Integration of digital twins to simulate community health scenarios and evaluate the effectiveness of public health interventions led by pharmacists

Public health emergencies such as the 2019 coronavirus disease (COVID-19) have increased the focus on digital technology. A digital twin (DT) is a cybernetic representation of a physical object (e.g., a person or a community) that helps us comprehend the complexity of the real thing and makes it easier to foresee, avoid, track, and improve its actual performance. To prepare for a new age of reliable and precise public health and medical care, DT technology can propel a much-needed radical transformation of conventional electronic health and medical records, which centre on individuals and their aggregates covering populations. Hence, this paper presents the Digital Twin-based Public Health Intervention Model (DT-PHIM) to simulate community health led by pharmacists. The use of digital twins in healthcare has the potential to revolutionize workflow analysis and resource allocation, ultimately resulting in better patient care and more efficient operations. In addition, healthcare providers may hone their abilities and rehearse intricate treatments in a secure and realistic environment with the help of digital twins. There is great promise for digital twin technology to revolutionize healthcare by boosting innovation, patient safety, and overall patient outcomes. On top of that, DTs may help doctors and other medical staff, such as hospital pharmacists, make decisions by simulating how therapy would affect these patients. The experimental outcomes demonstrate that the suggested DT-PHIM model increases the patient data access ratio by 97.4%, the monitoring efficiency ratio by 96.2% and the decision-making ratio by 98.2% compared to other existing models.