Stop Wasting Healthcare Data! Prepare It for AI Like a Pro

Welcome to the Data Management newsletter!

Is Your Healthcare Data AI-Ready? Here’s How to Prepare

Healthcare organizations rely on essential applications such as electronic health records, customer relationship management tools, and enterprise resource planning systems to maintain operations. These applications represent a significant investment and house vast amounts of valuable data. However, this often results in data being isolated within separate systems, limiting its potential for broader insights.

As healthcare organizations integrate artificial intelligence into their workflows—particularly to support overburdened clinical teams amid financial constraints—data quality and governance remain key priorities. Across industries, businesses are reassessing their data strategies in preparation for AI adoption. A 2024 Harvard Business Review Analytic Services study, sponsored by Amazon Web Services, found that nearly half (49%) of organizations are focused on improving data quality and cleansing, while 41% are enhancing governance policies and standards.

The healthcare sector follows this trend, with a strong emphasis on data strategy as AI becomes increasingly central. While an organization's size may influence its data management approach—impacting available personnel and financial resources—the primary factor is data maturity. Having established governance practices and analytics capabilities can be more critical than sheer scale. Larger organizations may have an advantage in some cases, but smaller entities with advanced data maturity can also excel.

There are several frameworks available to assess an organization's readiness for AI adoption. The updated HIMSS Analytics Maturity Assessment Model helps healthcare providers evaluate their preparedness, while Gartner provides broader benchmarking tools. Partnering with experts is often the best approach to assess and enhance current data strategies.

To maximize the value of existing data management and analytics capabilities, organizations should consider integrating their applications into a modern data platform. Cloud adoption and modernized data infrastructure will also be essential for optimizing data collection, storage, management, and movement.

Prioritizing the Human Element in Data Governance

Effective data governance is fundamental to treating data as a valuable asset. It establishes how data is managed, protected, and utilized, making it a core organizational function rather than an afterthought. Strong governance fosters collaboration between business and technical teams, clarifies responsibilities, and aligns with AI governance—ensuring standards address bias, transparency, and risk.

The human aspect of governance is just as critical as the technical side. Engaging stakeholders who depend on data-driven solutions is key. Organizations must consider workforce training and education to facilitate AI adoption, evaluate how AI can shift employees’ focus from routine tasks to more strategic work, and define clear processes for implementing AI in specific workflows.

Cultural change is also necessary. It’s natural for employees to feel hesitant about new technologies, so organizations must communicate AI expectations clearly, test specific use cases, and foster a culture that embraces change rather than fears it. Defining AI use cases with measurable ROI and tracking outcomes is essential for long-term success.

Ultimately, organizations must bridge the gap between technology and its real-world impact on business and clinical processes. Strengthening this connection will be crucial for driving effective data and analytics strategies in the future.

Thank you for reading

Data management team