Try : Insurtech, Application Development

AgriTech(1)

Augmented Reality(21)

Clean Tech(9)

Customer Journey(17)

Design(45)

Solar Industry(8)

User Experience(68)

Edtech(10)

Events(34)

HR Tech(3)

Interviews(10)

Life@mantra(11)

Logistics(5)

Manufacturing(3)

Strategy(18)

Testing(9)

Android(48)

Backend(32)

Dev Ops(11)

Enterprise Solution(33)

Technology Modernization(9)

Frontend(29)

iOS(43)

Javascript(15)

AI in Insurance(38)

Insurtech(66)

Product Innovation(58)

Solutions(22)

E-health(12)

HealthTech(24)

mHealth(5)

Telehealth Care(4)

Telemedicine(5)

Artificial Intelligence(153)

Bitcoin(8)

Blockchain(19)

Cognitive Computing(8)

Computer Vision(8)

Data Science(23)

FinTech(51)

Banking(7)

Intelligent Automation(27)

Machine Learning(48)

Natural Language Processing(14)

expand Menu Filters

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

Cancel

Knowledge thats worth delivered in your inbox

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.

Image Source

  • 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.

Cancel

Knowledge thats worth delivered in your inbox

Loading More Posts ...
Go Top
ml floating chatbot