Background and aims: The metabolic syndrome (MetS) is a clustering of cardio-metabolic risk factors for cardiovascular disease. It is important to identify individuals with the MetS early and initiate interventions long before adverse conditions occur. Our previous studies found that missing or incomplete data that should be entered into the electronic health record (EHR) by nursing staff could lead to the under-diagnosis of the MetS. This study aimed to determine whether a five-component intervention to improve EHR data entry would increase the completeness of data, particularly height, weight, and blood pressure, needed to diagnose the MetS.
Design: Quasi-experimental design with pre-test, intervention, and post-test sessions.
Setting: Two family medicine residency training clinics serving mainly African-American patients in Atlanta, Georgia, United States.
Subjects and methods: Four nurses and four certified medical assistants attended pre-test, intervention, and post-test sessions. Data of 279 patients at pre-test and 246 patients at post-test were collected and analysed. The pre-test and post-test data completion rates of data entry were compared using rates and Wald chi(2)-test.
Main outcome measures: Rate of patients with information documented in the EHR on blood pressure, weight, and height at pre-test and post-test.
Results: There was a statistically significant increase in the recording of height from pre-test to post-test (46.6% versus 96.7%, P <0.001) and the recording of blood pressure from pre-test to post-test (96.8% versus 99.2%, P <0.05).
Conclusions: The intervention led to an improvement in the entry of pertinent EHR data among nurses and medical assistants in this primary care setting. This increase improved the ability to identify patients who met the criteria for the MetS.