Eric S. Kirkendall

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Background Although electronic health records (EHRs) have the potential to provide a foundation for quality and safety algorithms, few studies have measured their impact on automated adverse event (AE) and medical error (ME) detection within the neonatal intensive care unit (NICU) environment. Objective This paper presents two phenotyping AE and ME(More)
BACKGROUND AND OBJECTIVE Nephrotoxic medication exposure represents a common cause of acute kidney injury (nephrotoxin-AKI) in hospitalized children. Systematic serum creatinine (SCr) screening has not been routinely performed in children receiving nephrotoxins, potentially leading to underestimating nephrotoxin-AKI rates. We aimed to accurately determine(More)
OBJECTIVES To evaluate and characterize the Global Trigger Tool's (GTT's) utility in a pediatric population; to measure the rate of harm at our institution and compare it with previously established trigger tools and benchmark rates; and to describe the distribution of harm of the detected events. METHODS Per the GTT methodology, 240 random inpatient(More)
Microbiology study results are necessary for conducting many comparative effectiveness research studies. Unlike core laboratory test results, microbiology results have a complex structure. Federating and integrating microbiology data from six disparate electronic medical record systems is challenging and requires a team of varied skills. The PHIS+(More)
BACKGROUND Nephrotoxic medication-associated acute kidney injury (NTMx-AKI) is a costly clinical phenomenon and more common than previously recognized. Prior efforts to use technology to identify AKI have focused on detection after renal injury has occurred. OBJECTIVES Describe an approach and provide a technical framework for the creation of(More)
OBJECTIVES An efficient and reliable process for measuring harm due to medical care is needed to advance pediatric patient safety. Several pediatric studies have assessed the use of trigger tools in varying inpatient environments. Using the Institute for Healthcare Improvement's adult-focused Global Trigger Tool as a model, we developed and pilot tested a(More)
OBJECTIVE To evaluate a proposed natural language processing (NLP) and machine-learning based automated method to risk stratify abdominal pain patients by analyzing the content of the electronic health record (EHR). METHODS We analyzed the EHRs of a random sample of 2100 pediatric emergency department (ED) patients with abdominal pain, including all with(More)