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MantraTalks Podcast with Richard Roy Mendonce: Covid-19 & the Disruption in Healthcare

11 minutes, 21 seconds read

The outbreak of COVID-19 has put immense pressure on the healthcare sector. The supply chain of medical supplies was hit. The sudden surge of patients made it difficult to manage the hospital operations. Since priority had to be given to COVID patients, regular consults and elective surgeries were delayed. 

To go one step further and understand the disruption in healthcare amidst these adverse conditions, we interviewed Mr. Richard Roy Mendonce, Head Digital Strategy at Yashoda Hospitals to shed light on the role of technology in combating the current challenges faced by healthcare and possible mitigation strategies.

Mr. Richard Roy Mendonce has a strong domain expertise within the Healthcare Industry and has successfully infused digital transformations in various organizations like Columbia Asia Group of Hospitals, Sakra World Hospital, and Manipal Hospitals Group that ensured better customer experience and increased business. 

A Digital Strategist, he currently leads the digital efforts at Yashoda Hospitals, which is among the oldest and biggest healthcare groups in the region. He has nearly a decade of experience in digital marketing, digital strategy and digital transformation, with a distinctive ability to develop highly effective and measurable strategies that drive revenue growth, new customers, brand awareness and reputation. 

Constantly inspired & fascinated by the dynamics of the digital landscape, he has developed a skill set built on the art of leveraging digital technologies focused to deliver positive user experiences and achieve business objectives. In 2019, he was awarded as one of the 50 Most Influential Strategy Leaders by COM Global at World Marketing Congress.

Connect with Mr. Richard Roy Mendonce – LinkedIn

Watch the interview: 

The excerpt from the interview:

Covid-19 & the Disruption in Healthcare

Many hospitals are reassessing their digital marketing strategy and budgets in light of the uncertain economic situation. Most healthcare organizations can benefit from taking this time to strategize and plan for the future, rather than putting the brakes on. Please share some key insights into the changing patient behavior and the steps you are taking to reach them? Also, How will the healthcare marketing landscape change Beyond COVID-19?

Mr. Richard: In terms of healthcare, especially telemedicine, COVID-19 has completely cut down the channel of visiting doctors in-person for a consult. Lack of options has increased more acceptance towards Telemedicine. A couple of months back, we compared the benefits and comfort of direct consultation to an online one. We were reluctant to have those experiences but now acceptance has increased. 

Another thing I feel is —  we do not need high-end technology or equipment. When we hear of telemedicine, what comes first to our mind is jazzy computers, high-tech connections, software, etc.; but that is not the case. Even a simple SMS/call/WhatsApp call is enough to connect with a doctor. We don’t really need any high-end equipment to start a telemedicine service. 

Today, most of the spending is being diverted to digital channels rather than traditional offline ones and it will continue to happen. Digital channels are more trackable, more efficient, and more controllable. Even digital connect to engage with offline channels is gaining momentum. Healthcare set-ups will have offline referral networks, business partners. Traditionally, there would be a sales team who go meet and connect with them. Now with the social distancing and lockdowns, even that connection is replaced with digital connections such as webinars, video calls, etc. 

Communication in marketing has also changed. Before COVID-19, the communication was “Don’t ignore your health, come to us”. During the COVID-19 situation, the communication was “Come to us only if it is an emergency, it’s better to stay at home”. Post COVID-19, the communication might be- “Wherever you are, we are accessible, come to us or use our online services.” 

Telemedicine in a Post-Pandemic India

In the short time since the Pandemic began, the impact of social distancing norms has changed our daily lives & routines. Due to which, services like live remote consultations and telemedicine are getting more attention. Telemedicine is likely a permanent beneficiary of the pandemic. Do you think it will remain a key mode of healthcare delivery even after restrictions are lifted? Are there other digitally-enabled services that can potentially find greater adoption in a Post-Pandemic India?

Mr. Richard: Telemedicine will continue to be one of the modes of care delivery but that will not replace the existing care delivery system. Rather, it will be a mix of both. People will opt for telemedicine for the initial consultation (a non-serious one) and post-treatment follow-ups or review visits or to update on reports. People might get accustomed to telemedicine services but I think that will never replace serious conditions or surgical specialty where doctors need to examine personally to deliver proper care. 

In terms of acceptance level of technology, there has been wider acceptance for non-clinical support systems. For example, chatbots in place to address customer service and AI-driven platforms to check symptoms and guide the patient to respective specialists. This is not for prescriptions, but to enable patients to help themselves in availing services. 

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

Medical supplies: Another area where digital platforms should have a wider scale of adoption is traveling for non-essential medical supplies. Pharma delivery is one sector that can go entirely digital. We can also have a format where physical stores are eliminated. Delivery can be from warehouse to customer. 

Diagnostics: Apart from radiology, diagnostics can go completely digital. Home care such as remote ICUs, remote monitoring could have potentially greater adoption in the current scenario. 

Disruption in healthcare will also include technologies to strengthen medical education and training.

Operational Challenges in Healthcare

From the operations point of view, digital transformation alone cannot help in preparing for an outbreak of this scale. The reality is we also have to be prepared for the possibility of a next Pandemic wave. The pandemic itself is testing the digital readiness and operational resilience of hospitals, in digitizing services and bringing innovation into healthcare. 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?

Mr. Richard: We all know that the entire system was not geared up for a pandemic of this scale. Hospitals are facing both operational and clinical challenges. However, I’ll address this one particular issue from a digital angle. 

The biggest challenge for any hospital is the lack of a digital care platform and is still heavily dependent on paper-based systems. Now we know that anything can be sanitized but how do we sanitize paper documents. Patients have to carry these documents, touch them, and exchange multiple hands which can be potential carriers of the virus. Now it is more important to keep all the medical records digitized. 

Another aspect is the nature of this virus which is highly communicable and is leading to the community spread of this disease. Therefore, hospitals have a responsibility to maintain data at a patient-level so that contact tracing becomes much more easier and automated. So, maybe a symptom can be added as a trigger in the system and automatically do a contact tracing and give a list of people they can reach out to.

Yet another aspect in healthcare which is prone to change is remote working. Most of the industries such as IT have already geared up for remote working but healthcare has not. Many of the processes still need people coming to the office and working on a computer that is in the network. So, the disruption in healthcare relies on digital platforms to ensure that staff is efficiently deployed.

Changes in the Patient Experience

Both outpatient and in-patient treatment for all major non-communicable diseases including emergencies have declined. Going forward, as the country tries to resume life in the New Normal, industries like retail are experimenting with touchless interfaces to boost the customer’s confidence in shopping in-store. What changes, if any, do you foresee to the physical patient experience?

Mr. Richard: Wherever possible, currently hospitals are trying to minimize contact. Like airports, one can print their boarding pass, even hospitals can ask the patients not to wait in a line at the reception but rather book an appointment and make payments online. Once the appointment is booked, patients can just come and wait for the doctor’s call. We have seen multiple robotic-assisted surgeries where contact with the patient is avoided. Similarly, some technologies may come up taking vitals from the patient in a no-contact manner. There are hospitals in the country that have introduced innovative robots who screen patients coming to the hospitals. There are lots of innovations possible in this area. 

Role of AR, VR and AI in Digital Healthcare 

Huge volumes of data are flowing into the cloud, not just from doctors’ offices and imaging centers, but also from remote devices and sensors worn or operated by patients. By harnessing the vast amounts of data and putting it to work in applications, it helps care providers to improve effectiveness and efficiencies. Do you see technologies like AR/VR/AI playing a role in the future of digital healthcare in India? Can you share some examples of areas that Yashoda Hospitals has begun experimentation or implementation with these technologies?

Mr. Richard: Artificial intelligence, Machine Learning, Augmented Reality, Virtual Reality, Cloud systems, etc. are the buzzwords these days. I do believe that these technologies will pick pace in the healthcare industry as well. But I see a challenge there. Though all the data is on the cloud, the data is held by individual stakeholders and corporations. And standardization of data is the biggest challenge right now. 

So, any company which is working towards utilizing these technologies should first look at technologies that can bring data on one platform which is usable, accessible, and standardized without compromising confidential information of the patient. In terms of innovation at Yashoda hospitals, we are working on a couple of them such as AI-based radiology systems, optimizing customer journeys in hospitals, manpower planning, etc. 

Related: Medical Image Management: DICOM Images Sharing Process

Let’s take the patient discharge process for instance. Transitioning a customer from ‘in-patient’ to ‘out-patient’ is a significant challenge for any hospital, since it involves multiple departments. You’ve even stated before that it takes the integrated view of HIS (hospital information systems), EMR (electronic medical records), inventory, billing, and real-time updates of treatment progress to facilitate discharge at the click of a button. What is your experience in the transformation process and the ground realities of addressing this critical pain point? 

Mr. Richard: Theoretically speaking, the discharge process takes a lot of time but the reason it takes so much time is because it involves multiple stakeholders at a time- internal as well as external. It further gets complicated when the insurance is involved. I think all healthcare providers are looking to simplify the discharge process. The only way it is possible is having technology cut across stakeholders and in real-time. So wherever possible, we can avoid these internal communication delays. 

Return to Normal: The way forward

As hospitals plan for the complicated return stage (once restrictions are lifted), the volume of footfalls, testing, etc. will gradually increase. What advice can you share with other healthcare leaders to prepare their organization on the frontline to manage specific risks regarding employee safety, patient outcomes, etc? What investments (in remote patient monitoring, medical equipment, CRM systems, etc.) should healthcare organizations be making to respond to ‘the return to normal’?

Mr. Richard: I think that the precautionary steps taken by most of the healthcare providers are commendable. It is much better than in other countries across the world. We are in touch with a few of the major chains and the precautions that are being taken are phenomenal. Starting from thermal screens and fever clinics at the entrance, social distancing blogs; we have implemented Cluster Systems within our hospitals. It is a system where the employees are clustered in certain areas to minimize cross-contamination between employees. 

In terms of investment in technology, clinical data can be good to start working on. A good EMR system that seamlessly integrates and exchanges data between all relevant information systems is the need of the time. This investment will not just be in terms of technology but also behavioral change. 

So the system has to be friendly to seamlessly capture the data and make it available across systems. Using data efficiently is important to guide clinical decision support, developing user experience protocols and creating empowerment for the patient. 

Summing up

COVID-19 has changed a lot in us. The lockdown has unlocked a lot of things. It is a good time to innovate. Essential services would be a keyword used for a very long time now in every aspect. Be it shopping, be it food, be it health. And social distancing will be a new lifestyle. 

In this session, Mr. Richard shared insights on the disruption in healthcare and the importance of technological innovations in the new normal for hospitals.


AI is going to be essential for Insurers to gain that competitive edge in the post-pandemic world. Check out Hitee — an industry-pecific chatbot for driving customer engagement. For your specific requirements, please feel free to write to us at hello@mantralabsglobal.com.

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AI Code Assistants: Revolution Unveiled

AI code assistants are revolutionizing software development, with Gartner predicting that 75% of enterprise software engineers will use these tools by 2028, up from less than 10% in early 2023. This rapid adoption reflects the potential of AI to enhance coding efficiency and productivity, but also raises important questions about the maturity, benefits, and challenges of these emerging technologies.

Code Assistance Evolution

The evolution of code assistance has been rapid and transformative, progressing from simple autocomplete features to sophisticated AI-powered tools. GitHub Copilot, launched in 2021, marked a significant milestone by leveraging OpenAI’s Codex to generate entire code snippets 1. Amazon Q, introduced in 2023, further advanced the field with its deep integration into AWS services and impressive code acceptance rates of up to 50%. GPT (Generative Pre-trained Transformer) models have been instrumental in this evolution, with GPT-3 and its successors enabling more context-aware and nuanced code suggestions.

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  • Adoption rates: By 2023, over 40% of developers reported using AI code assistants.
  • Productivity gains: Tools like Amazon Q have demonstrated up to 80% acceleration in coding tasks.
  • Language support: Modern AI assistants support dozens of programming languages, with GitHub Copilot covering over 20 languages and frameworks.
  • Error reduction: AI-powered code assistants have shown potential to reduce bugs by up to 30% in some studies.

These advancements have not only increased coding efficiency but also democratized software development, making it more accessible to novice programmers and non-professionals alike.

Current Adoption and Maturity: Metrics Defining the Landscape

The landscape of AI code assistants is rapidly evolving, with adoption rates and performance metrics showcasing their growing maturity. Here’s a tabular comparison of some popular AI coding tools, including Amazon Q:

Amazon Q stands out with its specialized capabilities for software developers and deep integration with AWS services. It offers a range of features designed to streamline development processes:

  • Highest reported code acceptance rates: Up to 50% for multi-line code suggestions
  • Built-in security: Secure and private by design, with robust data security measures
  • Extensive connectivity: Over 50 built-in, managed, and secure data connectors
  • Task automation: Amazon Q Apps allow users to create generative AI-powered apps for streamlining tasks

The tool’s impact is evident in its adoption and performance metrics. For instance, Amazon Q has helped save over 450,000 hours from manual technical investigations. Its integration with CloudWatch provides valuable insights into developer usage patterns and areas for improvement.

As these AI assistants continue to mature, they are increasingly becoming integral to modern software development workflows. However, it’s important to note that while these tools offer significant benefits, they should be used judiciously, with developers maintaining a critical eye on the generated code and understanding its implications for overall project architecture and security.

AI-Powered Collaborative Coding: Enhancing Team Productivity

AI code assistants are revolutionizing collaborative coding practices, offering real-time suggestions, conflict resolution, and personalized assistance to development teams. These tools integrate seamlessly with popular IDEs and version control systems, facilitating smoother teamwork and code quality improvements.

Key features of AI-enhanced collaborative coding:

  • Real-time code suggestions and auto-completion across team members
  • Automated conflict detection and resolution in merge requests
  • Personalized coding assistance based on individual developer styles
  • AI-driven code reviews and quality checks

Benefits for development teams:

  • Increased productivity: Teams report up to 30-50% faster code completion
  • Improved code consistency: AI ensures adherence to team coding standards
  • Reduced onboarding time: New team members can quickly adapt to project codebases
  • Enhanced knowledge sharing: AI suggestions expose developers to diverse coding patterns

While AI code assistants offer significant advantages, it’s crucial to maintain a balance between AI assistance and human expertise. Teams should establish guidelines for AI tool usage to ensure code quality, security, and maintainability.

Emerging trends in AI-powered collaborative coding:

  • Integration of natural language processing for code explanations and documentation
  • Advanced code refactoring suggestions based on team-wide code patterns
  • AI-assisted pair programming and mob programming sessions
  • Predictive analytics for project timelines and resource allocation

As AI continues to evolve, collaborative coding tools are expected to become more sophisticated, further streamlining team workflows and fostering innovation in software development practices.

Benefits and Risks Analyzed

AI code assistants offer significant benefits but also present notable challenges. Here’s an overview of the advantages driving adoption and the critical downsides:

Core Advantages Driving Adoption:

  1. Enhanced Productivity: AI coding tools can boost developer productivity by 30-50%1. Google AI researchers estimate that these tools could save developers up to 30% of their coding time.
IndustryPotential Annual Value
Banking$200 billion – $340 billion
Retail and CPG$400 billion – $660 billion
  1. Economic Impact: Generative AI, including code assistants, could potentially add $2.6 trillion to $4.4 trillion annually to the global economy across various use cases. In the software engineering sector alone, this technology could deliver substantial value.
  1. Democratization of Software Development: AI assistants enable individuals with less coding experience to build complex applications, potentially broadening the talent pool and fostering innovation.
  2. Instant Coding Support: AI provides real-time suggestions and generates code snippets, aiding developers in their coding journey.

Critical Downsides and Risks:

  1. Cognitive and Skill-Related Concerns:
    • Over-reliance on AI tools may lead to skill atrophy, especially for junior developers.
    • There’s a risk of developers losing the ability to write or deeply understand code independently.
  2. Technical and Ethical Limitations:
    • Quality of Results: AI-generated code may contain hidden issues, leading to bugs or security vulnerabilities.
    • Security Risks: AI tools might introduce insecure libraries or out-of-date dependencies.
    • Ethical Concerns: AI algorithms lack accountability for errors and may reinforce harmful stereotypes or promote misinformation.
  3. Copyright and Licensing Issues:
    • AI tools heavily rely on open-source code, which may lead to unintentional use of copyrighted material or introduction of insecure libraries.
  4. Limited Contextual Understanding:
    • AI-generated code may not always integrate seamlessly with the broader project context, potentially leading to fragmented code.
  5. Bias in Training Data:
    • AI outputs can reflect biases present in their training data, potentially leading to non-inclusive code practices.

While AI code assistants offer significant productivity gains and economic benefits, they also present challenges that need careful consideration. Developers and organizations must balance the advantages with the potential risks, ensuring responsible use of these powerful tools.

Future of Code Automation

The future of AI code assistants is poised for significant growth and evolution, with technological advancements and changing developer attitudes shaping their trajectory towards potential ubiquity or obsolescence.

Technological Advancements on the Horizon:

  1. Enhanced Contextual Understanding: Future AI assistants are expected to gain deeper comprehension of project structures, coding patterns, and business logic. This will enable more accurate and context-aware code suggestions, reducing the need for extensive human review.
  2. Multi-Modal AI: Integration of natural language processing, computer vision, and code analysis will allow AI assistants to understand and generate code based on diverse inputs, including voice commands, sketches, and high-level descriptions.
  3. Autonomous Code Generation: By 2027, we may see AI agents capable of handling entire segments of a project with minimal oversight, potentially scaffolding entire applications from natural language descriptions.
  4. Self-Improving AI: Machine learning models that continuously learn from developer interactions and feedback will lead to increasingly accurate and personalized code suggestions over time.

Adoption Barriers and Enablers:

Barriers:

  1. Data Privacy Concerns: Organizations remain cautious about sharing proprietary code with cloud-based AI services.
  2. Integration Challenges: Seamless integration with existing development workflows and tools is crucial for widespread adoption.
  3. Skill Erosion Fears: Concerns about over-reliance on AI leading to a decline in fundamental coding skills among developers.

Enablers:

  1. Open-Source Models: The development of powerful open-source AI models may address privacy concerns and increase accessibility.
  2. IDE Integration: Deeper integration with popular integrated development environments will streamline adoption.
  3. Demonstrable ROI: Clear evidence of productivity gains and cost savings will drive enterprise adoption.
  1. AI-Driven Architecture Design: AI assistants may evolve to suggest optimal system architectures based on project requirements and best practices.
  2. Automated Code Refactoring: AI tools will increasingly offer intelligent refactoring suggestions to improve code quality and maintainability.
  3. Predictive Bug Detection: Advanced AI models will predict potential bugs and security vulnerabilities before they manifest in production environments.
  4. Cross-Language Translation: AI assistants will facilitate seamless translation between programming languages, enabling easier migration and interoperability.
  5. AI-Human Pair Programming: More sophisticated AI agents may act as virtual pair programming partners, offering real-time guidance and code reviews.
  6. Ethical AI Coding: Future AI assistants will incorporate ethical considerations, suggesting inclusive and bias-free code practices.

As these trends unfold, the role of human developers is likely to shift towards higher-level problem-solving, creative design, and AI oversight. By 2025, it’s projected that over 70% of professional software developers will regularly collaborate with AI agents in their coding workflows1. However, the path to ubiquity will depend on addressing key challenges such as reliability, security, and maintaining a balance between AI assistance and human expertise.

The future outlook for AI code assistants is one of transformative potential, with the technology poised to become an integral part of the software development landscape. As these tools continue to evolve, they will likely reshape team structures, development methodologies, and the very nature of coding itself.

Conclusion: A Tool, Not a Panacea

AI code assistants have irrevocably altered software development, delivering measurable productivity gains but introducing new technical and societal challenges. Current metrics suggest they are transitioning from novel aids to essential utilities—63% of enterprises now mandate their use. However, their ascendancy as the de facto standard hinges on addressing security flaws, mitigating cognitive erosion, and fostering equitable upskilling. For organizations, the optimal path lies in balanced integration: harnessing AI’s speed while preserving human ingenuity. As generative models evolve, developers who master this symbiosis will define the next epoch of software engineering.

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