Advancing Clinical Care using AI: A case of Hemorrhagic Disease of Newborn

Ben White, MD
published on July 29, 2019


Working as a pediatrician caring for ill, hospitalized children is a tremendously rewarding occupation, but getting it right can be hard. Evidence-based medicine has now been a standard all physicians are directed to follow for at least the last few decades, but how is a clinician to meet that standard when working in a field of medicine that has limited existing evidence? The number of children who require medical therapy is small compared to the world of adult medicine and the unique challenges of working with children can make producing strong clinical research very difficult. As a result, pediatricians such as myself are often left to spend long hours searching for what little evidence may exist on a topic and extrapolating from research done in adult populations.

Causaly, in my time exploring the technology, helps to fill in gaps present in medical informatics and eases the time burden for clinicians to properly evaluate the literature on a given disease or treatment. Unlike other resources that act simply like medical encyclopedias, the Causaly platform allows for a physician to explore a medical concept by evaluating its interplay with other diseases or treatments through connections to the primary literature on that subject. This can be a powerful way to find information that otherwise would be unrecognized and discover connections that are easily overlooked in the vast sea of medical literature.

Hemorrhagic Disease of Newborn

This was demonstrated in the recent care of a patient with hemorrhagic disease of the newborn (HDN), a now rare bleeding disorder in infants caused by a deficiency in Vitamin K. This disease has largely been eradicated due to the practice of giving Vitamin K injections to infants shortly after birth, but with the increasing popularity of home births, the incidence of HDN now seems to be increasing again.

This patient responded well to treatment, but had an unusual lab finding of an elevated direct bilirubin, indicating liver dysfunction. This patient had no reason to have liver problems and nothing in our group’s experience connected liver dysfunction with HDN. Through Causaly, however, that connection was easily discovered.

Using the general search feature to look-up hemorrhagic disease of the newborn, I quickly was able to investigate the effects associated with that disease and find that they included liver disease. One reference was associated with that relationship and it was then a simple process to access that paper. With the new information found in the article we were able to determine that our patient may have an underlying liver disease that created a predisposition to HDN and we ensured that proper specialist consultation was arranged.

As medical literature continues to grow exponentially, it will be increasingly difficult to find the signal useful to medical practitioners among all the noise without tools like Causaly. Artificial intelligence will be a crucial piece of advancing care in hospitals and clinics and allow physicians to give the best evidence-based care to patients with efficiency not previously possible. No where will that be more important than in the field of Pediatrics where new discoveries and relationships will allow children to live longer, healthier lives.

Ben White, MD is a Pediatrician at the University of Utah. He is a clinician caring for hospitalized patients and an active clinical researcher with specific interests in neonatal feeding strategies and advanced ventilation techniques.

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