Computerized Decision Support for the Cardiovascular Clinician

Case presentation: A 76-year-old woman with coronary artery disease, left ventricular systolic dysfunction (ejection fraction 30%), obesity, and a history of deep vein thrombosis presents with dyspnea and hypoxemia. The combination of physical examination findings of an S3 heart sound, rales in the lower half of both lung fields, and peripheral edema; chest radiograph evidence of cardiomegaly and pulmonary edema; and a pro-brain natriuretic peptide level of 2150 pg/mL (normal <350 pg/mL) confirms the diagnosis of decompensated heart failure. She is admitted to the cardiology service for diuretic therapy and optimization of her heart failure regimen. Although bed rest is included in her written admission orders, the orders do not include venous thromboembolism (VTE) prophylaxis. While entering the orders into the online medical record and provider order-entry program, the medical house officer caring for the patient receives an electronic alert identifying the patient as high-risk for VTE and recommending that she be prescribed prophylaxis.Computerized decision support systems are finding an increasing number of applications in both the hospital and ambulatory care settings owing to continuing advances in medical informatics technology. A strong foundation in evidence-based medicine and well-established clinical guidelines make the practice of cardiovascular medicine ideally suited to capitalize on the benefits of computerized decision support systems. Computerized decision support strategies have already been implemented successfully in several areas of cardiovascular care, including VTE prevention,1–3 pulmonary embolism risk stratification,4 dyslipidemia screening and treatment,5 and anticoagulation management.6Although most clinicians believe that the use of medical informatics technology, including computerized decision support, should lead to safer, more efficient, and higher-quality care, only a small proportion of US medical centers have adopted such systems.7 Commonly cited barriers to implementation of computerized decision support include the capital investment necessary to purchase medical informatics technology and the …