AI Solutions Can Reduce Medical Error

AI Solutions Can Reduce Medical Error

Dealing with medical error is an ongoing challenge facing health systems. In addition to having potentially serious consequences for patients, there are also considerable resources expended to fix these mistakes and adjust care. AI provides solutions by equipping healthcare professionals with tools that improve accuracy and catch errors early. This week’s Domain Knowledge highlights various ways AI can help reduce the risk of medical error.

There is a vast collection of data that contributes to any patient care decision. With all this data, there is bound to be human error either in recording or interpreting. AI is capable of recording and analyzing patient data in much more efficient ways to ensure nothing goes unnoticed that may impact a patient care decision.

Electronic health records (EHR) contain patient information used in providing care. However, that patient data is often contained in different EHR systems within the same hospital and these systems don’t always play nice, making data communication difficult. AI helps pull EHR data together to ensure nothing gets lost in communication between systems.

Peer review is the standard method for double-checking imaging results and ensuring an accurate clinical diagnosis. When done by radiologists, only 3 – 5% of all the diagnostic images receive a second review. In this case study on screening for lung nodules, AI runs as a safety net in the background, performs a second read on all images, and can flag anything that is undocumented. This is also one of the easiest ways to integrate AI, as the quality workflow doesn’t interrupt the daily work of the radiologist.

Overdiagnosis is a potentially costly form of medical error that is often overlooked. In the case of breast cancer, AI can be used to personalize care for patients to ensure they aren’t screened or treated with excessive amounts of radiation. Along the same lines, a Case Western Reserve University-led team of scientists has used artificial intelligence (AI) to identify which patients with certain head and neck cancers would benefit from reducing the intensity of treatments such as radiation therapy and chemotherapy.

That’s a wrap for this week’s review of news and happenings in the healthcare AI space. In closing, I’ll leave you with an invitation to learn more about the benefits of using an AI Hub to manage multiple applications across your clinical needs and offer you a personal demo of Ferrum’s platform and growing AI catalog.

If you have AI tips, suggestions, or resources you’d like to share leave us a note below, and please feel free to suggest topics you would like to see covered in future posts.

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Alex Uy

Alex is a recent UC San Diego graduate with a degree in economics and communications. His focus is digital marketing, and he has a passion for technology driven healthcare solutions.

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