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

Building Consumer Trust in the Digital Healthcare Era

By :
6 minutes, 54 seconds read

Since the onset of the COVID-19 Pandemic, consumers have prioritized health above other needs. Healthcare organizations offering Digital Health solutions, need to find the right fit for both clinicians and patients. Consumer trust gaps between customers and adoption of digital health tools are fairly large due to privacy and security issues, poor first digital health experience, and lack of trust for tech companies. Following the launch of National Digital Health Mission, a LocalCircles survey found that around 60% of people want Digital Health Ids but are hesitant to share personal details beyond health and medical records. DigiLocker consent management framework will be followed to frame a proper consent management for people to ensure privacy of data.

How can Consumer Trust in Digital Healthcare be Built?

As the pandemic is strengthening the digital health space, now is a great time to strengthen the consumer trust in digital healthcare industry. Below are few ways that can be adopted:

1. Cross-industry Partnership:

With nowhere else to go customers have taken up the digital offerings to keep themselves safe. COVID-19 has served as a catalyst for people who are still hesitant to adopt digital health tools. This has led to cross-industry partnership among many companies to strengthen consumer trust in digital healthcare industry:

1. Medtronic collaborated with Manipal Hospital, Bangalore for ERAS deal, under the Value-Based Healthcare (VBHC) initiative in India. Enhanced Recovery After Surgery (ERAS) increases the recovery rate of patients, thus reducing in-hospital stay and decreasing complication rate.  

2. LTTS partnered with Agappe Diagnostic, an in-vitro diagnostics company and launched an automated blood cell counter that would lower the diagnostic cost. LTTS has also partnered with GITA and DST (Department of Science and Technology) to form robotic endo-training kits. 

2. Privacy & Data Security

Only 45% of healthcare consumers trust tech companies in sharing personal data, according to a recent Accenture survey. As the healthcare data privacy and security bill is yet to come into effect in India, protecting consumer data has become very  important.  Healthtech startups like Practo and Healthifyme have recognized the importance of consumer trust in digital healthcare industry to ensure confidentiality: 

  1. Practo is the only Indian healthtech company which is ISO 27000 certified. It has regular  internal and external audits. As the company serves foreign countries as well, Practo’s servers comply with the Health Insurance Portability and Accountability Act (hipaa). They also have 256 bit encryption code to encrypt data files. 
  2. Wildflower Healthcare used Data Theorem App Secure, a mobile application security solution that provides security coverage. Data theorem performs static and dynamic analysis of iOS and android apps to detect flaws, security vulnerabilities and insecure open source libraries. It helped Wildflower identify and close 73 security issues and remove 11 harmful third-party libraries.

As the mobile health app sector is growing rapidly, there are certain measures that can be taken to ensure privacy of data:

  1. Two-factor authentication– a two-factor authentication increases app’s security by asking the user for two-step authentication. For example, a combination of either username/password and SMS, or username/password and email verification code can be implemented for apps. 
  2. SSL technology- SSL technology can be used to encrypt the data being transmitted between the app and server. 
  3. Audit Trail for EMR- regular audit trail enables tracking of all attempts to access patient data. Certain factors to keep in mind while auditing are the type and date of data accessed, and source from where the data was accessed.

3. Building trust through medical professionals-

Healthcare providers need to be well acquainted with digital health tools before recommending them to their patients. According to an Accenture survey, only 11% of people indicated that their regular healthcare provider has recommended digital tools to manage their health. 

Physicians are adopting digital health tools to provide televisits or virtual visits, a trend that has progressively increased in recent years. The growing trend can be attributed to improvements in Point of care or workflow enhancements, Clinical decision support, Patient engagement, Remote monitoring, and management for improved care and Remote monitoring for efficiency. Before recommending digital health tools, doctors should consider whether they can provide healthcare remotely and if certain technology is seamlessly integrable to their everyday practise. In the case of mHealth such as apps that help patients with diabetes and control blood glucose levels, assist those with back pain in performing exercises, and one-on-one therapeutic sessions, physicians are starting to offer a combination of well-being oriented tools in tandem with their professional advice. To gain physicians’ confidence in such tools, applications should be developed together with or by the medical professionals and based on scientific evidence which at present constitute only a tiny percentage of all “health apps”. 

To ensure people received quality healthcare during lockdown, Practo joined hands with thousands of experienced doctors to provide free online telemedicine solutions. More than 2500 patients received remote healthcare through sharing of images, discussions through texts, voice and video calls. Such an initiative has been able to raise consumer trust in digital healthcare industry, especially in telemedicine .  

4. Demand for Value-added services –

Due to COVID-19, the growing customer demands has led to the fast growth of the healthtech industry. People have experienced noticeable change in their health as health apps have helped to keep track of health issues and permit doctors to help people self-manage their health. Monitoring of steps, calories intake, medication doses have made it easier for remote checkup. The pandemic has brought a great opportunity for healthtech companies to meet the rising demands that can lead to the strengthening of trust. Moreover, 57% of consumers are open to receiving remote virtual care at home. Wearable devices and mobile health apps have witnessed a surge in use as they have facilitated healthcare by providing at-home virtual care:

  1. Cure.fit- professional health trainers provide daily guidance in yoga and DIY exercise.The app enables customers to keep a daily check on their health by serving as a healthy food delivery app and by comparing health status with competitors.
  2. 1 mg- it is an online medical store and healthcare app. Through the app, customers can book appointments, avail discounts while purchasing medicine,receive health tips and search doctors nearby.
  3. GoQii-  it is a wearable device that has an OLED display and 360 motion sensor that measure activity and sleep quality. It is a fitness tracker that nudges customers to have a healthier lifestyle.   

5. Personalization of healthcare delivery –

As pandemic has changed the way customers access care, dynamic demands are gradually making the healthtech industry flexible to provide customized care any time. Flexibility confirms the growing consumer trust in digital healthcare industry as their ever-changing demands are being met constantly. 

Healthtech companies have brought innovations to provide door-step services as follows:

  1. Hinge Health- it is a patient-driven digital hospital that launched a digital clinic for back and joint pain. It provides one-one-one sessions with therapists, personalized health coaching and a sensor technology that acts as a real-time health tracker. 
  2. Hello Heart- it is a hypertension smartphone solution provider that gives personalized peer-reviewed explanations for the results, without having to talk to anyone over the phone. It helps people to monitor sugar levels, blood pressure using AI technology, and provides health tips in smartphones.    

Conclusion

When it comes to healthcare delivery, there are many digital processes operating beyond what the customer sees. A layer of transparency must be built into processes that directly contends with a user’s sensitive health data. Organizations that monitor & report on digital trust with real-time accountability will be the new normal for value creation in the digital era, where personal information has become an economic commodity by itself. As digital health consumers become more informed, managing data use and protecting privacy will be an important barometer of how healthcare enterprises are trusted by their wider stakeholders.

To know about how healthcare industry is bringing hospitals to a customer’s doorstep, watch our webinar on Digital Health Beyond COVID-19.

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
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