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Jul 8
Sand Technologies
Strengthening healthcare systems has always been a key priority for leaders worldwide. Today, AI is increasingly reshaping healthcare by improving patient outcomes, streamlining operations and transforming how care is delivered. But its impact depends on one essential factor: data. Without clean, accessible, and high-quality data, the full potential of AI in healthcare will remain out of reach.
The healthcare sector is one of the most data-rich industries in the world. Nearly 30% of all global data, including electronic health records, lab results, patient monitoring and insurance, is generated by this sector. The data collected by this industry is critical not only for individual patient care but also for managing public health, streamlining operations and empowering healthcare professionals to make data-informed strategic decisions.
But having data isn’t the same as using it well. Many healthcare facilities globally struggle to effectively utilize the large volumes of data they generate and collect. Some of the key challenges they face include dealing with fragmented systems, inconsistent, missing or inaccurate data formats and outdated infrastructure. The lack of real-time access to data, data security concerns and complex regulatory requirements add further pressure.
At its core, healthcare is about decisions: what to diagnose, how to treat and when to intervene. Data powers those decisions, helping clinicians and countries identify patterns, manage risk, track outcomes and drive operational efficiency. Without the right data, care is compromised and development is stifled as health systems and populations are weakened.
Healthcare providers worldwide are increasingly turning to AI to enhance care, achieve better patient outcomes and streamline services. In the U.S., more than half of healthcare organizations have either implemented generative AI or are in the early stages of deploying it. So, how are healthcare professionals utilizing this technology?
One of AI’s most valuable roles in healthcare is improving decision-making. Tools like the Rural Health Operating System (RHOS) connect clinical operations with AI-driven insights to strengthen service delivery, optimize resources and guide faster, more informed responses.
Similarly, nurses are using AI to streamline data workflows and access timely insights in familiar formats. By combining secure data platforms with local capacity building and expert support, these systems help shape effective policies and interventions.
AI is also playing a key role in process and infrastructure planning. Digital twins of hospitals and patient pathways are being used to simulate different scenarios and support better decision-making. Additionally, predictive maintenance tools help monitor biomedical equipment to prevent failures and minimize service disruptions.
On the frontline, AI is helping healthcare professionals do more with less. In certain settings, AI is helping healthcare workers accelerate decisions on patient care and resource allocation. In the UK, for instance, AI has been used to identify patients in need of urgent hospital transfers, enabling quicker response times and more efficient use of beds, ambulances and clinical staff.
While these examples demonstrate what AI can do, they all rely on one key factor: high-quality data. Whether it’s connecting clinical operations or predicting urgent care needs, each use case relies on data that is abundant, accurate, timely and well-organized. Without that foundation, AI systems can’t deliver reliable insights and risk adding complexity instead of clarity.
Consider the example of an AI model designed to identify high-risk patients in a rural clinic. If the data is incomplete or scattered, the model may miss key signs or raise false alarms. However, with clean, connected data, it becomes a real-time support tool that helps health workers act quickly and with greater confidence.
Ultimately, data is the operational layer that connects clinical intent with intelligent action. Its value doesn’t come from volume alone, but from ensuring the data works when and where it’s needed so that AI can work well too.
The starting point for strengthening healthcare data isn’t the data itself; it’s gaining clarity on strategic priorities. Whether the goal is to improve patient outcomes, optimize resource use or enhance system-wide decision-making, healthcare organizations need to define the problems they’re trying to solve before turning to the data. Doing so ensures that data efforts are tied to real operational needs and answer real business questions.
As healthcare providers set priorities, it’s essential to avoid taking on everything at once. Focusing on high-impact use cases, such as patient flow, ICU capacity or staff allocation, can deliver early wins that build confidence, secure buy-in and create momentum for broader transformation.
Once priorities are clear, the next step is to audit existing data, assess its quality, structure, accessibility and evaluate how well it supports those specific goals. This helps identify what’s usable, what’s missing and where to invest.
In many healthcare systems, valuable data is scattered across disconnected platforms like EHRs, imaging systems, lab databases and more. These silos limit the value of AI. To unlock insights, healthcare organizations need infrastructure that brings these pieces together.
A critical step in this process is building a health intelligence layer: a centralized system of insights that connects and organizes data from clinical, operational, and IT systems. This layer becomes the foundation for all downstream AI use cases, helping organizations to move from reactive problem-solving to proactive, system-wide improvement.
Technology alone isn’t enough. To maximize the benefits of data and AI, healthcare organizations must invest in their people by developing their skills, fostering a shared understanding and promoting new ways of working. This includes training clinical and operational staff to interpret and act on data-driven insights, as well as equipping analysts and IT teams with the necessary tools and frameworks to support AI systems.
Capacity building also means fostering a data-informed culture where teams at all levels are empowered to make informed decisions using data in their day-to-day activities. As data becomes a more integral part of healthcare delivery, organizations that invest in workforce readiness will be better positioned to adopt and scale AI effectively.
Real impact from AI in healthcare doesn’t start with algorithms; it starts with strategic objectives supported by the right data. Without strong, structured and accessible data aligned with health system priorities, even the most advanced tools fall short of their potential. However, when that foundation is in place, AI can help healthcare systems make faster decisions, utilize resources more effectively and improve outcomes at scale.
Achieving this requires a shift in how data is managed, shared and used across the healthcare system. It also calls for collaboration with partners who understand both the complexity of healthcare and how to design AI that fits into it. With the right approach, healthcare organizations can turn data into one of their most valuable strategic assets.
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