©2015 MFMER | slide-1 The Effect of an Automated Point of Care Tool on Diagnosis and Management of Childhood Obesity in Primary Care Natalie Gentile, MD.

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Presentation transcript:

©2015 MFMER | slide-1 The Effect of an Automated Point of Care Tool on Diagnosis and Management of Childhood Obesity in Primary Care Natalie Gentile, MD PGY-2 Family Medicine STFM Annual Spring Conference 2016 May 3, 2016 Minneapolis, MN

©2015 MFMER | slide-2 Disclosures None

©2015 MFMER | slide-3 Background Greater than 1 in 3 children in the United States are considered obese or overweight Link to metabolic disturbances secondary to fat accumulation  type 2 diabetes, hypertension, and cardiovascular disease Childhood obesity and overweight is often underdiagnosed by primary care providers

©2015 MFMER | slide-4 GDMS Generic Disease Management System (GDMS) at Mayo Clinic, Rochester, MN Point of care tool that extracts information from the electronic medical record to alert the health care team and the patient of preventive services September 2010: GDMS started being used for Employee Community Health (ECH) pediatric patients Identified patient’s BMI percentile, reported whether or not it was in the healthy range (</=85th percentile)

©2015 MFMER | slide-5 Purpose Examine rates of weight-related diagnosis and management before and after implementation of the electronic point of care tool

©2015 MFMER | slide-6 Methods Retrospectively extracted data from electronic medical records Children ages 2 to 18 years BMI at or above 95 th percentile Four primary care sites in ECH IRP: Search terms for weight related diagnoses and management related to nutrition and physical activity counseling Years 2009, 2011 and 2013

©2015 MFMER | slide-7 Methods Good concordance between manual and electronic searches (50 charts per year) GEE logistic regression-association between patient characteristics and weight-related diagnosis and management

©2015 MFMER | slide-8 Weight Classification Among Years Body mass index percentile for age and gender 2009 (N=9,944)2011 (N=10,306)2013 (N=10,227) N (%) Obese (>/= 95th)938 (9.4)1029 (10.0)980 (9.6) Moderate (95th -98th)757 (7.6)822 (8.0)776 (7.6) Severe (>/= 99th)181 (1.8)207 (2.0)204 (2.0) OBESE PATIENTS ONLY Weight Diagnosis*427 (45.5)539 (52.3)494 (50.4) Counseling*627 (66.8)775 (75.2)763 (77.9) *Cochran-Armitage Trend Test : p=0.03 for weight diagnosis and <0.001 for counseling

©2015 MFMER | slide-9 Weight-related diagnosis and management plan over time for obese children Year Weight-related Diagnosis Weight-related Management plan OR (95% CI) 2009 (N=938)ref 2011 (N=1029)1.37 (1.17, 1.61)1.53 (1.27, 1.84) 2013 (N=980)1.24 (1.05, 1.48)1.80 (1.48, 2.19) Adjusted for age, sex, race, severity of obesity, Medicaid status and practice confounders (provider specialty and provider type)

©2015 MFMER | slide-10 Take home point The implementation of a point of care tool regarding BMI was associated with improvement in diagnosis and management of childhood obesity

©2015 MFMER | slide-11 Discussion In 2010, the USPSTF published recommendations for obesity in children and adolescents. Provider fatigue may have become an issue by 2013 due to the increase in GDMS prompts after its implementation. Providers may have become more aware of what to do but just didn’t document it Stigma of pediatric obesity Time constraints

©2015 MFMER | slide-12 Implications for Primary Care Increasing awareness, diagnosis, and management of obesity is essential to improve health outcomes of obese pediatric patients Proper identification of overweight and obesity in the pediatric and adolescent population is the first step before intervention, prevention and/or treatment can take place

©2015 MFMER | slide-13 Limitations Electronic medical records are not used in every outpatient practice Lack of control for documentation Location in chart Undocumented diagnosis and counseling despite taking place

©2015 MFMER | slide-14 Next Steps We are currently looking at selective lab screening for obesity-related comorbidities such as lipid panel, transaminases, and glucose.

©2015 MFMER | slide-15 Acknowledgements Seema Kumar, MD (mentor) Valeria Cristiani, MD Brian Lynch, MD Patrick Wilson (statistician) Amy Weaver (statistician) Lila Rutten, PhD Deb Jacobson, MS Swetha Sriram

©2015 MFMER | slide-16 Questions & Discussion