Making HIM Auditing Faster with Computers and AI

Healthcare is always changing with new tech, rules, and better ways of doing things. Because of this, how we check quality in health records (HIM) needs to change too. Quality checks aren't just about following rules anymore. They are now important for getting better, working faster, and helping patients more. To learn about these new changes, we did research. We looked at studies about using AI and computers in quality checks, examples of places doing it well, and the rules for health records. This article talks about the important changes happening in quality checks for health records. It helps people in HIM know what's coming.

One big change in checking quality is using computers and AI more. AI can handle simple audit tasks like finding data, analyzing it, and creating reports. This frees up HIM professionals to do more complex and important work. This automation saves time and money and also makes fewer errors.

AI tools can analyze large amounts of information quickly and accurately. They can find patterns and problems that human checkers might miss. For example, AI can find mistakes in medical coding, spot possible fraud, and point out areas where rules aren't being followed. This lets HIM professionals fix issues early and reduce risks.

However, AI in healthcare auditing can be complicated. Things like different kinds of data, working with older systems, and needing to constantly watch the AI can affect how well it works.

Several organizations are successfully using AI in their quality checks. For example, Siemens uses AI to predict when equipment will fail to improve repairs. GE also uses AI for quality control in areas like hospitals. These examples show that AI can change HIM quality auditing, making it more efficient, accurate, and proactive.

Using AI in HIM auditing also creates challenges. Organizations must ensure AI is accurate and reliable, avoid bias in data analysis, and protect patient privacy. To reduce these risks, good quality checks and testing are important.

This includes carefully

1. Checking AI models before using them,
2. Monitoring their performance, and
3. Training HIM professionals to use them properly.

Also, it’s not always easy to move AI research into real healthcare use. We need strong scientific evaluations of AI models in healthcare to make sure they are safe, effective, and fit into how clinics work.

More to come about Emerging Trends in Quality Auditing in Health Information Management