, 38] we recognize that an ED visit could be appropriate and necessary for some of these children, especially those without primary care.
Access to primary care and insurance status influence ED utilization.[14, 39, 40, 41] A fragmented healthcare system with poor access to primary care is strongly associated with utilization of the ED for nonurgent care. A high ED revisit rate might be indicative of poor coordination between ED and outpatient services.[9, 39, 42, 43, 44, 45, 46] Our study's finding of increased risk of return visit if the index visit occurred on a Friday or Saturday, and a decreased likelihood of subsequent admission when a patient returns on a Sunday, may suggest limited or perceived limited access to the PCP over a weekend. Although insured patients tend to use the ED less often for nonemergent cases, even when patients have PCPs, they might still choose to return to the ED out of convenience.[47, 48] This may be reflected in our finding that, when adjusted for insurance status and PCP density, patients who lived closer to the hospital were more likely to return, but less likely to be admitted, thereby suggesting proximity as a factor in the decision to return. It is also possible that patients residing further away returned to another institution. Although PCP density did not seem to be associated with revisits, patients who lived in areas with higher PCP density were less likely to be admitted when they returned. In this study, there was a stepwise gradient in the effect of PCP density on the odds of being hospitalized on return with those patients in areas with fewer PCPs being admitted at higher rates on return. Guttmann et al.,[40] in a recent study conducted in Canada where there is universal health insurance, showed that children residing in areas with higher PCP densities had higher rates of PCP visits but lower rates of ED visits compared to children residing in areas with lower PCP densities. It is possible that emergency physicians have more confidence that patients will have dedicated follow‐up when a PCP can be identified. These findings suggest that the development of PCP networks with expanded access, such as alignment of office hours with parent need and patient/parent education about PCP availability, may reduce ED revisits. Alternatively, creation of centralized hospital‐based urgent care centers for evening, night, and weekend visits may benefit both the patient and the PCP and avoid ED revisits and associated costs.
Targeting and eliminating disparities in care might also play a role in reducing ED revisits. Prior studies have shown that publicly insured individuals, in particular, frequently use the ED as their usual source of care and are more likely to return to the ED within 72 hours of an initial visit.[23, 39, 44, 49, 50] Likewise, we found that patients with public insurance were more likely to return but less likely to be admitted on revisit. After controlling for disease severity and other demographic variables, patients with public insurance and of lower socioeconomic status still had lower odds of being hospitalized following a revisit. This might also signify an increase of avoidable hospitalizations among patients of higher SES or with private insurance. Further investigation is needed to explore the reasons for these differences and to identify effective interventions to eliminate disparities.
Our findings have implications for emergency care, ambulatory care, and the larger healthcare system. First, ED revisits are costly and contribute to already overburdened EDs.[10, 11] The average ED visit incurs charges that are 2 to 5 times more than an outpatient office visit.[49, 50] Careful coordination of ambulatory and ED services could not only ensure optimal care for patients, but could save the US healthcare system billions of dollars in potentially avoidable healthcare expenditures.[49, 50] Second, prior studies have demonstrated a consistent relationship between poor access to primary care and increased use of the ED for nonurgent conditions.[42] Publicly insured patients have been shown to have disproportionately increased difficulty acquiring and accessing primary care.[41, 42, 47, 51] Furthermore, conditions with high ED revisit rates are similar to conditions reported by Berry et al.4 as having the highest hospital readmission rates such as cancer, sickle cell anemia, seizure, pneumonia, asthma, and gastroenteritis. This might suggest a close relationship between 72‐hour ED revisits and 30‐day hospital readmissions. In light of the recent expansion of health insurance coverage to an additional 30 million individuals, the need for better coordination of services throughout the entire continuum of care, including primary care, ED, and inpatient services, has never been more important.[52] Future improvements could explore condition‐specific revisit or readmission rates to identify the most effective interventions to reduce the possibly preventable returns.
This study has several limitations. First, as an administrative database, PHIS has limited clinical data, and reasons for return visits could not be assessed. Variations between hospitals in diagnostic coding might also lead to misclassification bias. Second, we were unable to assess return visits to a different ED. Thus, we may have underestimated revisit frequency. However, because children are generally more likely to seek repeat care in the same hospital,[3] we believe our estimate of return visit rate approximates the actual return visit rate; our findings are also similar to previously reported rates. Third, for the PCP density factor, we were unable to account for types of insurance each physician accepted and influence on return rates. Fourth, return visits in our sample could have been for conditions unrelated to the diagnosis at index visit, though the short timeframe considered for revisits makes this less likely. In addition, the crowding index does not include the proportion of occupied beds at the precise moment of the index visit. Finally, this cohort includes only children seen in the EDs of pediatric hospitals, and our findings may not be generalizable to all EDs who provide care for ill and injured children.
We have shown that, in addition to previously identified patient level factors, there are visit‐level and access‐related factors associated with pediatric ED return visits. Eighty percent are discharged again, and almost one‐fifth of returning patients are admitted to the hospital. Admitted patients tend to be younger, sicker, chronically ill, and live farther from the hospital. By being aware of patients' comorbidities, PCP access, as well as certain diagnoses associated with high rates of return, physicians may better target interventions to optimize care. This may include having a lower threshold for hospitalization at the initial visit for children at high risk of return, and communication with the PCP at the time of discharge to ensure close follow‐up. Our study helps to provide benchmarks around ED revisit rates, and may serve as a starting point to better understand variation in care. Future efforts should aim to find creative solutions at individual institutions, with the goal of disseminating and replicating successes more broadly. For example, investigators in Boston have shown that the use of a comprehensive home‐based asthma management program has been successful in decreasing emergency department visits and hospitalization rates.[53] It is possible that this approach could be spread to other institutions to decrease revisits for patients with asthma. As a next step, the authors have undertaken an investigation to identify hospital‐level characteristics that may be associated with rates of return visits.
Acknowledgements
The authors thank the following members of the PHIS ED Return Visits Research Group for their contributions to the data analysis plan and interpretation of results of this study: Rustin Morse, MD, Children's Medical Center of Dallas; Catherine Perron, MD, Boston Children's Hospital; John Cheng, MD, Children's Healthcare of Atlanta; Shabnam Jain, MD, MPH, Children's Healthcare of Atlanta; and Amanda Montalbano, MD, MPH, Children's Mercy Hospitals and Clinics. These contributors did not receive compensation for their help with this work.
Disclosures
A.T.A. and A.M.S. conceived the study and developed the initial study design. All authors were involved in the development of the final study design and data analysis plan. C.W.T. collected and analyzed the data. A.T.A. and C.W.T. had full access to all of the data and take responsibility for the integrity of the data and the accuracy of the data analysis. All authors were involved in the interpretation of the data. A.T.A. drafted the article, and all authors made critical revisions to the initial draft and subsequent versions. A.T.A. and A.M.S. take full responsibility for the article as a whole. The authors report no conflicts of interest.
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Returns to the hospital following recent encounters, such as an admission to the inpatient unit or evaluation in an emergency department (ED), may reflect the natural progression of a disease, the quality of care received during the initial admission or visit, or the quality of the underlying healthcare system.[1, 2, 3, 4, 5, 6, 7, 8, 9, 10] Although national attention has focused on hospital readmissions,[3, 4, 5, 6, 7, 11, 12] ED revisits are a source of concern to emergency physicians.[8, 9] Some ED revisits are medically necessary, but revisits that may be managed in the primary care setting contribute to ED crowding, can be stressful to patients and providers, and increase healthcare costs.[10, 11, 12] Approximately 27 million annual ED visits are made by children, accounting for over one‐quarter of all ED visits in the United States, with a reported ED revisit rate of 2.5% to 5.2%.[2, 13, 14, 15, 16, 17, 18, 19, 20] Improved understanding of the patient‐level or visit‐level factors associated with ED revisits may provide an opportunity to enhance disposition decision making at the index visit and optimize site of and communication around follow‐up care.
Previous studies on ED revisits have largely been conducted in single centers and have used variable visit intervals ranging between 48 hours and 30 days.[2, 13, 16, 18, 21, 22, 23, 24, 25] Two national studies used the National Hospital Ambulatory Medical Care Survey, which includes data from both general and pediatric EDs.[13, 14] Factors identified to be associated with increased odds of returning were: young age, higher acuity, chronic conditions, and public insurance. One national study identified some diagnoses associated with higher likelihood of returning,[13] whereas the other focused primarily on infectious diseaserelated diagnoses.[14]
The purpose of this study was to describe the prevalence of return visits specifically to pediatric EDs and to investigate patient‐level, visit‐level, and healthcare systemrelated factors that may be associated with return visits and hospitalization at return.
METHODS
Study Design and Data Source
This retrospective cohort study used data from the Pediatric Health Information System (PHIS), an administrative database with data from 44 tertiary care pediatric hospitals in 27 US states and the District of Columbia. This database contains patient demographics, diagnoses, and procedures as well as medications, diagnostic imaging, laboratory, and supply charges for each patient. Data are deidentified prior to inclusion; encrypted medical record numbers allow for the identification of individual patients across all ED visits and hospitalizations to the same hospital. The Children's Hospital Association (Overland Park, KS) and participating hospitals jointly assure the quality and integrity of the data. This study was approved by the institutional review board at Boston Children's Hospital with a waiver for informed consent granted.
Study Population and Protocol
To standardize comparisons across the hospitals, we included data from 23 of the 44 hospitals in PHIS; 7 were excluded for not including ED‐specific data. For institutions that collect information from multiple hospitals within their healthcare system, we included only records from the main campus or children's hospital when possible, leading to the exclusion of 9 hospitals where the data were not able to be segregated. As an additional level of data validation, we compared the hospital‐level ED volume and admission rates as reported in the PHIS to those reported to a separate database (the Pediatric Analysis and Comparison Tool). We further excluded 5 hospitals whose volume differed by >10% between these 2 data sources.
Patients <18 years of age who were discharged from these EDs following their index visit in 2012 formed the eligible cohort.
Key Outcome Measures
The primary outcomes were return visits within 72 hours of discharge from the ED, and return visits resulting in hospitalization, including observation status. We defined an ED revisit as a return within 72 hours of ED discharge regardless of whether the patient was subsequently discharged from the ED on the return visit or hospitalized. We assessed revisits within 72 hours of an index ED discharge, because return visits within this time frame are likely to be related to the index visit.[2, 13, 16, 21, 22, 24, 25, 26]
Factors Associated With ED Revisits
A priori, we chose to adjust for the following patient‐level factors: age (<30 days, 30 days<1 year, 14 years, 511 years, 1217 years), gender, and socioeconomic status (SES) measured as the zip codebased median household income, obtained from the 2010 US Census, with respect to the federal poverty level (FPL) (<1.5 FPL, 1.52 FPL, 23 FPL, and >3 FPL).[27] We also adjusted for insurance type (commercial, government, or other), proximity of patient's home zip code to hospital (modeled as the natural log of the geographical distance to patient's home address from the hospital), ED diagnosis‐based severity classification system score (1=low severity, 5=high severity),[28] presence of a complex chronic condition at the index or prior visits using a validated classification scheme,[15, 29, 30, 31] and primary care physician (PCP) density per 100,000 in the patient's residential area (modeled as quartiles: very low, <57.2; low, 57.267.9; medium, 68.078.7; high, >78.8). PCP density, defined by the Dartmouth Atlas of Health Care,[32, 33, 34] is the number of primary care physicians per 100,000 residents (PCP count) in federal health service areas (HSA). Patients were assigned to a corresponding HSA based on their home zip code.
Visit‐level factors included arrival time of index visit (8:01 am 4:00 pm, 4:01 pm12:00 am, 12:01 am8 am representing day, evening, and overnight arrival, respectively), day of the week, season, length of stay (LOS) in the ED during the index visit, and ED crowding (calculated as the average daily LOS/yearly average LOS for the individual ED).[35] We categorized the ED primary diagnosis for each visit using the major diagnosis groupings of a previously described pediatric ED‐specific classification scheme.[36] Using International Classification of Diseases, Ninth Revision (ICD‐9) codes, we identified the conditions with the highest ED revisit rates.
Statistical Analyses
Categorical variables describing the study cohort were summarized using frequencies and percentages. Continuous variables were summarized using mean, median, and interquartile range values, where appropriate. We used 2 different hierarchical logistic regression models to assess revisit rates by patient‐ and visit‐level characteristics. The initial model included all patients discharged from the ED following the index visit and assessed for the outcome of a revisit within 72 hours. The second model considered only patients who returned within 72 hours of an index visit and assessed for hospitalization on that return visit. We used generalized linear mixed effects models, with hospital as a random effect to account for the presence of correlated data (within hospitals), nonconstant variability (across hospitals), and binary responses. Adjusted odds ratios with 95% confidence intervals were used as summary measures of the effect of the individual adjusters. Adjusters were missing in fewer than 5% of patients across participating hospitals. Statistical analyses were performed using SAS version 9.3 (SAS Institute Inc., Cary, NC); 2‐sided P values <0.004 were considered statistically significant to account for multiple comparisons (Bonferroni‐adjusted level of significance=0.0038).
RESULTS
Patients
A total of 1,610,201 patients <18 years of age evaluated across the 23 PHIS EDs in 2012 were included in the study. Twenty‐one of the 23 EDs have academic affiliations; 10 are located in the South, 6 in the Midwest, 5 in the West, and 2 in the Northeast region of the United States. The annual ED volume for these EDs ranged from 25,090 to 136,160 (median, 65,075; interquartile range, 45,28085,206). Of the total patients, 1,415,721 (87.9%) were discharged following the index visit and comprised our study cohort. Of these patients, 47,294 (revisit rate: 3.3%) had an ED revisit within 72 hours. There were 4015 patients (0.3%) who returned more than once within 72 hours, and the largest proportion of these returned with infection‐related conditions. Of those returning, 37,999 (80.3%) were discharged again, whereas 9295 (19.7%) were admitted to the hospital (Figure 1). The demographic and clinical characteristics of study participants are displayed in Table 1.
| Index Visit, n=1,415,721, n (%) | Return Visits Within 72 Hours of Discharge, n=47,294, 3.3% | ||
|---|---|---|---|
| Return to Discharge, n (%) | Return to Admission, n (%) | ||
| |||
| Gender, female | 659,417 (46.6) | 17,665 (46.5) | 4,304 (46.3) |
| Payor | |||
| Commercial | 379,403 (26.8) | 8,388 (22.1) | 3,214 (34.6) |
| Government | 925,147 (65.4) | 26,880 (70.7) | 5,786 (62.3) |
| Other | 111,171 (7.9) | 2,731 (7.2) | 295 (3.2) |
| Age | |||
| <30 days | 19,217 (1.4) | 488 (1.3) | 253 (2.7) |
| 30 days to <1 year | 216,967 (15.3) | 8,280 (21.8) | 2,372 (25.5) |
| 1 year to 4 years | 547,083 (38.6) | 15,542 (40.9) | 3,187 (34.3) |
| 5 years to 11 years | 409,463 (28.9) | 8,906 (23.4) | 1,964 (21.1) |
| 12 years to 17 years | 222,991 (15.8) | 4,783 (12.6) | 1,519 (16.3) |
| Socioeconomic statusa | |||
| <1.5 times FPL | 493,770 (34.9) | 13,851 (36.5) | 2,879 (31.0) |
| 1.5 to 2 times FPL | 455,490 (32.2) | 12,364 (32.5) | 2,904 (31.2) |
| 2 to 3 times FPL | 367,557 (26.0) | 9,560 (25.2) | 2,714 (29.2) |
| >3 times FPL | 98,904 (7.0) | 2,224 (5.9) | 798 (8.6) |
| Primary care physician density per 100,000 patients | |||
| Very low | 351,798 (24.9) | 8,727 (23.0) | 2,628 (28.3) |
| Low | 357,099 (25.2) | 9,810 (25.8) | 2,067 (22.2) |
| Medium | 347,995 (24.6) | 10,186 (26.8) | 2,035 (21.9) |
| High | 358,829 (25.4) | 9,276 (24.4) | 2,565 (27.6) |
| CCC present, yes | 125,774 (8.9) | 4,446 (11.7) | 2,825 (30.4) |
| Severity score | |||
| Low severity (0,1,2) | 721,061 (50.9) | 17,310 (45.6) | 2,955 (31.8) |
| High severity (3,4,5) | 694,660 (49.1) | 20,689 (54.5) | 6,340 (68.2) |
| Time of arrival | |||
| Day | 533,328 (37.7) | 13,449 (35.4) | 3,396 (36.5) |
| Evening | 684,873 (48.4) | 18,417 (48.5) | 4,378 (47.1) |
| Overnight | 197,520 (14.0) | 6,133 (16.1) | 1,521 (16.4) |
| Season | |||
| Winter | 384,957 (27.2) | 10,603 (27.9) | 2,844 (30.6) |
| Spring | 367,434 (26.0) | 9,923 (26.1) | 2,311 (24.9) |
| Summer | 303,872 (21.5) | 8,308 (21.9) | 1,875 (20.2) |
| Fall | 359,458 (25.4) | 9,165 (24.1) | 2,265 (24.4) |
| Weekday/weekend | |||
| Monday | 217,774 (15.4) | 5,646 (14.9) | 1,394 (15) |
| Tuesday | 198,220 (14.0) | 5,054 (13.3) | 1,316 (14.2) |
| Wednesday | 194,295 (13.7) | 4,985 (13.1) | 1,333 (14.3) |
| Thursday | 191,950 (13.6) | 5,123 (13.5) | 1,234 (13.3) |
| Friday | 190,022 (13.4) | 5,449 (14.3) | 1,228 (13.2) |
| Saturday | 202,247 (14.3) | 5,766 (15.2) | 1,364 (14.7) |
| Sunday | 221,213 (15.6) | 5,976 (15.7) | 1,426 (15.3) |
| Distance from hospital in miles, median (IQR) | 8.3 (4.614.9) | 9.2 (4.917.4) | 8.3 (4.614.9) |
| ED crowding score at index visit, median (IQR) | 1.0 (0.91.1) | 1.0 (0.91.1) | 1.0 (0.91.1) |
| ED LOS in hours at index visit, median (IQR) | 2.0 (1.03.0) | 3.0 (2.05.0) | 2.0 (1.03.0) |
ED Revisit Rates and Revisits Resulting in Admission
In multivariate analyses, compared to patients who did not return to the ED, patients who returned within 72 hours of discharge had higher odds of revisit if they had the following characteristics: a chronic condition, were <1 year old, a higher severity score, and public insurance. Visit‐level factors associated with higher odds of revisits included arrival for the index visit during the evening or overnight shift or on a Friday or Saturday, index visit during times of lower ED crowding, and living closer to the hospital. On return, patients were more likely to be hospitalized if they had a higher severity score, a chronic condition, private insurance, or were <30 days old. Visit‐level factors associated with higher odds of hospitalization at revisit included an index visit during the evening and overnight shift and living further from the hospital. Although the median SES and PCP density of a patient's area of residence were not associated with greater likelihood of returning, when they returned, patients residing in an area with a lower SES and higher PCP densities (>78.8 PCPs/100,000) had lower odds of being admitted to the hospital. Patients whose index visit was on a Sunday also had lower odds of being hospitalized upon return (Table 2).
| Characteristic | Adjusted OR of 72‐Hour Revisit (95% CI), n=1,380,723 | P Value | Adjusted OR of 72‐Hour Revisit Admissions (95% CI), n=46,364 | P Value |
|---|---|---|---|---|
| ||||
| Gender | ||||
| Male | 0.99 (0.971.01) | 0.2809 | 1.02 (0.971.07) | 0.5179 |
| Female | Reference | Reference | ||
| Payor | ||||
| Government | 1.14 (1.111.17) | <0.0001 | 0.68 (0.640.72) | <0.0001 |
| Other | 0.97 (0.921.01) | 0.1148 | 0.33 (0.280.39) | <0.0001 |
| Private | Reference | Reference | ||
| Age group | ||||
| 30 days to <1 year | 1.32 (1.221.42) | <0.0001 | 0.58 (0.490.69) | <0.0001 |
| 1 year to 5 years | 0.89 (0.830.96) | 0.003 | 0.41 (0.340.48) | <0.0001 |
| 5 years to 11 years | 0.69 (0.640.74) | <0.0001 | 0.40 (0.330.48) | <0.0001 |
| 12 years to 17 years | 0.72 (0.660.77) | <0.0001 | 0.50 (0.420.60) | <0.0001 |
| <30 days | Reference | Reference | ||
| Socioeconomic statusa | ||||
| % <1.5 times FPL | 0.96 (0.921.01) | 0.0992 | 0.82 (0.740.92) | 0.0005 |
| % 1.5 to 2 times FPL | 0.98 (0.941.02) | 0.2992 | 0.83 (0.750.92) | 0.0005 |
| % 2 to 3 times FPL | 1.02 (0.981.07) | 0.292 | 0.88 (0.790.97) | 0.01 |
| % >3 times FPL | Reference | Reference | ||
| Severity score | ||||
| High severity, 4, 5, 6 | 1.43 (1.401.45) | <0.0001 | 3.42 (3.233.62) | <0.0001 |
| Low severity, 1, 2, 3 | Reference | Reference | ||
| Presence of any CCC | ||||
| Yes | 1.90 (1.861.96) | <0.0001 | 2.92 (2.753.10) | <0.0001 |
| No | Reference | Reference | ||
| Time of arrival | ||||
| Evening | 1.05 (1.031.08) | <0.0001 | 1.37 (1.291.44) | <0.0001 |
| Overnight | 1.19 (1.151.22) | <0.0001 | 1.84 (1.711.97) | <0.0001 |
| Day | Reference | Reference | ||
| Season | ||||
| Winter | 1.09 (1.061.11) | <0.0001 | 1.06 (0.991.14) | 0.0722 |
| Spring | 1.07 (1.041.10) | <0.0001 | 0.98 (0.911.046) | 0.4763 |
| Summer | 1.05 (1.021.08) | 0.0011 | 0.93 (0.871.01) | 0.0729 |
| Fall | Reference | Reference | ||
| Weekday/weekend | ||||
| Thursday | 1.02 (0.9821.055) | 0.3297 | 0.983 (0.8971.078) | 0.7185 |
| Friday | 1.08 (1.041.11) | <0.0001 | 1.03 (0.941.13) | 0.5832 |
| Saturday | 1.08 (1.041.12) | <0.0001 | 0.89 (0.810.97) | 0.0112 |
| Sunday | 1.02 (0.991.06) | 0.2054 | 0.81 (0.740.89) | <0.0001 |
| Monday | 1.00 (0.961.03) | 0.8928 | 0.98 (0.901.07) | 0.6647 |
| Tuesday | 0.99 (0.951.03) | 0.5342 | 0.93 (0.851.02) | 0.1417 |
| Wednesday | Reference | Reference | ||
| PCP ratio per 100,000 patients | ||||
| 57.267.9 | 1.00 (0.961.04) | 0.8844 | 0.93 (0.841.03) | 0.1669 |
| 68.078.7 | 1.00 (0.951.04) | 0.8156 | 0.86 (0.770.96) | 0.0066 |
| >78.8 | 1.00 (0.951.04) | 0.6883 | 0.82 (0.730.92) | 0.001 |
| <57.2 | Reference | Reference | ||
| ED crowding score at index visitb | ||||
| 2 | 0.92 (0.900.95) | <0.0001 | 0.96 (0.881.05) | 0.3435 |
| 1 | Reference | Reference | ||
| Distance from hospitalc | ||||
| 3.168, 23.6 miles | 0.95 (0.940.96) | <0.0001 | 1.16 (1.121.19) | <0.0001 |
| 2.168, 8.7 miles | Reference | Reference | ||
| ED LOS at index visitb | ||||
| 3.7 hours | 1.003 (1.0011.005) | 0.0052 | NA | |
| 2.7 hours | Reference | |||
Diagnoses Associated With Return Visits
Patients with index visit diagnoses of sickle cell disease and leukemia had the highest proportion of return visits (10.7% and 7.3%, respectively). Other conditions with high revisit rates included infectious diseases such as cellulitis, bronchiolitis, and gastroenteritis. Patients with other chronic diseases such as diabetes and with devices, such as gastrostomy tubes, also had high rates of return visits. At return, the rate of hospitalization for these conditions ranged from a 1‐in‐6 chance of hospitalization for the diagnoses of a fever to a 1‐in‐2 chance of hospitalization for patients with sickle cell anemia (Table 3).
| Major Diagnostic Subgroup | No. of Index ED Visit Dischargesa | 72‐Hour Revisit, % (95% CI) | Admitted on Return, % (95% CI) |
|---|---|---|---|
| |||
| Sickle cell anemia | 2,531 | 10.7 (9.511.9) | 49.6 (43.755.6) |
| Neoplastic diseases, cancer | 536 | 7.3 (5.19.5) | 36 (2151) |
| Infectious gastrointestinal diseases | 802 | 7.2 (5.49.0) | 21 (1031) |
| Devices and complications of the circulatory systemb | 1,033 | 6.9 (5.38.4) | 45 (3457) |
| Other hematologic diseasesb | 1,538 | 6.1 (4.97.3) | 33 (2443) |
| Fever | 80,626 | 5.9 (5.76.0) | 16.3 (15.217.3) |
| Dehydration | 7,362 | 5.4 (5.25.5) | 34.6 (30.139) |
| Infectious respiratory diseases | 72,652 | 5.4 (5.25.5) | 28.6 (27.230) |
| Seizures | 17,637 | 5.3 (4.95.6) | 33.3 (30.336.4) |
| Other devices and complicationsb | 1,896 | 5.3 (4.36.3) | 39.0 (29.448.6) |
| Infectious skin, dermatologic and soft tissue diseases | 40,272 | 4.7 (4.55) | 20.0 (18.221.8) |
| Devices and complications of the gastrointestinal systemb | 4,692 | 4.6 (4.05.2) | 24.7 (18.930.4) |
| Vomiting | 44,730 | 4.4 (4.24.6) | 23.7 (21.825.6) |
| Infectious urinary tract diseases | 17,020 | 4.4 (4.14.7) | 25.9 (22.729) |
| Headache | 19,016 | 4.3 (4.14.6) | 28.2 (25.131.3) |
| Diabetes mellitus | 1,531 | 4.5 (3.35.3) | 29 (1840) |
| Abdominal pain | 39,594 | 4.2 (44.4) | 24.8 (22.726.8) |
| Other infectious diseasesb | 647 | 4.2 (2.65.7) | 33 (1651) |
| Gastroenteritis | 55,613 | 4.0 (3.84.1) | 20.6 (18.922.3) |
DISCUSSION
In this nationally representative sample of free‐standing children's hospitals, 3.3% of patients discharged from the ED returned to the same ED within 72 hours. This rate is similar to rates previously published in studies of general EDs.[11, 15] Of the returning children, over 80% were discharged again, and 19.7% were hospitalized, which is two‐thirds more than the admission rate at index visit (12%). In accordance with previous studies,[14, 16, 25] we found higher disease severity, presence of a chronic condition, and younger age were strongly associated with both the odds of patients returning to the ED and of being hospitalized at return. Patients who were hospitalized lived further away from the hospital and were of a higher SES. In this study, we show that visit‐level and access‐related factors are also associated with increased risk of return, although to a lesser degree. Patients seen on a weekend (Friday or Saturday) were found to have higher odds of returning, whereas those seen initially on a Sunday had lower odds of hospitalization at return. In this study, we also found that patients seen on the evening or night shifts at the index presentation had a significant association with return visits and hospitalization at return. Additionally, we found that although PCP density was not associated with the odds of returning to the ED, patients from areas with a higher PCP density were less likely to be admitted at return. In addition, by evaluating the diagnoses of patients who returned, we found that many infectious conditions commonly seen in the ED also had high return rates.
As previously shown,[23] we found that patients with complex and chronic diseases were at risk for ED revisits, especially patients with sickle cell anemia and cancer (mainly acute leukemia). In addition, patients with a chronic condition were 3 times more likely to be hospitalized when they returned. These findings may indicate an opportunity for improved discharge planning and coordination of care with subspecialty care providers for particularly at‐risk populations, or stronger consideration of admission at the index visit. However, admission for these patients at revisit may be unavoidable.
Excluding patients with chronic and complex conditions, the majority of conditions with high revisit rates were acute infectious conditions. One national study showed that >70% of ED revisits by patients with infectious conditions had planned ED follow‐up.[13] Although this study was unable to assess the reasons for return or admission at return, children with infectious diseases often worsen over time (eg, those with bronchiolitis). The relatively low admission rates at return for these conditions, despite evidence that providers may have a lower threshold for admission when a patient returns to the ED shortly after discharge,[24] may reflect the potential for improving follow‐up at the PCP office. However, although some revisits may be prevented,[37, 38] we recognize that an ED visit could be appropriate and necessary for some of these children, especially those without primary care.
Access to primary care and insurance status influence ED utilization.[14, 39, 40, 41] A fragmented healthcare system with poor access to primary care is strongly associated with utilization of the ED for nonurgent care. A high ED revisit rate might be indicative of poor coordination between ED and outpatient services.[9, 39, 42, 43, 44, 45, 46] Our study's finding of increased risk of return visit if the index visit occurred on a Friday or Saturday, and a decreased likelihood of subsequent admission when a patient returns on a Sunday, may suggest limited or perceived limited access to the PCP over a weekend. Although insured patients tend to use the ED less often for nonemergent cases, even when patients have PCPs, they might still choose to return to the ED out of convenience.[47, 48] This may be reflected in our finding that, when adjusted for insurance status and PCP density, patients who lived closer to the hospital were more likely to return, but less likely to be admitted, thereby suggesting proximity as a factor in the decision to return. It is also possible that patients residing further away returned to another institution. Although PCP density did not seem to be associated with revisits, patients who lived in areas with higher PCP density were less likely to be admitted when they returned. In this study, there was a stepwise gradient in the effect of PCP density on the odds of being hospitalized on return with those patients in areas with fewer PCPs being admitted at higher rates on return. Guttmann et al.,[40] in a recent study conducted in Canada where there is universal health insurance, showed that children residing in areas with higher PCP densities had higher rates of PCP visits but lower rates of ED visits compared to children residing in areas with lower PCP densities. It is possible that emergency physicians have more confidence that patients will have dedicated follow‐up when a PCP can be identified. These findings suggest that the development of PCP networks with expanded access, such as alignment of office hours with parent need and patient/parent education about PCP availability, may reduce ED revisits. Alternatively, creation of centralized hospital‐based urgent care centers for evening, night, and weekend visits may benefit both the patient and the PCP and avoid ED revisits and associated costs.
Targeting and eliminating disparities in care might also play a role in reducing ED revisits. Prior studies have shown that publicly insured individuals, in particular, frequently use the ED as their usual source of care and are more likely to return to the ED within 72 hours of an initial visit.[23, 39, 44, 49, 50] Likewise, we found that patients with public insurance were more likely to return but less likely to be admitted on revisit. After controlling for disease severity and other demographic variables, patients with public insurance and of lower socioeconomic status still had lower odds of being hospitalized following a revisit. This might also signify an increase of avoidable hospitalizations among patients of higher SES or with private insurance. Further investigation is needed to explore the reasons for these differences and to identify effective interventions to eliminate disparities.
Our findings have implications for emergency care, ambulatory care, and the larger healthcare system. First, ED revisits are costly and contribute to already overburdened EDs.[10, 11] The average ED visit incurs charges that are 2 to 5 times more than an outpatient office visit.[49, 50] Careful coordination of ambulatory and ED services could not only ensure optimal care for patients, but could save the US healthcare system billions of dollars in potentially avoidable healthcare expenditures.[49, 50] Second, prior studies have demonstrated a consistent relationship between poor access to primary care and increased use of the ED for nonurgent conditions.[42] Publicly insured patients have been shown to have disproportionately increased difficulty acquiring and accessing primary care.[41, 42, 47, 51] Furthermore, conditions with high ED revisit rates are similar to conditions reported by Berry et al.4 as having the highest hospital readmission rates such as cancer, sickle cell anemia, seizure, pneumonia, asthma, and gastroenteritis. This might suggest a close relationship between 72‐hour ED revisits and 30‐day hospital readmissions. In light of the recent expansion of health insurance coverage to an additional 30 million individuals, the need for better coordination of services throughout the entire continuum of care, including primary care, ED, and inpatient services, has never been more important.[52] Future improvements could explore condition‐specific revisit or readmission rates to identify the most effective interventions to reduce the possibly preventable returns.
This study has several limitations. First, as an administrative database, PHIS has limited clinical data, and reasons for return visits could not be assessed. Variations between hospitals in diagnostic coding might also lead to misclassification bias. Second, we were unable to assess return visits to a different ED. Thus, we may have underestimated revisit frequency. However, because children are generally more likely to seek repeat care in the same hospital,[3] we believe our estimate of return visit rate approximates the actual return visit rate; our findings are also similar to previously reported rates. Third, for the PCP density factor, we were unable to account for types of insurance each physician accepted and influence on return rates. Fourth, return visits in our sample could have been for conditions unrelated to the diagnosis at index visit, though the short timeframe considered for revisits makes this less likely. In addition, the crowding index does not include the proportion of occupied beds at the precise moment of the index visit. Finally, this cohort includes only children seen in the EDs of pediatric hospitals, and our findings may not be generalizable to all EDs who provide care for ill and injured children.
We have shown that, in addition to previously identified patient level factors, there are visit‐level and access‐related factors associated with pediatric ED return visits. Eighty percent are discharged again, and almost one‐fifth of returning patients are admitted to the hospital. Admitted patients tend to be younger, sicker, chronically ill, and live farther from the hospital. By being aware of patients' comorbidities, PCP access, as well as certain diagnoses associated with high rates of return, physicians may better target interventions to optimize care. This may include having a lower threshold for hospitalization at the initial visit for children at high risk of return, and communication with the PCP at the time of discharge to ensure close follow‐up. Our study helps to provide benchmarks around ED revisit rates, and may serve as a starting point to better understand variation in care. Future efforts should aim to find creative solutions at individual institutions, with the goal of disseminating and replicating successes more broadly. For example, investigators in Boston have shown that the use of a comprehensive home‐based asthma management program has been successful in decreasing emergency department visits and hospitalization rates.[53] It is possible that this approach could be spread to other institutions to decrease revisits for patients with asthma. As a next step, the authors have undertaken an investigation to identify hospital‐level characteristics that may be associated with rates of return visits.
Acknowledgements
The authors thank the following members of the PHIS ED Return Visits Research Group for their contributions to the data analysis plan and interpretation of results of this study: Rustin Morse, MD, Children's Medical Center of Dallas; Catherine Perron, MD, Boston Children's Hospital; John Cheng, MD, Children's Healthcare of Atlanta; Shabnam Jain, MD, MPH, Children's Healthcare of Atlanta; and Amanda Montalbano, MD, MPH, Children's Mercy Hospitals and Clinics. These contributors did not receive compensation for their help with this work.
Disclosures
A.T.A. and A.M.S. conceived the study and developed the initial study design. All authors were involved in the development of the final study design and data analysis plan. C.W.T. collected and analyzed the data. A.T.A. and C.W.T. had full access to all of the data and take responsibility for the integrity of the data and the accuracy of the data analysis. All authors were involved in the interpretation of the data. A.T.A. drafted the article, and all authors made critical revisions to the initial draft and subsequent versions. A.T.A. and A.M.S. take full responsibility for the article as a whole. The authors report no conflicts of interest.
Returns to the hospital following recent encounters, such as an admission to the inpatient unit or evaluation in an emergency department (ED), may reflect the natural progression of a disease, the quality of care received during the initial admission or visit, or the quality of the underlying healthcare system.[1, 2, 3, 4, 5, 6, 7, 8, 9, 10] Although national attention has focused on hospital readmissions,[3, 4, 5, 6, 7, 11, 12] ED revisits are a source of concern to emergency physicians.[8, 9] Some ED revisits are medically necessary, but revisits that may be managed in the primary care setting contribute to ED crowding, can be stressful to patients and providers, and increase healthcare costs.[10, 11, 12] Approximately 27 million annual ED visits are made by children, accounting for over one‐quarter of all ED visits in the United States, with a reported ED revisit rate of 2.5% to 5.2%.[2, 13, 14, 15, 16, 17, 18, 19, 20] Improved understanding of the patient‐level or visit‐level factors associated with ED revisits may provide an opportunity to enhance disposition decision making at the index visit and optimize site of and communication around follow‐up care.
Previous studies on ED revisits have largely been conducted in single centers and have used variable visit intervals ranging between 48 hours and 30 days.[2, 13, 16, 18, 21, 22, 23, 24, 25] Two national studies used the National Hospital Ambulatory Medical Care Survey, which includes data from both general and pediatric EDs.[13, 14] Factors identified to be associated with increased odds of returning were: young age, higher acuity, chronic conditions, and public insurance. One national study identified some diagnoses associated with higher likelihood of returning,[13] whereas the other focused primarily on infectious diseaserelated diagnoses.[14]
The purpose of this study was to describe the prevalence of return visits specifically to pediatric EDs and to investigate patient‐level, visit‐level, and healthcare systemrelated factors that may be associated with return visits and hospitalization at return.
METHODS
Study Design and Data Source
This retrospective cohort study used data from the Pediatric Health Information System (PHIS), an administrative database with data from 44 tertiary care pediatric hospitals in 27 US states and the District of Columbia. This database contains patient demographics, diagnoses, and procedures as well as medications, diagnostic imaging, laboratory, and supply charges for each patient. Data are deidentified prior to inclusion; encrypted medical record numbers allow for the identification of individual patients across all ED visits and hospitalizations to the same hospital. The Children's Hospital Association (Overland Park, KS) and participating hospitals jointly assure the quality and integrity of the data. This study was approved by the institutional review board at Boston Children's Hospital with a waiver for informed consent granted.
Study Population and Protocol
To standardize comparisons across the hospitals, we included data from 23 of the 44 hospitals in PHIS; 7 were excluded for not including ED‐specific data. For institutions that collect information from multiple hospitals within their healthcare system, we included only records from the main campus or children's hospital when possible, leading to the exclusion of 9 hospitals where the data were not able to be segregated. As an additional level of data validation, we compared the hospital‐level ED volume and admission rates as reported in the PHIS to those reported to a separate database (the Pediatric Analysis and Comparison Tool). We further excluded 5 hospitals whose volume differed by >10% between these 2 data sources.
Patients <18 years of age who were discharged from these EDs following their index visit in 2012 formed the eligible cohort.
Key Outcome Measures
The primary outcomes were return visits within 72 hours of discharge from the ED, and return visits resulting in hospitalization, including observation status. We defined an ED revisit as a return within 72 hours of ED discharge regardless of whether the patient was subsequently discharged from the ED on the return visit or hospitalized. We assessed revisits within 72 hours of an index ED discharge, because return visits within this time frame are likely to be related to the index visit.[2, 13, 16, 21, 22, 24, 25, 26]
Factors Associated With ED Revisits
A priori, we chose to adjust for the following patient‐level factors: age (<30 days, 30 days<1 year, 14 years, 511 years, 1217 years), gender, and socioeconomic status (SES) measured as the zip codebased median household income, obtained from the 2010 US Census, with respect to the federal poverty level (FPL) (<1.5 FPL, 1.52 FPL, 23 FPL, and >3 FPL).[27] We also adjusted for insurance type (commercial, government, or other), proximity of patient's home zip code to hospital (modeled as the natural log of the geographical distance to patient's home address from the hospital), ED diagnosis‐based severity classification system score (1=low severity, 5=high severity),[28] presence of a complex chronic condition at the index or prior visits using a validated classification scheme,[15, 29, 30, 31] and primary care physician (PCP) density per 100,000 in the patient's residential area (modeled as quartiles: very low, <57.2; low, 57.267.9; medium, 68.078.7; high, >78.8). PCP density, defined by the Dartmouth Atlas of Health Care,[32, 33, 34] is the number of primary care physicians per 100,000 residents (PCP count) in federal health service areas (HSA). Patients were assigned to a corresponding HSA based on their home zip code.
Visit‐level factors included arrival time of index visit (8:01 am 4:00 pm, 4:01 pm12:00 am, 12:01 am8 am representing day, evening, and overnight arrival, respectively), day of the week, season, length of stay (LOS) in the ED during the index visit, and ED crowding (calculated as the average daily LOS/yearly average LOS for the individual ED).[35] We categorized the ED primary diagnosis for each visit using the major diagnosis groupings of a previously described pediatric ED‐specific classification scheme.[36] Using International Classification of Diseases, Ninth Revision (ICD‐9) codes, we identified the conditions with the highest ED revisit rates.
Statistical Analyses
Categorical variables describing the study cohort were summarized using frequencies and percentages. Continuous variables were summarized using mean, median, and interquartile range values, where appropriate. We used 2 different hierarchical logistic regression models to assess revisit rates by patient‐ and visit‐level characteristics. The initial model included all patients discharged from the ED following the index visit and assessed for the outcome of a revisit within 72 hours. The second model considered only patients who returned within 72 hours of an index visit and assessed for hospitalization on that return visit. We used generalized linear mixed effects models, with hospital as a random effect to account for the presence of correlated data (within hospitals), nonconstant variability (across hospitals), and binary responses. Adjusted odds ratios with 95% confidence intervals were used as summary measures of the effect of the individual adjusters. Adjusters were missing in fewer than 5% of patients across participating hospitals. Statistical analyses were performed using SAS version 9.3 (SAS Institute Inc., Cary, NC); 2‐sided P values <0.004 were considered statistically significant to account for multiple comparisons (Bonferroni‐adjusted level of significance=0.0038).
RESULTS
Patients
A total of 1,610,201 patients <18 years of age evaluated across the 23 PHIS EDs in 2012 were included in the study. Twenty‐one of the 23 EDs have academic affiliations; 10 are located in the South, 6 in the Midwest, 5 in the West, and 2 in the Northeast region of the United States. The annual ED volume for these EDs ranged from 25,090 to 136,160 (median, 65,075; interquartile range, 45,28085,206). Of the total patients, 1,415,721 (87.9%) were discharged following the index visit and comprised our study cohort. Of these patients, 47,294 (revisit rate: 3.3%) had an ED revisit within 72 hours. There were 4015 patients (0.3%) who returned more than once within 72 hours, and the largest proportion of these returned with infection‐related conditions. Of those returning, 37,999 (80.3%) were discharged again, whereas 9295 (19.7%) were admitted to the hospital (Figure 1). The demographic and clinical characteristics of study participants are displayed in Table 1.
| Index Visit, n=1,415,721, n (%) | Return Visits Within 72 Hours of Discharge, n=47,294, 3.3% | ||
|---|---|---|---|
| Return to Discharge, n (%) | Return to Admission, n (%) | ||
| |||
| Gender, female | 659,417 (46.6) | 17,665 (46.5) | 4,304 (46.3) |
| Payor | |||
| Commercial | 379,403 (26.8) | 8,388 (22.1) | 3,214 (34.6) |
| Government | 925,147 (65.4) | 26,880 (70.7) | 5,786 (62.3) |
| Other | 111,171 (7.9) | 2,731 (7.2) | 295 (3.2) |
| Age | |||
| <30 days | 19,217 (1.4) | 488 (1.3) | 253 (2.7) |
| 30 days to <1 year | 216,967 (15.3) | 8,280 (21.8) | 2,372 (25.5) |
| 1 year to 4 years | 547,083 (38.6) | 15,542 (40.9) | 3,187 (34.3) |
| 5 years to 11 years | 409,463 (28.9) | 8,906 (23.4) | 1,964 (21.1) |
| 12 years to 17 years | 222,991 (15.8) | 4,783 (12.6) | 1,519 (16.3) |
| Socioeconomic statusa | |||
| <1.5 times FPL | 493,770 (34.9) | 13,851 (36.5) | 2,879 (31.0) |
| 1.5 to 2 times FPL | 455,490 (32.2) | 12,364 (32.5) | 2,904 (31.2) |
| 2 to 3 times FPL | 367,557 (26.0) | 9,560 (25.2) | 2,714 (29.2) |
| >3 times FPL | 98,904 (7.0) | 2,224 (5.9) | 798 (8.6) |
| Primary care physician density per 100,000 patients | |||
| Very low | 351,798 (24.9) | 8,727 (23.0) | 2,628 (28.3) |
| Low | 357,099 (25.2) | 9,810 (25.8) | 2,067 (22.2) |
| Medium | 347,995 (24.6) | 10,186 (26.8) | 2,035 (21.9) |
| High | 358,829 (25.4) | 9,276 (24.4) | 2,565 (27.6) |
| CCC present, yes | 125,774 (8.9) | 4,446 (11.7) | 2,825 (30.4) |
| Severity score | |||
| Low severity (0,1,2) | 721,061 (50.9) | 17,310 (45.6) | 2,955 (31.8) |
| High severity (3,4,5) | 694,660 (49.1) | 20,689 (54.5) | 6,340 (68.2) |
| Time of arrival | |||
| Day | 533,328 (37.7) | 13,449 (35.4) | 3,396 (36.5) |
| Evening | 684,873 (48.4) | 18,417 (48.5) | 4,378 (47.1) |
| Overnight | 197,520 (14.0) | 6,133 (16.1) | 1,521 (16.4) |
| Season | |||
| Winter | 384,957 (27.2) | 10,603 (27.9) | 2,844 (30.6) |
| Spring | 367,434 (26.0) | 9,923 (26.1) | 2,311 (24.9) |
| Summer | 303,872 (21.5) | 8,308 (21.9) | 1,875 (20.2) |
| Fall | 359,458 (25.4) | 9,165 (24.1) | 2,265 (24.4) |
| Weekday/weekend | |||
| Monday | 217,774 (15.4) | 5,646 (14.9) | 1,394 (15) |
| Tuesday | 198,220 (14.0) | 5,054 (13.3) | 1,316 (14.2) |
| Wednesday | 194,295 (13.7) | 4,985 (13.1) | 1,333 (14.3) |
| Thursday | 191,950 (13.6) | 5,123 (13.5) | 1,234 (13.3) |
| Friday | 190,022 (13.4) | 5,449 (14.3) | 1,228 (13.2) |
| Saturday | 202,247 (14.3) | 5,766 (15.2) | 1,364 (14.7) |
| Sunday | 221,213 (15.6) | 5,976 (15.7) | 1,426 (15.3) |
| Distance from hospital in miles, median (IQR) | 8.3 (4.614.9) | 9.2 (4.917.4) | 8.3 (4.614.9) |
| ED crowding score at index visit, median (IQR) | 1.0 (0.91.1) | 1.0 (0.91.1) | 1.0 (0.91.1) |
| ED LOS in hours at index visit, median (IQR) | 2.0 (1.03.0) | 3.0 (2.05.0) | 2.0 (1.03.0) |
ED Revisit Rates and Revisits Resulting in Admission
In multivariate analyses, compared to patients who did not return to the ED, patients who returned within 72 hours of discharge had higher odds of revisit if they had the following characteristics: a chronic condition, were <1 year old, a higher severity score, and public insurance. Visit‐level factors associated with higher odds of revisits included arrival for the index visit during the evening or overnight shift or on a Friday or Saturday, index visit during times of lower ED crowding, and living closer to the hospital. On return, patients were more likely to be hospitalized if they had a higher severity score, a chronic condition, private insurance, or were <30 days old. Visit‐level factors associated with higher odds of hospitalization at revisit included an index visit during the evening and overnight shift and living further from the hospital. Although the median SES and PCP density of a patient's area of residence were not associated with greater likelihood of returning, when they returned, patients residing in an area with a lower SES and higher PCP densities (>78.8 PCPs/100,000) had lower odds of being admitted to the hospital. Patients whose index visit was on a Sunday also had lower odds of being hospitalized upon return (Table 2).
| Characteristic | Adjusted OR of 72‐Hour Revisit (95% CI), n=1,380,723 | P Value | Adjusted OR of 72‐Hour Revisit Admissions (95% CI), n=46,364 | P Value |
|---|---|---|---|---|
| ||||
| Gender | ||||
| Male | 0.99 (0.971.01) | 0.2809 | 1.02 (0.971.07) | 0.5179 |
| Female | Reference | Reference | ||
| Payor | ||||
| Government | 1.14 (1.111.17) | <0.0001 | 0.68 (0.640.72) | <0.0001 |
| Other | 0.97 (0.921.01) | 0.1148 | 0.33 (0.280.39) | <0.0001 |
| Private | Reference | Reference | ||
| Age group | ||||
| 30 days to <1 year | 1.32 (1.221.42) | <0.0001 | 0.58 (0.490.69) | <0.0001 |
| 1 year to 5 years | 0.89 (0.830.96) | 0.003 | 0.41 (0.340.48) | <0.0001 |
| 5 years to 11 years | 0.69 (0.640.74) | <0.0001 | 0.40 (0.330.48) | <0.0001 |
| 12 years to 17 years | 0.72 (0.660.77) | <0.0001 | 0.50 (0.420.60) | <0.0001 |
| <30 days | Reference | Reference | ||
| Socioeconomic statusa | ||||
| % <1.5 times FPL | 0.96 (0.921.01) | 0.0992 | 0.82 (0.740.92) | 0.0005 |
| % 1.5 to 2 times FPL | 0.98 (0.941.02) | 0.2992 | 0.83 (0.750.92) | 0.0005 |
| % 2 to 3 times FPL | 1.02 (0.981.07) | 0.292 | 0.88 (0.790.97) | 0.01 |
| % >3 times FPL | Reference | Reference | ||
| Severity score | ||||
| High severity, 4, 5, 6 | 1.43 (1.401.45) | <0.0001 | 3.42 (3.233.62) | <0.0001 |
| Low severity, 1, 2, 3 | Reference | Reference | ||
| Presence of any CCC | ||||
| Yes | 1.90 (1.861.96) | <0.0001 | 2.92 (2.753.10) | <0.0001 |
| No | Reference | Reference | ||
| Time of arrival | ||||
| Evening | 1.05 (1.031.08) | <0.0001 | 1.37 (1.291.44) | <0.0001 |
| Overnight | 1.19 (1.151.22) | <0.0001 | 1.84 (1.711.97) | <0.0001 |
| Day | Reference | Reference | ||
| Season | ||||
| Winter | 1.09 (1.061.11) | <0.0001 | 1.06 (0.991.14) | 0.0722 |
| Spring | 1.07 (1.041.10) | <0.0001 | 0.98 (0.911.046) | 0.4763 |
| Summer | 1.05 (1.021.08) | 0.0011 | 0.93 (0.871.01) | 0.0729 |
| Fall | Reference | Reference | ||
| Weekday/weekend | ||||
| Thursday | 1.02 (0.9821.055) | 0.3297 | 0.983 (0.8971.078) | 0.7185 |
| Friday | 1.08 (1.041.11) | <0.0001 | 1.03 (0.941.13) | 0.5832 |
| Saturday | 1.08 (1.041.12) | <0.0001 | 0.89 (0.810.97) | 0.0112 |
| Sunday | 1.02 (0.991.06) | 0.2054 | 0.81 (0.740.89) | <0.0001 |
| Monday | 1.00 (0.961.03) | 0.8928 | 0.98 (0.901.07) | 0.6647 |
| Tuesday | 0.99 (0.951.03) | 0.5342 | 0.93 (0.851.02) | 0.1417 |
| Wednesday | Reference | Reference | ||
| PCP ratio per 100,000 patients | ||||
| 57.267.9 | 1.00 (0.961.04) | 0.8844 | 0.93 (0.841.03) | 0.1669 |
| 68.078.7 | 1.00 (0.951.04) | 0.8156 | 0.86 (0.770.96) | 0.0066 |
| >78.8 | 1.00 (0.951.04) | 0.6883 | 0.82 (0.730.92) | 0.001 |
| <57.2 | Reference | Reference | ||
| ED crowding score at index visitb | ||||
| 2 | 0.92 (0.900.95) | <0.0001 | 0.96 (0.881.05) | 0.3435 |
| 1 | Reference | Reference | ||
| Distance from hospitalc | ||||
| 3.168, 23.6 miles | 0.95 (0.940.96) | <0.0001 | 1.16 (1.121.19) | <0.0001 |
| 2.168, 8.7 miles | Reference | Reference | ||
| ED LOS at index visitb | ||||
| 3.7 hours | 1.003 (1.0011.005) | 0.0052 | NA | |
| 2.7 hours | Reference | |||
Diagnoses Associated With Return Visits
Patients with index visit diagnoses of sickle cell disease and leukemia had the highest proportion of return visits (10.7% and 7.3%, respectively). Other conditions with high revisit rates included infectious diseases such as cellulitis, bronchiolitis, and gastroenteritis. Patients with other chronic diseases such as diabetes and with devices, such as gastrostomy tubes, also had high rates of return visits. At return, the rate of hospitalization for these conditions ranged from a 1‐in‐6 chance of hospitalization for the diagnoses of a fever to a 1‐in‐2 chance of hospitalization for patients with sickle cell anemia (Table 3).
| Major Diagnostic Subgroup | No. of Index ED Visit Dischargesa | 72‐Hour Revisit, % (95% CI) | Admitted on Return, % (95% CI) |
|---|---|---|---|
| |||
| Sickle cell anemia | 2,531 | 10.7 (9.511.9) | 49.6 (43.755.6) |
| Neoplastic diseases, cancer | 536 | 7.3 (5.19.5) | 36 (2151) |
| Infectious gastrointestinal diseases | 802 | 7.2 (5.49.0) | 21 (1031) |
| Devices and complications of the circulatory systemb | 1,033 | 6.9 (5.38.4) | 45 (3457) |
| Other hematologic diseasesb | 1,538 | 6.1 (4.97.3) | 33 (2443) |
| Fever | 80,626 | 5.9 (5.76.0) | 16.3 (15.217.3) |
| Dehydration | 7,362 | 5.4 (5.25.5) | 34.6 (30.139) |
| Infectious respiratory diseases | 72,652 | 5.4 (5.25.5) | 28.6 (27.230) |
| Seizures | 17,637 | 5.3 (4.95.6) | 33.3 (30.336.4) |
| Other devices and complicationsb | 1,896 | 5.3 (4.36.3) | 39.0 (29.448.6) |
| Infectious skin, dermatologic and soft tissue diseases | 40,272 | 4.7 (4.55) | 20.0 (18.221.8) |
| Devices and complications of the gastrointestinal systemb | 4,692 | 4.6 (4.05.2) | 24.7 (18.930.4) |
| Vomiting | 44,730 | 4.4 (4.24.6) | 23.7 (21.825.6) |
| Infectious urinary tract diseases | 17,020 | 4.4 (4.14.7) | 25.9 (22.729) |
| Headache | 19,016 | 4.3 (4.14.6) | 28.2 (25.131.3) |
| Diabetes mellitus | 1,531 | 4.5 (3.35.3) | 29 (1840) |
| Abdominal pain | 39,594 | 4.2 (44.4) | 24.8 (22.726.8) |
| Other infectious diseasesb | 647 | 4.2 (2.65.7) | 33 (1651) |
| Gastroenteritis | 55,613 | 4.0 (3.84.1) | 20.6 (18.922.3) |
DISCUSSION
In this nationally representative sample of free‐standing children's hospitals, 3.3% of patients discharged from the ED returned to the same ED within 72 hours. This rate is similar to rates previously published in studies of general EDs.[11, 15] Of the returning children, over 80% were discharged again, and 19.7% were hospitalized, which is two‐thirds more than the admission rate at index visit (12%). In accordance with previous studies,[14, 16, 25] we found higher disease severity, presence of a chronic condition, and younger age were strongly associated with both the odds of patients returning to the ED and of being hospitalized at return. Patients who were hospitalized lived further away from the hospital and were of a higher SES. In this study, we show that visit‐level and access‐related factors are also associated with increased risk of return, although to a lesser degree. Patients seen on a weekend (Friday or Saturday) were found to have higher odds of returning, whereas those seen initially on a Sunday had lower odds of hospitalization at return. In this study, we also found that patients seen on the evening or night shifts at the index presentation had a significant association with return visits and hospitalization at return. Additionally, we found that although PCP density was not associated with the odds of returning to the ED, patients from areas with a higher PCP density were less likely to be admitted at return. In addition, by evaluating the diagnoses of patients who returned, we found that many infectious conditions commonly seen in the ED also had high return rates.
As previously shown,[23] we found that patients with complex and chronic diseases were at risk for ED revisits, especially patients with sickle cell anemia and cancer (mainly acute leukemia). In addition, patients with a chronic condition were 3 times more likely to be hospitalized when they returned. These findings may indicate an opportunity for improved discharge planning and coordination of care with subspecialty care providers for particularly at‐risk populations, or stronger consideration of admission at the index visit. However, admission for these patients at revisit may be unavoidable.
Excluding patients with chronic and complex conditions, the majority of conditions with high revisit rates were acute infectious conditions. One national study showed that >70% of ED revisits by patients with infectious conditions had planned ED follow‐up.[13] Although this study was unable to assess the reasons for return or admission at return, children with infectious diseases often worsen over time (eg, those with bronchiolitis). The relatively low admission rates at return for these conditions, despite evidence that providers may have a lower threshold for admission when a patient returns to the ED shortly after discharge,[24] may reflect the potential for improving follow‐up at the PCP office. However, although some revisits may be prevented,[37, 38] we recognize that an ED visit could be appropriate and necessary for some of these children, especially those without primary care.
Access to primary care and insurance status influence ED utilization.[14, 39, 40, 41] A fragmented healthcare system with poor access to primary care is strongly associated with utilization of the ED for nonurgent care. A high ED revisit rate might be indicative of poor coordination between ED and outpatient services.[9, 39, 42, 43, 44, 45, 46] Our study's finding of increased risk of return visit if the index visit occurred on a Friday or Saturday, and a decreased likelihood of subsequent admission when a patient returns on a Sunday, may suggest limited or perceived limited access to the PCP over a weekend. Although insured patients tend to use the ED less often for nonemergent cases, even when patients have PCPs, they might still choose to return to the ED out of convenience.[47, 48] This may be reflected in our finding that, when adjusted for insurance status and PCP density, patients who lived closer to the hospital were more likely to return, but less likely to be admitted, thereby suggesting proximity as a factor in the decision to return. It is also possible that patients residing further away returned to another institution. Although PCP density did not seem to be associated with revisits, patients who lived in areas with higher PCP density were less likely to be admitted when they returned. In this study, there was a stepwise gradient in the effect of PCP density on the odds of being hospitalized on return with those patients in areas with fewer PCPs being admitted at higher rates on return. Guttmann et al.,[40] in a recent study conducted in Canada where there is universal health insurance, showed that children residing in areas with higher PCP densities had higher rates of PCP visits but lower rates of ED visits compared to children residing in areas with lower PCP densities. It is possible that emergency physicians have more confidence that patients will have dedicated follow‐up when a PCP can be identified. These findings suggest that the development of PCP networks with expanded access, such as alignment of office hours with parent need and patient/parent education about PCP availability, may reduce ED revisits. Alternatively, creation of centralized hospital‐based urgent care centers for evening, night, and weekend visits may benefit both the patient and the PCP and avoid ED revisits and associated costs.
Targeting and eliminating disparities in care might also play a role in reducing ED revisits. Prior studies have shown that publicly insured individuals, in particular, frequently use the ED as their usual source of care and are more likely to return to the ED within 72 hours of an initial visit.[23, 39, 44, 49, 50] Likewise, we found that patients with public insurance were more likely to return but less likely to be admitted on revisit. After controlling for disease severity and other demographic variables, patients with public insurance and of lower socioeconomic status still had lower odds of being hospitalized following a revisit. This might also signify an increase of avoidable hospitalizations among patients of higher SES or with private insurance. Further investigation is needed to explore the reasons for these differences and to identify effective interventions to eliminate disparities.
Our findings have implications for emergency care, ambulatory care, and the larger healthcare system. First, ED revisits are costly and contribute to already overburdened EDs.[10, 11] The average ED visit incurs charges that are 2 to 5 times more than an outpatient office visit.[49, 50] Careful coordination of ambulatory and ED services could not only ensure optimal care for patients, but could save the US healthcare system billions of dollars in potentially avoidable healthcare expenditures.[49, 50] Second, prior studies have demonstrated a consistent relationship between poor access to primary care and increased use of the ED for nonurgent conditions.[42] Publicly insured patients have been shown to have disproportionately increased difficulty acquiring and accessing primary care.[41, 42, 47, 51] Furthermore, conditions with high ED revisit rates are similar to conditions reported by Berry et al.4 as having the highest hospital readmission rates such as cancer, sickle cell anemia, seizure, pneumonia, asthma, and gastroenteritis. This might suggest a close relationship between 72‐hour ED revisits and 30‐day hospital readmissions. In light of the recent expansion of health insurance coverage to an additional 30 million individuals, the need for better coordination of services throughout the entire continuum of care, including primary care, ED, and inpatient services, has never been more important.[52] Future improvements could explore condition‐specific revisit or readmission rates to identify the most effective interventions to reduce the possibly preventable returns.
This study has several limitations. First, as an administrative database, PHIS has limited clinical data, and reasons for return visits could not be assessed. Variations between hospitals in diagnostic coding might also lead to misclassification bias. Second, we were unable to assess return visits to a different ED. Thus, we may have underestimated revisit frequency. However, because children are generally more likely to seek repeat care in the same hospital,[3] we believe our estimate of return visit rate approximates the actual return visit rate; our findings are also similar to previously reported rates. Third, for the PCP density factor, we were unable to account for types of insurance each physician accepted and influence on return rates. Fourth, return visits in our sample could have been for conditions unrelated to the diagnosis at index visit, though the short timeframe considered for revisits makes this less likely. In addition, the crowding index does not include the proportion of occupied beds at the precise moment of the index visit. Finally, this cohort includes only children seen in the EDs of pediatric hospitals, and our findings may not be generalizable to all EDs who provide care for ill and injured children.
We have shown that, in addition to previously identified patient level factors, there are visit‐level and access‐related factors associated with pediatric ED return visits. Eighty percent are discharged again, and almost one‐fifth of returning patients are admitted to the hospital. Admitted patients tend to be younger, sicker, chronically ill, and live farther from the hospital. By being aware of patients' comorbidities, PCP access, as well as certain diagnoses associated with high rates of return, physicians may better target interventions to optimize care. This may include having a lower threshold for hospitalization at the initial visit for children at high risk of return, and communication with the PCP at the time of discharge to ensure close follow‐up. Our study helps to provide benchmarks around ED revisit rates, and may serve as a starting point to better understand variation in care. Future efforts should aim to find creative solutions at individual institutions, with the goal of disseminating and replicating successes more broadly. For example, investigators in Boston have shown that the use of a comprehensive home‐based asthma management program has been successful in decreasing emergency department visits and hospitalization rates.[53] It is possible that this approach could be spread to other institutions to decrease revisits for patients with asthma. As a next step, the authors have undertaken an investigation to identify hospital‐level characteristics that may be associated with rates of return visits.
Acknowledgements
The authors thank the following members of the PHIS ED Return Visits Research Group for their contributions to the data analysis plan and interpretation of results of this study: Rustin Morse, MD, Children's Medical Center of Dallas; Catherine Perron, MD, Boston Children's Hospital; John Cheng, MD, Children's Healthcare of Atlanta; Shabnam Jain, MD, MPH, Children's Healthcare of Atlanta; and Amanda Montalbano, MD, MPH, Children's Mercy Hospitals and Clinics. These contributors did not receive compensation for their help with this work.
Disclosures
A.T.A. and A.M.S. conceived the study and developed the initial study design. All authors were involved in the development of the final study design and data analysis plan. C.W.T. collected and analyzed the data. A.T.A. and C.W.T. had full access to all of the data and take responsibility for the integrity of the data and the accuracy of the data analysis. All authors were involved in the interpretation of the data. A.T.A. drafted the article, and all authors made critical revisions to the initial draft and subsequent versions. A.T.A. and A.M.S. take full responsibility for the article as a whole. The authors report no conflicts of interest.
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- , , , , , Newman L. Impact of socioeconomic status on hospital use in New York City. Health Aff (Millwood). 1993;12:162–173.
- , , , , . Primary care physician supply and children's health care use, access, and outcomes: findings from Canada. Pediatrics. 2010;125:1119–1126.
- , , , et al. Insurance status and access to urgent ambulatory care follow‐up appointments. JAMA. 2005;294:1248–1254.
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- , . Geographic variation in access to care—the relationship with quality. N Engl J Med. 2012;367:3–6.
- , , , , . Trends and characteristics of US emergency department visits, 1997–2007. JAMA. 2010;304:664–670.
- , , , , . Ambulatory visits to hospital emergency departments. Patterns and reasons for use. 24 Hours in the ED Study Group. JAMA. 1996;276:460–465.
- , , , , . Emergency department workload increase: dependence on primary care? J Emerg Med. 2010;38:279–285.
- Network for Excellence in Health Innovation. Leading healthcare research organizations to examine emergency department overuse. New England Research Institute, 2008. Available at: http://www.nehi.net/news/310‐leading‐health‐care‐research‐organizations‐to‐examine‐emergency‐department‐overuse/view. Accessed October 4, 2013.
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- , , , et al. Community asthma initiative: evaluation of a quality improvement program for comprehensive asthma care. Pediatrics. 2012;129:465–472.
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