Video Abstract

Video Abstract

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BACKGROUND:

Patient safety concerns over the past 2 decades have prompted widespread efforts to reduce adverse events (AEs). It is unclear whether these efforts have resulted in reductions in hospital-wide AE rates. We used a validated safety surveillance tool, the Global Assessment of Pediatric Patient Safety, to measure temporal trends (2007–2012) in AE rates among hospitalized children.

METHODS:

We conducted a retrospective surveillance study of randomly selected pediatric inpatient records from 16 teaching and nonteaching hospitals. We constructed Poisson regression models with hospital random intercepts, controlling for patient age, sex, insurance, and chronic conditions, to estimate changes in AE rates over time.

RESULTS:

Examining 3790 records, reviewers identified 414 AEs (19.1 AEs per 1000 patient days; 95% confidence interval [CI] 17.2–20.9) and 210 preventable AEs (9.5 AEs per 1000 patient days; 95% CI 8.2–10.8). On average, teaching hospitals had higher AE rates than nonteaching hospitals (26.2 [95% CI 23.7–29.0] vs 5.1 [95% CI 3.7–7.1] AEs per 1000 patient days, P < .001). Chronically ill children had higher AE rates than patients without chronic conditions (33.9 [95% CI 24.5–47.0] vs 14.0 [95% CI 11.8–16.5] AEs per 1000 patient days, P < .001). Multivariate analyses revealed no significant changes in AE rates over time. When stratified by hospital type, neither teaching nor nonteaching hospitals experienced significant temporal AE rate variations.

CONCLUSIONS:

AE rates in pediatric inpatients are high and did not improve from 2007 to 2012. Pediatric AE rates were substantially higher in teaching hospitals as well as in patients with more chronic conditions.

What’s Known on This Subject:

Rates of harm in pediatrics remain high despite nationwide efforts to improve patient safety. Interventions have reduced errors at the local level, but it remains unclear if pediatric patient safety is improving nationally and how harm rates differ by setting.

What This Study Adds:

In this retrospective surveillance study of pediatric inpatient safety, harm rates (19.1 adverse events per 1000 patient days overall) varied between teaching and nonteaching hospitals but not over time. Harm rates have not meaningfully changed despite widespread attention to safety.

Despite >15 years of US investment in improving patient safety, harm due to medical care remains a leading cause of death and injury.1,4 In 2010, the Office of the Inspector General of the US Department of Health and Human Services estimated that 180 000 deaths, nearly half of them preventable, occur annually among Medicare beneficiaries due to medical care.5 Recently, the authors of an article aggregated results across patient safety surveillance studies and estimated that 210 000 to 444 000 deaths due to medical care occur annually6; in another article, the authors extrapolated from several studies that medical care could be the third leading cause of death in the United States.7 Although the authors of these studies give only broad estimates of patient safety–related deaths, they call attention to the issue of ongoing harm in the inpatient environment.

Pediatrics has enjoyed many enviable accomplishments with respect to patient safety. There are several reports of preventable adverse event (AE) improvements at institutional, state, and national levels.8,11 Many interventions, such as implementing computerized provider order entry systems, enhancing situational awareness, using the electronic medical record to monitor bundle compliance and suggest needed interventions, implementing handoff programs, and limiting residents’ work shifts, have reduced errors at a local level.12,17 Efforts to reduce specific AEs have also been successful; for example, the rates of many hospital-acquired conditions (HACs) are improving after bundle creation and implementation, most notably within Solutions for Patient Safety, a large group of pediatric hospitals targeting HACs.18,24 However, overall AE rates across conditions are still high, and it remains unclear whether significant improvements in safety outcomes broadly are occurring nationally despite noted gains in specific areas.25 

AE rates among hospitalized children likewise are high, although limited data exist from across US hospital types and settings. Multisite studies of NICU and PICU patients revealed high rates of harm, with 74 and 203 AEs per 100 patients in NICU and PICU admissions, respectively.26,27 The authors of a 2012 Canadian study aggregated rates of AEs in pediatric teaching and nonteaching inpatient settings, revealing an overall rate of 9.2 AEs per 100 admissions when excluding temporary (ie, nonsevere, resolving harm not impacting length of stay) AEs.28 More recent work revealed AE rates of 40 harms per 100 admissions in 6 teaching children’s hospitals.29 Data on hospital-wide AE rates for children over time across teaching and nonteaching centers are lacking.

To better understand trends in rates of AEs and preventable AEs among US children, we measured rates in a random sample of hospitalized children in 16 teaching and nonteaching hospitals. In addition, we compared AE rates by hospital type (teaching versus nonteaching) to account for differences in patient population and academic status and evaluated the role of patients’ chronic conditions in AE rates.

We applied the Global Assessment of Pediatric Patient Safety (GAPPS) Trigger Tool30 to medical records of randomly selected patients discharged between January 2007 and December 2012 in 16 Pediatric Research in Inpatient Settings Network hospitals from all 4 major US geographic regions. Consistent with recent work, we defined AEs as “unintended physical injury (resulting from or contributed to) by medical care that required additional monitoring, treatment, or hospitalization, or that resulted in death.”5,30,32 GAPPS was developed and tested by the Center of Excellence for Pediatric Quality Measurement at Boston Children’s Hospital.30 Study procedures were approved by each hospital’s institutional review board.

We recruited acute care hospitals to participate in the GAPPS study through an invitation to Pediatric Research in Inpatient Settings Network sites.33 Those expressing interest were included on the basis of geography and teaching status.34 

Each participating hospital randomly selected 10 admissions (lasting ≥24 hours) in each quarter from January 2007 through December 2012 (240 records per hospital). Hospitals gathered a list of all inpatient charts for each target quarter and selected them by using a random number generator. Hospitals excluded patients >18 years old and those admitted primarily for psychiatric or rehabilitation care from the eligible population.25,29,35 

A team of hospital-based primary reviewers (nurses) and secondary reviewers (physicians) were trained on the GAPPS process and conducted record reviews.30 At each hospital, the primary reviewer assessed selected medical records using the GAPPS manual trigger list. The list consists of 27 “triggers,” clues found in patient records that suggest the possibility of medically induced harm. When primary reviewers found a trigger (eg, naloxone administration), they investigated the record to determine whether an AE due to medical care had occurred (eg, hypopnea due to an opioid overdose resulting from a prescribing error) or not (eg, naloxone used to reverse a heroin overdose). Consistent with earlier investigations,25,29,35 we included injuries present at admission (ie, those that had occurred before admission to the study hospital, including outpatient care and care that occurred at another hospital prior to transfer) and those that occurred during the hospitalization to determine total AE burden.36 The order of record review was randomized so reviewers’ accumulation of experience with GAPPS over time would not confound analyses of changes in AE rates. Primary reviewers presented each suspected AE to 2 secondary reviewers who independently judged whether an AE had occurred; they also assessed its severity (National Coordinating Council for Medication Error Reporting and Prevention [NCCMERP] Index) and preventability (4-point Likert scale).25,27,29,30,37,38 Secondary reviewers came to a consensus on all cases for which they had initial disagreement.

We examined the distribution of overall and preventable AEs by severity of AEs. AEs with NCCMERP severity categories of F-I were considered high severity AEs (Fig 1).37 We compared AEs between hospital types and clinical characteristics (Fig 2) and used chronic condition indicators (CCIs) to classify diagnoses as chronic or not chronic on the basis of International Classification of Diseases, Ninth Edition, Clinical Modification codes.39 We investigated CCI rates over time by using a Poisson regression model to determine if there was a change in patient complexity during the study period. To compare AE rates per 1000 patient days and 100 admissions among hospital types (teaching versus nonteaching) and patient groups with CCIs, we performed Poisson regression models. Additionally, we adjusted for CCIs to compare between teaching and nonteaching hospitals because teaching hospitals had a significantly higher proportion of patients with ≥1 chronic condition than nonteaching hospitals. We had complete data on chronic conditions from 15 of 16 hospitals.

FIGURE 1

Severity of all AEs and preventable AEs. NCCMERP categories are as follows: E, contributed to or resulted in temporary harm to the patient and required intervention; F, contributed to or resulted in temporary harm to the patient and required initial or prolonged hospitalization; G, contributed to or resulted in permanent patient harm; H, required intervention to sustain life; I, contributed to or resulted in the patient’s death.

FIGURE 1

Severity of all AEs and preventable AEs. NCCMERP categories are as follows: E, contributed to or resulted in temporary harm to the patient and required intervention; F, contributed to or resulted in temporary harm to the patient and required initial or prolonged hospitalization; G, contributed to or resulted in permanent patient harm; H, required intervention to sustain life; I, contributed to or resulted in the patient’s death.

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FIGURE 2

Distribution of AEs by hospital and clinical characteristics. The values presented are unadjusted. High-severity AEs are defined as NCCMERP categories F to I. A, All AEs per 1000 patient days. B, All AEs per 100 admissions. C, Preventable AEs per 1000 patient days. D, Preventable AEs per 100 admissions. E, High-severity AEs per 1000 patient days. F, High-severity AEs per 100 admissions. ** P < .01; *** P < .001.

FIGURE 2

Distribution of AEs by hospital and clinical characteristics. The values presented are unadjusted. High-severity AEs are defined as NCCMERP categories F to I. A, All AEs per 1000 patient days. B, All AEs per 100 admissions. C, Preventable AEs per 1000 patient days. D, Preventable AEs per 100 admissions. E, High-severity AEs per 1000 patient days. F, High-severity AEs per 100 admissions. ** P < .01; *** P < .001.

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To assess changes in the number of AEs per 1000 patient days and AEs per 100 admissions over time, we performed Poisson regression models with hospital random intercepts to account for hospital-level clustering and a term indicating hospital discharge date (24 quarters over 6 years). To account for any temporal changes in AE rate due to changes in patient demographics and chronic illness over time, we conducted additional regression analyses, controlling for age, sex, insurance, and CCIs.

Projecting 25 AEs per 100 admissions based on previous studies,25 we anticipated having 80% power to detect temporal changes in AEs equivalent to a 25% relative reduction in AEs from 2007 to 2012, at a 2-sided 0.05 significance level.

A total of 3790 admissions from 16 hospitals were analyzed for changes in AE rates over time; 1880 (49.6%) patients were in nonteaching hospitals and 1910 (50.4%) in teaching hospitals. Patient demographics were previously described.30 A total of 414 AEs were detected in 3790 admissions, representing 19.0 AEs (95% confidence interval [CI] 17.3–21.0) per 1000 patient days and 10.9 AEs (95% CI 9.9–12.0) per 100 admissions; 8.0% of unique admissions experienced 1 or more AEs. AEs most frequently occurred as a result of hospital-acquired infections (77 AEs), intravenous line complications (60 AEs), gastrointestinal harms (49 AEs), respiratory-related harms (53 AEs), and other causes (49 AEs) (Table 1).

TABLE 1

Types and Number of AEs Identified by GAPPS

Type of AEAll AEsPreventable AEs
EFGHITotalEFGHITotal
Hospital-acquired infection             
 Total infections 34 42 77 24 35 59 
 Surgical site infection 14 15 13 14 
 Central line–associated blood stream infection 11 11 
 Sepsis and/or bacteremia unrelated to catheter 10 10 
 Catheter-associated UTI 
 Ventilator-associated pneumonia 
 Nosocomial pneumonia, not ventilator related 
 Clostridium difficile colitis 
 Hospital-acquired viral illness 
 Other hospital-acquired infection 10 11 
 Intravenous catheter complication 55 60 18 21 
Respiratory system             
 Total events 10 17 25 53 10 13 26 
 Acute respiratory failure 14 
 Respiratory distress, not acute failure 12 
 Reintubation within 24 h of planned extubation 
 Unplanned extubation 
 Pneumothorax, hemothorax, and/or subcutaneous air 
 Atelectasis 
 Postextubation stridor 
 Bronchospasm 
 Pulmonary embolus 
 Other respiratory 
Gastrointestinal system             
 Total events 31 18 49 17 10 27 
 Constipation 12 13 11 12 
 Nausea and/or vomiting 13 
 Diarrhea 
 Jaundice and/or hepatic insult 
 Ileus 
 Pancreatitis 
 Other GI 
Surgical or obstetrical event             
 Total events 11 21 36 14 19 
 Unplanned return to surgery 
 Failed procedure 
 Postoperative hemorrhage 
 Wound dehiscence 
 Vascular injury 
 Laceration or other injury of organ 
 Fetal or neonatal complications associated with delivery 
 Postoperative hematoma 
 Surgical anastomosis failure 
 Other surgical or obstetrical 
Renal or endocrine system             
 Total events 14 12 26 13 
 Hypoglycemia 
 Acute renal failure 
 Dehydration and/or oliguria 
 Fluid overload 
 Metabolic acidosis 
 Other renal, fluids, and/or endocrine 
Neurologic system             
 Total events 11 22 
 Oversedation 10 
 Withdrawal symptoms 
 Seizures 
 Inadequate sedation and/or anxiolysis 
 Inadequate analgesia 
 Other neurologic 
Cardiovascular system             
 Total events 21 10 
 Hypotension 
 Cardiac arrest 
 Arrhythmias and/or conduction abnormality 
 Hypertension 
 Pulmonary edema 
 Other cardiovascular event 
Hematologic system             
 Total events 13 21 
 Hemorrhage 11 
 Hematoma 
 Thromboembolic event: venous 
 Thromboembolic event: arterial 
 Other hematologic 
Other types of AEs             
 Total events 31 14 49 12 20 
 Allergic reaction 12 17 
 Rash (nonallergic) 
 Pressure ulcer 
 Fall 
 Death 
 Pyrexia 
 Hypothermia 
 Tube complication 
 Other harm type not listed above 
Type of AEAll AEsPreventable AEs
EFGHITotalEFGHITotal
Hospital-acquired infection             
 Total infections 34 42 77 24 35 59 
 Surgical site infection 14 15 13 14 
 Central line–associated blood stream infection 11 11 
 Sepsis and/or bacteremia unrelated to catheter 10 10 
 Catheter-associated UTI 
 Ventilator-associated pneumonia 
 Nosocomial pneumonia, not ventilator related 
 Clostridium difficile colitis 
 Hospital-acquired viral illness 
 Other hospital-acquired infection 10 11 
 Intravenous catheter complication 55 60 18 21 
Respiratory system             
 Total events 10 17 25 53 10 13 26 
 Acute respiratory failure 14 
 Respiratory distress, not acute failure 12 
 Reintubation within 24 h of planned extubation 
 Unplanned extubation 
 Pneumothorax, hemothorax, and/or subcutaneous air 
 Atelectasis 
 Postextubation stridor 
 Bronchospasm 
 Pulmonary embolus 
 Other respiratory 
Gastrointestinal system             
 Total events 31 18 49 17 10 27 
 Constipation 12 13 11 12 
 Nausea and/or vomiting 13 
 Diarrhea 
 Jaundice and/or hepatic insult 
 Ileus 
 Pancreatitis 
 Other GI 
Surgical or obstetrical event             
 Total events 11 21 36 14 19 
 Unplanned return to surgery 
 Failed procedure 
 Postoperative hemorrhage 
 Wound dehiscence 
 Vascular injury 
 Laceration or other injury of organ 
 Fetal or neonatal complications associated with delivery 
 Postoperative hematoma 
 Surgical anastomosis failure 
 Other surgical or obstetrical 
Renal or endocrine system             
 Total events 14 12 26 13 
 Hypoglycemia 
 Acute renal failure 
 Dehydration and/or oliguria 
 Fluid overload 
 Metabolic acidosis 
 Other renal, fluids, and/or endocrine 
Neurologic system             
 Total events 11 22 
 Oversedation 10 
 Withdrawal symptoms 
 Seizures 
 Inadequate sedation and/or anxiolysis 
 Inadequate analgesia 
 Other neurologic 
Cardiovascular system             
 Total events 21 10 
 Hypotension 
 Cardiac arrest 
 Arrhythmias and/or conduction abnormality 
 Hypertension 
 Pulmonary edema 
 Other cardiovascular event 
Hematologic system             
 Total events 13 21 
 Hemorrhage 11 
 Hematoma 
 Thromboembolic event: venous 
 Thromboembolic event: arterial 
 Other hematologic 
Other types of AEs             
 Total events 31 14 49 12 20 
 Allergic reaction 12 17 
 Rash (nonallergic) 
 Pressure ulcer 
 Fall 
 Death 
 Pyrexia 
 Hypothermia 
 Tube complication 
 Other harm type not listed above 

By using the NCCMERP Index, 218 AEs (52.7%) were determined to be category E (contributed to or resulted in temporary harm to the patient and required intervention), and 146 (35.3%) were category F (contributed to or resulted in temporary harm to the patient and required initial or prolonged hospitalization). Five (1.2%) AEs resulted in permanent harm (category G), 42 (10.1%) were life-threatening (category H), and 3 (0.7%) caused or contributed to a patient’s death (category I; Fig 1).37 In our sample, 9.2% of all AEs were present on admission.

Of the 414 AEs, 210 (50.7%) were preventable, representing 9.5 preventable AEs (95% CI 8.2–10.8) per 1000 patient days and 5.5 preventable AEs (95% CI 4.8–6.3) per 100 admissions. Among unique admissions, 4.5% experienced 1 or more preventable AEs. Preventable harm types were similar to overall AEs (Table 1).

Of the 210 preventable AEs, 93 (22.5%) were NCCMERP Index26 category E; 94 (22.7%) were category F; 2 (0.5%) were category G, 20 (4.8%) were category H, and 1 (0.24%) was category I (Fig 1). Of all preventable AEs, 12.4% were present on admission.

We found no significant change in patient CCI count over the study time period (P = .35). A comparison of patients with at least 1 chronic condition to those who did not have 1 revealed that patients with a chronic condition had a higher number of incidences of AEs (Fig 2).40 Overall, patients with 3 or more body systems affected by a chronic condition had higher AE rates than patients without any chronic condition (33.9 [95% CI 24.5–47.0] vs 14.0 [95% CI 11.8–16.5] AEs per 1000 patient days, 30.5 [95% CI 22.3–41.7] vs 6.9 [95% CI 5.8–8.1] AEs per 100 admissions, both P < .001). Similarly, compared with patients without any chronic conditions, patients with 3 or more body systems affected by a chronic condition had higher preventable AE rates (19.8 [95% CI 12.9–30.3] vs 6.5 [95% CI 5.1–8.4] AEs per 1000 patient days, 18.0 [95% CI 11.9–27.0] vs 3.2 [95% CI 2.5–4.1] AEs per 100 admissions, both P < .001) and higher high-severity AE rates (18.8 [95% CI 12.1–29.2] vs 6.2 [95% CI 4.8–8.0] AEs per 1000 patient days, 16.4 [95% CI 10.7–25.2] vs 3.1 [95% CI 2.4–3.9] AEs per 100 admissions, both P < .001; Fig 2).

The patient populations between hospital types differed, with 50.6% of patients in teaching hospitals having at least 1 chronic condition vs 30.1% of patients in nonteaching hospitals (P < .001); the same comparison for ≥2 chronic conditions was 19.5% vs 4.0% (P < .001). For all AEs, teaching hospitals had substantially higher unadjusted rates than nonteaching hospitals by a factor of 5.29 (95% CI 3.77–7.42; P < .001) AEs per 1000 patient days and 10.71 (95% CI 7.63–15.03; P < .001) AEs per 100 admissions (Table 2). Unadjusted preventable AE rates were higher in teaching hospitals by a factor of 5.58 (95% CI 3.39–9.2; P < .001) AEs per 1000 patient days and 11.59 (95% CI 7.04–19.07; P < .001) AEs per 100 admissions. Similarly, unadjusted high-severity AE rates were higher in teaching hospitals by a factor of 3.77 (95% CI 2.41–5.9; P < .001) AEs per 1000 patient days and 7.73 (95% CI 4.94–12.08; P < .001) AEs per 100 admissions. Adjusting for chronic conditions, the significant differences between overall, preventable, and high-severity harm rates in teaching and nonteaching hospitals remained (Table 2).

TABLE 2

Rates of Unadjusted and Adjusted AEs by Hospital Type

Nonteaching Hospitals Rate (95% CI)Teaching Hospitals Rate (95% CI)aRelative Incidence
Unadjusted (95% CI)PAdjusted (95% CI)P
Per 100 patients       
 All AE 1.97 (1.43–2.72) 21.08 (18.99–23.4) 10.71 (7.63–15.03) <.001 9.09 (6.43–12.86) <.001 
 Preventable AE 0.9 (0.56–1.45) 10.48 (9.04–12.15) 11.59 (7.04–19.07) <.001 9.46 (5.69–15.75) <.001 
 Severe AE 1.17 (0.77–1.78) 9.04 (7.71–10.61) 7.73 (4.94–12.08) <.001 6.38 (4.02–10.12) <.001 
Per 1000 patient d       
 All AE 5.13 (3.72–7.09) 27.14 (24.41–30.18) 5.29 (3.77–7.42) <.001 4.88 (3.43–6.94) <.001 
 Preventable AE 2.36 (1.47–3.79) 13.17 (11.31–15.34) 5.58 (3.39–9.2) <.001 5.00 (2.97–8.41) <.001 
 Severe AE 3.05 (2.01–4.64) 11.51 (9.78–13.54) 3.77 (2.41–5.9) <.001 3.33 (2.08–5.35) <.001 
Nonteaching Hospitals Rate (95% CI)Teaching Hospitals Rate (95% CI)aRelative Incidence
Unadjusted (95% CI)PAdjusted (95% CI)P
Per 100 patients       
 All AE 1.97 (1.43–2.72) 21.08 (18.99–23.4) 10.71 (7.63–15.03) <.001 9.09 (6.43–12.86) <.001 
 Preventable AE 0.9 (0.56–1.45) 10.48 (9.04–12.15) 11.59 (7.04–19.07) <.001 9.46 (5.69–15.75) <.001 
 Severe AE 1.17 (0.77–1.78) 9.04 (7.71–10.61) 7.73 (4.94–12.08) <.001 6.38 (4.02–10.12) <.001 
Per 1000 patient d       
 All AE 5.13 (3.72–7.09) 27.14 (24.41–30.18) 5.29 (3.77–7.42) <.001 4.88 (3.43–6.94) <.001 
 Preventable AE 2.36 (1.47–3.79) 13.17 (11.31–15.34) 5.58 (3.39–9.2) <.001 5.00 (2.97–8.41) <.001 
 Severe AE 3.05 (2.01–4.64) 11.51 (9.78–13.54) 3.77 (2.41–5.9) <.001 3.33 (2.08–5.35) <.001 

We adjusted AE rates for chronic conditions by using CCI count.

a

The aggregated adjusted and unadjusted AE rates in this table are for the 15 hospitals for which we had complete chronic conditions data. One teaching hospital was excluded.

Multivariate analyses that were controlled for demographic characteristics and CCIs revealed no significant changes in overall, preventable, or high-severity AE rates over time. Poisson regression in which hospital-level clustering and changes over time were accounted for revealed a nonsignificant 1.2% increase per year in rate of AEs per 1000 patient days (relative increase in risk per year = 1.012 [95% CI 1.00–1.03]; P = .10; Fig 3A), and AEs per 100 admissions likewise did not change (risk factor = 1.00 [95% CI 0.98–1.017]; P = .998; Fig 4A). Similarly, preventable AEs (risk factor = 1.00 per 1000 patient days [95% CI 0.98–1.02]; P = .96) and high-severity AEs (risk factor = 1.00 [95% CI 0.98–1.02]; P = .85) revealed no significant changes over time (Figs 3C, 3E, 4C, and 4E). When stratified by hospital type, neither teaching nor nonteaching hospitals experienced significant temporal trends in overall or preventable AEs per 1000 patient days (Fig 3 B and D).

FIGURE 3

Rates of all AEs, preventable AEs, and high-severity AEs per 1000 patient days, according to quarter. The values presented are unadjusted. A, All AEs. B, By hospital type, all AEs. C, Preventable AEs. D, By hospital type, preventable AEs. E, High-severity AEs. High-severity AEs are defined as NCCMERP categories F to I. F, By hospital type, high-severity AEs. Qtr, quarter.

FIGURE 3

Rates of all AEs, preventable AEs, and high-severity AEs per 1000 patient days, according to quarter. The values presented are unadjusted. A, All AEs. B, By hospital type, all AEs. C, Preventable AEs. D, By hospital type, preventable AEs. E, High-severity AEs. High-severity AEs are defined as NCCMERP categories F to I. F, By hospital type, high-severity AEs. Qtr, quarter.

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FIGURE 4

Rates of all AEs, preventable AEs, and high-severity AEs per 100 admissions, according to quarter. The values presented are unadjusted. A, All AEs. B, By hospital type, all AEs. C, Preventable AEs. D, By hospital type, preventable AEs. E, High-severity AEs. High-severity AEs are defined as NCCMERP categories F to I. F, By hospital type, high-severity AEs. Qtr, quarter.

FIGURE 4

Rates of all AEs, preventable AEs, and high-severity AEs per 100 admissions, according to quarter. The values presented are unadjusted. A, All AEs. B, By hospital type, all AEs. C, Preventable AEs. D, By hospital type, preventable AEs. E, High-severity AEs. High-severity AEs are defined as NCCMERP categories F to I. F, By hospital type, high-severity AEs. Qtr, quarter.

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In a study of 16 teaching and nonteaching hospitals that care for children across the United States, we found that AEs due to medical care remained common from 2007 to 2012, and rates did not decrease significantly over time. We also found that patients with more chronic conditions and therefore higher complexity were at increased risk for harm. To our knowledge, there have been no previous pediatric studies in which changes in hospital-wide rates of AEs were tracked in a nationally distributed sample of patients from nonteaching and teaching hospitals.

The authors of recent adult studies have found that rates of both AEs due to medical care and some HACs are declining.41,42 These studies involved focused efforts to address specific types of harm. However, the authors of multicenter studies of hospitalized adults using a broader approach (trigger tools to measure harm rates across the entire population of hospitalized patients) have found that harm rates have not changed over time.5,25 

In the years since the Institute of Medicine’s call to improve safety, there have been many efforts to improve the safety of US health care. There have been substantial strides in combatting nosocomial infections, surgical complications, and other discrete HACs over the past decade.43,47 Transitioning from focused successes in addressing patient safety events (eg, the reduction of hospital-associated infections) to system-wide reductions in AE rates will require more comprehensive safety measurements, such as those provided by GAPPS, and interventions that address a wider range of patient safety problems.48 

We found wide variation in AE rates in teaching and nonteaching hospitals; the reasons are unclear, but major differences in the frequency of chronic conditions and the types and severity of illness in the 2 types of hospitals may explain the difference. Previous studies in pediatric inpatient populations indicate that chronic conditions represent an important risk factor for AEs; hospitals that serve patient populations with increased levels of complexity and chronic disease have higher AE rates regardless of teaching status.49,51 As is the case with adult populations, pediatric patients with multiple chronic conditions are more likely to have higher complexity and are more frequently seen in teaching than in nonteaching hospitals.52 Consistent with previous literature, our hypothesis is supported by our observations that teaching hospitals had almost 5 times the number of patients with 2 or more chronic conditions than nonteaching hospitals and that patients with more chronic conditions tend to have higher AE rates. Severity of illness may also contribute to this variation. Although we were able to adjust AE rates for teaching versus nonteaching hospitals to some degree by accounting for number of CCIs, it is important to note that this variable provides limited ability to adjust. In addition to caring for patients with different numbers of CCIs, teaching and nonteaching hospitals care for patients with different levels of acute illness severity and different types of diseases. Moreover, the quantity of medications and interventions used over an admission and the level of difficulty of surgeries or procedures required differ across settings. Our measure, the number of CCIs, has the potential to capture only a small portion of these important distinctions. The differing rates of harm between teaching and nonteaching hospitals may well rest with different levels of complexity and severity. However, it may also be true that the care afforded to pediatric patients at nonteaching hospitals is safer. We are not able to determine the reasons for the difference in harm rates; additional investigation is warranted to explore possible explanations.

Neither teaching nor nonteaching centers experienced improvements over the 6-year span studied, suggesting that effectively controlling pediatric patient safety problems has proven similarly difficult in both settings. Targeted efforts to identify those at risk for AEs, such as those with multiple chronic conditions, may help to decrease AE rates over time in much the same way that targeting patients at risk for readmission early in an admission may help mitigate readmission rates.35 

A reliable safety measurement strategy is essential to determining whether future efforts to enhance patient safety produce their intended effect. Voluntary event reporting, the mainstay of inpatient safety tracking, and administrative coding tools have proven unreliable for tracking most safety events.29,53,55 Trigger tools, and the GAPPS tool in particular, have been shown to perform far better than alternative methods of measuring AEs.5,30,56 The major impediment to their use is feasibility because clinician manual review is resource intensive. However, systematic surveillance of patient safety events by using GAPPS or another trigger tool is the most reliable way identified to date to consistently measure AEs. As trigger tool use becomes more commonplace, however, it will be important that improvement in patient safety measurement continues to occur as new harms emerge over time and harms previously thought nonpreventable become preventable as new interventions emerge. Another important consideration in improving care would be to identify unnecessary care.57,58 

Our study has several limitations. First, this study is limited to analysis of data collected from 2007 to 2012, and it is possible that trends in AE prevalence have changed since then. Secondly, although GAPPS is reliable and specific, and although trigger tools are more sensitive than other methods, the sensitivity of GAPPS, especially in the hands of inexperienced reviewers, is suboptimal.30,59 The true rate of AEs may be higher than indicated in our study; by their nature, record review–based methodologies are limited to the information provided in the medical record. Automation of the trigger identification process may help by reducing the need for the initial phase of reviewer abstraction (ie, initial identification of the trigger). It is likely that some AEs were missed when a trigger was not associated with the specific event. Such omissions can be addressed by continually iterating the GAPPS trigger list and methods. Conversely, ∼25% of AEs were discovered during chart review without an associated trigger, a rate consistent with previous trigger tool studies.25 AEs identified without a trigger help drive new trigger development. Iterative changes over time will improve the acuity of the GAPPS trigger list. Nonetheless, random chart sampling is far less efficient than the current GAPPS method. Finally, lower than anticipated AE rates meant that the power to detect our originally stated 25% reduction in AEs was somewhat reduced. Even with the lower baseline AE rates, we had 80% power to detect a reduction of ∼7% in AE rates per year over the 6-year period. Although we cannot be certain that there was not a small reduction over time, plots of the rates are more consistent with no improvement whatsoever.

AEs due to medical care were common among hospitalized children from 2007 to 2012, and AE rates did not appear to be decreasing. AEs were more common in patients with increased complexity and in teaching compared with nonteaching hospitals. Although a growing body of literature has demonstrated substantial reductions in certain types of AEs in hospitals, it appears that additional efforts are needed to achieve improvements in the safety of all care for hospitalized children.

     
  • AE

    adverse event

  •  
  • CCI

    chronic condition indicator

  •  
  • CI

    confidence interval

  •  
  • GAPPS

    Global Assessment of Pediatric Patient Safety

  •  
  • HAC

    hospital-acquired condition

  •  
  • NCCMERP

    National Coordinating Council for Medication Error Reporting and Prevention

NCCMERP categories are as follows: E, contributed to or resulted in temporary harm to the patient and required intervention; F, contributed to or resulted in temporary harm to the patient and required initial or prolonged hospitalization; G, contributed to or resulted in permanent patient harm; H, required intervention to sustain life; I, contributed to or resulted in the patient’s death. GI, gastrointestinal; SQ, xxx; UTI, urinary tract infection.

Drs Stockwell and Landrigan conceptualized and designed the study, coordinated and supervised the data collection, conducted initial analyses, drafted the initial manuscript, and reviewed and revised the manuscript; Drs Toomey and Schuster conceptualized and designed the study, obtained funding, coordinated and supervised the data collection, conducted initial analyses, drafted the initial manuscript, and reviewed and revised the manuscript; Mr Loren, Ms Quinn, Ms Ashrafzadeh, Ms Wang, and Ms Wu collected the data, conducted the initial analyses, and reviewed and revised the manuscript; Ms Jang collected the data, conducted initial analyses, drafted the initial manuscript, and reviewed and revised the manuscript; Drs Sharek, Classen, and Srivastava drafted and critically reviewed the manuscript; Dr Parry coordinated and supervised the data collection, conducted initial analyses, and critically reviewed the manuscript; and all authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.

FUNDING: Supported by the US Department of Health and Human Services Agency for Healthcare Research and Quality and Centers for Medicare and Medicaid Services, Children’s Health Insurance Program Reauthorization Act of 2009 Pediatric Quality Measures Program Centers of Excellence, under grant U18 HS 020513 (principal investigator: Dr Schuster). The content is solely the responsibility of the authors and does not necessarily represent the official views of the Agency for Healthcare Research and Quality. Funded by the National Institutes of Health (NIH).

COMPANION PAPER: A companion to this article can be found online at www.pediatrics.org/cgi/doi/10.1542/peds.2018-0954.

We thank the GAPPS National Field Test sites: Boston Children’s Hospital, Children’s Hospital Colorado, Children’s National Medical Center, Cincinnati Children’s Hospital Medical Center, Grand View Hospital, Hillcrest Hospital, Lucile Packard Children’s Hospital Stanford, Mary Washington Hospital, New York Presbyterian/Weill Cornell Medical Center, Progress West Hospital, Providence St. Peter Hospital, Silver Cross Hospital, South Shore Hospital, University of Florida Health Shands Children’s Hospital, Utah Valley Regional Medical Center, and Western Virginia University Hospitals.

We thank all of our collaborators from the GAPPS Study Group. The following GAPPS Study Group members are nonauthor contributors. GAPPS Study Group members who are also authors have been denoted with asterisks (*).

GAPPS Study Site Teams:

Boston Children’s Hospital, Boston, Massachusetts: Shannon Cottreau, BSN, RN, CPN, Alisa Khan, MD, MPH, Christopher P. Landrigan, MD, MPH*, Colleen Madden, BSN, RN; Children’s Hospital Colorado, Aurora, Colorado: Laurie Kohring, PNP, Denise Pickard, MSN, RN, Eric Tham, MD, Amy Tyler, MD; Children’s National Medical Center, Washington, District of Columbia: Jeremy Kern, MD, Marie King, BSN, RN, CPN, Valere Lemon, RN, BSN, MBA, Kavita Parikh, MD; Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio: Eric S. Kirkendall, MD, MBI, FAAP, Fran Laube, BSN, MS, Serena Phillips, BSN, RN, Kathleen Walsh, MD, MSc; Grand View Hospital, Sellersville, Pennsylvania: Andrew S. Chu, MD, Julia Harkness, MD, Kathy Peca, RN, Kathy Shafer, BSN, RN; Hillcrest Hospital, Mayfield Heights, Ohio: Carrie Cuomo, CNP, Rachna May, MD, Marin Waynar, MD, Martha Williams, CNP; Lucile Packard Children’s Hospital Stanford, Palo Alto, California: Deborah Franzon, MD, Krisa Hoyle, MPH, FNP, Tua Palangyo, RN, BA, MSN, Paul Sharek, MD, MPH*; Mary Washington Hospital, Fredericksburg, Virginia: Francisco Alvarez, MD, Kristen Lewis, BSN, RN, CPN, CPHON, Allison Markowsky, MD, Nancy Young, BSN, RNC; New York Presbyterian Hospital/Weill-Cornell Medical Center, New York, New York: Kristen Critelli, MD, Jillian Konarski, RNC, Beth Matucci, RN, Jennie Ono, MD, Nena Osorio, MD; Progress West Hospital, O’Fallon, Missouri: Jennifer Bates, BSN, RN, Sarah Lenhardt, MD, Cassandra Pruitt, MD, Dawn Spell, BSN; Providence St. Peter Hospital, Olympia, Washington: Rasa Izadnegahdar, MD, MPH, Rebecca M. Jennings, MD, Sheri Keahey, MSN, RN, Theresa Miller, BSN; Silver Cross Hospital, New Lenox, Illinois: Nicole Anania, DO, Judy Black, MD, Kimberly Medlin, BSN, MSN, Theresa Sawyer, RN-AND; South Shore Hospital, South Weymouth, Massachusetts: Lindsey Burghardt, MD, Lynn D’Angelo, MSN, DNP, Mark L. Waltzman, MD, Faye Weir, BSN, MSN; University of Florida Health Shands Children’s Hospital, Gainesville, Florida: Shelley Collins, MD, FAAP, Michele N. Lossius, MD, FAAP, Jennifer Rackley, RN, Hillary Rohrs, ARNP, MSN, CPNP; Utah Valley Regional Medical Center, Provo, Utah: Deb Bracken, MSN, Russell J. Osguthorpe, MD, Karen Singson, RN, MSN, CIC; Western Virginia University Hospitals, Morgantown, West Virginia: Anjum Ahmed, RN, Juanita Fox, ADN, BS, Jeffrey Lancaster, MD, Tara Matthews, ADN, RN, Kamakshya P. Patra, MBBS, MD, Robert Riley, RN.

Members of the Expert Panel:

David Bundy, MD, MPH: Academic Pediatric Association, Medical University of South Carolina Children’s Hospital (Charleston, SC). S. Todd Callahan, MD, MPH: Society for Adolescent Health and Medicine, Monroe Carell Jr. Children’s Hospital at Vanderbilt (Nashville, TN). Emi Datuin-Pal, RN, BSN, MSHSA, MBA: The Joint Commission (Oakbrook Terrace, IL). Carol Haraden, PhD: Institute for Healthcare Improvement (Boston, MA). Laura Knobel, MD: American Academy of Family Physicians (Leawood, KS). Rita Pickler, PhD, RN, PNP-BC, FAAN: American Nurses Association, Cincinnati Children’s Hospital Medical Center (Cincinnati, OH). Xavier Sevilla, MD, MBA: Consumers Advancing Patient Safety, Catholic Health Initiatives (Chicago, IL). Jennifer Slayton, MSN, RN: National Patient Safety Foundation, Monroe Carell Jr. Children’s Hospital at Vanderbilt (Nashville, TN). Glenn Takata, MD, MS: American Academy of Pediatrics, Children’s Hospital Los Angeles (Los Angeles, CA).

External expert reviewers who participated in the GAPPS National Field Test:

Lee M. Adler, DO: Adventist Health System, College of Medicine, University of Central Florida (Orlando, FL). Diedre A. Rahn, RN: Mayo Clinic (Rochester, MN). Roger K. Resar, MD: Institute for Healthcare Improvement, Pascal Metrics (Boston, MA). Katherine R. Zigmont, RN: CRICO, Brigham and Women’s Hospital (Boston, MA).

Members of the GAPPS Advisory Committee:

Hema Bisarya, MHSA, RD: Children’s Hospital Association (Lenexa, KS). David C. Classen, MD, MS*: Pascal Metrics, University of Utah (Salt Lake City, UT). Rajendu Srivastava, MD, MPH*: Intermountain Healthcare, University of Utah (Salt Lake City, UT).

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Competing Interests

POTENTIAL CONFLICT OF INTEREST: Drs Stockwell and Classen disclose that they are employees for Pascal Metrics, a Patient Safety Organization; the other authors have indicated they have no potential conflicts of interest to disclose.

FINANCIAL DISCLOSURE: The authors have indicated they have no financial relationships relevant to this article to disclose.