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- Adverse Events (1)
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AHRQ Research Studies
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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
Results
1 to 9 of 9 Research Studies DisplayedLiu L, Ni Y, Zhang N
Mining patient-specific and contextual data with machine learning technologies to predict cancellation of children's surgery.
The objectives of this study were: 1) to develop predictive models of last-minute surgery cancellation, utilizing machine learning technologies, from patient-specific and contextual data from two distinct pediatric surgical sites of a single institution; and 2) to identify specific key predictors that impact children's risk of day-of-surgery cancellation. The study demonstrated the capacity of machine learning models for predicting pediatric patients at risk of last-minute surgery cancellation and providing useful insight into root causes of cancellation. The author’s approach offers the promise of targeted interventions to significantly decrease both healthcare costs and families' negative experiences.
AHRQ-funded; HS024983.
Citation: Liu L, Ni Y, Zhang N .
Mining patient-specific and contextual data with machine learning technologies to predict cancellation of children's surgery.
Int J Med Inform 2019 Sep;129:234-41. doi: 10.1016/j.ijmedinf.2019.06.007..
Keywords: Children/Adolescents, Data, Electronic Health Records (EHRs), Health Information Technology (HIT), Surgery
Lindell RB, Nishisaki A, Weiss SL
Comparison of methods for identification of pediatric severe sepsis and septic shock in the Virtual Pediatric Systems Database.
This study compared the use of Virtual Pediatric Systems with traditional use of International Classification of Diseases, 9th edition (ICD) to identify children with severe sepsis or septic shock in PICU settings. Two different systems were compared “Martin” and “Angus”. Both showed good agreement, but ICD9 identified a smaller more accurate cohort of children. Additional analysis of discrepancies between the reference standard the two virtual systems showed that prospective screening missed 66 patients who were diagnosed with severe sepsis or severe shock. Once they were included in the standard cohort, agreement improved with a positive predictive value of 70%.
AHRQ-funded; HS024511; HS022464.
Citation: Lindell RB, Nishisaki A, Weiss SL .
Comparison of methods for identification of pediatric severe sepsis and septic shock in the Virtual Pediatric Systems Database.
Crit Care Med 2019 Feb;47(2):e129-e35. doi: 10.1097/ccm.0000000000003541..
Keywords: Children/Adolescents, Intensive Care Unit (ICU), Data, Sepsis
Durojaiye AB, McGeorge NM, Puett LL
Mapping the flow of pediatric trauma patients using process mining.
The purpose of this study was to describe a process mining approach for mapping the inhospital flow of pediatric trauma patients, to identify and characterize the major patient pathways and care transitions, and to identify opportunities for patient flow and triage improvement. Process mining was successfully applied to derive process maps from trauma registry data and to identify opportunities for trauma triage improvement and optimization of PICU use.
AHRQ-funded; HS023837.
Citation: Durojaiye AB, McGeorge NM, Puett LL .
Mapping the flow of pediatric trauma patients using process mining.
Appl Clin Inform 2018 Jul;9(3):654-66. doi: 10.1055/s-0038-1668089..
Keywords: Trauma, Children/Adolescents, Transitions of Care, Data
Eisler L, Huang G, Lee KM
Identification of perioperative pulmonary aspiration in children using quality assurance and hospital administrative billing data.
This study aims to identify the incidence of and risk factors for perioperative aspiration in children using quality assurance data supplemented by administrative billing records, and to examine the utility of billing data as a supplementary data source. The investigators found that International Classification of Diseases, Ninth Revision codes for aspiration used as a secondary data source were nonspecific for perioperative aspiration, but when combined with record review yielded a 30% increase in identified cases of aspiration over quality assurance data alone.
AHRQ-funded; HS022941.
Citation: Eisler L, Huang G, Lee KM .
Identification of perioperative pulmonary aspiration in children using quality assurance and hospital administrative billing data.
Paediatr Anaesth 2018 Mar;28(3):218-25. doi: 10.1111/pan.13319..
Keywords: Adverse Events, Children/Adolescents, Data, Pneumonia, Respiratory Conditions
Arthur KC, Lucenko BA, Sharkova IV
Using state administrative data to identify social complexity risk factors for children.
Researchers aimed to test the feasibility of using an integrated state agency administrative database to identify social complexity risk factors and examine their relationship to emergency department (ED) use. They concluded that State administrative data can be used to identify social complexity risk factors associated with higher rates of ED use among Medicaid-insured children.
AHRQ-funded; HS020506.
Citation: Arthur KC, Lucenko BA, Sharkova IV .
Using state administrative data to identify social complexity risk factors for children.
Ann Fam Med 2018 Jan;16(1):62-69. doi: 10.1370/afm.2134.
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Keywords: Children/Adolescents, Data, Emergency Department, Healthcare Utilization, Risk
Bush RA, Connelly CD, Perez A
Extracting autism spectrum disorder data from the electronic health record.
This study uses electronic health record (EHR) data to examine medical utilization and track outcomes among children with Autism Spectrum Disorder (ASD). The study also identifies challenges inherent in designing inclusive algorithms for identifying individuals with ASD and demonstrates the utility of employing multiple extractions to improve the completeness and quality of EHR data when conducting research.
AHRQ-funded; HS022404.
Citation: Bush RA, Connelly CD, Perez A .
Extracting autism spectrum disorder data from the electronic health record.
Appl Clin Inform 2017 Jul 19;8(3):731-41. doi: 10.4338/aci-2017-02-ra-0029..
Keywords: Autism, Children/Adolescents, Data, Health Information Technology (HIT), Electronic Health Records (EHRs)
Wilcox HC, Kharrazi H, Wilson RF
Data linkage strategies to advance youth suicide prevention: a systematic review for a National Institutes of Health Pathways to Prevention Workshop.
This review sought to identify and describe data systems that can be linked to data from prevention studies to advance youth suicide prevention research. It concluded that there is untapped potential to evaluate and enhance suicide prevention efforts by linking suicide prevention data with existing data systems. However, sparse availability of data dictionaries and lack of adherence to standard data elements limit this potential.
AHRQ-funded; 290201200007I.
Citation: Wilcox HC, Kharrazi H, Wilson RF .
Data linkage strategies to advance youth suicide prevention: a systematic review for a National Institutes of Health Pathways to Prevention Workshop.
Ann Intern Med 2016 Dec 6;165(11):779-85. doi: 10.7326/m16-1281.
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Keywords: Behavioral Health, Children/Adolescents, Data, Evidence-Based Practice, Prevention
Chien AT, Kuhlthau KA, Toomey SL
Development of the children with disabilities algorithm.
The researchers developed the Children with Disabilities algorithm (CWDA), which uses International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes to identify CWD. They concluded that ICD-9-CM codes can be classified by their likelihood of indicating CWD. CWDA triangulates well with parent report and physician assessment of child disability status. CWDA is a new tool that can be used to assess care quality for CWD.
AHRQ-funded; HS020513.
Citation: Chien AT, Kuhlthau KA, Toomey SL .
Development of the children with disabilities algorithm.
Pediatrics 2015 Oct;136(4):e871-8. doi: 10.1542/peds.2015-0228..
Keywords: Children/Adolescents, Quality of Care, Data, Children/Adolescents
Neff JM, Clifton H, Popalisky J
Stratification of children by medical complexity.
The investigators stratified children using the software, Clinical Risk Groups (CRGs), in a tertiary children's hospital and a state's Medicaid claims data into 3 condition groups: complex chronic disease; noncomplex chronic disease, and nonchronic disease. They concluded that CRGs can be used to stratify children receiving care at a tertiary care hospital according to complexity in both hospital and Medicaid administrative data.
AHRQ-funded; HS020506.
Citation: Neff JM, Clifton H, Popalisky J .
Stratification of children by medical complexity.
Acad Pediatr 2015 Mar-Apr;15(2):191-6. doi: 10.1016/j.acap.2014.10.007.
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Keywords: Children/Adolescents, Chronic Conditions, Data, Electronic Health Records (EHRs), Children/Adolescents