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MantraTalks Podcast with Parag Sharma: Delivering Digital-first Health Experiences for Patient Care in the New Normal

6 minutes read

The healthcare industry took the brunt of the Covid-19 pandemic from the very beginning. It was, and still is, a humongous task for hospitals to deal with the rising number of COVID patients as well as handling the regular consults. 

To delve deeper into the state of healthcare in the COVID times, we interviewed Parag Sharma, CEO, Mantra Labs Pvt Ltd. Parag shares his insights on how technology can help in delivering digital-first health experiences for patient care in the New Normal.

Parag is a product enthusiast and tinkerer at heart and has been at the forefront of developing innovative products especially in the field of AI. He also holds over ten years of experience working in the services line and has been instrumental in launching several startups in the Internet & Mobile space. His rich domain expertise and innovative leadership have helped Mantra climb to the top 100 innovative InsurTechs in the World – selected by FinTech Global. 

Catch the interview:  

Connect with Parag- LinkedIn

COVID-19 and Its impact on Healthcare Organizations

Considering the COVID situation, according to you how has COVID-19 impacted the IT & service operations among healthcare organizations?

Parag:  Since the onset of COVID-19, the healthcare sector has been deeply impacted. Institutions are facing a serious crunch in manpower. IT support systems which were usually manned and managed by a large team of IT professionals are not available in the same strength. Resource allocation’ is one of the biggest concerns due to physical and mental exhaustion of the healthcare workforce. 

Hospitals are facing issues such as operational disruption due to staff quarantine, supply-chain delays and sudden decline in patient footfalls, difficulty in sustaining fixed costs, etc. People are not comfortable getting out of the safety confinements of their homes due to the rising risk of getting infected with the virus. Hospitals will have to reassess their future strategy and budgets in light of the uncertain economic situation.

Preparing for the Future

What can hospitals do to ensure the continuity of their customer-facing operations in the wake of a second Pandemic wave?

Parag: There are many things that hospitals can do to manage themselves in this hour of crisis. Being more digital than what they are would be one step forward for all of them. They can bring their IT systems to the cloud so that the person can access data and manage their work remotely. They can enable their patients to book appointments and enquire about services through apps and chatbots which won’t require them to call the reception or come to the hospital. These are some of the services which hospitals can provide to their customers with minimum physical contact. 

Related: Manipal Hospital’s move to a self-service healthcare mobile application

Hospitals can extend Telehealth services to their patients. Recently, telehealth has proved to be useful especially when there is asymmetry between the number of patients and healthcare providers. I think it will be very useful for healthcare institutions to deploy telehealth solutions to provide medical facilities to people who have so far been outside the benefits of healthcare.

New Expectations in Health Experiences

Is consumer behavior defined by the ‘new normal’ going to change the way we access healthcare from this point on?

Parag: Yes, people will expect a completely different way to access healthcare services from now on. Hospitals should gear-up and rise to this occasion. The pandemic has also provided a new opportunity to adopt a completely different approach in the way healthcare is delivered. They always felt that medical care cannot be provided remotely but now this is happening and people are appreciating remote healthcare services. Hospitals and healthcare institutions are convinced that telehealth and remote care will be more successful soon.

Technology in Healthcare can Bridge Operational Gaps

What are the operational challenges, as far as digital capabilities go, that hospitals are facing currently? And, what steps must they take to bridge these gaps?

Parag: Operational challenges are not just digital challenges. But a lot of these challenges can be addressed with technology. For example, Electronic Health Records which hospitals manage within the premises can be moved to the cloud so that the person can access these records on the cloud itself and need not come to the hospital. 

Related: Medical Image Management: DICOM Images Sharing Process

Secondly, if you deploy telehealth and telemedicine solutions, irrespective of where your patients are or doctors are, hospitals can deliver the required care to its patients. You can even extend your diagnostics services to your patients by giving them an application through which they can seamlessly book appointments for consults, diagnostics, or pathological services and resolve their queries, etc. Simply by giving a seamless interface either through bots or applications can go a long way in providing better health experiences to the customers.

Role of Chatbots in Superior Customer Experiences

According to you, what role does chatbots powered by Artificial Intelligence have in the Healthcare CX landscape?

Parag: Chatbots are the simplest example of the implementation of AI-based technology in healthcare. There are a lot of things which bots can do simplistically. For example, if a patient wants to book an appointment with the doctors, instead of going through a complex web applications and interfaces, what if I can simply write “I want to book an appointment with the doctor Dr. XYZ at 4 pm” and the bot can figure out in case the time slot is available with that particular doctor, it will confirm the appointment followed by a payment process if the payment has to be made upfront. 

Apart from this, you can extend your bots to provide e-consultations where doctors can do remote consultations via audio and video features of a chatbot. So there is a huge scope for bots beyond answering routine queries by customers or booking appointments. It does not stop just there. You can extend chatbot functionalities to support functions such as admin, HR, finance, and business process efficiency so that they can provide better services to their customers.

Related: Healthcare Chatbots: Innovative, Efficient, and Low-cost Care

Chatbot Use Cases in Healthcare

Could you tell us some possible bot use cases for delivering better customer experiences to digital health users?

Parag: Apart from booking appointments and resolving customer queries, these bots can conduct remote consultations, internal processes, health symptom checker, out-patient video consultation, second opinion consultation, ordering medicines, psychological counseling & mental wellness, scenario-based risk advice, Heroism Recognition for employees, etc. Also, it can be further extended to help patients enquire about health insurance related queries, and all the interactions between insurance companies and hospitals can be provided to the patient. 

Related: Healthcare & Hospitals Use Cases | Digital Health

The Road Ahead

COVID-19 has forced hospitals to revise patient support strategy with limited operational staff that is bringing every day a new challenge. A way out is to heavily rely on digital innovation.

In India we have a disparity between the no. of healthcare providers and care seekers. Without technology, I don’t think there is any way healthcare institutions will be able to scale to a level where they can provide meaningful services to such a large number of people. Hospitals can invest in setting up an information exchange; making the process as seamless as possible; and removing all possible inefficiencies from the supply chain through technology.

Future growth for hospitals will come from digital technology because patients will opt more for digital platforms. And it is up to hospitals to catch up with the pace at which modern technology is developing. We, at Mantra Labs, have achieved several use cases including hospitals/diagnostic centers that are able to deliver superior health experiences.

Check out the webinar on ‘Digital Health Beyond COVID-19: Bringing the Hospital to the Customer’ on our YouTube channel

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Machines That Make Up Facts? Stopping AI Hallucinations with Reliable Systems

There was a time when people truly believed that humans only used 10% of their brains, so much so that it fueled Hollywood Movies and self-help personas promising untapped genius. The truth? Neuroscientists have long debunked this myth, proving that nearly all parts of our brain are active, even when we’re at rest. Now, imagine AI doing the same, providing information that is untrue, except unlike us, it doesn’t have a moment of self-doubt. That’s the bizarre and sometimes dangerous world of AI hallucinations.

AI hallucinations aren’t just funny errors; they’re a real and growing issue in AI-generated misinformation. So why do they happen, and how do we build reliable AI systems that don’t confidently mislead us? Let’s dive in.

Why Do AI Hallucinations Happen?

AI hallucinations happen when models generate errors due to incomplete, biased, or conflicting data. Other reasons include:

  • Human oversight: AI mirrors human biases and errors in training data, leading to AI’s false information
  • Lack of reasoning: Unlike humans, AI doesn’t “think” critically—it generates predictions based on patterns.

But beyond these, what if AI is too creative for its own good?

‘Creativity Gone Rogue’: When AI’s Imagination Runs Wild

AI doesn’t dream, but sometimes it gets ‘too creative’—spinning plausible-sounding stories that are basically AI-generated fake data with zero factual basis. Take the case of Meta’s Galactica, an AI model designed to generate scientific papers. It confidently fabricated entire studies with fake references, leading Meta to shut it down in three days.

This raises the question: Should AI be designed to be ‘less creative’ when AI trustworthiness matters?

The Overconfidence Problem

Ever heard the phrase, “Be confident, but not overconfident”? AI definitely hasn’t.

AI hallucinations happen because AI lacks self-doubt. When it doesn’t know something, it doesn’t hesitate—it just generates the most statistically probable answer. In one bizarre case, ChatGPT falsely accused a law professor of sexual harassment and even cited fake legal documents as proof.

Take the now-infamous case of Google’s Bard, which confidently claimed that the James Webb Space Telescope took the first-ever image of an exoplanet, a factually incorrect statement that went viral before Google had to step in and correct it.

There are more such multiple instances where AI hallucinations have led to Human hallucinations. Here are a few instances we faced.

When we tried the prompt of “Padmavaat according to the description of Malik Muhammad Jayasi-the writer ”

When we tried the prompt of “monkey to man evolution”

Now, if this is making you question your AI’s ability to get things right, then you should probably start looking have a checklist to check if your AI is reliable.

Before diving into solutions. Question your AI. If it can do these, maybe these will solve a bit of issues:

  • Can AI recognize its own mistakes?
  • What would “self-awareness” look like in AI without consciousness?
  • Are there techniques to make AI second-guess itself?
  • Can AI “consult an expert” before answering?

That might be just a checklist, but here are the strategies that make AI more reliable:

Strategies for Building Reliable AI

1. Neurosymbolic AI

It is a hybrid approach combining symbolic reasoning (logical rules) with deep learning to improve factual accuracy. IBM is pioneering this approach to build trustworthy AI systems that reason more like humans. For example, RAAPID’s solutions utilize this approach to transform clinical data into compliant, profitable risk adjustment, improving contextual understanding and reducing misdiagnoses.

2. Human-in-the-Loop Verification

Instead of random checks, AI can be trained to request human validation in critical areas. Companies like OpenAI and Google DeepMind are implementing real-time feedback loops where AI flags uncertain responses for review. A notable AI hallucination prevention use case is in medical AI, where human radiologists verify AI-detected anomalies in scans, improving diagnostic accuracy.

3. Truth Scoring Mechanism

IBM’s FactSheets AI assigns credibility scores to AI-generated content, ensuring more fact-based responses. This approach is already being used in financial risk assessment models, where AI outputs are ranked by reliability before human analysts review them.

4. AI ‘Memory’ for Context Awareness

Retrieval-Augmented Generation (RAG) allows AI to access verified sources before responding. This method is already being used by platforms like Bing AI, which cites sources instead of generating standalone answers. In legal tech, RAG-based models ensure AI-generated contracts reference actual legal precedents, reducing AI accuracy problems.

5. Red Teaming & Adversarial Testing

Companies like OpenAI and Google regularly use “red teaming”—pitting AI against expert testers who try to break its logic and expose weaknesses. This helps fine-tune AI models before public release. A practical AI reliability example is cybersecurity AI, where red teams simulate hacking attempts to uncover vulnerabilities before systems go live 

The Future: AI That Knows When to Say, “I Don’t Know”

One of the most important steps toward reliable AI is training models to recognize uncertainty. Instead of making up answers, AI should be able to respond with “I’m unsure” or direct users to validated sources. Google DeepMind’s Socratic AI model is experimenting with ways to embed self-doubt into AI.

Conclusion:

AI hallucinations aren’t just quirky mistakes—they’re a major roadblock in creating trustworthy AI systems. By blending techniques like neurosymbolic AI, human-in-the-loop verification, and retrieval-augmented generation, we can push AI toward greater accuracy and reliability.

But here’s the big question: Should AI always strive to be 100% factual, or does some level of ‘creative hallucination’ have its place? After all, some of the best innovations come from thinking outside the box—even if that box is built from AI-generated data and machine learning algorithms.

At Mantra Labs, we specialize in data-driven AI solutions designed to minimize hallucinations and maximize trust. Whether you’re developing AI-powered products or enhancing decision-making with machine learning, our expertise ensures your models provide accurate information, making life easier for humans

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