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©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 on theme: "©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."— Presentation transcript:

1 ©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 MAFP Spring Refresher April 14 and 15, 2016

2 ©2015 MFMER | slide-2 Disclosures None

3 ©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

4 ©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)

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

6 ©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 Search terms for weight related diagnoses and management related to nutrition and physical activity counseling Years 2009, 2011 and 2013 GEE logistic regression

7 ©2015 MFMER | slide-7 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

8 ©2015 MFMER | slide-8 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)

9 ©2015 MFMER | slide-9 Discussion The implementation of a point of care tool regarding BMI was associated with improvement in diagnosis and management of childhood obesity Diagnosis with obesity was associated with a significantly higher likelihood of receiving management counseling (OR =1.33)

10 ©2015 MFMER | slide-10 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

11 ©2015 MFMER | slide-11 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

12 ©2015 MFMER | slide-12 Next Steps Assess effect of identification and management of weight related diagnoses on measurable change in BMI We are currently looking at selective lab screening for obesity-related comorbidities such as lipid panel, transaminases, and glucose.

13 ©2015 MFMER | slide-13 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

14 ©2015 MFMER | slide-14 Questions & Discussion


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