Introducing HealthIT.com: Making Sense of Healthcare AI

In this episode

• Start with the work an AI tool is meant to improve.
• Include review, correction, workflow fit and implementation in the evaluation.
• Distinguish the existence of a CPT code from coverage and payment.

Explore the sources and practical guides

• Clinical AI Brief 001: AI and CPT codes
• Evaluating ambient clinical documentation
• Healthcare AI evaluation worksheet
• Editorial standards and relationships

Full transcript

HealthIT.com helps clinicians, practice leaders, and builders make sense of healthcare AI.

We bring together research and practical guidance to help you understand the tools available and the evidence behind them.

Our aim is to help you decide where these tools could make a meaningful difference for patients and providers.

When we consider a new AI tool, the most useful starting point is the work we want to improve. A clear description of the problem gives us a better way to judge whether a product deserves our attention.

For example, a practice might be exploring a tool that helps prepare clinical notes. The promise of saving time is appealing, but the evaluation needs to include the time spent checking and correcting the result. It should also consider whether the tool fits naturally into the visit and supports the conversation between the patient and the clinician.

The people doing that work should help define what success would look like. Their experience can reveal whether a promising demonstration translates into something useful during an ordinary working day.

Practice leaders also need a realistic picture of implementation. That includes the training people will need, the responsibilities for reviewing the output, and the ongoing cost of operating the service. The organization should decide in advance how it will recognize a useful result and when it would change course.

HealthIT.com brings together product research, practical evaluation guides, and Clinical AI Briefs to support that process. We want readers to be able to examine the sources behind a claim and understand where the evidence is limited. A product appearing in the resource does not mean that we endorse it.

Our first Clinical AI Brief looks at AI and CPT codes. It explains why the existence of a code is a different question from whether a service is covered or paid for. Those distinctions matter when someone is building a business case around a new technology.

The broader lesson is to connect evidence to the decision in front of us. A published result can be informative, but we still need to understand how closely it matches the setting where a tool will be used. We should be clear about what is known and what still needs to be evaluated.

For patients, the practical test is whether the change helps them understand their care and get the support they need. For providers, it is whether the technology helps them do their work well and strengthens the relationship with the people they care for.

You can explore the research and Clinical AI Briefs at HealthIT.com. We welcome feedback that helps us make the evidence clearer and the resource more useful.

Better evidence should support better decisions and better care.