Integration of physical abuse clinical decision support at 2 general emergency departments.

OBJECTIVE: The study sought to develop and evaluate an electronic health record-based child abuse clinical decision support system in 2 general emergency departments. MATERIALS AND METHODS: A combination of a child abuse screen, natural language processing, physician orders, and discharge diagnoses were used to identify children <2 years of age with injuries suspicious for physical abuse. Providers received an alert and were referred to a physical abuse order set whenever a child triggered the system. Physician compliance with clinical guidelines was compared before and during the intervention. RESULTS: A total of 242 children triggered the system, 86 during the preintervention and 156 during the intervention. The number of children identified with suspicious injuries increased 4-fold during the intervention (P < .001). Compliance was 70% (7 of 10) in the preintervention period vs 50% (22 of 44) in the intervention, a change that was not statistically different (P = .55). Fifty-two percent of providers said that receiving the alert changed their clinical decision making. There was no relationship between compliance and provider or patient demographics. CONCLUSIONS: A multifaceted child abuse clinical decision support system resulted in a marked increase in the number of young children identified as having injuries suspicious for physical abuse in 2 general emergency departments. Compliance with published guidelines did not change; we hypothesize that this is related to the increased number of children identified with suspicious, but less serious injuries. These injuries were likely missed preintervention. Tracking compliance with guidelines over time will be important to assess whether compliance increases as physician comfort with evaluation of suspected physical abuse in young children improves.

Integration of physical abuse clinical decision support into the electronic health record at a Tertiary Care Children's Hospital.

Objective: To evaluate the effect of a previously validated electronic health record-based child abuse trigger system on physician compliance with clinical guidelines for evaluation of physical abuse. Methods: A randomized controlled trial (RCT) with comparison to a preintervention group was performed. RCT-experimental subjects' providers received alerts with a direct link to a physical abuse-specific order set. RCT-control subjects' providers had no alerts, but could manually search for the order set. Preintervention subjects' providers had neither alerts nor access to the order set. Compliance with clinical guidelines was calculated. Results: Ninety-nine preintervention subjects and 130 RCT subjects (73 RCT-experimental and 57 RCT-control) met criteria to undergo a physical abuse evaluation. Full compliance with clinical guidelines was 84% pre-intervention, 86% in RCT-control group, and 89% in RCT-experimental group. The physical abuse order set was used 43 times during the 7-month RCT. When the abuse order set was used, full compliance was 100%. The proportion of cases in which there was partial compliance decreased from 10% to 3% once the order set became available (P = .04). Male gender, having >10 years of experience and completion of a pediatric emergency medicine fellowship were associated with increased compliance. Discussion/Conclusion: A child abuse clinical decision support system comprised of a trigger system, alerts and a physical abuse order set was quickly accepted into clinical practice. Use of the physical abuse order set always resulted in full compliance with clinical guidelines. Given the high baseline compliance at our site, evaluation of this alert system in hospitals with lower baseline compliance rates will be more valuable in assessing the efficacy in adherence to clinical guidelines for the evaluation of suspected child abuse.

Development of an electronic medical record-based child physical abuse alert system.

Objective: Physical abuse is a leading cause of pediatric morbidity and mortality. Physicians do not consistently screen for abuse, even in high-risk situations. Alerts in the electronic medical record may help improve screening rates, resulting in early identification and improved outcomes. Methods: Triggers to identify children < 2 years old at risk for physical abuse were coded into the electronic medical record at a freestanding pediatric hospital with a level 1 trauma center. The system was run in "silent mode"; physicians were unaware of the system, but study personnel received data on children who triggered the alert system. Sensitivity, specificity, and negative and positive predictive values of the child abuse alert system for identifying physical abuse were calculated. Results: Thirty age-specific triggers were embedded into the electronic medical record. From October 21, 2014, through April 6, 2015, the system was in silent mode. All 226 children who triggered the alert system were considered subjects. Mean (SD) age was 9.1 (6.5) months. All triggers were activated at least once. Sensitivity was 96.8% (95% CI, 92.4-100.0%), specificity was 98.5% (95% CI, 98.3.5-98.7), and positive and negative predictive values were 26.5% (95% CI, 21.2-32.8%) and 99.9% (95% CI, 99.9-100.0%), respectively, for identifying children < 2 years old with possible, probable, or definite physical abuse. Discussion/Conclusion: Triggers embedded into the electronic medical record can identify young children with who need to be evaluated for physical abuse with high sensitivity and specificity.

Validation of the Pittsburgh Infant Brain Injury Score for Abusive Head Trauma.

BACKGROUND: Abusive head trauma is the leading cause of death from physical abuse. Misdiagnosis of abusive head trauma as well as other types of brain abnormalities in infants is common and contributes to increased morbidity and mortality. We previously derived the Pittsburgh Infant Brain Injury Score (PIBIS), a clinical prediction rule to assist physicians deciding which high-risk infants should undergo computed tomography of the head. METHODS: Well-appearing infants 30 to 364 days of age with temperature <38.3°C, no history of trauma, and a symptom associated with an increased risk of having a brain abnormality were eligible for enrollment in this prospective, multicenter clinical prediction rule validation. By using a predefined neuroimaging paradigm, subjects were classified as cases or controls. The sensitivity, specificity, and negative and positive predictive values of the rule for prediction of brain injury were calculated. RESULTS: A total of 1040 infants were enrolled: 214 cases and 826 controls. The 5-point PIBIS included abnormality on dermatologic examination (2 points), age ≥3.0 months (1 point), head circumference >85th percentile (1 point), and serum hemoglobin <11.2g/dL (1 point). At a score of 2, the sensitivity and specificity for abnormal neuroimaging was 93.3% (95% confidence interval 89.0%-96.3%) and 53% (95% confidence interval 49.3%-57.1%), respectively. CONCLUSIONS: Our data suggest that the PIBIS accurately identifies infants who would benefit from neuroimaging to evaluate for brain injury. An implementation analysis is needed before the PIBIS can be integrated into clinical practice.