Clin Nutr Res.  2019 Oct;8(4):255-264. 10.7762/cnr.2019.8.4.255.

Clinical Decision Supports in Electronic Health Records to Promote Childhood Obesity-Related Care: Results from a 2015 Survey of Healthcare Providers

• Harrison M
• Lundeen EA
• Belay B
• Goodman AB

• KMID: 2461093
• DOI: http://doi.org/10.7762/cnr.2019.8.4.255

Abstract

Obesity-related clinical decision support tools in electronic health records (EHRs) can improve pediatric care, but the degree of adoption of these tools is unknown. DocStyles 2015 survey data from US pediatric healthcare providers (n = 1,156) were analyzed. Multivariable logistic regression identified provider characteristics associated with three EHR functionalities: automatically calculating body mass index (BMI) percentile (AUTO), displaying BMI trajectory (DISPLAY), and flagging abnormal BMIs (FLAG). Most providers had EHRs (88%). Of those with EHRs, 90% reporting having AUTO, 62% DISPLAY, and 54% FLAG functionalities. Only provider age was associated with all three functionalities. Compared to providers aged > 54 years, providers < 40 years had greater odds for: AUTO (adjusted odds ratio [aOR], 3.0; 95% confidence interval [CI], 1.58-5.70), DISPLAY (aOR, 2.07; 95% CI, 1.38-3.12), and FLAG (aOR, 1.67; 95% CI, 1.14-2.44). Future investigations can elucidate causes of lower adoption of EHR functions that display growth trajectories and flag abnormal BMIs.

Keyword

Childhood obesity; Childhood overweight; Adolescent; Electronic health record; Decision supports

MeSH Terms

Adolescent
Body Mass Index
Decision Support Systems, Clinical*
Delivery of Health Care*
Electronic Health Records*
Health Personnel*
Humans
Logistic Models
Odds Ratio
Pediatric Obesity

Figure

• 

  Figure 1 Analytic sample flow chart (DocStyles, 2015). OB/GYN, obstetrician/gynecologist; EHR, electronic health record.

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