AI in Healthcare: Balancing Innovation and Practicality for Health Systems

The rapid adoption of Artificial Intelligence (AI) is transforming industries globally, and healthcare is no exception. For health systems, navigating the AI landscape requires a strategic approach that balances innovation with practical implementation. Scott MacLean, Senior Vice President and CIO at MedStar Health, a major integrated delivery system in the US, offers a valuable perspective on this challenge, categorising health systems into "takers," "shakers," or "makers" in their AI journey. His insights are particularly relevant for Indian healthcare providers and technology startups looking to leverage AI effectively.

MacLean's philosophy, developed over eight years of building MedStar Health's IT environment, prioritises fewer tools used better. This disciplined approach extends to AI, where the focus is on strategic adoption rather than merely accumulating new technologies. For Indian health systems, where resource optimisation and impactful deployment are paramount, this perspective underscores the importance of a clear AI strategy.

Defining the AI Archetypes

MacLean's framework helps organisations understand their current position and future aspirations in AI adoption:

"Takers" are health systems that primarily consume off-the-shelf AI solutions. They integrate existing AI products into their workflows, benefiting from readily available innovations without significant in-house development. This approach can be efficient for quick wins and leveraging proven technologies.

"Shakers" are organisations that adapt and customise existing AI tools to better fit their unique needs. They might integrate multiple AI solutions, develop bespoke connectors, or modify open-source models. This group actively shapes how AI functions within their ecosystem.

"Makers" are at the forefront, developing novel AI algorithms and solutions in-house. These systems often have robust research and development capabilities and aim to create proprietary AI applications that address specific, unmet needs. This path requires significant investment in talent and infrastructure.

Key facts

Characteristic	Description
Source	Becker's Hospital Review
Key Figure	Scott MacLean, CIO, MedStar Health
Core Concept	AI 'taker, shaker or maker' balance
Application	Strategic AI adoption in healthcare

Relevance for Indian Healthcare

India's diverse healthcare landscape, ranging from large urban multi-specialty hospitals to rural clinics, presents unique opportunities and challenges for AI adoption. MacLean's "taker, shaker, or maker" framework provides a useful lens for Indian healthcare entities to define their AI strategy:

For many Indian hospitals and clinics, especially those in resource-constrained settings, being a "taker" of well-tested, cost-effective AI solutions could be the most pragmatic starting point. This could involve adopting AI-powered diagnostic tools, administrative assistants, or patient engagement platforms that are already proven in the market. Focus on solutions with clear ROI and minimal integration friction.

As organisations mature, they might evolve into "shakers," customising AI models for specific local needs, such as optimising patient flow in crowded public hospitals or tailoring diagnostic AI for prevalent regional diseases. This approach requires some in-house technical expertise or strong partnerships with Indian MedTech startups.

A select few, particularly advanced research institutions, large hospital chains, or AI startups collaborating with healthcare providers, might aim to be "makers." This involves developing new AI models for drug discovery, personalised medicine, or predictive analytics for public health challenges specific to India. The IndiaAI Mission and initiatives like the National Digital Health Mission can further support these efforts.

Challenges and Opportunities for Indian Teams

Indian teams in healthcare, MedTech startups, and AI development face several considerations when implementing AI:

Data availability and quality: India has a vast amount of healthcare data, but its digitisation, standardisation, and accessibility remain challenges. Robust data governance and privacy frameworks (e.g., under the Digital Personal Data Protection Act) are crucial.

Talent gap: While India has a strong IT talent pool, specialised AI talent in healthcare, particularly clinical informaticists, is still developing. Skilling initiatives and collaborations between medical colleges and technical institutions are vital.

Interoperability: Integrating AI solutions with existing legacy systems in hospitals can be complex. Standardised APIs and health information exchange protocols are essential for seamless adoption.

Ethical considerations: Ensuring fairness, transparency, and accountability in AI applications, especially in diagnostics and treatment recommendations, is paramount. Indian regulatory bodies will play a key role in setting guidelines.

The Path Forward

For Indian health systems, the key takeaway from MacLean's approach is the importance of a deliberate and focused AI strategy. Instead of chasing every new AI tool, organisations should:

Assess needs: Identify specific clinical, operational, or administrative pain points that AI can genuinely address.
Start small, scale fast: Pilot AI solutions in controlled environments, measure impact, and then scale successful implementations.
Build capabilities: Invest in data infrastructure, cybersecurity, and upskilling staff to manage and leverage AI effectively.
Collaborate: Partner with Indian AI startups, research institutions, and technology providers to co-create solutions relevant to the local context.

The AI "taker, shaker, or maker" framework provides a valuable blueprint for strategic AI adoption in healthcare. For India, this means carefully selecting the right approach based on organisational maturity, resource availability, and specific healthcare challenges, ultimately driving impactful and sustainable AI integration.

Source:beckershospitalreview.com – The AI ‘taker, shaker or maker’ balance for health systems (https://www.beckershospitalreview.com/healthcare-information-technology/ai/the-ai-taker-shaker-or-maker-balance-for-health-systems/)