Telehealth Effectively Diagnoses / Manages Fetal Congenital Heart Disease in Rural Patients

A recent study of 368 pregnant mothers, led by Bettina Cuneo, MD, director of perinatal cardiology and fetal cardiac telemedicine at Children's Hospital Colorado, found that fetal congenital heart disease (CHD) was correctly identified and successfully managed according to evidence-based risk stratification. In addition, parents achieved a dramatic cost benefit and patient/physician satisfaction was high.

This study, which was performed in conjuiction with St. Mary's Maternal Fetal Medicine and OBGYN Clinic, which is about 250 miles and two mountain passes away from the Children's Hospital Colorado's Heart Institute, is the first to look at the effects of a telecardiology program on the detection and risk stratification of fetal CHD in a medically underserved area. The program used real-time video consultation between medical experts in the clinic and pediatric cardiologists at Children's Hospital Colorado.

"With these findings, we can now confidently say that neither diagnostic quality nor patient satisfaction were sacrificed with telecardiology as opposed to in-person visits," said Dr. Cuneo. "The study also suggests that a telecardiology program is feasible and has economic advantages for the parents while keeping care close to home."

Specific findings include:

  • Obstetric sonographers who participated in the study improved their identification of fetal CHD;
  • Mothers who are carrying a fetus with suspected CHD were seen the same day even if outside the virtual clinic hours;
  • All mothers preferred their fetal cardiac evaluation to take place locally rather than traveling to the distant center;
  • The estimated total cost to parents for fetal cardiac evaluation at the distant center was 10-times greater than that of telecardiology; and
  • 100% of pregnancies with fetal CHD were correctly risk stratified

CHD is the most common birth defect and a significant cause of perinatal morbidity and mortality. Prenatal diagnosis of CHD helps ensure the optimal delivery site for the mother and baby. For example, a fetus identified with CHD that requires immediate intervention may benefit from delivery at a cardiac center of excellence, while those with stable CHD may safely deliver at a community hospital. This anticipatory care improves postnatal outcomes and effectively utilizes medical resources by delivering low-risk fetuses locally.

"While 90% of pregnant women in the United States receive at least one ultrasound during their pregnancy, fewer than 50% of infants with CHD requiring immediate postnatal intervention are diagnosed before birth," said Dr. Cuneo. "Since the lowest CHD detection rates are in rural and medically underserved areas, we are thrilled to show that telecardiology programs can effectively diagnose and manage CHD cases in those communities."

Bettina Cuneo, Christina Olson, Caitlin Haxel, Lisa Howley, Amy Gagnon, D Benson, Alexander Kaizer, J Thomas.
Risk Stratification of Fetal Cardiac Anomalies in an Underserved Population Using Telecardiology.
Obstetrics & Gynecology. 134(5):1096–1103, NOVEMBER 2019. doi: 10.1097/AOG.0000000000003502.

Most Popular Now

Unlocking the 10 Year Health Plan

The government's plan for the NHS is a huge document. Jane Stephenson, chief executive of SPARK TSL, argues the key to unlocking its digital ambitions is to consider what it...

Alcidion Grows Top Talent in the UK, wit…

Alcidion has today announced the addition of three new appointments to their UK-based team, with one internal promotion and two external recruits. Dr Paul Deffley has been announced as the...

AI can Find Cancer Pathologists Miss

Men assessed as healthy after a pathologist analyses their tissue sample may still have an early form of prostate cancer. Using AI, researchers at Uppsala University have been able to...

AI, Full Automation could Expand Artific…

Automated insulin delivery (AID) systems such as the UVA Health-developed artificial pancreas could help more type 1 diabetes patients if the devices become fully automated, according to a new review...

New Training Year Starts at Siemens Heal…

In September, 197 school graduates will start their vocational training or dual studies in Germany at Siemens Healthineers. 117 apprentices and 80 dual students will begin their careers at Siemens...

How AI could Speed the Development of RN…

Using artificial intelligence (AI), MIT researchers have come up with a new way to design nanoparticles that can more efficiently deliver RNA vaccines and other types of RNA therapies. After training...

MIT Researchers Use Generative AI to Des…

With help from artificial intelligence, MIT researchers have designed novel antibiotics that can combat two hard-to-treat infections: drug-resistant Neisseria gonorrhoeae and multi-drug-resistant Staphylococcus aureus (MRSA). Using generative AI algorithms, the research...

AI Hybrid Strategy Improves Mammogram In…

A hybrid reading strategy for screening mammography, developed by Dutch researchers and deployed retrospectively to more than 40,000 exams, reduced radiologist workload by 38% without changing recall or cancer detection...

Penn Developed AI Tools and Datasets Hel…

Doctors treating kidney disease have long depended on trial-and-error to find the best therapies for individual patients. Now, new artificial intelligence (AI) tools developed by researchers in the Perelman School...

Are You Eligible for a Clinical Trial? C…

A new study in the academic journal Machine Learning: Health discovers that ChatGPT can accelerate patient screening for clinical trials, showing promise in reducing delays and improving trial success rates. Researchers...

Global Study Reveals How Patients View M…

How physicians feel about artificial intelligence (AI) in medicine has been studied many times. But what do patients think? A team led by researchers at the Technical University of Munich...

New AI Tool Addresses Accuracy and Fairn…

A team of researchers at the Icahn School of Medicine at Mount Sinai has developed a new method to identify and reduce biases in datasets used to train machine-learning algorithms...