Bruce Rosenthal1, Janet Skrbin1, Janet Fromkin1, Emily Heineman1, Tom McGinn2, Rudolph Richichi3, Rachel P Berger1. 1. Department of Pediatrics, UPMC Children's Hospital of Pittsburgh of University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA. 2. Department of Medicine, Hofstra Northwell School of Medicine, Manhasset, New York, USA. 3. Statistical Analysis and Measurement Consultants, Lanexa, VA.
Abstract
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.
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.
Authors: Srinivasan Suresh; Richard A Saladino; Janet Fromkin; Emily Heineman; Tom McGinn; Rudolph Richichi; Rachel P Berger Journal: J Am Med Inform Assoc Date: 2018-07-01 Impact factor: 4.497
Authors: Maureen A Smythe; Trupti P Mehta; John M Koerber; Lisa L Forsyth; Elizabeth Sykes; Lindsey R Corbets; Susan M Melendy; Rajul Parikh Journal: Am J Health Syst Pharm Date: 2012-02-01 Impact factor: 2.637
Authors: Emalee G Flaherty; Jeannette M Perez-Rossello; Michael A Levine; William L Hennrikus Journal: Pediatrics Date: 2014-01-27 Impact factor: 7.124
Authors: Carmen E Guerra; J Sanford Schwartz; Katrina Armstrong; Jamin S Brown; Chanita Hughes Halbert; Judy A Shea Journal: J Gen Intern Med Date: 2007-10-16 Impact factor: 5.128
Authors: Gunjan Tiyyagura; Andrea G Asnes; John M Leventhal; Eugene D Shapiro; Marc Auerbach; Wei Teng; Emily Powers; Amy Thomas; Daniel M Lindberg; Justin McClelland; Carol Kutryb; Thomas Polzin; Karen Daughtridge; Virginia Sevin; Allen L Hsiao Journal: Acad Pediatr Date: 2021-11-12 Impact factor: 2.993
Authors: Braja G Patra; Mohit M Sharma; Veer Vekaria; Prakash Adekkanattu; Olga V Patterson; Benjamin Glicksberg; Lauren A Lepow; Euijung Ryu; Joanna M Biernacka; Al'ona Furmanchuk; Thomas J George; William Hogan; Yonghui Wu; Xi Yang; Jiang Bian; Myrna Weissman; Priya Wickramaratne; J John Mann; Mark Olfson; Thomas R Campion; Mark Weiner; Jyotishman Pathak Journal: J Am Med Inform Assoc Date: 2021-11-25 Impact factor: 7.942
Authors: Craig D Newgard; Peter E Fischer; Mark Gestring; Holly N Michaels; Gregory J Jurkovich; E Brooke Lerner; Mary E Fallat; Theodore R Delbridge; Joshua B Brown; Eileen M Bulger Journal: J Trauma Acute Care Surg Date: 2022-04-27 Impact factor: 3.697
Authors: Thomas McGinn; David A Feldstein; Isabel Barata; Emily Heineman; Joshua Ross; Dana Kaplan; Safiya Richardson; Barbara Knox; Amanda Palm; Francesca Bullaro; Nicholas Kuehnel; Linda Park; Sundas Khan; Benjamin Eithun; Rachel P Berger Journal: Int J Med Inform Date: 2020-12-10 Impact factor: 4.730