Dr. Bots- How Conversational AI can solve some of the biggest challenges in Clinical trials

Doc: Hi Brian, how are you doing today?

Brian: Yeah, ok, not that great.

Doc: Oh, I am sorry to hear that. Are you feeling any of the following symptoms?

a. Fatigue

b. Palpitations

c. Nausea

d. Dizziness

Brian: Yes, actually feeling a little dizzy.

Doc: Hmm. On a scale of 1–5, with 5 being the highest, how dizzy would you say you are feeling?

Brian: Around 3

Doc: Ok, got it. Mild to medium dizziness is common in this regimen. Since we just started the new medication 2 days back, let’s wait for a bit and see how it goes. In the meantime, drink water and get some rest.

Brian: Ok

Doc: Great. Take care and I’ll check on you again in the evening.

Brian: Thanks Doc.

Doc: You’re welcome. Feel free to reach out anytime! Have a great day!

The above chat conversation between a patient and his doctor seems ideal, doesn’t it — Initiated by a doctor to check on his patient, showing concern but also providing useful information in a crisp format and alleviating the patient’s apprehension about the side-effects of the drug.

However, how many healthcare professionals really have the time to provide such detailed level of care? In some developing nations, physicians are seeing as many as 150 patients a day, giving them almost no time to check on the progress of patients at home. This might still be ok in cases with mild to moderate illnesses. However, in Clinical trials, where new drugs are being tested in patients for efficacy, it is more important than ever to continuously monitor patients, and keep a check for adverse effects.

In a recent study, it was reported that more than 50% of the patients who were admitted into a clinical trial, dropped out before the trial was completed. This is after the expensive procedures of selection, enrolment, and training of the patients and caretakers; a huge cost to the trial.

This huge churn could be attributed to a number of reasons — patients not wanting to travel every month to visit the trial hospital, losing interest, not adhering to the protocols, or facing issues that are not resolved due to lack of time and resources at the investigator’s end.

In order to fix the patient funnel, the industry is now looking at various virtual and AI based solutions. This is where Conversational AI could have a huge impact on the industry.

Chatbots could provide cradle to grave solutions for all stakeholders in clinical trials. From creating awareness about trials, to giving information about the new drug, bots can dissipate information fast and in a customized fashion. It can onboard new participants, help set up their appointments and even send behavioural nudges to take medicines on time.

Chatbots can also be extremely valuable for symptomatic data collection that is not usually collected by wearable devices. Bots could prompt questions regarding the participant’s general health, like in the case above, and collect data in real time. This could be especially relevant for collecting adverse event data that needs to be addressed and accounted for in real time.

Bots can save both the trial participants and the clinicians a lot of time and cognitive energy, by reducing patient visits at site, increasing adherence and reducing patient drop out by keeping them engaged and answering queries quickly and efficiently.

The efficacy of bots could be further enhanced by deploying them on platforms that are already being used by participants, such as WhatsApp or Facebook messenger. This would reduce the initial resistance of downloading and learning a new app at the patient’s end.

At Fluid AI, we are working on some of these very solutions, trying to adapt them to the Indian market, as well as the world in general. With its humanized bots that are omnichannel, can chat in multiple regional languages and seamlessly handover to human agents for escalation, Fluid AI bots can solve some of the pressing issues in Decentralized Clinical trials, Pharmacovigilance and Medical management (Healthcare/Drug Development) at large.

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