Q&A With Hansa Bhargava, MD, Chief Medical Officer at Medscape Education

At the DTX Summit held in Washington DC on June 6-9, 2023, Dr. Hansa Bhargava participated in a panel discussion about putting the patient and provider first in the field of digital therapeutics. After the event, she sat with MD&T to further discuss how education around new therapies is impacting patients and providers.

(MDT:) When it comes to new technologies and the medical industry, whose needs should come first: the patient or the provider?

Bhargava: For adoption and adaption of new technologies, including digital technologies, it’s so important to increase awareness. We’ve done that with mental health, with the campaign increasing awareness over the past several years. It’s really important for patients to know about new technologies, but clinicians have to be part of the equation. Ultimately, patients trust clinicians and they will go to clinicians to ask about things that could be potential solutions to their diseases.

(MDT:) How can digital health options improve the typical time that it takes new therapies to be fully adopted?

Bhargava: Solutions and therapies typically take 17 years, from publication to practice. That means you can publish a paper on how effective a therapy is and it won’t actually get prescribed in a clinician’s office for 17 years. That’s a really long time. For digital therapeutics, it’s important to increase awareness for clinicians, doctors, nurses, and other providers on the healthcare team.

You can use an omnichannel approach. You need to go where they are and incentivize them to learn about and use these new solutions and therapies. Whether that’s through providing continuing education credits, discussing cases on social media (without dispelling the privacy of the patients), or at live conferences, that’s where we need to be to let them know that this exists.

Lastly, there’s a need for an educational curriculum around digital health, AI, and innovations. It’s a gap in education right now and we need to address it so that clinicians come on board as well.

(MDT:) The term “AI” comes with preconceived notions. The way that it’s used in the health sphere, however, is different than how people perceive it from science fiction. Could you elaborate on how AI is impacting the health industry?

Bhargava: This is why we want to educate people in the healthcare industry about AI, and to do so in a manner that is easy to understand. A lot of the AI experts have written textbooks and know it from a deep level, but we need a basic understanding of what it is. If it’s done properly and used properly, it could improve patient care and wellbeing.

One of the things that I’ve seen is that a new patient will come in and I don’t know their history. They’re telling me that they’re taking their medication every single day, but is that actually true? Life happens to all of us, and people forget to take their medications for many different reasons. If I had the data to see if the patient was taking their medication because of a sensor on a device or an application on a phone, that can make a difference in how I deliver care.

The other example I use is what if a patient comes in and I’m not sure what they have. If there was a database that could help me figure out the most likely diagnosis and best treatments to recommend, that could make a difference. As long as I know that that data files the power the AI are diverse and full, that could help me as a burned-out health care provider. I’ll still be making the decision, but now I’ve got some support.

(MDT:) So AI is often being used to help make more confident diagnoses, but to make the actual diagnosis, correct?

Bhargava: I’ve heard people say that they like to think of AI as not artificial intelligence, but augmented intelligence. It’s going to help you with your efficiency and decision making, but it’s not going to make the diagnosis.

We also have to be educated around AI because we need to be aware of the fallout. For AI, one of the fallouts is that if there’s not enough diversity in the data set, or there’s incorrect, then that’s going to influence the data. Trash in, trash out. We, as clinicians and patients, need to know the limitation of AI as well.

A great example is ChatGPT, where it’s been caught making things up. We can’t just take it at face value, we need to make sure that we know enough about it to make sure we get the best results.