Original Research DIGITAL HEALTH Weighing the odds: Assessing underdiagnosis of adult obesity via electronic medical record problem list omissions Akshat Kapoor 1 , Juhee Kim 2 , Xiaoming Zeng 1 , Susie T Harris 1 and Andrew Anderson 3 Abstract 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. Keywords Obesity, body mass index, electronic medical record, medical records, weight management, clinical decision support Submission date: 2 January 2020; Acceptance date: 18 March 2020 Introduction Obesity continues to be a major epidemic in the USA, affecting individuals across diverse demographics and spectrums. 1 The continued growth of the obesity epi- demic is particularly concerning because obesity can have a physical, psychological, as well as social impact on one’s well-being. 2 Obesity is also known to be associated with several significant health issues, such as hypertension, 3 sleep apnea, 4 type II diabetes, 5 car- diovascular disease, 6 and even mortality. 7 1 Health Services and Information Management, East Carolina University, USA 2 Graduate School of Governance, Sungkyunkwan University, Republic of Korea 3 Network Systems & Support Services, East Carolina University, USA Corresponding author: Akshat Kapoor, Health Services and Information Management, East Carolina University, 600 Moye Blvd. (Mail Stop 668), Greenville, NC 27834, USA. Email: kapoora16@ecu.edu Digital Health Volume 6: 1–8 ! The Author(s) 2020 Article reuse guidelines: sagepub.com/journals- permissions DOI: 10.1177/2055207620918715 journals.sagepub.com/home/dhj Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/ open-access-at-sage).