Approximately 20% of all children in the US will have at least one Emergency Department (ED) visit each year.Patients and caregivers expect clinicians to deliver high quality emergency care, yet many children do notreceive appropriate care under the current system. The Institute of Medicine report, 'Emergency Care forChildren: Growing Pains,' notes that the delivery of care should be built on a strong foundation in whichemergency care is based on scientific evidence, data are collected so clinicians can learn from pastexperience, and system performance is monitored to ensure quality.While EDs routinely collect substantialpatient data with the potential to provide information on the quality of care provided, the capacity to capture,analyze and report these data back to front-line clinicians capable of acting to improve health care delivery hasbeen limited.Leveraging advances in health information technology to access patient-centric clinical data,researchers can measure and identify variation in performance and outcomes, enhancing the ability to designand implement interventions to improve outcomes and quality of care across multiple settings. The increasinguse of the electronic health record (EHR) in EDs provides a unique opportunity. Utilizing the infrastructure ofthe Pediatric Emergency Care Applied Research Network (PECARN), this proposal will innovatively captureEHR data to implement and report performance measures with the ultimate goal of improving the quality ofemergency care for children. This proposal will develop an emergency care visit registry for pediatric patientsfrom EHR clinical data which will serve as the foundation for the current and future studies, precluding theneed for resource-intensive chart review. Emergency care performance measures will be derived from registrydata and we will measure variability among sites and individual clinicians. Data from the registry and qualitativemethods will be used to set benchmarks of care. We will design site- and clinician-level Quality PerformanceMeasure Report Cards that will be generated from the registry and distributed monthly. The registry will beutilized to evaluate the performance measures in a prospective manner for improvement in the measuresthemselves as well as decreased variation in performance across practitioners. Significant improvements byindividual practitioners should also manifest as improvement at the site level. We will test the hypothesis thatproviding regular performance measure feedback will improve performance and decrease variation among EDclinicians using a staggered time-series study. The proposed project, thus, has enormous potential to improveour ability to evaluate systems of health care delivery, as well as to lead to improvements in the quality of careprovided to acutely ill or injured children. Although this proposal centers on health care delivered to children inEDs, the principles delineated apply to all components of the healthcare system that collect electronic patientdata. Accordingly, this proposal should be viewed as having wide applicability for comparative effectivenessresearch and quality improvement across all healthcare domains.
Every day; 80;000 children seek care in emergency departments (EDs); these patients and caregivers expectemergency clinicians to deliver high quality care; yet many children do not receive appropriate treatment underthe current system. We intend to establish a data registry from electronic health records to collect and reportquality measures of emergency care provided to children. We will establish measurable benchmarks andimplement a clinician feedback intervention to improve performance; this proposed project will allow systematicand widespread collection and reporting of performance and outcomes and is critical to allow clinicians andemergency care stakeholders to improve care beyond the local level.
| Goyal, Monika K; Johnson, Tiffani J; Chamberlain, James M et al. (2017) Racial and Ethnic Differences in Antibiotic Use for Viral Illness in Emergency Departments. Pediatrics 140: |
| Grundmeier, Robert W; Masino, Aaron J; Casper, T Charles et al. (2016) Identification of Long Bone Fractures in Radiology Reports Using Natural Language Processing to support Healthcare Quality Improvement. Appl Clin Inform 7:1051-1068 |
