This clinical trial focuses on testing the efficacy of different digital interventions to promote re-engagement in cancer-related long-term follow-up care for adolescent and young adult (AYA) survivors of childhood cancer.
This research project aims to enhance the safety of childbirth by using advanced computer models to predict the risk of postpartum hemorrhage (PPH). PPH is a significant concern for mothers during and after delivery. Current risk assessment tools are basic and do not adapt to changing conditions. This study will investigate whether a new and recently validated model for predicting PPH, combined with a provider-facing Best Practice Advisory (BPA) regarding currently recommended strategies triggered by an increased predicted risk, can improve perinatal outcomes. This study will compare the current category based risk assessment tool with a new, enhanced prediction model which calculates risk based on 21 factors, automatically updates as new information becomes available during labor and, if elevated, provides a provider-facing Best Practice Advisory (BPA) recommending consideration of strategies that are institutionally agreed to represent high-quality practice. Investigators hypothesize that the enhanced care approach will result in improved perinatal outcomes. The goal of the study is to improve the wellbeing of mothers during childbirth by harnessing the power of modern technology and data analysis.
Logistic Regression Prediction Model Vs. Standard of Care for Prediction of Postpartum Hemorrhage - a Pragmatic Randomized Controlled Trial
Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.
| Inclusion Criteria | Exclusion Criteria |
|---|---|
|
|
Sponsor: Holly Ende
These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.