Afib After Open Heart Surgery: A Case Study

Databases have become an essential part of contemporary society. Harvey, Rowan, Harrison, and Black (2010) stated that databases consists of collections of data in which each record is stored in a certain format, allowing for sorting and organization of such data to generate information and knowledge. Therefore, in order to capture information and identify patients at risk for developing atrial fibrillation (AFib) after open heart surgery a database plan will need to be developed. The aim of developing this database plan is for early detection and implementing preventive measures to decrease the prevalence of AFib following open heart surgery. This paper will discuss the information management problem, conceptual data model, planned data entities, justification or rationale for entities, entity-relationship (E-R) diagram, entity identifier, relationships between entities, and provide three query questions in the database.
Information Management Problem
The incidence of AFib following coronary artery bypass graft (CABG) is an information management problem that could be resolved with a database. According to Mostafa, EL-Haddad, Shenoy, and Tuliani (2012), in the
(2012), an upsurge in mortality and morbidity rate is usually seen and is related to the development of post CABG AFib. This is sufficient reason to screen preoperative CABG patients and start prophylactic treatment to prevent postoperative AFib. By using the screening tool, the patients can be medicated before and after the surgery to decrease the incidence of developing AFib. The nurse plays a pivotal role in assessing and documenting the patient information preoperatively in the electronic health records (EHRs). The screening tools, plus the intended open heart surgery are the main criteria that will alert the surgeon to the patient most at risk for developing AFib following heart surgery (Alqahtani, 2010). Thus, they need to start preventive treatments