Perioperative/Operative

This project will evaluate the feasibility of implementing a quality improvement-driven, health information technology-enabled method for capturing patient-reported outcomes (PROs) after ambulatory surgical procedures.

The research team developed and tested algorithms that can predict postoperative adverse outcomes with a high degree of accuracy.

This research applied machine learning to develop a model predicting surgical cancellations among pediatric patients, and found the feasibility in using these algorithms as a cost-effective quality-improvement measure.

This project will develop a clinical decision support tool for the perioperative setting.

This project developed a patient-centric tool called the Surgical Risk Preoperative Assessment System to estimate the risk of adverse operative outcomes.

This project evaluated the impact of an inpatient portal used by cardiac patients and found that patients using the portal had a lower 30-day hospital readmission rate.

Developed a detailed plan for the implementation and support of informatics tools in regional health centers including the creation of informatics tools to manage institutional surgical care information, creation of a multi-institutional partnership to manage both the informatics and surgical quality improvement programs, and the development of an economic model related to the business and safety benefits.