Harnessing Louisiana Electronic Medical Records for Pediatric Obesity Research

Transcription

1 Harnessing Louisiana Electronic Medical Records for Pediatric Obesity Research Amanda E. Staiano, Ph.D., M.P.P. Pennington Biomedical Research Center October 28, 2015 Childhood Obesity and Public Health Conference 2015

2 Presentation Outline The Era of Digital Data What can we do with these data? Improve screening and treatment Identify high risk patients/groups Engage patients in healthcare What are the strengths and limitations?

3 What is an EMR?

4 HOW CAN WE USE THESE DATA? 1. Improve screening and treatment 2. Identify high risk patients/groups 3. Engage patients in healthcare

5 1. IMPROVE SCREENING AND TREATMENT OPTIONS Obesity Toolkit Access Health Louisiana

6 Pediatric Obesity Prevalence Louisiana Pediatric Obesity U.S. LA Health SBHCs HPFA Bogalusa Year RWJF F as in Fat 2012

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8 Barlow et al. Pediatrics 2007;120:s164-92

9 Use of EMR Improves Clinical Care 19% of physicians aware of recommendations, only 3% adhere to all of them (Kologatla & Adams, 2004) Barriers include (Rattay et al., 2009): low self-efficacy, inadequate tools/resources, lack knowledge/skills, lack of time, competing priorities, insufficient reimbursement, and lack of awareness of community resources. Kolagotla & Adams, Obes Res 2004;12(2): Rattay et al., Pediatrics 2009;123:S100-7

10 Use of EMR Improves Clinical Care In 740k pediatric visits, EMR decision tools (Coleman et al., 2012): BMI measurement: 66% to 94% Obesity diagnosis: 12% to 61% Counseling rates: 1% to 50% RCT: Decision alerts, counseling template, and diagnosis order set: Obesity diagnosis: 7% to 22% Weight-specific counseling: 15% to 27% (Tang et al., 2012) Physician & staff training needed (Coleman et al., 2012) Keehbauch et al., Clin Pediatr 2012;51(1):31-8 Tang et al., JGIM 2012;27(8):933-9 Coleman et al., J of Peds 2012;160(6):918-22

11 Prevent, Identify, Treat Childhood Obesity Require BMI assessment; prompt if elevated Link to screenings; follow-up visits Online database of community resources Online counseling Monitor patients more frequently

12 Partnership with Access Health Louisiana Washington FQHC, Bogalusa St. Tammany FQHCs, Slidell, Covington St. Charles FQHCs, Luling (2), Norco, Kenner Ruth Fertel/Tulane FQHC, New Orleans Albert Cammon Middle School/St. Rose Elementary SBHC, St. Rose Warren Easton SBHC, New Orleans Bonnabel High School SBHC, Kenner John Ehret High School SBHC, Marrero Joshua Butler Elementary School SBHC, Westwego Riverdale High School SBHC, Jefferson West Jefferson High School SBHC, Harvey St. Bernard FQHC, Chalmette Belle Chasse FQHC, Belle Chasse

13 2. IDENTIFY HIGH RISK PATIENTS/GROUPS LSU Hospitals and Clinics

14 Percentage with Diabetes CDC s Division of Diabetes Translation. National Diabetes Surveillance System available at Slides from Dr. Jackie Stephens Number with Diabetes (Millions) Number and Percentage of U.S. Population with Diagnosed Diabetes, Percentage with Diabetes Number with Diabetes Year

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17 % of adults with diabetes

18 Louisiana s Chronic Disease Burden These 3 diseases alone cost the state $10.6 billion/year. *Am. Diabetes Assn 2014, National Center for Chronic Disease Prevention 2010, Caregiving 2013.

19 Adult-Onset Diabetes? 0.24 per 1000 or 1 child in every 4000 African Americans have 6x higher prevalence than Whites

20 LSU Hospitals & Clinics LSU Data Management Evaluation Database (DMED) Since 1990, 1.6 million unique patients (35% of Louisiana population) Under- and uninsured population 46% free care, 10% self-pay, 20% Medicaid, 14% Medicare, 10% commercial insurance

21 LSU DMED n = 1.6 mil Diagnoses Procedures Encounter Data Labs and Pathology Medications, Allergies, Immunizations Demographics Blood Pressure and Anthropometry Tobacco Use and Smoking Cessation

22 Characteristics of pediatric sample Average Diagnosis Age 15.2 y for type 1 diabetes 16.3 y for type 2 diabetes Unpublished data; In preparation

23 3. ENGAGE PATIENTS IN THEIR OWN HEALTHCARE PROPEL & REACHNet

24 PI: Peter Katzmarzyk, Ph.D. Test a 2-y obesity treatment program delivered in primary care setting to an underserved population Identify patients that qualify Reports of patient health data during study enrollment

25 REACHnet: Research Action for Health Network (Formerly known as the Louisiana Clinical Data Research Network) PI: Thomas Carton, Ph.D. Slides provided by Beth Nauman, MPH, PhD Research Director Louisiana Public Health Institute Research Action for Health Network

26 Research Action for Health Network PCORnet

27 REACHnet An informatics and stakeholder engagement infrastructure for multi-site research in Louisiana and Texas Goal: To facilitate the efficient conduct of patient-centered comparative effectiveness research by establishing a data network containing clinical records for more than 1 million patients PARTNERS Research Action for Health Network

28 Research Action for Health Network Common Data Model

29 WHAT ARE THE STRENGTHS AND LIMITATIONS OF DIGITAL DATA?

30 Strengths Large cohorts of chronic diseases and risk factors Cost effective Focus on ethnic minority groups and those at/near the poverty line Assessments actually conducted in clinical settings Can improve clinical diagnosis & treatment

31 Limitations Self-selected appointments Physician-selected assessments Retrospective data Limited validity and reliability

32 Concluding Thoughts "Evidence is the cornerstone of a high-performing healthcare system." - Institute of Medicine

33 Concluding Thoughts "Evidence is the cornerstone of a high-performing healthcare system." - Institute of Medicine Glaeser et al., NBER 2014; Working Paper No

34 Acknowledgements Our Lady of the Lake Children s Hospital

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