Aviv Y Landau1, Ashley Blanchard2, Kenrick Cato3, Nia Atkins4, Stephanie Salazar5, Desmond U Patton6, Maxim Topaz7. 1. Data Science Institute, Columbia University, New York, New York, USA. 2. New York Presbyterian Morgan Stanley Children's Hospital, Columbia University Irving Medical Center, New York, New York, USA. 3. Department of Emergency Medicine, School of Nursing, Columbia University, New York, New York, USA. 4. Columbia College, Columbia University, New York, New York, USA. 5. Columbia School of Social Work, Columbia University, New York, New York, USA. 6. Data Science Institute, Columbia School of Social Work, Columbia University, New York, New York, USA. 7. Data Science Institute, Columbia University School of Nursing, Columbia University, New York, New York, USA.
Abstract
OBJECTIVE: The study provides considerations for generating a phenotype of child abuse and neglect in Emergency Departments (ED) using secondary data from electronic health records (EHR). Implications will be provided for racial bias reduction and the development of further decision support tools to assist in identifying child abuse and neglect. MATERIALS AND METHODS: We conducted a qualitative study using in-depth interviews with 20 pediatric clinicians working in a single pediatric ED to gain insights about generating an EHR-based phenotype to identify children at risk for abuse and neglect. RESULTS: Three central themes emerged from the interviews: (1) Challenges in diagnosing child abuse and neglect, (2) Health Discipline Differences in Documentation Styles in EHR, and (3) Identification of potential racial bias through documentation. DISCUSSION: Our findings highlight important considerations for generating a phenotype for child abuse and neglect using EHR data. First, information-related challenges include lack of proper previous visit history due to limited information exchanges and scattered documentation within EHRs. Second, there are differences in documentation styles by health disciplines, and clinicians tend to document abuse in different document types within EHRs. Finally, documentation can help identify potential racial bias in suspicion of child abuse and neglect by revealing potential discrepancies in quality of care, and in the language used to document abuse and neglect. CONCLUSIONS: Our findings highlight challenges in building an EHR-based risk phenotype for child abuse and neglect. Further research is needed to validate these findings and integrate them into creation of an EHR-based risk phenotype.
OBJECTIVE: The study provides considerations for generating a phenotype of child abuse and neglect in Emergency Departments (ED) using secondary data from electronic health records (EHR). Implications will be provided for racial bias reduction and the development of further decision support tools to assist in identifying child abuse and neglect. MATERIALS AND METHODS: We conducted a qualitative study using in-depth interviews with 20 pediatric clinicians working in a single pediatric ED to gain insights about generating an EHR-based phenotype to identify children at risk for abuse and neglect. RESULTS: Three central themes emerged from the interviews: (1) Challenges in diagnosing child abuse and neglect, (2) Health Discipline Differences in Documentation Styles in EHR, and (3) Identification of potential racial bias through documentation. DISCUSSION: Our findings highlight important considerations for generating a phenotype for child abuse and neglect using EHR data. First, information-related challenges include lack of proper previous visit history due to limited information exchanges and scattered documentation within EHRs. Second, there are differences in documentation styles by health disciplines, and clinicians tend to document abuse in different document types within EHRs. Finally, documentation can help identify potential racial bias in suspicion of child abuse and neglect by revealing potential discrepancies in quality of care, and in the language used to document abuse and neglect. CONCLUSIONS: Our findings highlight challenges in building an EHR-based risk phenotype for child abuse and neglect. Further research is needed to validate these findings and integrate them into creation of an EHR-based risk phenotype.
Authors: Sarah A Collins; Daniel M Stein; David K Vawdrey; Peter D Stetson; Suzanne Bakken Journal: J Biomed Inform Date: 2011-02-02 Impact factor: 6.317
Authors: Nirav K Pandya; Keith D Baldwin; Hayley Wolfgruber; Denis S Drummond; Harish S Hosalkar Journal: J Pediatr Orthop B Date: 2010-11 Impact factor: 1.041
Authors: Emily Putnam-Hornstein; Eunhye Ahn; John Prindle; Joseph Magruder; Daniel Webster; Christopher Wildeman Journal: Am J Public Health Date: 2021-04-15 Impact factor: 9.308
Authors: Mary Catherine Beach; Somnath Saha; Jenny Park; Janiece Taylor; Paul Drew; Eve Plank; Lisa A Cooper; Brant Chee Journal: J Gen Intern Med Date: 2021-03-22 Impact factor: 6.473