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What does the Digital & Connected Patient Experience of Tomorrow look like?

Over the last two years, between setting up new hospitals, handling the patient load, rearranging floors, and turning ICUs into covid wards quickly, the healthcare ecosystem faced a paradigm shift. Virtual visits that seemed like a mere possibility a few years ago, turned into reality in just a matter of months. Hospitals turned up at doorsteps and digital consultations became the new normal. The pandemic gave momentum to the rapid adoption of newer technologies by both providers and patients.

The Healthcare of Tomorrow

According to Deloitte, by 2040, health care as we know it today will cease to exist and the focus will shift from ‘healthcare’ to ‘health’. While it’ll be impossible to eradicate disease and illness completely, early detection, proactive intervention, and progress tracking will help to prevent serious consequences and promote well-being. 

Smartwatch market share is expected to reach $ 95.78 Billion by 2028 and register a CAGR of 19.1%, according to Emergen Research. A 71-year-old woman in the US collapsed while she was alone, but the Apple Watch’s fall detection feature was able to warn her son and first responders. When she was taken to the health center, she was detected with a mass in her lungs that was cancerous. The future of healthcare will be strongly empowered by the digital revolution where the focus will be more on wellness rather than illness.

What does a Digital Patient of tomorrow want?

Patient loyalty is directly linked with their overall experience. According to Accenture, “Two-thirds of patients are likely to switch to a new health system if their expectations are not met.”

Ideal Patient Journey

Let’s look at what a future healthcare consumer is looking for:

  1. Omnichannel Experience

According to Mantra Labs report, “healthcare providers that successfully initiate conversations, advise, engage and then close over multiple channels can potentially retain up to 7X more customers.”

Earlier healthcare customers relied more on in-person visits and consultations. But with change in consumer dynamics in the past two years, industries have shifted to omnichannel engagement strategy to reach out to their customers who now expect a similar experience in healthcare as well. They want flexibility and control to communicate with their providers on their own terms over all the channels via chat, web, email, text, and call.

  1. Digital Infrastructure is an absolute necessity

Covid-19 has taught us that there is an urgent need to build a strong Digital Infrastructure for a pandemic-like situation in the future.

Global Digital Health Funding

A study by CB Insights says, “Global digital investments in healthcare went record-high of $57.2 billion in 2021, a 79% jump from the $32 billion raised globally in 2020.” The number will keep going higher every year as there is a huge demand-supply gap in the healthcare industry. Providers would be better aligned with their patient’s demands if they invested in digital front-office transformation. This would also increase overall cost efficiencies.

Recently, ₹200 Cr has been allocated by the Indian government to set up an open platform for the National Digital Health Ecosystem (NDHE) which will include an exhaustive list of digital registries of health providers and health facilities, unique health identity, consent framework, and universal access to healthcare. This will create a much-needed interactive and transparent platform for healthcare providers and seekers to manage stacks of health data in the country.

  1. Insurance & Financing

When it comes to healthcare, people have been compelled to pay for their healthcare coverage out of their own wallets, especially in developing countries like India. According to research conducted by the Public Health Foundation of India, healthcare-related expenses push 4% of India’s population below the poverty line every year. This creates an urgent necessity for insurance and healthcare partnerships to go beyond working in silos and integrate with each other for creating a better patient journey.

What does a Future Health workforce want?

There has been a massive shift in not just consumers’ but providers’ mindsets too. The health workforce has been the fastest to adapt and evolve into this new digital healthcare setting. 

Coming out of this crisis, knowing what they want has become critical for healthcare organizations. 

Digital Health Provider Experience
Source: Mantra Labs Whitepaper
  1. Technology that benefits clinicians rather than the other way around

Collaboration solutions with real-time video and audio capabilities are rated as a significant sales conversation accelerator by 57% of healthcare agents. 

Accenture found that since COVID-19, 60% of patients want to use technology more for their healthcare. 

Given the fact that AI adoption rates surged by 51% in 2021, usage rates remain low. This shows that there’s a huge scope for the industry leaders to make conversational AI a better partner for healthcare providers.

  1. Regular training to upskill the workforce

Healthcare providers need to upgrade not just their technical skills but their soft skills as well to connect with the patient at a deeper level. With multitudes of data available to the doctor, what’s important for them is to train their clinicians and workforce to learn to process that data in a timely and meaningful way during the consultation. 

Conclusion

“The global healthcare interoperability solutions market is expected to grow from $ 2.9 billion in 2021 to $ 5.7 billion by 2026, growing at a CAGR of 13.9% during the forecast period 2021-2026”, according to marketsandmarkets.

Global Healthcare Market Trends
Source: marketsandmarkets

Factors like lack of unified patient data, soaring patient demand, and an overburdened legacy health system have resulted in disjointed care experiences. The interoperability between different healthcare systems will facilitate healthcare practitioners to see a complete panoramic picture of their patients. 

Health experts need to strike the right balance between digital and physical channels because the human touch will always take the center stage. 

Going forward, the health industry requires a framework that allows them to remain agile during the healthcare crisis and be tech ready to provide a connected patient experience.

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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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