PACT Involvement in Cardiology Patients

Official title: Early PACT Involvement in Cardiology Patients Using Machine Learning

Summary: The goal of this trial is to determine the effectiveness of a machine-learning (ML) model predicting a serious cardiac event within the next three months, when compared pre- versus post-deployment, in pediatric cardiac inpatients. The main questions it aims to answer are whether deployment of the ML model: 1. Increases PACT consultation within the next three months among admissions without PACT involvement in the previous 100 days 2. Increases PACT consultation or visit within the next three months among those who experience a serious cardiac event during this period 3. Decreases time to PACT consultation or visit among those seen by PACT during this period 4. Decreases the incidence of death in the intensive care unit (ICU) 5. Increases documentation of goals of care High-risk cardiology patients will be identified by an ML model each morning. If the patient has been seen by the PACT team within the past year, the update will go to the PACT team members. If the patient hasn't been seen by the PACT team, the email will be sent to the cardiology physician in charge of the patient. This physician will decide whether a PACT consultation is necessary based on their clinical judgment. If so, a referral will be made using the usual process. Outcomes of the identified patients will be compared pre- and post-deployment.

Conditions: Machine Learning, Cardiovascular Outcome, Pediatric Palliative Care, Pediatric Cardiology

Keywords: quality of life, cardiovascular outcomes, machine learning, prediction models, pediatric

Study type: INTERVENTIONAL

Primary purpose: SUPPORTIVE CARE

Phases: NA

Enrollment: 1000 (ESTIMATED)

Status: RECRUITING

Completion: 2027-10-16 (ESTIMATED)