19 Aug AI Shouldn’t Automate Healthcare’s Data Problems

Top Takeaways

• AI can accelerate healthcare workflows, but it can also magnify longstanding problems with fragmented data, redundant processes, and inconsistent information. 
• Healthcare organizations should evaluate the data and workflows behind a process before using AI to automate it.
• AI-ready healthcare data must be complete, standardized, normalized, and usable across systems and applications.
• A trusted enterprise data foundation can support AI while also improving quality reporting, analytics, population health, and other healthcare priorities.
• AI implementation offers healthcare organizations an opportunity to simplify how data is collected and used rather than automating processes that no longer serve their intended purpose.

Healthcare organizations are moving quickly to introduce artificial intelligence into clinical and operational workflows. From ambient documentation and clinical decision support to quality reporting, analytics, and administrative automation, AI is creating opportunities to reduce burden and make better use of healthcare data.

But introducing AI into an existing workflow can also reveal just how complicated that workflow has become.

A recent Healthcare Innovation article explores this issue through the lens of nursing documentation. Over time, electronic flowsheets have accumulated hundreds of discrete data elements, duplicated assessments, and multiple ways of documenting similar information. As healthcare organizations begin introducing ambient AI into these environments, longstanding problems with the underlying data and workflows are becoming more visible.

The lesson extends well beyond nursing documentation. Healthcare organizations preparing for AI should understand the data and processes supporting a workflow before attempting to automate it. Otherwise, AI may simply help an inefficient process operate faster.

AI Can Expose Problems That Were Already There

Healthcare technology rarely starts with a clean slate. Systems and workflows evolve over many years as organizations respond to new regulations, reporting requirements, clinical needs, technologies, and organizational priorities. New data fields and processes are added, while older ones may remain in place long after their original purpose has changed.

The result can be considerable complexity. The article points out that structured documentation was intended to improve reporting, quality measurement, and analytics, yet many healthcare organizations still rely on manual chart abstraction for quality and regulatory programs. More structured data has not necessarily produced more actionable information.

AI brings those issues to the surface because it depends on the information and processes already in place.

When similar information is captured differently across systems, records contain duplicate or incomplete data, or workflows require extensive manual intervention, an AI application must navigate that complexity too.

Start With the Data Behind the Workflow

Before introducing AI into a healthcare workflow, organizations should examine the information that supports it.

That includes understanding where data originates, how consistently it is captured, whether terminology is standardized, how patient information is reconciled across systems, and how much manual preparation occurs before that information can actually be used.

These questions become especially important when AI applications rely on information from multiple clinical and administrative sources.

An organization may have access to EHR, claims, laboratory, pharmacy, HIE, and other healthcare data, but those sources were not necessarily designed to work together. Differences in formats, coding, terminology, and patient identity can limit how confidently information can be used.

AI-ready data requires more than access. Healthcare organizations need information that has been aggregated, normalized, validated, and made usable across the enterprise.

Don’t Automate Work That No Longer Adds Value

AI implementation also creates an opportunity to reconsider the workflow itself.

The Healthcare Innovation article makes this point directly in discussing nursing documentation. Before optimizing a workflow, organizations should determine whether it still serves its intended purpose. AI implementation teams often uncover duplicate documentation, redundant questions, outdated fields, and similar information being collected differently by multiple departments.

That same discipline can be applied across healthcare. Rather than asking only where AI can save time, organizations can examine why a process requires so much effort in the first place. Some workflows may be candidates for automation, while others may need to be simplified, redesigned, or eliminated.

Taking that approach can help organizations avoid investing in AI simply to preserve processes created for an earlier era of healthcare technology.

One Trusted Data Foundation Can Support Multiple Priorities

The underlying data work required for AI can also create value well beyond a single application.

Healthcare organizations increasingly need the same clinical and claims information for quality reporting, population health, value-based care, payer and provider collaboration, analytics, digital quality measurement, and AI.

Building separate data pipelines for every new initiative adds cost and complexity while requiring organizations to repeatedly address many of the same data quality problems.

A trusted enterprise data foundation offers a different approach. Once healthcare information has been aggregated, normalized, validated, and reconciled, it can support multiple downstream applications and workflows.

That makes AI readiness part of a much broader data strategy rather than another standalone technology project.

Use AI as an Opportunity to Improve the Foundation

Healthcare AI will continue advancing quickly, and organizations have good reason to explore where it can improve care, reduce administrative burden, and increase efficiency.

The introduction of AI also provides an opportunity to examine the systems, workflows, and healthcare data that have accumulated over decades.

Organizations that take the time to simplify unnecessary processes and improve the quality and usability of their data will create a stronger foundation for AI and many other healthcare priorities. The goal should be to make healthcare work better, not simply make existing processes run faster.

Start by Assessing Your AI-Ready Data

Before healthcare organizations expand AI across clinical and operational workflows, they need a clear understanding of whether the data supporting those applications is ready.

The IMAT Health Data Quality Assessment provides a measurable baseline of an organization’s data health and identifies gaps that could limit AI, analytics, quality reporting, and other initiatives. Powered by the IMAT Intelligence platform, the assessment evaluates five areas: data integration, normalization, completeness, accuracy, and AI readiness.

Organizations receive a documented baseline of their current data health, key findings and risk areas, targeted opportunities for improvement, and practical recommendations for achieving AI-ready data. The result is a roadmap for addressing data issues before they become larger problems downstream.

Contact IMAT Solutions to request a Health Data Quality Assessment and learn whether your data foundation is ready to support AI, analytics, and smarter healthcare decision making.

 

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