Weighing the Odds: Assessing Underdiagnosis of Adult Obesity via Electronic Medical Record Problem List Omissions
Open accessAbstract
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.
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Documentation errors, especially data omissions, can influence diagnostic decisions and lead to inappropriate treatments.
“Documentation errors, especially data omissions, can influence diagnostic decisions and lead to inappropriate treatments [174,175].”
From Toward Safer Diagnoses: A SEIPS-Based Narrative Review of Diagnostic Errors (2026) · cites this paper as [174]
The Introduction (page unavailable) says accurate obesity documentation supports clinical decisions about screening, prescribing, and targeted therapies; it also reports that patients whose obesity is documented are more likely to receive treatment and management. The paper examines obesity-related problem-list omissions, but it does not show that documentation errors lead to inappropriate treatments, as opposed to missed or delayed obesity care. (LLM, conf=0.86)
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- Authors
- Kapoor, A., Kim, J., Zeng, X., Harris, S. T., & Anderson, A.
- Affiliations
- East Carolina University (US)
- Sungkyunkwan University (KR)
- Corresponding author
- Kapoor, Akshat
- Venue
- DIGITAL HEALTH
- Publisher
- SAGE Publications
- Published
- 2020-01
- Volume
- 6
- Pages
- 2055207620918715-2055207620918715
- ISSN
- 2055-2076 (ISSN-L)
- PMC
- PMC7153175
- OpenAlex
- W3016260035
- Cited by
- 33
- References
- 32
- Citations by year
- 2026: 12 · 2025: 11 · 2024: 3 · 2023: 2 · 2022: 2 · 2021: 3
- FWCI
- 1.54
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Digital Health (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
- Health Sciences › Nursing › Issues, ethics and legal aspects › Nursing Diagnosis and Documentation
- Keywords
- adult obesity, underdiagnosis, electronic health records, problem list, clinical documentation, automated clinical decision support, body mass index, patient characteristics
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