AI in Healthcare Needs Good Data

According to the World Economic Forum, hospitals​​ generate 50 petabytes of data per year, yet 97% of it sits untouched. That’s a massive missed opportunity. Imagine the potential if all that data, from patient records to lab results and operational details, was put to work. The challenge? Making it AI-ready. Preparing healthcare data for AI can unlock smarter decision-making and more impactful results. It’s time to stop letting valuable insights sit idle.

What Types of Data Exist in Healthcare?

Healthcare runs on data. From patient records and lab results to inventory, scheduling, insurance claims, and billing, an endless flow of information is waiting to be organized. But first, let’s break it down into three types of data you’ll encounter.

Structured Data

Think of spreadsheets and databases. Neat. Organized. Easy to work with. AI loves structured data because it can quickly analyze rows, columns, and attributes to uncover patterns and produce insights. Examples include patient demographics, billing info, medication lists, and clinical outcomes.

Unstructured Data

Here’s where things get messy. Unstructured data doesn’t follow a set format. It hides in emails, notes, voice recordings, and even medical images like X-rays and CT scans. While it’s trickier to work with, this data is just as important. It holds critical context and details that structured data can’t capture.

Semi-Structured Data

The hybrid. Semi-structured data sits somewhere in between. Think of EHRs with structured metadata paired with freeform notes or medical images attached to charts and lists. It’s flexible, complex, and packed with potential.

AI thrives when it has a clear view of the data landscape. Understanding these data types can unlock new ways to optimize processes, improve patient care, and drive innovation.

Why Quality Data is Non-Negotiable in AI

AI tools are only as good as the data they work with. Quality data is essential. Without it, AI can’t deliver the results you count on. Think about it. If symptom lists aren’t aligned, AI can’t spot key patterns for a diagnosis. AI can’t sort invoices into revenue reports if they lack consistent variables.

The Four Pillars of Quality Data

The four pillars help define quality data when preparing data sets for AI.

Accuracy

Data must accurately reflect the real world. This includes symptom lists, care histories, inventory numbers, current schedules, and all other details. Without accurate data, AI cannot provide precise analysis.

Completeness

Complete data is key. To get the most out of AI, every data set must be treated equally and analyzed thoroughly. Blank entries? They only hold the system back.

Timeliness

Enter data on time and, better yet, in real-time. Whether during care, processing, or automation, staying current ensures the AI works with up-to-the-minute insights, not outdated information.

Standardization

Data uniformity allows the AI to draw accurate cross-comparisons and compile complete reports. Non-uniform data may result in outliers, omitted data, and inaccurate conclusions.

For AI to deliver, the correct data is non-negotiable. Inaccurate or incomplete data? That leads to bad decisions. Outdated data? It throws off timely reports, insights, and logistics. And unstandardized data stops AI from using the whole picture. Think of accuracy, completeness, timeliness, and standardization as the foundation for success.

The Risks of Bad Data in AI

Bad data comes in all shapes and sizes, whether inaccurate information or unnecessary details that limit AI’s ability to perform. And the fallout? Think biased decisions driven by flawed assumptions, misdiagnoses based on poor symptom tracking, or complete operational chaos thanks to delayed logistics updates or messy inventory records.

Garbage In, Garbage Out

In IT, there's a saying: "Garbage In, Garbage Out" (GI/GO). Simply put, poor-quality data leads to poor-quality results. The same goes for AI. If you want accurate analysis or smoother operations, you need precise, well-structured data to back it up.

Building a Solid Data Foundation for AI

Get your data in shape for AI integration. A strong data foundation is the key to unlocking the full potential of your healthcare AI tools. Start by building a straightforward, step-by-step process that helps your team clean and standardize data for accuracy and consistency.

1. Create High-Quality Data Sets

Make sure every data set is complete and uniform. This can mean completing incomplete data sets, translating non-uniform data sources into uniform data records, and removing outdated data that is no longer accurate or known to be precise.

2. Data Governance

Develop a comprehensive approach to creating uniform and high-quality data. This includes how data is recorded and stored, uniform variables and formats, and a data lifecycle to ensure the timely removal of old and potentially outdated data.

3. Ensure Interoperability

Make sure all data sets use the same format and variables. CDPs like Salesforce Data Cloud can help make structured and unstructured data more interoperable by parsing the details and arranging them in uniform data sets.

4. Stop Bias

Filter sentiment and bias from unstructured data. Before ingesting your data sets into the AI, remove assumptions, old data, and details that should not qualify as data. This will give the AI a clean and objective data set to draw evidence-based conclusions.

5. Monitor Data

Don't just prepare existing data. Ensure that all future data meets the same qualifications and that new data is entered as soon as possible. Then, continue to monitor data sets for quality.

The Role of Healthcare Tech Consultants

Cleaning an entire hospital's data? That’s no small task. The good news is you don’t have to tackle it alone or manually. Healthcare tech consultants are here to help. They specialize in building AI-ready data systems, setting up clean, standardized datasets, and ensuring you're always on the right side of regulations like HIPAA. Platforms like Salesforce Data Cloud make this process even smoother. They unify data from EHRs, marketing tools, ERPs, and other healthcare systems into an AI-friendly infrastructure, accelerating your organization's AI readiness.

The Bottom Line

AI provides powerful tools for modern healthcare organizations, from improving revenue management to identifying trends and potential diagnoses in patient health records. By addressing data issues and implementing strong governance, healthcare organizations can unlock AI's full potential. Of course, AI's potential in healthcare can only shine when paired with complete, accurate, and reliable data.

Are you ready to prepare your healthcare organization's data for AI? Our healthcare tech consultants are here to help. Schedule a consultation with Penrod to get your healthcare data ready for AI.