Weighing the odds: Assessing underdiagnosis of adult obesity via electronic medical record problem list omissions

BACKGROUND: Obesity is a continuing national epidemic, and the condition can have a physical, psychological, as well as social impact on one’s well-being. Consequently, it is critical to diagnose and document obesity accurately in the patient’s electronic medical record (EMR), so that the information can be used and shared to improve clinical decision making and health communication and, in turn, the patient’s prognosis. It is therefore worthwhile identifying the various factors that play a role in documenting obesity diagnosis and the methods to improve current documentation practices. METHOD: We used a retrospective cross-sectional design to analyze outpatient EMRs of patients at an academic outpatient clinic. Obese patients were identified using the measured body mass index (BMI; ≥30 kg/m(2)) entry in the EMR, recorded at each visit, and checked for documentation of obesity in the EMR problem list. Patients were categorized into two groups (diagnosed or undiagnosed) based on a documented diagnosis (or omission) of obesity in the EMR problem list and compared. RESULTS: A total of 10,208 unique patient records of obese patients were included for analysis, of which 4119 (40%) did not have any documentation of obesity in their problem list. Chi-square analysis between the diagnosed and undiagnosed groups revealed significant associations between documentation of obesity in the EMR and patient characteristics. CONCLUSION: EMR designers and developers must consider employing automated decision support techniques to populate and update problem lists based on the existing recorded BMI in the EMR in order to prevent omissions occurring from manual entry.