This study discussed the impressions of clinician and patient participants on the implementation of an AI trained to recognize early heart failure exacerbation. The AI sensor used had shown in a previous study to accurately predict heart failure rehospitalization up to one week before actual rehospitalization. The 27 patient participants of this study were enrolled at the time of discharge from a heart failure hospitalization and wore the noninvasive sensor continuously for nine months. When early signs of heart failure decompensation were detected for patients in the experimental group, a notification was sent to one of the 13 participating clinicians. The clinicians’ response protocol aimed to alter the patient’s treatment regimen, with the hopes of preventing subsequent rehospitalization. Following the 90-day implementation, patients and clinicians were interviewed for feedback on the technology and response protocol’s functionality, efficacy, and clarity. Clinicians generally agreed with the technology’s practicality and the importance of noninvasive early detection of heart failure exacerbation. However, clinicians also had various critiques, especially regarding the lack of patient data and “cause” accompanied with the notification. This generally led to increased time spent looking up patient history data, especially when the clinicians did not personally know the patient, and trying to identify a cause manually through questioning the patient about symptoms. If no physical signs such as changes in vitals or weight accompanied the notification, clinicians tended to choose to “watch and wait,” negating the purpose of detecting exacerbation before these changes occur. Potential improvements included adding important patient information into the notification and improving the AI’s “explainability” so clinicians will be more likely to proceed with treatment alterations.