A new study published in JAMA Network Open last week described how a machine-learning (ML) tool accurately identified a set of biomarkers for neonatal opioid withdrawal syndrome (NOWS) using newborn...
As many healthcare organizations move toward value-based care, they must create and implement strategies to improve the quality of care and reduce the risk of adverse patient outcomes in the short and long term. Strategies differ by type...
A study published in JAMA Network Open concluded that a newly developed symptom-based screening tool could detect asthma risk levels among pediatric patients as well as persistent wheezing symptoms and...
A new study published in BMJ Health & Care Informatics shows that machine-learning (ML) models can accurately predict autism spectrum disorder (ASD) risk in children 18 to 30 months old using...
A study published in Lancet Digital Health found that a machine-learning algorithm identified the differences between multisystem inflammatory syndrome in children (MIS-C) and Kawasaki Disease (KD),...
Developed by researchers at Tulane University and described in a study published in Nature Biomedical Engineering, a new blood testing system displayed the ability to enhance the pediatric tuberculosis...
A new study published in JAMA Pediatrics found that an artificial intelligence (AI)-based clinical decision-making tool can help improve antibiotic stewardship for diarrheal disease in settings with...
A study published in JAMA Network Open last month found that a prediction model could accurately forecast health-related quality of life (HRQOL) among adult survivors of childhood cancer using...
Researchers have found a significant association among social determinants of health (SDOH), prehospital pediatric encounters, and EMS transport decisions.
Screening children for SDOH has become more...
Children's Hospital of Philadelphia (CHOP) has announced the launch of a new analytics platform that will use pediatric cancer data to streamline and accelerate cancer drug development.
With...
A study published this week in Nature Communications shows that an automated clinical decision support tool for genetic disease diagnosis and treatment can provide accurate results and disease...
Ohio-based Akron Children’s Hospital has launched a strategic, multi-year partnership with data and analytics technology company Health Catalyst to advance population health and improve patient...
A study published in Scientific Reports earlier this month found that artificial intelligence (AI)-based software approved for use in interpreting adult chest radiographs achieved high performance on...
Despite a slight inconsistency in satisfaction rates among different ethnic groups, a study from Academic Pediatrics reported a relatively high rate of approval from parents regarding the use of...
New research has found that an artificial intelligence (AI)-based medical device can assist clinicians in primary care settings accurately diagnose autism spectrum disorder (ASD) in children up to 6...
A deep-learning model can accurately predict pediatric no-shows using data from patient EHRs and local weather information, which can then be used to implement no-show prevention measures, according to...
While exploring the possibilities of integrating machine learning into clinical decision-making, a JAMA Network study found that novel machine learning-driven workflows helped improve test ordering...
Researchers from Johns Hopkins Medicine and NORC at the University of Chicago found that the Agency for Healthcare Research and Quality's (AHRQ) Safety Program for Improving Antibiotic Use is...
Children’s Hospital Los Angeles received a three-year, $2 million grant from the state of California to conduct a precision medicine study screening children for adverse childhood experiences...
Using a predictive analytics model, providers could proactively identify pediatric patients at risk of developing blood clots or venous thromboembolisms (VTEs), according to a study published in...
