A new study demonstrates that UNAFIED (Undiagnosed Atrial Fibrillation prediction using Electronic health Data), a highly accurate artificial intelligence (AI) prediction model which uses machine learning to parse information acquired from a patient's electronic health record (EHR) to predict whether a patient has or might develop detectable AFib within the following two years, can be easily integrated into the healthcare workflow.
In the study of clinical implementation, UNAFIED was integrated into the EHR system of a busy cardiology clinic, enabling the algorithm upon which UNAFIED is based to calculate the predicted risk for each patient individually.
If the risk factor was found to be above a certain threshold, the model provided visual indicators to the cardiologist that the patient might have an elevated risk of undetected AFib or of developing AFib within the next two years.
The workflow also provides recommendations such as performing follow-up heart rhythm and other testing as well as presenting ways to document within the EHR for higher risk or that a patient may actually be experiencing AFib, even if the condition had been previously ruled out.
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