Disparities in Patient Safety Voluntary Event Reporting: A Scoping Review.

Voluntary event reporting (VER) systems underestimate the incidence of safety events and often capture only serious events. A limited amount of data is collected through these systems, and they may be inadequate to characterize disparities in reported safety events. We conducted a scoping review of the literature to summarize the state of the evidence as it relates to differences in safety events and safety event reporting by age, gender, and race. Using a broad-based query, a systematic search for published, peer-reviewed literature that discusses patient safety event reporting and differences by age, gender, race, and socioeconomic status was conducted. Based on modified Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 283 studies underwent title and abstract review, yielding 56 studies for full text review. After full text review, 23 studies were carefully reviewed individually, grouped thematically, and summarized to highlight the most pertinent findings. The studies reviewed yielded important insights, particularly with regard to race, gender, and the ways events are identified. Patients from minoritized groups may be less likely to have events reported and more likely to suffer serious events. Some studies found differences in rates of reporting safety events for female vs. male providers. The rate of VER is consistently lower than the rate of events identified through identified using automated detection. The current literature describing VER data shows disparities by race, language, age, and gender for patients and providers. Further research and systematic change are needed to specifically study these disparities to guide health care institutions on ways to mitigate bias and deliver more equitable care.

Association of the Child Opportunity Index and Inpatient Illness Severity in the United States, 2018-2019.

OBJECTIVE: Children residing in impoverished neighborhoods have reduced access to health care resources. Our objective was to identify potential associations between Child Opportunity Index (COI), a composite score of neighborhood characteristics, and inpatient severity of illness and clinical trajectory among United States (US) children. METHODS: This retrospective cohort study assessed data using the Pediatric Health Information System Registry from 2018 to 2019. Primary exposure variable was COI level (range: very low [CO1 1], low [COI 2], moderate [COI 3], high [COI 4], and very high [COI 5]). Markers of inpatient clinical severity included index mortality, Pediatric Intensive Care Unit (PICU) admission, invasive mechanical ventilation (IMV), and hospital length of stay (LOS). Subgroup analysis of COI and clinical outcome variation by United States Census Geographic Regions was conducted. Adjusted regression analysis was utilized to understand associations between COI and inpatient clinical severity outcomes. RESULTS: Of the 132,130 encounters, 44% resided in very low or low COI neighborhoods. In adjusted models, very low COI was associated with increased mortality (aOR: 1.35, 95% CI: 1.05-1.74, P = .018), PICU admission (aOR: 1.06, 95% CI: 1.02-1.11, P = 0.014), IMV (aOR: 1.12, 95% CI: 1.04-1.21, P = .002), and higher hospital LOS (P = .045). Regional variation by COI depicted the East North Central region having the highest rate of mortality (20.5%), P < .001, and PICU admissions (23%), P = .014. CONCLUSIONS: Our multicenter, retrospective study highlights the interaction between neighborhood-level deprivation and worsened health disparities, indicating a need for prospective study.

A Novel Comprehensive Algorithm for Evaluation of PICU Patients With New Fever or Instability.

OBJECTIVES: There is variation in microbiology testing among PICU patients with fever offering opportunities to reduce avoidable testing and treatment. Our objective is to describe the development and assess the impact of a novel comprehensive testing algorithm to support judicious testing practices and expanded diagnostic differentials for PICU patients with new fever or instability. DESIGN: A mixed-methods quality improvement study. SETTING: Single-center academic PICU and pediatric cardiac ICU. SUBJECTS: Admitted PICU patients and physicians. INTERVENTIONS: A multidisciplinary team developed a clinical decision-support algorithm. MEASUREMENTS AND MAIN RESULTS: We evaluated blood, endotracheal, and urine cultures, urinalyses, and broad-spectrum antibiotic use per 1,000 ICU patient-days using statistical process control charts and incident rate ratios (IRRs) and assessed clinical outcomes 24 months pre- and 18 months postimplementation. We surveyed physicians weekly for 12 months postimplementation. Blood cultures declined by 17% (IRR, 0.83; 95% CI, 0.77-0.89), endotracheal cultures by 26% (IRR, 0.74; 95% CI, 0.63-0.86), and urine cultures by 36% (IRR, 0.64; 95% CI, 0.56-0.73). There was an anticipated rise in urinalysis testing by 23% (IRR, 1.23; 95% CI, 1.14-1.33). Despite higher acuity and fewer brief hospitalizations, mortality, hospital, and PICU readmissions were stable, and PICU length of stay declined. Of the 108 physician surveys, 46 replied (43%), and 39 (85%) recently used the algorithm; 0 reported patient safety concerns, two (4%) provided constructive feedback, and 28 (61%) reported the algorithm improved patient care. CONCLUSIONS: A comprehensive fever algorithm was associated with reductions in blood, endotracheal, and urine cultures and anticipated increase in urinalyses. We detected no patient harm, and physicians reported improved patient care.

Body Mass Index Category and Adverse Events in Hospitalized Children.

OBJECTIVE: To identify associations between patient body mass index (BMI) category and adverse event (AE) rate, severity, and preventability in a cohort of children discharged from an academic children's hospital. METHODS: We identified patients 2 to 17 years old consecutively discharged between June and October 2018. Patient age, sex, height, and weight were used to categorize patients as having underweight, normal weight, overweight, or obesity. We used the Global Assessment of Pediatrics Patient Safety trigger tool to identify AEs, which were scored for harm and preventability. The primary outcome was the rate of AEs; these were compared with Poisson regression. We used multivariable logistic regression to model event preventability. RESULTS: We reviewed 834 encounters in 680 subjects; 51 (7.5%) had underweight, 367 (54.0%) had normal weight, 112 (16.5%) had overweight, and 150 (22.1%) had obesity. Our cohort experienced 270 AEs, with an overall rate of 69.7 (61.8-78.5) AEs per 1000 patient-days: 67.7 (46.4-98.7) in underweight, 70.0 (59.4-82.4) in normal weight, 58.6 (42.5-79.7) in overweight, and 80.4 (62.5-103.6) in obesity, P = .46. No associations were seen between BMI category and AE severity. Children with obesity had an increased rate of preventable AEs (P < .01), but this association did not persist in the multivariable model. CONCLUSIONS: In this single-center study, we did not find associations between BMI category and rate, severity, or preventability of AEs.

Culture Ordering for Patients with New-onset Fever: A Survey of Pediatric Intensive Care Unit Clinician Practices.

INTRODUCTION: Accurate assessment of infection in critically ill patients is vital to their care. Both indiscretion and under-utilization of diagnostic microbiology testing can contribute to inappropriate antibiotic administration or delays in diagnosis. However, indiscretion in diagnostic microbiology cultures may also lead to unnecessary tests that, if false-positive, would incur additional costs and unhelpful evaluations. This quality improvement project objective was to assess pediatric intensive care unit (PICU) clinicians' attitudes and practices around the microbiology work-up for patients with new-onset fever. METHODS: We developed and conducted a self-administered electronic survey of PICU clinicians at a single institution. The survey included 7 common clinical vignettes of PICU patients with new-onset fever and asked participants whether they would obtain central line blood cultures, peripheral blood cultures, respiratory aspirate cultures, cerebrospinal fluid cultures, urine cultures, and/or urinalyses. RESULTS: Forty-seven of 54 clinicians (87%) completed the survey. Diagnostic specimen ordering practices were notably heterogeneous. Respondents unanimously favored a decision-support algorithm to guide culture specimen ordering practices for PICU patients with fever (100%, N = 47). A majority (91.5%, N = 43) indicated that a decision-support algorithm would be a means to align PICU and consulting care teams when ordering culture specimens for patients with fever. CONCLUSION: This survey revealed variability of diagnostic specimen ordering practices for patients with new fever, supporting an opportunity to standardize practices. Clinicians favored a decision-support tool and thought that it would help align patient management between clinical team members. The results will be used to inform future diagnostic stewardship efforts.

Racial, Ethnic, and Socioeconomic Disparities in Patient Safety Events for Hospitalized Children.

OBJECTIVES: Previous studies have revealed racial/ethnic and socioeconomic disparities in quality of care and patient safety. However, these disparities have not been examined in a pediatric inpatient environment by using a measure of clinically confirmed adverse events (AEs). In this study, we do so using the Global Assessment of Pediatric Patient Safety (GAPPS) Trigger Tool. METHODS: GAPPS was applied to medical records of randomly selected pediatric patients discharged from 16 hospitals in the Pediatric Research in Inpatient Settings Network across 4 US regions from January 2007 to December 2012. Disparities in AEs for hospitalized children were identified on the basis of patient race/ethnicity (black, Latino, white, or other; N = 17 336 patient days) and insurance status (public, private, or self-pay/no insurance; N = 19 030 patient days). RESULTS: Compared with hospitalized non-Latino white children, hospitalized Latino children experienced higher rates of all AEs (Latino: 30.1 AEs per 1000 patient days versus white: 16.9 AEs per 1000 patient days; P ≤ .001), preventable AEs (Latino: 15.9 AEs per 1000 patient days versus white: 8.9 AEs per 1000 patient days; P = .002), and high-severity AEs (Latino: 12.6 AEs per 1000 patient days versus white: 7.7 AEs per 1000 patient days; P = .02). Compared with privately insured children, publicly insured children experienced higher rates of preventable AEs (public: 12.1 AEs per 1000 patient days versus private: 8.5 AEs per 1000 patient days; P = .02). No significant differences were observed among other groups. CONCLUSIONS: The GAPPS analysis revealed racial and/or ethnic and socioeconomic disparities in rates of AEs experienced by hospitalized children across a broad range of geographic and hospital settings. Further investigation may reveal underlying mechanisms of these disparities and could help hospitals reduce harm.

Using Length of Stay to Understand Patient Flow for Pediatric Inpatients.

OBJECTIVES: Develop and test a new metric to assess meaningful variability in inpatient flow. METHODS: Using the pediatric administrative dataset, Pediatric Health Information System, that quantifies the length of stay (LOS) in hours, all inpatient and observation encounters with 21 common diagnoses were included from the calendar year 2013 in 38 pediatric hospitals. Two mutually exclusive composite groups based on diagnosis and presence or absence of an ICU hospitalization termed Acute Care Composite (ACC) and ICU Composite (ICUC), respectively, were created. These composites consisted of an observed-to-expected (O/E) LOS as well as an excess LOS percentage (ie, the percent of day beyond expected). Seven-day all-cause risk-adjusted rehospitalizations was used as a balancing measure. The combination of the ACC, the ICUC, and the rehospitalization measures forms this new metric. RESULTS: The diagnosis groups in the ACC and the ICUC included 113,768 and 38,400 hospitalizations, respectively. The ACC had a median O/E LOS of 1.0, a median excess LOS percentage of 23.9% and a rehospitalization rate of 1.7%. The ICUC had a median O/E LOS of 1.1, a median excess LOS percentage of 32.3%, and rehospitalization rate of 4.9%. There was no relationship of O/E LOS and rehospitalization for either ACC or ICUC. CONCLUSIONS: This metric shows variation among hospitals and could allow a pediatric hospital to assess the performance of inpatient flow.

Using a Pediatric Trigger Tool to Estimate Total Harm Burden Hospital-acquired Conditions Represent.

INTRODUCTION: To improve patient safety, the Centers for Medicare & Medicaid Services (CMS) has promoted systematically measuring and reporting harm due to patient care. The CMS's Partnership for Patients program identified 9 hospital-acquired conditions (HACs) for reduction, to make care safer, more reliable, and less costly. However, the proportion of inpatient pediatric harm represented by these HACs is unknown. METHODS: We conducted a retrospective review of 240 harms previously identified using the Pediatric All-Cause Harm Measurement Tool, a trigger tool that is applied to medical records to comprehensively identify harms. The original sample included 600 randomly selected patients from 6 children's hospitals in February 2012. Patients with rehabilitation, obstetric, newborn nursery, and psychiatric admissions were excluded. The 240 identified harms were classified as a HAC if the event description potentially met the definition of 1 of the 9 CMS-defined HACs. HAC assessment was performed independently by 2 coauthors and compared using Cohen's Kappa. RESULTS: Two hundred forty harms across 6 children's hospitals were identified in February 2012 using a pediatric global trigger tool. Agreement between the coauthors on HAC classification was high (Kappa = 0.77). After reconciling differences, of the 240 identified harms, 58 (24.2%; 95% confidence interval: 9.1-31.7%) were classified as a CMS-defined HAC. CONCLUSIONS: One-fourth of all harms detected by a pediatric-specific trigger tool are represented by HACs. Although substantial effort is focused on identifying and minimizing HACs, to better understand and ultimately mitigate harm, more comprehensive harm identification and quantification may be needed to address events unidentified using this approach.

Adverse Events in Hospitalized Pediatric Patients.

: media-1vid110.1542/5789657761001PEDS-VA_2017-3360Video Abstract 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.

National trends in safety performance of electronic health record systems in children's hospitals.

Objective: To evaluate the safety of computerized physician order entry (CPOE) and associated clinical decision support (CDS) systems in electronic health record (EHR) systems at pediatric inpatient facilities in the US using the Leapfrog Group's pediatric CPOE evaluation tool. Methods: The Leapfrog pediatric CPOE evaluation tool, a previously validated tool to assess the ability of a CPOE system to identify orders that could potentially lead to patient harm, was used to evaluate 41 pediatric hospitals over a 2-year period. Evaluation of the last available test for each institution was performed, assessing performance overall as well as by decision support category (eg, drug-drug, dosing limits). Longitudinal analysis of test performance was also carried out to assess the impact of testing and the overall trend of CPOE performance in pediatric hospitals. Results: Pediatric CPOE systems were able to identify 62% of potential medication errors in the test scenarios, but ranged widely from 23-91% in the institutions tested. The highest scoring categories included drug-allergy interactions, dosing limits (both daily and cumulative), and inappropriate routes of administration. We found that hospitals with longer periods since their CPOE implementation did not have better scores upon initial testing, but after initial testing there was a consistent improvement in testing scores of 4 percentage points per year. Conclusions: Pediatric computerized physician order entry (CPOE) systems on average are able to intercept a majority of potential medication errors, but vary widely among implementations. Prospective and repeated testing using the Leapfrog Group's evaluation tool is associated with improved ability to intercept potential medication errors.

Comparison of Clinical Risk Factors Among Pediatric Patients With Single Admission, Multiple Admissions (Without Any 7-Day Readmissions), and 7-Day Readmission.

BACKGROUND AND OBJECTIVE: Readmissions have received increasing attention. The goal of this study was to identify demographic and clinical factors associated with hospital utilization and 7-day readmissions. METHODS: This retrospective case-control study of inpatient and observation encounters was conducted at a freestanding children's hospital. Over a 1-year period, patients were categorized into 3 groups: patients with a single admission, patients with multiple admissions without any 7-day readmissions, and patients with at least one 7-day readmission. Factors associated with risk of future hospital utilization were determined, and post hoc testing was performed to compare groups. RESULTS: Patients with a single admission had statistically significant lower numbers of medications at admission and discharge, lower rates of home health care at admission and discharge, and fewer diagnosis codes during index admission than patients with multiple admissions. There were no statistically significant differences among patients with multiple admissions between those with and without 7-day readmissions. CONCLUSIONS: This study found that patients with multiple admissions were similar regardless of whether they had any 7-day readmissions. Because patients with multiple admissions seemed to represent a single high-risk group, it is possible that many readmissions represent a consequence of medical complexity rather than a failure of care. Further studies are necessary to determine if providing targeted interventions to high-risk patients will lower their future hospital utilization.

Unplanned ICU Transfers from Inpatient Units: Examining the Prevalence and Preventability of Adverse Events Associated with ICU Transfer in Pediatrics.

Background Adverse events have been associated with unplanned intensive care unit (ICU) transfers in adults. Objective To examine trends in unplanned ICU transfers in pediatrics resulting from adverse events. Design, Setting, Patients Retrospective observational study of pediatric and cardiac ICU transfers from acute care units during a 2-year period in a tertiary care children's hospital. Methods Transfers were identified via electronic health record query and investigated for adverse events. Predefined adverse events included ICU transfers within 12 hours of admission to an acute care unit, readmissions to an ICU within 24 hours, and cardiopulmonary arrest on an acute care unit. Other adverse events examined were not predefined. Adverse events were evaluated for preventability and categorized by type, diagnosis, time of day and weekday versus weekend occurrence, and level of associated patient harm. Results There were 1,008 ICU transfers during the study period; 67% were unplanned. Of the unplanned transfers, 32% were attributed to adverse events, 35% of which were preventable. Unplanned transfers associated with a high rate of preventable adverse events included readmission to an ICU within 24 hours (58%, p = 0.002) and ICU transfer within 12 hours of acute care admission (34%). Conclusions We observed a high rate of preventable adverse events associated with unplanned pediatric ICU transfers, many of which were due to inappropriate triage. Readmission to an ICU within 24 hours of transfer to an acute care unit was significantly associated with preventability.

Effect of a Real-Time Pediatric ICU Safety Bundle Dashboard on Quality Improvement Measures.

BACKGROUND: Patient daily goal sheets have been shown to improve compliance with hospital policies but might not represent the dynamic nature of care delivery in the pediatric ICU (PICU) setting. A study was conducted at Children's National Health System (Washington, DC) to determine the effect of a visible, unitwide, real-time dashboard on timeliness of compliance with quality and safety measures. METHODS: An automated electronic health record (EHR)- querying tool was created to assess compliance with a PICU Safety Bundle. Querying of the EHR for compliance and updating of the dashboard automatically occurred every five minutes. A real-time visual display showed data on presence of consent for treatment, restraint orders, presence of urinary catheters, deep venous thrombosis (DVT) prophylaxis, Braden Q score, and medication reconciliation. Baseline compliance and duration of noncompliance was established during three time periods: the first, before activation of the dashboard; the second, at one month following activation of the dashboard; and the third, at three months after activation. RESULTS: A total of 450 patients were included in the analysis. Between the first and third time periods, the median time from PICU admission to obtaining treatment consent decreased by 49%, from 393 to 202 minutes (p=.05). The number of patients with urinary catheters in place>96 hours decreased from 16 (32%) in Period 1 to 11 (19%) for Periods 2 and 3 combined (p=.01). Completion of medication reconciliation improved from 80% in the first time period to 93% and 92%, respectively, in the subsequent two periods (p=.002). There was no difference between the three periods in presence of restraint orders, DVT prophylaxis, or development or worsening of pressure ulcers. CONCLUSIONS: A unitwide dashboard can increase awareness for potential interventions, affecting patient safety in the PICU in a dynamic manner.