Publications by authors named "Christine VanZandbergen"
Importance: Statins reduce the risk of major adverse cardiovascular events, but less than one-half of individuals in America who meet guideline criteria for a statin are actively prescribed this medication.
Objective: To evaluate whether nudges to clinicians, patients, or both increase initiation of statin prescribing during primary care visits.
Design, Setting, And Participants: This cluster randomized clinical trial evaluated statin prescribing of 158 clinicians from 28 primary care practices including 4131 patients.
Purpose: Integrating genomic data into the electronic health record (EHR) is key for optimally delivering genomic medicine.
Methods: The PennChart Genomics Initiative (PGI) at the University of Pennsylvania is a multidisciplinary collaborative that has successfully linked orders and results from genetic testing laboratories with discrete genetic data in the EHR. We quantified the use of the genomic data within the EHR, performed a time study with genetic counselors, and conducted key informant interviews with PGI members to evaluate the effect of the PGI's efforts on genetics care delivery.
Importance: Statin therapy is underused for many patients who could benefit.
Objective: To evaluate the effect of passive choice and active choice interventions in the electronic health record (EHR) to promote guideline-directed statin therapy.
Design, Setting, And Participants: Three-arm randomized clinical trial with a 6-month preintervention period and 6-month intervention.
Rationale: We implemented an electronic early warning and response system (EWRS) to improve detection of and response to severe sepsis. Sustainability of such a system requires stakeholder acceptance. We hypothesized that clinicians receiving such alerts perceive them to be useful and effective.
View Article and Find Full Text PDFBackground: Early recognition and timely intervention significantly reduce sepsis-related mortality.
Objective: Describe the development, implementation, and impact of an early warning and response system (EWRS) for sepsis.
Design: After tool derivation and validation, a preimplementation/postimplementation study with multivariable adjustment measured impact.
Background: Identification of patients at high risk for readmission is a crucial step toward improving care and reducing readmissions. The adoption of electronic health records (EHR) may prove important to strategies designed to risk stratify patients and introduce targeted interventions.
Objective: To develop and implement an automated prediction model integrated into our health system's EHR that identifies on admission patients at high risk for readmission within 30 days of discharge.