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Emerging Healthcare Delivery Models

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5 minutes, 30 seconds read

Wearables, IoT devices, healthcare apps have significantly increased digital health access points for customers. The evolving nature of the digital health industry in India can be witnessed through the improved personalization and value-added services delivered to the consumers. To further multiply the digital health touchpoints, new healthcare delivery models are coming up to cater to specific healthcare needs and deliver a satisfying experience. Below are a few emerging healthcare delivery models: 

1. On-Demand Healthcare

The healthcare industry is beginning to welcome on-demand services for consumers who are already accustomed to receiving services anytime and anywhere from demand-driven brands such as Zomato, Uber, etc. Among the various digital healthcare services, e-pharmacies in India such as Medlife, Pharmeasy, have made a significant profit by making medicines and drugs available to customers in no time.

Quick booking of appointments, receiving digital medical reports, settling medical fees online, scheduling doctor visits, all of this can be done through smartphones. On-demand healthcare is gathering attention among consumers, especially millennials, needing mental health counseling, follow-up appointments, and quick recovery from wounds and minor illnesses. Due to the provision of healthcare anytime and anywhere, reduced expenses due to lessened hospital visits, deep interaction with patients, the on-demand healthcare model is growing. Express Care offered by Cleveland Clinic allows patients to consult virtual doctors in matters of non-life-threatening conditions like allergies, rashes, or back pain.   

This model also allows medical practitioners to work flexibly, depending on their schedule. It also lets the doctors appoint and cancel the dates of visits depending on their availability. For instance, besides being a clinical app for patients, Doctor on Demand has features such as email, payment system, messaging, etc. that help doctors to manage their patients.

2. Social Health Networks or Peer-based health networks

This model allows people and medical professionals to share views and hold discussions on health-related issues. This model also allows the doctors to address people’s health issues without any geographical barrier, motivates people to share their health experiences, and facilitates health literacy. As people belonging to different age groups are well versed and active on social media, social health network serves as an effective solution to encourage people to look after their health and provide emotional support to others. Any platform based on this model can also push people to create awareness through health-related campaigns. 

This model also facilitates maximizing health-related conversations and allows the exchanging of information among patients, medical professionals, and doctors- one-to-many and many-to-many conversations between patients-patients, doctors-patients, doctors-doctors. Even though this model is still at its developing stage, it is sure to gain momentum because people are starting to be upfront about their health problems, especially mental health issues. Organizations following this model can also leverage health data and provide effective health solutions. HealthUnlocked is a social networking service that empowers patients and promotes relevant health-related content. It focuses on building online support groups where patients can give health recommendations and insights to other patients. Medikoe launched We, a social network that publishes health-related content from qualified doctors and allows patients to connect with doctors. It is like any other social media where people can follow health professionals, and search, share, like health-related articles. It also encourages people to take up health challenges and updates people on upcoming health campaigns. 

3. Proactive Intervention

Chronic diseases account for more than 55% of total deaths in India. Increasing awareness and the use of healthcare technologies are prompting people to opt for preventive healthcare services to manage unexpected health issues. Proactive care lessens the chances of health deterioration through active dialogues between doctors and patients. Telemedicine is one such way through which health outcomes can be improved. As it facilitates easier transmission of patient data and increased access to HCPs, it holds the potential to reduce the mortality rate in India. This instant healthcare service provider model also allows remote monitoring of patients as health records can be transmitted in no time. 

As the model is compatible with wearable sensors, medical information such as blood pressure, heart rate, etc. can be monitored easily which helps in the early detection of diseases such as asthma, hypertension, heart-related diseases. Fitness trackers that are compatible with mobile applications help people in monitoring their health data as well. Health Care Originals developed ADAMM, an intelligent asthma monitoring wearable system that is attached to the upper body. It detects symptoms of asthma attacks- body temperature, heartbeat, cough rate, etc. Rubi Life is a MedTech company that uses nanotechnology in an elastic maternity band to monitor fetal activity in high-risk pregnancies. It sends alerts to the mother’s phone in order to prevent premature births, stillbirths, and to also avoid negative outcomes.  

4. Personalised Medicine & genomics

This model aims to improve the effectiveness of medicine by leveraging the patient’s health history, genetic characteristics, and lifestyle. Personalized medicine, also known as precision medicine improves health outcomes without any side effects. The unique genetic composition of patients helps in predicting disease and curing it before it starts affecting the body. This model disrupts the one-size-fits-all model as it takes into consideration the genomic composition and forms a pattern by observing a body’s reaction to drug dosages, thus promising accuracy. 

This model also reduces the trial and error inefficiencies and proves to be beneficial for medical professionals as it can reduce the failure and cost of pharmaceutical trials.  

K&H Personalised Medicine Clinic is the only healthcare facility in Hyderabad, India that provides personalized healthcare based on genomics, DNA analysis, and a patient’s medical history. As the genes are affected by diet, exercise, stress levels, and environmental factors, the K&H clinic takes into consideration all these factors to form a proper treatment strategy.

Conclusion

The emerging healthcare delivery models are aiming for cost-effective solutions that can save time and instantly cater to patient needs digitally. The digital healthcare market is growing at a compound annual growth of 27.41% during 2019-2024, and according to a recent report by McKinsey Global Institute, telemedicine services in India have the potential to replace in-person consultations by 30-40%. This shows that people are appreciating contactless solutions and are used to immediate gratification. While some of the healthcare delivery models are still at their nascent stage in India, some are experiencing good growth. As digital health consumers are starting to demand more, the emerging healthcare models have to make sure that they cater to their diverse needs instantly and efficiently.  Moreover, as healthcare facilities are unevenly distributed, the upcoming healthcare delivery systems should also make sure to maximize their touchpoints in order to reach every corner of the country.

Know about our work in Digital Health and how we have helped clients such as Suraksha Diagnostics, Abbvie, Religare Health Insurance, and SBI Health Insurance build mobile and web applications improving their operational efficiency and customer experience.

Further Readings:

  1. Building Consumer Trust in the Digital Healthcare Era
  2. HealthTech 101: How are Healthcare Technologies Reinventing Patient Care
  3. Virtual health: Delivering care through technology
  4. How Mobile Micro-Health Insurance can unlock ‘Digital for Bharat’?

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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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