Impact of Personalized Healthcare Messaging on Patient Outcomes and Medical Communication Effectiveness

Abstract

Involving patients, ensuring they follow their therapy, and enhancing their overall health all depend on effective communication in healthcare. Sending broad messages and notes seldom is one of the traditional medical communication techniques that always appeal to patients. Fewer of them follow their medication regimens, fewer of them react, and more of them have to return to the hospital as result. The research investigates how tailored healthcare message motivated by artificial intelligence affects It generates health messages unique to every patient by aggregating Natural Language Processing (NLP), Machine Learning (ML), and Electronic Health Records (EHR). Using a 500-person sample divided in two, the proposed approach was tested: 250 persons utilised regular means of communication while 250 others received tailored messages from artificial intelligence. The personalised message group had notably higher responses rates (82.5% vs. 55.3%), drug adherence (89.4% vs. 67.8%), and patient contentment (8.9 vs. 6.7 out of 10). Furthermore declining from 21.4% (standard) to 12.3% (personalised) and from 18.5% to 8.2% were hospital readmissions and missed appointments. These findings indicate that tailored healthcare messaging driven by artificial intelligence greatly increase patient engagement, enable adherence to their treatment plan, and save healthcare expenditures. The paper also covers moral concerns, data security difficulties, and system capacity for expansion. It underlines the importance of striking a balance in healthcare communication between artificial intelligence and human control. The findings reveal that messaging systems driven by artificial intelligence and natural language processing might transform the way modern healthcare is provided, therefore opening a more adaptable and patient-centered communication channel.