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Quality Improvement
Improving Healthcare Quality with User-Centric Patient Portals
The goal of this project is to develop, validate, and evaluate a framework of patient information needs for patient portals.
AcademyHealth Electronic Data Methods Forum (EDM) for Comparative Effectiveness Research
The Electronic Data Methods Forum is charged with advancing the national dialogue on the infrastructure and methods of health research and quality improvement using electronic clinical data, with the goal of improving patient care and outcomes.
Trial of Aggregate Data Extraction for Maintenance of Certification and Raising Quality
The overall objective of this study is to make quality reporting a byproduct of ambulatory care and ongoing quality improvement.
Novel IT To Create Patient-Integrated Quality Improvement
This project will create and evaluate a tool that gathers patient and family member feedback on pediatric care. The tool will make the feedback rapidly available to providers through a dashboard to enable timely and responsive safety improvement efforts.
Using the Electronic Health Record To Identify Children Likely To Suffer Last-Minute Surgery Cancellation
This project will apply machine learning against a large data set to develop a model to both understand and predict surgical cancellations on individual pediatric patients at two pediatric surgical sites.
Electronic Health Record Use, Work Environments, and Patient Outcomes
Focusing on the work environment of nurses, this project will study the organizational conditions under which electronic health records function best in hospitals and their potential to improve the outcomes of medical-surgical patients.
Enhancing an EMR-Based Real-Time Sepsis Alert System Performance Through Machine Learning
This project will use machine learning to enhance an existing sepsis clinical decision support tool to improve the early detection of sepsis.
Surgical Risk Preoperative Assessment System
This project will develop a patient-centric tool called the SUrgical Risk Preoperative Assessment System (SURPAS) to provide quantitative estimates of the risk of adverse operative outcomes to the patient, surgical team, and relevant hospital personnel.
NLP to Improve Accuracy and Quality of Dictated Medical Documents
This project will study the impact of errors in medical documents on quality of care and develop innovative natural language processing methods to automatically detect errors so that physicians can correct the documents before finalizing them in the electronic health record.
Improving Anxiety Detection in Pediatrics Using Health Information Technology
This project will build and pilot an anxiety module within an existing clinical decision support system to automate concurrent administration of validated screening instruments for anxiety and attention deficit hyperactivity disorder among pediatric patients.
