Notes from the Round Table on
Deep Tech in Healthcare : Strategies for Scaling Adoption
Background
Nasscom along with the HealthTech community organized a HealthTech roundtable at the Nasscom Future Forge event at Bengaluru on 17th Oct 2024. The attendees included medical doctors as well as healthtech companies including startups, and other segments associated with healthcare. This document summarizes the discussion at the roundtable along with some key recommendations.
Pointer to roundtable on the Nasscom Future Forge agenda:
https://nasscomfutureforge2024.sched.com/event/1oQgC/deep-tech-in-healthcare-strategies-for-scaling-adoption-invite-only
Nasscom Future Forge event website:
https://nasscom.in/futureforge/
Participants:
- Moderators: Ravi Padaki, Bharat Gera
- Vijay Anand
- Dr. Uma Nambiar
- Dr. Prashant Sathe
- Jeyendran Venugopal
- Dr. Tony Raj
- Navratan Katariya
- Dr. Janardhan
- Dr. Sridhar Bodapati
- Dr. Alok Modi
- Dr. Ilin Kinimi
- Dr. Srinivasa Gunda
- Ashwin Amarapuram
- Dolcy Dhar
- Harshini Zaveri
- Dr. Suvrankar Datta
- Parag Agarwal
- Avinash Jois
- Shivani Modi
Here is a quick summary of the success stories and challenges shared by the participants
Success Stories
1. Digital Transformation in Hospitals:
- A neurosurgeon shared success in establishing India's first paperless hospital, demonstrating the shift to digital records with a focus on clinician training and adoption.
- Success in automating nursing records through digital transformation in a Hyderabad-based hospital was achieved by prioritizing administrative processes like rostering.
2. AI in Tertiary Care:
- A startup used AI to analyze MRIs quickly, providing insights for neurosurgery in a fraction of the usual time, improving decision-making efficiency.
- AI-assisted diagnostic tools for rural primary care have helped doctors remotely diagnose conditions like heart issues using simplified digital devices.
3. Innovations for Special Needs and Training:
- E-learning solutions for neurodivergent children, including those with motor disabilities, have been well-received, offering drag-and-drop activities for better engagement.
- Use of AI tools in creating educational scenarios and patient summaries has helped in training junior doctors and managing clinical records efficiently.
4. Improved Clinical Workflows:
- An AI-based platform for NICU patients to monitor brain oxygenation levels has been effectively implemented, providing critical insights for neonatal care.
- Adoption of speech-to-text tools in radiology reporting has streamlined documentation, reducing turnaround time for reports.
5. Integration with Hospital Workflows
- AI augmented Radiology software were integrated with the existing workflow of a tertiary care hospital by providing additional on premise services and smooth personalized integration with the existing RIS and PACS of the hospital.
- Security reasons often make existing solution providers skeptical of uploading their scans on cloud and providing hybrid solutions is the key to success.
Challenges
1. Adoption Barriers in Hospitals:
- Resistance from senior clinicians, especially high-profile ones, to shift from traditional methods to digital solutions like EMRs, remains a significant challenge.
- Many hospitals rely on outdated or legacy systems that lack integration capabilities, making it hard to adopt new tech solutions that require data sharing through APIs.
- Lack of early adopters in healthcare - Very few hospitals are interested in experimenting and evaluating benefits of new tech.
- Lack of awareness of new technological capabilities due to bandwidth issues limit appreciating the benefits and ROI of tech.
- Lack of consolidated strategy to understand consumer trends and evolving digital native business models.
2. Validation and Trust in AI:
- Clinicians emphasized the need for rigorous validation of AI tools, particularly for clinical decision support, as unvalidated tools can harm patient trust.
- The challenge of ensuring data privacy and security when using AI models remains a major obstacle, with both hospitals and tech companies wary of data leaks.
- There are no standard evaluation frameworks that will help hospitals to evaluate AI technologies that ensures alignment with org goals.
3. Scaling and Reaching Rural Areas:
- While urban hospitals are slowly adopting new technologies, rural and tier-2/3 hospitals struggle with the availability of advanced diagnostic tools and digital health solutions.
- The gap between tech developers and healthcare practitioners often leads to solutions that do not fully meet the needs of the practitioners, especially in less urbanized areas.
4. Commercialization and Price Sensitivity:
- Many innovative solutions face hurdles in commercialization due to the high price sensitivity of the Indian market, making it difficult to justify costs to smaller healthcare providers.
- The lengthy decision-making process in hospitals, including multiple layers of approval, slows down the adoption of new technologies.
- The tech firms also don't recognize the value of bringing doctors on board. They expect medical advice for free.
Recommendations
To address the challenges identified in adopting deep tech in healthcare, here are some recommendations:
For Government bodies [1]
1. Enhance Validation and Regulatory Support and Awareness:
- Create a Validation Framework for AI in Healthcare: Collaborate with regulatory bodies to develop standard guidelines for validating AI tools and digital health solutions. This can include setting up testbeds or sandboxes where new technologies can be tested in real-world environments.
- Establish Certification Programs: Develop certification programs for AI tools in healthcare, focusing on clinical effectiveness, data privacy, and integration standards. Certification can build trust among hospitals and clinicians, facilitating wider adoption.
- Support from Medical Associations: Encourage medical associations to endorse validated digital health tools and AI models, making it easier for practitioners to trust and adopt new solutions.
- Educational programs at School & College level to ensure our upcoming workforce is ready to be deployed in any section of HealthTech community.
2. Encourage Grassroots Innovations and Focus on Rural Health:
- Incubate Rural Healthcare Tech Solutions: Provide targeted support to startups and innovators working on healthcare solutions tailored for rural and semi-urban settings. This can include funding, mentorship, and access to field data.
- Deploy Mobile Health Units: Use technology to set up mobile health units that can offer diagnostics and telemedicine services in remote areas. These can be equipped with AI diagnostic tools and connected to urban centers for consultation.
- Community Health Workers Training: Train community health workers to use simple AI-based diagnostic tools, which can extend the reach of medical expertise to rural areas and help address the shortage of doctors.
3. Promote Adoption Through Incentives:
- Incentivize Early Adopters: Offer discounted or trial periods for new technologies in hospitals to encourage early adoption and allow them to see the value of the tools without an upfront financial burden.
- Funding for Research-Backed Startups: Create a fund dedicated to startups that have a strong research backing and potential to solve specific healthcare challenges, thereby encouraging more research-driven innovations.
Partnership between Hospitals and Tech vendors
1. Improve Data Sharing and Privacy Standards:
- Develop Open APIs and Data Interoperability Standards: Establish open APIs and standards for data interoperability across healthcare systems, making it easier for new tech solutions to integrate with existing hospital systems.
- Privacy-First AI Solutions: Innovate privacy-centric AI models like federated learning that keep patient data secure while still allowing for AI-driven insights. This can ease concerns about data security and enable the use of AI in clinical settings.
- Data Co-operatives: Create data cooperatives where hospitals and tech firms can share anonymized data for AI model training and improvement. This can help in building robust AI tools without compromising patient confidentiality.
2. Create Awareness Campaigns and Case Studies:
- Showcase Success Stories: Highlight success stories of hospitals or clinics that have effectively implemented digital solutions and AI tools. This can be done through webinars, conferences, and industry publications, helping others see the practical benefits.
- Patient Education Campaigns: Engage patients directly through awareness campaigns about the benefits of digital health solutions, helping them become more open to using tech-enabled services like telemedicine.
For Hospitals
1. Focus on Training and Adoption Support:
- Clinician Training Programs: Implement targeted training programs for doctors and nurses to help them become comfortable with new technologies. These can include hands-on workshops, peer mentoring, and digital literacy sessions.
- Champion Model for Tech Adoption: Encourage hospitals to identify key clinicians or “champions” who can lead the adoption of new tech tools. This has been effective when one or two respected figures embrace the change and influence others.
- Create an indexed and common problem statement registry to enable tech vendors and entrepreneurs to get a more effective and nuanced understanding of what to solve, and for who.
For Technology vendors
- Build human centric experiences
- Develop User-Friendly Interfaces: Tech developers should work closely with healthcare professionals to create user-friendly interfaces and tools that align with the workflow of doctors and nurses, minimising the learning curve.
- Adopt Responsible AI practices to inform the user they are interacting with AI and not a human!
These initiatives can help create a more integrated, efficient, and innovative healthcare ecosystem, ultimately leading to better patient care and more effective use of technology. By focusing on collaboration, training, validation, and addressing financial constraints, the community can overcome many of the existing challenges in adopting deep tech in healthcare.
Navratan Katariya from NASSCOM has requested members to share their top use cases where they can provide support, guidance, and execute on the clinical front. The aim is to co-create replicable lighthouse projects that leverage innovative solutions from deep-tech startups, ultimately delivering high-impact outcomes. NASSCOM plans to execute 2-3 such projects, over a period of 6 to 9 months. The goal is to create success stories worth celebrating and showcasing at the next Future Forge event.
The initiative is designed not only to drive impactful collaborations within the RT group but also to set a precedent for other stakeholders, including hospitals and government bodies, to replicate these models. By sharing the insights and experiences gained, the initiative hopes to inspire widespread adoption and foster a culture of innovation in the clinical domain.
