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Virtual health: Delivering care through technology

8 minutes, 52 seconds read

Virtual Care, Telehealth, Telemedicine, etc. are terms used very synonymously. Indeed they are interrelated, however, Virtual Care is a broader term in which healthcare providers use digital tools to communicate and deliver care to their patients. Telehealth and Telemedicine are a part of Virtual Care where doctors deliver care to their patients, remotely via phone, video, or instant messaging. Virtual health includes care delivery beyond video consultation where hospitals provide services using technology such as wearables for remote monitoring, instruments for post-op care and second opinions, e-pharma services, and medical information, etc. 

The outbreak of the COVID-19 pandemic gave an impetus to Virtual Care, but even in the Pre-COVID time, the Healthcare sector was slowly gearing up for this next wave in care delivery. What COVID-19 did was, help patients get acclimatized to the digital health tools and services. 

What does Virtual Health help with?

The pandemic has brought the burning issues of the healthcare sector to center stage. Patient experience and access to healthcare services are key differentiators for people while choosing a healthcare provider. Let’s take a look at some of these issues addressed by technology in the healthcare sector-

The increasing number of patients

Apart from the pandemic, there’s already been a rise in the number of patients due to drastic changes in lifestyle and food habits, an increase in pollution levels, increase in new types of viruses, etc. This has caused undue stress on healthcare institutions and workers and has led to the deterioration of the quality of patient care. Virtual Health technology such as mobile healthcare apps, EHR (Electronic Health Record), video conferencing, etc. has helped reduce the pressure on hospitals.

Difficulty in traveling for old patients

The pace of life is increasing at a rapid rate. It is getting insanely difficult for the elder population to navigate through the traffic and commute long distances for a check-up. Many times, they have to depend on their family members to take them to hospitals. Moreover, they are at risk of exposure to viruses in hospitals and clinics. Now that they have had the experience of virtual consultations, they prefer care delivery at home rather than going to hospitals.

Chronic Diseases treatment

The number of people above the age of 45 face health issues. Some patients are suffering from chronic diseases regardless of age. Regular monitoring of their vitals is very important. Moreover people now prefer Virtual healthcare services which are easily accessible and save a lot of time, effort, and money. Now that people have found these services effective, they will opt for online consults rather than frequent in-person visits. 

Post-op Care

The duration of post-operative care is quite long and tedious. If given a choice, people will lean towards wearables which will help keep doctors posted on the status of the treatment. Many times, the cost of post-op care is more than the actual treatment and sometimes is not covered under insurance. Virtual care-delivery services will help reduce the financial burden of people going through these treatments.

Follow-ups/Second opinion  

Some health conditions need multiple follow-ups and second opinions to figure out the right approach to treat the issue. It is much easier for patients to do follow-up consults virtually rather than going through the tedious process of appointment booking, commuting, and waiting for their turn. It helps reduce the queue outside the doctor’s office as well. Some health issues need a second opinion, sometimes both by patients and doctors. Virtual Healthcare technologies make it possible for them to take second opinions from doctors all over the world. With electronic records and image sharing, doctors can diagnose the problem better.

What does Virtual Health include?

Virtual Health can be broadly divided into below applications-

mHealth Applications

Mobile health applications have widespread use. From symptom checkers to appointment booking, from fitness trackers to uploading medical records, from video conferencing features to chatbot integrations, mobile healthcare apps in India are on a rise mainly because of easy accessibility for the tech-savvy customers. According to a study by NCBI, among the 22 selected mobile healthcare apps in India, Practo, mfine, DocsApp, 1mg, Netmeds, Lybrate, MediBuddy, and Medlife were found to be the eight most popular ones with over a million downloads and on average four-plus user rating out of five. All the above apps are mainly being used for online consults. This just goes about showing that people prefer having homecare services instead of stepping out. 

E-Triage Tools

The rising number of patients with different stages of COVID symptoms was a task to deal with. E-triage software here enables hospitals to triage patients into different sections when there’s an overload of patients at a particular time. Now, in the case of home care, e-triage tools help patients to access the gravity of their health condition and notify the healthcare provider accordingly. Such tools help reduce A&E waiting time and improve NHS performance. Many companies are building healthcare software integrating the E-triage module within EHR, telemedicine, clinical decision making, billing, etc. In India, Persistent Systems’ cutting edge platform has a Nurse Triage system that enables nurses to see the queue of patients and triage via phone calls. Once the calls are done, a triage report is generated and sent to care providers. Many leading doctors feel that AI in image triage will see a boost in near future.

Remote Patient Monitoring 

There are multiple benefits such as reduced post-op expenditure, time wastage, less exposure to other diseases, etc. The global remote patient monitoring devices market is expected to expand at a CAGR of 7.1% during the forecast period (2019–2027) according to Coherent Market Insights. Some of the top players in this space are Biotronik, Boston Scientific Corporation, CAS Medical Systems, CONTEC MEDICAL, Dragerwerk, GE Healthcare, Guangdong Biolight Meditech, Medtronic, Mindray Medical, Nihon Kohden, Philips Healthcare, Spacelabs Healthcare, Abbott. Companies such as GE Healthcare and Philips Healthcare have done a great job with building remote patient monitoring systems within the hospital premises as well as homecare for COVID patients. The main goal was to reduce the exposure of healthcare workers to at-risk patients. 

Synchronous and Asynchronous Telehealth

Synchronous telehealth, in other words, Telemedicine is where there is a live conversation between the patient and the doctor. Asynchronous telehealth involves the exchange of recorded data e.g. images, video, medical reports, pathology reports between patients and doctors, at times between doctors as well. Similar to mHealth space, companies like Practo, 1mg, Lybrate, Medlife, and Portea Medical in India are some of the top players in telehealth and telemedicine. Lybrate’s USP lies in CMS (Clinical Management System) which helps doctors with tedious tasks of managing patients and providing better care. Meanwhile, Portea Medical’s home consults and pharma delivery have more relevance with the audience as it combines technology with a touch of personalization. 

Digital Therapeutics

Digital Therapeutics delivers evidence-based therapies with the help of software which can be used both as a preventive measure as well as treatment application. The effectiveness of the medication and lifestyle changes on patients are monitored by leveraging technology. In India, major non-communicable diseases that account for 62% of the total mortality rate are CVD, diabetes, respiratory conditions, and cancer. Prominent global players in this space include Noom (US), Livongo Health (US), Omada Health (US), WellDoc (US), Pear Therapeutics (US), Proteus Digital Health (US), Propeller Health (US), Akili Interactive Labs (US), Better Therapeutics (US), etc. Omada Health is the pioneer in the DTx (Digital Therapeutics) that focused primarily on diabetes and pre-diabetes but now is branching out in the mental health space as well. In India, Altran (a part of Capgemini) is into building personalized DTx applications for clients. Whereas a start-up called Wellthy Therapeutics has ready solutions catering to multiple diseases.

Future of Virtual Health

Undoubtedly, there has been a massive increase in the adoption of Virtual Health technologies as people have gotten accustomed to the ease of certain services at home. In the coming future, mobile health apps, remote patient monitoring, and Digital therapeutics see a surge in demand from the customers. According to a study by Markets and Markets, “The global digital therapeutics market is projected to reach USD 6.9 billion by 2025 from USD 2.1 billion in 2020, at a CAGR of 26.7% during the forecast period (2020–2025).” A study by Fortune Business Insights, “The global mHealth market size is projected to reach USD 293.29 billion by 2026, exhibiting a CAGR of 29.1% during the forecast period.” A Research and Markets report says, “The remote patient monitoring market is expected to reach US$31.326 billion by the end of 2023.” Apart from the above, development in digital infrastructure such as virtual health stations where doctors can provide consultations globally, mobile ICUs, MRIs, X-rays, ultrasound equipment, the establishment of rural virtual care units reaching the remote areas of the country are some of the trends which will gain momentum. The focus would always lie upon the personalization of the virtual care experience for patients driven by data exchange and interoperability. 

Indeed, there are certain challenges to the implementation of these technologies, lack of infrastructure, and digital literacy amongst elders and lower strata of society. Many healthcare institutions still have inhibitions while investing in digital technologies fearing rejection from the customers. It will be crucial for care providers to choose the right partner for implementing these technologies and create awareness amongst people to adopt them.  

In a Nutshell

The success of virtual care relies on how well the digital experience is designed for the patient. “By 2025, as many as 95 percent of all customer interactions will be through channels supported by artificial intelligence (AI) technology” – Microsoft. The use of algorithms and AI for personalizing these experiences will be the key. 

Find out more about unchartered territories in ‘Blue Ocean’ of Digital Health. Watch our webinar hosted by Parag Sharma (CEO, Mantra Labs) where he shares his insights on untapped opportunities using digital self-care tools within behavioral healthcare & emotional wellness.

Further Readings:

  1. Reimagining Medical Diagnosis with Chatbots
  2. HealthTech 101: How are Healthcare Technologies Reinventing Patient Care
  3. What will be the state of the healthcare industry post pandemic?
  4. Healthcare Chatbots: Innovative, Efficient, and Low-cost Care
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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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