Patient Harm Events and Associated Cost Outcomes Reported to a Patient Safety Organization.

OBJECTIVES: The aims of the study were to describe inpatient harm events detected via an automatic electronic trigger system (ETS) and to consider their financial consequences. METHODS: Over a 27-month period, inpatient harm events were identified and documented in 1 healthcare system with 37 acute care facilities. Patients who experienced harm (all harm or preventable harm only) were compared with similar patients who did not. Clinical, financial, and demographic data were used to identify labor-adjusted direct variable costs (DVC) and potential differences in length of stay (LOS) associated with all-harm, preventable-harm, and nonharmed cohorts. Age-adjusted Charlson Comorbidity Index, case mix index, diagnosis-related groups, major diagnostic category, sex, age, location, diagnosis, adverse event category and subcategory, preventability, and harm severity were used to compare cohorts. Total harm events reported via the ETS and the health system's voluntary event reporting system were compared. RESULTS: Nearly 93,000 encounters for all-harm (n = 25,665) and nonharmed cohorts (n = 67,217) were compared by random sampling of diagnosis-related group-matched all-harm and nonharmed groups to ensure similar clinical conditions, as measured by Charlson Comorbidity Index and case mix index. Sampling (2 groups, n = 100 and n = 200) showed that increased LOS was associated with harm; yet other clinical comparators were similar across groups. the preventable-harm subcohort had longer LOS (10.7 versus 5.9 days) and higher DVC ($13,442 versus $8024) than the nonharmed cohort. Identification of harm events was nearly 6-fold higher with the ETS than with the voluntary event reporting system. CONCLUSIONS: Patients with preventable harm had increased LOS that was associated with higher DVC per preventable-harm encounter in a large US healthcare system.

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.

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.

A trigger tool to detect harm in pediatric inpatient settings.

OBJECTIVES: An efficient and reliable process for measuring harm due to medical care is needed to advance pediatric patient safety. Several pediatric studies have assessed the use of trigger tools in varying inpatient environments. Using the Institute for Healthcare Improvement's adult-focused Global Trigger Tool as a model, we developed and pilot tested a trigger tool that would identify the most common causes of harm in pediatric inpatient environments. METHODS: After formal training, 6 academic children's hospitals used this novel pediatric trigger tool to review 100 randomly selected inpatient records per site from patients discharged during the month of February 2012. RESULTS: From the 600 patient charts evaluated, 240 harmful events ("harms") were identified, resulting in a rate of 40 harms per 100 patients admitted and 54.9 harms per 1000 patient days across the 6 hospitals. At least 1 harm was identified in 146 patients (24.3% of patients). Of the 240 total events, 108 (45.0%) were assessed to have been potentially or definitely preventable. The most common patient harms were intravenous catheter infiltrations/burns, respiratory distress, constipation, pain, and surgical complications. CONCLUSIONS: Consistent with earlier rates of all-cause harm in adult hospitals, harm occurs at high rates in hospitalized children. Availability and use of an all-cause harm identification tool will establish the epidemiology of harm and will provide a consistent approach to assessing the effect of interventions on harms in hospitalized children.

Automated adverse event detection collaborative: electronic adverse event identification, classification, and corrective actions across academic pediatric institutions.

BACKGROUND: Historically, the gold standard for detecting medical errors has been the voluntary incident reporting system. Voluntary reporting rates significantly underestimate the number of actual adverse events in any given organization. The electronic health record (EHR) contains clinical and administrative data that may indicate the occurrence of an adverse event and can be used to detect adverse events that may otherwise remain unrecognized. Automated adverse event detection has been shown to be efficient and cost effective in the hospital setting. The Automated Adverse Event Detection Collaborative (AAEDC) is a group of academic pediatric organizations working to identify optimal electronic methods of adverse event detection. The Collaborative seeks to aggregate and analyze data around adverse events as well as identify and share specific intervention strategies to reduce the rate of such events, ultimately to deliver higher quality and safer care. The objective of this study is to describe the process of automated adverse event detection, report early results from the Collaborative, identify commonalities and notable differences between 2 organizations, and suggest future directions for the Collaborative. METHODS: In this retrospective observational study, the implementation and use of an automated adverse event detection system was compared between 2 academic children's hospital participants in the AAEDC, Children's National Medical Center, and Cincinnati Children's Hospital Medical Center. Both organizations use the EHR to identify potential adverse events as designated by specific electronic data triggers. After gathering the electronic data, a clinical investigator at each hospital manually examined the patient record to determine whether an adverse event had occurred, whether the event was preventable, and the level of harm involved. RESULTS: The Automated Adverse Event Detection Collaborative data from the 2 organizations between July 2006 and October 2010 were analyzed. Adverse event triggers associated with opioid and benzodiazepine toxicity and intravenous infiltration had the greatest positive predictive value (range, 47%- 96%). Triggers associated with hypoglycemia, coagulation disturbances, and renal dysfunction also had good positive predictive values (range, 22%-74%). In combination, the 2 organizations detected 3,264 adverse events, and 1,870 (57.3%) of these were preventable. Of these 3,264 events, clinicians submitted only 492 voluntary incident reports (15.1%). CONCLUSIONS: This work demonstrates the value of EHR-derived data aggregation and analysis in the detection and understanding of adverse events. Comparison and selection of optimal electronic trigger methods and recognition of adverse event trends within and between organizations are beneficial. Automated detection of adverse events likely contributes to the discovery of opportunities, expeditious implementation of process redesign, and quality improvement.

Automated detection of adverse events in children.

Voluntary reporting has been the standard method for identifying adverse events in hospitals, yet its effectiveness at identifying a comprehensive array of adverse events has always been in question. The electronic health record (EHR) contains clinical data that can be systematically reviewed to identify adverse events and improve adverse event detection. Active use of an automated trigger tool that is embedded in an EHR can identify systematic issues with delivery of high-risk medications and is cost-effective and efficient. Further development of an automated adverse event detection protocol for pediatrics is needed to apply this approach systematically across pediatric institutions.

Quality and safety in the intensive care unit.

Ensuring patient safety is becoming increasingly important for intensive care unit practitioners. The intensive care unit is particularly prone to medical errors because of the complexity of the patients, interdependence of the practitioners, and dependence on team functioning. This review provides historical perspectives, research foundations, and a practical "how to" guide to improving care in the intensive care unit. It also considers the organizational structure, the processes of care, and the occurrence of adverse outcomes in this setting. Effective intensive care unit quality and safety programs capitalize on institutional resources and have multidisciplinary input with clear leadership, input from quality improvement initiatives, a responsible yet nonpunitive culture, and data-driven assessment and monitoring to reduce medical errors. Intensive care unit practitioners need to capitalize on the benefits that patients and their families bring to the patient safety discourse. This provides opportunities for better understanding the risks of the intensive care unit and improving the consent process.