Documentation and Diagnosis of Overweight and Obesity in Electronic Health Records of Adult Primary Care Patients

Almost 69% of US adults are either overweight or obese (body mass index [BMI], calculated as weight in kilograms divided by height in meters squared, ≥25),¹ yet clinicians often fail to diagnose overweight and obesity or discuss weight management with their patients.^2-6 Many clinicians use electronic health records (EHRs), and adoption of EHRs has been increasing since the introduction of the Health Information Technology for Economic and Clinical Health (HITECH) Act in 2009.⁷ Electronic recording of vital signs—including height, weight, and BMI—is now one of the requirements for achieving “meaningful use” of EHRs,⁸ but few studies have examined rates of BMI documentation and diagnosis of overweight and obesity in EHR data. We conducted a retrospective study to examine these rates in the EHRs of adult primary care patients before the passing of the HITECH Act in 2009.

We evaluated patients at 25 primary care practices within a large academic care network in Boston, Massachusetts. We included adult patients (≥18 years) who had at least 2 visits with the same clinician between 2004 and 2008 and were not pregnant at the time of the visit. The study was approved by the Partners Human Research Committee.

Data were extracted from coded fields in the EHR. The primary outcome was documentation of at least 1 BMI in the appropriate coded EHR field at any time during the study period. Body mass index is calculated if patients have both height and weight; once a height has been entered, it is carried forward and used in subsequent calculations. Among patients with at least 1 BMI of at least 25 (overweight) or at least 30 (obese), we also examined whether they had a diagnosis of “overweight,” “obesity,” “weight gain,” or “weight management” on the EHR problem list at any time during the study period.

We computed frequencies of patient and clinician characteristics, documentation of BMI, and diagnosis of overweight and obesity. Multivariate logistic regression models with generalized estimating equations were used to examine associations of patient and clinician characteristics with documentation of BMI and diagnosis of overweight and obesity, accounting for clustering within health care providers and practices. All analyses were conducted using SAS statistical software (version 9.3).

A total of 219 356 patients were included in the analysis. The average age of patients at their first visit during the study period was 45.7 years, and the median number of visits per patient was 8. Among these patients, 65.9% had at least 1 BMI in the EHR (Table 1). Almost all of the missing BMI information was due to missing data on height; only 66.0% had at least 1 height in the EHR, whereas 90.6% had at least 1 weight in the EHR. Factors that were associated with documentation of BMI are shown in Table 1. Among patients with BMI in the EHR, 68.3% had at least 1 BMI of at least 25, and 34.4% had at least 1 BMI of at least 30. Of patients with a BMI of at least 25 or BMI of at least 30, 17.1% and 30.1%, respectively, had a diagnosis on their problem list. Factors that were associated with a diagnosis of overweight and obesity are shown in Table 2.

Approximately one-third of adult primary care patients in this population had no BMI in the EHR. Furthermore, very few overweight or obese patients had a diagnosis on the problem list. Our findings are consistent with those of previous studies showing that overweight and obesity are poorly documented and diagnosed by primary care clinicians,^2-6 but few prior studies have focused specifically on documentation and diagnosis of overweight and obesity in electronic health records. In a study by Rose et al,⁹ the rates of height, weight, and BMI documentation in the EHRs of primary care patients were very similar to those observed in our study, but they did not examine diagnosis of overweight and obesity. In another study, Bardia et al³ found that 19.9% of primary care patients with a BMI of at least 30 had obesity documented as a diagnosis in the EHR, which is lower than in our study. However, to be included in our study population, patients had to have seen the same primary care clinician at least twice during the 5-year study period.

Our study has several limitations. The analyses were cross-sectional, and we examined documentation of BMI and diagnosis of overweight and obesity only in coded EHR fields. In addition, we did not examine management of overweight and obesity because this is not typically documented in coded fields. However, in the study by Bardia et al,³ diagnosis of obesity was a strong predictor of formulation of an obesity plan.

In conclusion, many primary care patients lack documentation of BMI in the EHR, and most overweight and obese patients do not have a diagnosis on the problem list. Further research should focus on interventions to improve documentation of BMI and diagnosis and management of overweight and obesity in the primary care setting.

Corresponding Author: Heather J. Baer, SD, Division of General Internal Medicine and Primary Care, Brigham and Women’s Hospital, 1620 Tremont St, Boston, MA 02120 (hbaer@partners.org).

Published Online: July 8, 2013. doi:10.1001/jamainternmed.2013.7815.

Author Contributions: Dr Baer had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Baer, Karson, Bates.

Acquisition of data: Baer, Karson.

Analysis and interpretation of data: Baer, Karson, Soukup, Williams, Bates.

Drafting of the manuscript: Baer.

Critical revision of the manuscript for important intellectual content: Baer, Karson, Soukup, Williams, Bates.

Statistical analysis: Baer, Soukup.

Obtained funding: Baer.

Administrative, technical, and material support: Baer, Karson, Williams, Bates.

Study supervision: Baer, Bates.

Conflict of Interest Disclosures: Dr Bates is a coinventor on patent No. 6029138, held by Brigham and Women’s Hospital, on the use of decision support software for medical management, licensed to the Medicalis Corp. He holds a minority equity position in the privately held company Medicalis, which develops web-based decision support for radiology test ordering. He serves on the board for SEA Medical Systems, which makes intravenous pump technology. He serves as an advisor to Calgary Scientific, which makes technologies that enable mobility within EHRs. He is on the clinical advisory board for Zynx Inc, which develops evidence-based algorithms, and Patient Safety Systems, which provides a set of approaches to help hospitals improve safety. He is a consultant for EarlySense, which makes patient safety monitoring systems.

Funding/Support: Dr Baer was supported by a Mentored Research Scientist Career Development Award from the Agency for Healthcare Research and Quality (K01HS019789). Some funding for this work also was provided by the Eleanor and Miles Shore 50th Anniversary Fellowship for Scholars in Medicine from Harvard Medical School.

Role of the Sponsors: The funding organization had no role in the design and conduct of the study; in the collection, analysis, and interpretation of the data; or in the preparation, review, or approval of the manuscript.