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Electronic health records to support obesity-related patient care: Results from a survey of United States physicians
Preventive Medicine 77 (2015) 41–47
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Preventive Medicine journal homepage: www.elsevier.com/locate/ypmed
Electronic health records to support obesity-related patient care: Results from a survey of United States physicians Kayla L. Bronder b,⁎, Carrie A. Dooyema a, Stephen J. Onufrak a, Jennifer L. Foltz a,c a b c
Division of Nutrition, Physical Activity, and Obesity, Centers for Disease Control and Prevention, Atlanta, GA, USA The CDC Experience Applied Epidemiology Fellowship, Scientific Education and Professional Development Program Office, Centers for Disease Control and Prevention, Atlanta, GA, USA United States Public Health Service Commissioned Corps, Atlanta, GA, USA
a r t i c l e
i n f o
Available online 5 May 2015 Keywords: Electronic health records Obesity Body mass index
a b s t r a c t Objective. Obesity-related electronic health record functions increase the rates of measuring Body Mass Index, diagnosing obesity, and providing obesity services. This study describes the prevalence of obesity-related electronic health record functions in clinical practice and analyzes characteristics associated with increased obesity-related electronic health record sophistication. Methods. Data were analyzed from DocStyles, a web-based panel survey administered to 1507 primary care providers practicing in the United States in June, 2013. Physicians were asked if their electronic health record has specific obesity-related functions. Logistical regression analyses identified characteristics associated with improved obesity-related electronic health record sophistication. Results. Of the 88% of providers with an electronic health record, 83% of electronic health records calculate Body Mass Index, 52% calculate pediatric Body Mass Index percentile, and 32% flag patients with abnormal Body Mass Index values. Only 36% provide obesity-related decision support and 17% suggest additional resources for obesity-related care. Characteristics associated with having a more sophisticated electronic health record include age ≤45 years old, being a pediatrician or family practitioner, and practicing in a larger, outpatient practice. Conclusions. Few electronic health records optimally supported physician's obesity-related clinical care. The low rates of obesity-related electronic health record functions currently in practice highlight areas to improve the clinical health information technology in primary care practice. More work can be done to develop, implement, and promote the effective utilization of obesity-related electronic health record functions to improve obesity treatment and prevention efforts. Published by Elsevier Inc.
Background and significance The number of people affected by obesity in the United States is high, with an obesity prevalence of 34.9% for adults and 16.9% for youth in 2011–2012 (Ogden et al., 2013). National obesity treatment guidelines suggest annual screening for obesity as standard of care for adults (Moyer, 2012) and children aged 2 years and older (Barlow, 2007). However, it is estimated that among adults and children with clinical obesity, less than 30% of adults (Ma et al., 2009) and only 18% of obese children were diagnosed as obese during their primary care visit (Patel et al., 2010). Additionally, only 37% of obese adults received any obesity counseling (Ma et al., 2009). Several studies have demonstrated that obesity-related electronic health record (EHR) functions can assist providers in the screening and treatment of both adult (Baer et al., 2013) and childhood obesity (Smith et al., 2013). Obesity-related EHR functions increase the rates of assessing body mass index (BMI), diagnosing obesity,
and providing obesity counseling and treatment services (Baer et al., 2013; Adhikari et al., 2012; Bordowitz et al., 2007; Coleman et al., 2012; Keehbauch et al., 2012; Savinon et al., 2012; Ayash et al., 2013). The Health Information Technology for Economic and Clinical Health Act was passed in 2009 to spur adoption and utilization of EHRs in hospitals and outpatient clinics. This legislation includes funding to providers who adopt and use EHRs meeting specific requirements, known as Meaningful Use (MU) standards. Obesity screening was included in the MU standards; in order to receive incentive payments, EHRs are required to calculate and display BMI for adults and plot and display growth charts, including BMI, for children 0–20 years (Centers for Medicare and Medicaid Services HHS, 2012). Physicians have responded positively to the incentives with EHR adoption increasing from 17% in 2006 to 78% in 2013 among outpatient providers (Hsiao and Hing, 2012). Objective
⁎ Corresponding author at: University of Michigan, 1500 E. Medical Center Drive, D3230 MPB, SPC 5718, Ann Arbor, MI 48109-5718. E-mail address: [email protected] (K.L. Bronder).
http://dx.doi.org/10.1016/j.ypmed.2015.04.018 0091-7435/Published by Elsevier Inc.
While obesity-related EHR functions have been shown to help physicians diagnose and treat obesity, little is known about obesity-
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related functions available in EHRs in the U.S. The objectives of this study are to analyze EHR prevalence and characteristics of providers and practices with EHRs, to describe specific obesity-related EHR functions currently available in practice, and to identify characteristics associated with increased obesity-related EHR sophistication. Materials and methods
week, zip code to determine geographic region, and financial situation of the majority of their patients. In the 2013 DocStyles survey, physicians were asked “Does your electronic health record (EHR) provide the following supports for obesity-related care? If you have more than one EHR, answer based on your primary outpatient EHR.” Respondents could select all that apply for a series of twelve obesityrelated EHR functions grouped into four categories: screening functions, selfmanagement, decision supports, and additional resources (Fig. 1). Physicians were also allowed to responded “None of these” or “I do not have an EHR”.
Participants and data collection Data were collected through DocStyles, an annual web-based survey of U.S. healthcare providers from June 26th to July 25th, 2013. DocStyles is developed by Porter Novelli (Washington, DC), with input from agencies including the Centers for Disease Control and Prevention, which aid in survey question development. Physicians in the sample were drawn from the World One's Global Medical Panel of over 300,000 U.S. healthcare professionals and were selected to match the American Medical Association's (AMA) master file proportions for age, gender, and region. Eligible providers included those practicing in the U.S. for at least three years; actively seeing patients; and working in an individual, group or hospital-based practice. The study was exempted from IRB approval because no individual identifiers were included in the dataset. A total of 2657 were invited to participate and quotas were set to reach 1000 primary care physicians (internal and family medicine), 250 pediatricians, 250 OB/GYNs, and 250 Nurse Practitioners. A total of 1757 health professionals completed the survey, yielding a response rate of 74.0%. For this analysis Nurse Practitioners were excluded ( n = 250) because they were not asked to complete the survey module regarding EHRs, leaving a final analytical sample of 1507 physicians. When compared to the AMA master file, the sample had a higher proportion of males (71.7% DocStyles, 59.1% AMA) and younger physicians (average age 48.9 years DocStyles, 52.7 years AMA). Survey instrument Each year, a standard set of questions is fielded to all DocStyles survey participants querying basic demographic information of the physician and their practice. Physician demographic characteristics include gender, age, race, specialty, and weight status (calculated by self-reported height and weight). Practice characteristics include practice type, average number of patients seen per
Establishing levels of obesity-related EHR sophistication As shown in Fig. 1, obesity-related EHR sophistication was categorized into three increasingly stricter, overlapping levels of sophistication: Minimal, Basic, and Advanced. Previous studies have analyzed EHR sophistication in general and for women's health related functionality; however no studies have analyzed obesity-related EHR sophistication (DesRoches et al., 2008; Hsiao and Hing, 2014). As no obesity EHR sophistication criteria currently exist, the sophistication criteria developed for this study were based on previous studies of EHR sophistication as well as healthy weight standards (International HLS, 2013; Enterprise ItH, 2013) and practice recommendations (Barlow, 2007; NF K et al., 2007; Medicine Io, 2010). A Minimal Obesity EHR is defined if at a minimum, it calculates BMI and pediatric BMI percentiles, the two obesity-related requirements for MU incentive payments. An EHR is classified as Basic Obesity EHR if at a minimum, it performs the minimal functions plus displays BMI trajectory over time and flags patients with abnormal BMI values. An Advanced Obesity EHR performs all basic functions plus a least one function from each category of self-management, decision support, and additional resources. Statistical analysis Data analysis was performed using SAS Version 9.3. The prevalence of physicians with an EHR, and the various provider and practice characteristics were estimated. Differences between provider and practice characteristics were tested using Chi-squared tests with significance set at p b .05. After testing for confounding between variables, a multivariable logistic regression model was fit to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for the association between having an EHR with provider and practice characteristics. Next, physicians without an EHR were excluded to determine the prevalence of each of the twelve obesity-related EHR functions. Separate variables were then created for
Fig. 1. Survey questions from DocStyles 2013 and definitions of obesity-related sophistication levels for electronic health records (n = 1322).
K.L. Bronder et al. / Preventive Medicine 77 (2015) 41–47
each of the four categories (obesity screening, self-management, decision support, and additional resources) to determine the prevalence of EHRs that had at least one function in each of those categories. We also calculated the prevalence of each obesity function for three different specialty types of providers: pediatricians, primary care providers (PCPs, excluding pediatricians) that see children and PCPs that do not see children. Finally, the prevalence of physicians with an EHR meeting each level of sophistication was determined. Three independent logistic regression models (Minimal, Basic, Advanced) were fit for provider and practice characteristics as correlates of EHR sophistication.
Results Survey respondents were primarily male (71.7%), ≥ 45 years old (62.8%), and White non-Hispanic (66.0%) (Table 1). Nearly all (97.1%) of the physicians worked in an outpatient setting and 68.1% worked in group practices. A total of 87.7% of physicians in our analytic sample reported using an EHR in their main practice setting. Characteristics associated with having an EHR included age less than 45 years, not being a pediatrician, working primarily in the inpatient setting, and seeing 100 or more patients per week (Table 1).
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Table 2 Prevalence of obesity-related functions available in physician's primary outpatient electronic health record, from DocStyles 2013 (n = 1,322). Functions grouped by category
%
No obesity-related functions Obesity screening Automatically calculate BMIa Automatically calculate BMI percentile for pediatric patientsa Display BMI trajectory over time Flag patients with abnormal BMI values (overweight, obese) Self-management Print patient education materials (healthy eating, active living) Create a visit summary (lifestyle goals, obesity care plan) Decision support Prompt for best practices (screening for related conditions, labs, meds) Prompt for obesity prevention counseling Prompt for diagnostic codes for obesity or other co-morbid conditions Additional resources Refer to clinic-based obesity prevention personnel (nutritionist, trainer) Refer to community-based programs or resources Refer to support staff for case management
6.4 83.4 51.8 36.3 31.8 44.3 42.9 26.6 21.2 18.3 11.6 9.4 9.2
BMI, body mass index. a Denotes Meaningful Use BMI criteria.
Obesity-related EHR functions Six percent of EHRs had no obesity-related functions (Table 2). The proportion of EHRs with each of the twelve specific obesity-related
functions ranged from 9 to 83%. A total of 89% of physicians reported that their EHR had at least one obesity screening function (Fig. 2). Specifically, 83% had an EHR that calculated BMI from patient's height
Table 1 Characteristics of all respondents of DocStyles 2013 and frequency and odds of having an electronic health record.
All physicians Provider characteristics Gender Male Female Age (years) ≥45 b45 Race/ethnicity White, non-Hispanic Black, non-Hispanic Hispanic Asian Other Weight category Normal or underweight Overweight Obese Missing Specialty Pediatrician Family/general practitioners Internist Obstetrician/gynecologist Practice characteristics Practice Type Inpatient Individual outpatient Group outpatient Number of patients per week b100 ≥100 Region South Northeast Midwest West Finances of patients Very poor-lower middle class Lower middle class-middle class Middle class-upper middle class Upper middle class-affluent
All respondents
Respondents with an EHR
N (%)
%
1507 (100.0)
87.7
1081 (71.7) 426 (28.3)
88.0 87.1
1.00 0.87 (0.58–1.29)
946 (62.8) 561 (37.2)
83.5 94.8
1.00 2.77 (1.78–4.31)
994 (66.0) 37 (2.5) 67 (4.5) 317 (21.0) 92 (6.1)
84.8 89.2 97.0 93.4 92.4
1.00 2.03 (0.65–6.36) 4.90 (1.15–20.91) 1.94 (1.16–3.23) 1.86 (0.79–4.36)
579 (38.4) 484 (32.1) 121 (8.0) 323 (21.4)
87.6 88.2 86.8 87.6
1.00 1.25 (0.83–1.88) 1.42 (0.76–2.65) 0.91 (0.58–1.42)
250 (16.6) 566 (37.6) 440 (29.2) 251 (16.7)
82.4 87.8 93.2 83.3
1.00 1.92 (1.23–3.01) 3.34 (1.96–5.71) 1.73 (1.04–2.89)
125 (8.3) 356 (23.6) 1026 (68.1)
96.8 75.3 90.9
1.00 0.16 (0.06–0.47) 0.49 (0.17–1.43)
462 (30.7) 1045 (69.3)
84.8 89.0
1.00 1.57 (1.11–2.24)
494 (32.8) 395 (26.2) 331 (22.0) 287 (19.0)
87.0 85.8 88.2 90.9
1.00 0.86 (0.56–1.30) 1.05 (0.66–1.65) 1.46 (0.87–2.44)
234 (15.5) 603 (40.0) 573 (38.0) 97 (6.4)
91.9 87.6 86.4 86.6
1.00 0.66 (0.38–1.17) 0.62 (0.53–1.01) 0.48 (0.22–1.09)
OR (95% CI)
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Physicians with an EHR
No EHR 12%
88%
Obesity-related EHR Adv. Basic 5% 8% sophistication
Minimal 36%
Obesity Screening
Less than Minimal 51% None 11%
89%
Self-Management
None 43%
57%
Decision Support
None 64%
36%
Patient Referral Additional Resources
17%
None 83%
0%
100% with 100% of surveyed an EHR (n=1,322) physicians (n=1,507) Percent of physicians reporting EHR and obesity functions Note: Levels of sophistication are overlapping. Basic EHRs include functions of Minimal EHRs. Advanced EHRs include functions of Basic EHRs. EHR, electronic health record; Adv., Advanced
Fig. 2. Physicians with an electronic health record (EHR), prevalence of obesity-related sophistication, and obesity-related functions, DocStyles 2013 (n = 1322).
and weight. Fewer EHRs calculated pediatric BMI percentile (52%), displayed a patient's BMI trajectory over time (36%), or flagged a patient's BMI as abnormal if the patient is overweight or obese (32%). Of the physicians that see children (both pediatricians and PCPs that see children) 61% have an EHR that automatically calculates BMI percentile. Only 57% of physicians have EHRs with at least one selfmanagement support function, 36% of physicians reporting that their EHRs had at least one type of obesity-related decision support function and only 17% of EHRs have at least one type of additional resources function. Sophistication of obesity-related EHRs Of the physicians with an EHR, 49% had a Minimal Obesity EHR or better, indicating that their EHR meets the BMI requirements for Meaningful Use incentive payments. Only 13% had a Basic Obesity EHR or better, and 5% had an Advanced Obesity EHR (Table 3). Based on the independent logistic regression results for Minimal, Basic, and Advanced, controlling for provider and practice characteristics, physicians who were b 45 years old were more likely than older physicians to have a Minimal or Basic Obesity EHR or better. Family practitioners, internists, and OB/GYNs were less likely to have a Minimal Obesity EHR than pediatricians. Similarly, internists and OB/GYNS were less likely to have a Basic Obesity EHR than pediatricians. When analyzing the prevalence of the obesity-related functions by provider type, only 37% of pediatricians reported having an EHR that creates visit summaries. PCPs that see children had 1.45 (95% CI 1.1–2.1) higher odds of having an EHR that creates visit summaries. Finally, only 20% of pediatricians have an EHR that prompts for best practices. PCPs that see children had 1.59 (95% CI 1.1–2.4) higher odds of having an EHR that prompts for best practices and PCPs that do not see children had 1.65 (95% CI 1.1–2.6) higher odds. Providers of Asian ethnicity and providers seeing ≥100 patients per week were more likely to have an Advanced Obesity EHR. No differences were seen in any model for physician's weight status, region, or financial situation of the patient population. Discussion In this study 88% of providers reported having an EHR, similar to other nationally representative data showing EHR adoption rates among outpatient providers reached 78% in 2013 (Hsiao and Hing,
2012). We found that younger physicians and group-based practices have higher EHR adoption rates than older physicians and individual practices, also consistent with nationally representative studies (Hsiao et al., 2013; Leu et al., 2012; Decker et al., 2012). No differences in EHR adoption rates were found among geographic region or financial situation of the patient population in this or a previous nationally representative survey (Hsiao et al., 2013). This is the first study, to our knowledge, to evaluate obesity-related EHR functions in the U.S. The obesity related functionality of EHRs varied greatly ranging from 6% of EHRs having no obesity-related functions to 83% of the EHRs automatically calculating BMI. Fewer than half of all EHRs automatically calculate BMI and pediatric BMI percentile, which are the two Meaningful Use BMI criteria necessary to receive incentive payments. A 2011 national survey of office-based physicians, reported that 76% of physicians with EHRs said that their EHR meets all of the MU criteria (King et al., 2014). This disparity may indicate that while many physicians believe that their EHRs meet the MU criteria, many of these EHRs could be lacking the BMI or BMI percentile calculation functions. Approximately half of EHRs provide obesity-related self-management support. More sophisticated EHR functions such as self-management supports may empower patients to take control of their health behaviors to improve health outcomes (Dietz et al., 2007), however more research on this aspect is needed. Self-management support is a multifaceted approach that educates the patient about obesity, engages them in a healthy weight loss and management plan collaboratively developed with providers, and builds the patient's sense of confidence to take control of their weight (McGowan, 2012). Individually tailored patient education handouts have the potential to improve health behaviors, especially for nutrition education (Enwald and Huotari, 2010). Similarly, a visit summary allows the patient to take home their collaborative care plan to use as a reference and share with other health care providers. This sense of engagement and control in health care is essential to successful selfmanagement (Dietz et al., 2007). Despite health information technology advances, less than half of EHRs provide any type of printable patient education material or create a visit summary for patients. As such, there is a need to develop and implement obesity-related self-management supports within EHRs and evaluate the ability of EHRs to support obesity management on a large scale. Less than one out of every five EHRs provide any type of additional resources support to link patients to further obesity management.
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Table 3 Prevalence and characteristics of physicians with an Electronic Health Record (n = 1,322) meeting each level of obesity-related EHR sophistication or better. Minimal % All physicians with EHR (n = 1322) Provider characteristics Gender Male Female Age (years) ≥45 b45 Race/ethnicity White, non-Hispanic Black, non-Hispanic Hispanic Asian Other Weight category Normal or underweight Overweight Obese Missing Specialty Pediatrician Family/general practitioners Internists Obstetrician/Gynecologist Practice characteristics Practice type Inpatient Individual outpatient Group outpatient Number of patients per week b100 ≥100 Region South Northeast Midwest West Finances of patients Very poor-lower middle class Lower middle class-middle class Middle class-upper middle class Upper middle class-affluent
Basic OR (95% CI)
48.5
%
Advanced OR (95% CI)
13.3
%
OR (95% CI)
4.8
45.5 56.1
1.00 1.29 (0.98–1.71)
12.3 15.9
1.00 1.14 (0.78–1.66)
4.5 5.4
1.00 1.23 (0.68–2.23)
46.0 52.3
1.00 1.50 (1.16–1.94)
11.3 16.4
1.00 1.59 (1.12–2.24)
4.6 5.1
1.00 1.05 (0.60–1.82)
50.5 60.6 40.0 43.2 48.2
1.00 1.46 (0.68–3.15) 0.67 (0.38–1.17) 0.81 (0.60–1.10) 0.94 (0.58–1.55)
12.0 9.1 18.5 16.2 14.1
1.00 0.77 (0.23–2.63) 1.75 (0.88–3.49) 1.49 (1.00–2.23) 1.13 (0.58–2.23)
3.4 3.0 6.2 8.5 4.7
1.00 0.93 (0.12–7.22) 2.00 (0.66–6.05) 2.79 (1.54–5.06) 1.48 (0.49–4.47)
51.1 47.5 51.4 44.2
1.00 0.94 (0.70–1.25) 1.05 (0.66–1.68) 0.79 (0.58–1.09)
12.8 11.9 16.2 15.2
1.00 1.04 (0.68–1.58) 1.56 (0.84–2.88) 1.22 (0.79–1.88)
4.7 5.2 3.8 4.6
1.00 1.28 (0.68–2.42) 1.05 (0.34–3.24) 0.94 (0.46–1.90)
70.4 61.2 29.8 33.5
1.00 0.61 (0.42–0.88) 0.18 (0.13–0.27) 0.19 (0.12–0.29)
18.9 17.1 7.6 10.1
1.00 0.82 (0.53–1.29) 0.31 (0.18–0.53) 0.46 (0.26–0.83)
5.8 5.4 3.9 3.8
1.00 0.86 (0.41–1.79) 0.51 (0.23–1.15) 0.62 (0.24–1.59)
26.5 45.2 52.3
1.00 2.52 (1.47–4.33) 2.83 (1.74–4.59)
9.9 13.1 13.8
1.00 1.33 (0.63–2.83) 1.23 (0.63–2.42)
5.0 5.6 4.5
1.00 1.00 (0.34–2.94) 0.76 (0.29–1.99)
47.7 48.8
1.00 1.07 (0.82–1.40)
11.0 14.3
1.00 1.42 (0.96–2.09)
2.6 5.7
1.00 2.28 (1.11–4.64)
47.0 48.1 50.3 49.4
1.00 1.22 (0.89–1.67) 1.91 (0.86–1.66) 1.14 (0.81–1.60)
12.3 13.3 13.7 14.6
1.00 1.22 (0.78–1.90) 1.21 (0.76–1.91) 1.24 (0.78–1.97)
4.4 5.0 4.8 5.0
1.00 1.21 (0.21–2.42) 1.25 (0.60–2.60) 1.12 (0.53–2.35)
51.2 45.8 50.5 46.4
1.00 0.92 (0.65–1.34) 1.12 (0.78–1.60) 1.11 (0.64–1.93)
13.0 12.3 14.1 15.5
1.00 1.11 (0.38–1.82) 1.29 (0.78–2.13) 1.29 (0.78–2.13)
4.7 2.8 5.9 4.8
1.00 0.93 (0.42–2.08) 1.44 (0.66–3.15) 1.08 (0.32–3.70)
Note: Rows do not sum to 100% because categories are not mutually exclusive. OR, odds ratio; CI, confidence interval; EHR, electronic health record.
Linking patients with obesity treatment personnel such as nurses and dieticians can build a team of providers for the patient (Brown and Psarou, 2008; Pritchard et al., 1999). Team-based care provides added layers of support and follow-up for patients as they make difficult lifestyle changes to achieve and manage their healthy weight (Richman et al., 1996). Furthermore, connecting patients with community-based resources such as weight management groups, farmers markets, and parks can help strengthen the patient's social capital and may help support healthful behavior change (Brand et al., 2014). An EHR could assist a provider by generating lists of resources in the local community to provide to patients and link patients with local resources by sending electronic referrals to help ensure care coordination. Additional resources functions are under-represented in EHRs despite the potential to improve patient care and health outcomes. Basic obesity screening is a central component of practice for healthcare professionals, especially primary care specialties. This survey indicated that among physicians with EHR, internists and OB/GYNs were less likely than pediatricians to have a Basic Obesity EHR to facilitate obesity screening. This is driven by both the absence of the EHR's ability to calculate BMI percentile for children and to display BMI trajectory over time. Many internists and OBGYNs see patients younger than 20 years olds who need a gender
and age-specific growth charts to document BMI percentile and accurately diagnose childhood obesity. Of relevance for internists, there is a well-established association between obesity and multiple co-morbidities (Guh et al., 2009) and the health benefits of even small amounts of weight loss in adults are well documented (Hakala et al., 2000a,b; Lavie et al., 2009; Stenius-Aarniala et al., 2000; Wing et al., 2011). For OB-GYNs, obesity is associated with difficultly achieving and maintaining a pregnancy, gestational diabetes, as well as increased use of health-care during pregnancy (Chu et al., 2008; Maheshwari et al., 2007). The dangers of obesity during pregnancy emphasize the need to achieve a healthy weight before and during pregnancy which is essential to the health of mother and child (Chu et al., 2007; Heslehurst et al., 2008; Khan, 2012). It is well documented that weight management interventions during pregnancy can improve maternal and infant health outcomes without evidence of harm (Thangaratinam et al., 2012). While pediatricians are more likely to have a Basic Obesity EHR, they were less likely than other providers to have certain advanced the EHR functions of creating visit summaries or prompting for best practices. Obesity screening should be done at every well child visit (Moyer, 2012), and for children with a BMI greater than the 85th percentile for their gender and age, further treatment is indicated
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based national guidelines (Barlow, 2007). Pediatricians' EHRs could prompt for best practices based on a patient's BMI, gender, and age to remind providers on the next evidence based steps for treatment or recommended laboratory screening. Additionally, visit summaries could assist in care coordination and remind patients and families of goals set during the patient visit. For all provider types, targeted and overall efforts to improve the availability of EHR obesity functions could improve the diagnosis and treatment of obesity for multiple patient populations. This study is subject to several limitations. First, the DocStyles survey draws its sample from health professionals across the U.S. to match the American Medical Association Master file on demographic characteristics. While not nationally representative, the data matches national trends where data exist. Second, the data are self-reported, thus obesity-related functions might be available in a physician's EHR without their awareness. Gaps in perceived and actual functionality can identify areas for training to maximize EHR care supports. Similarly, providers were not asked the name of the specific EHR system they use. Finally, providers were asked about available EHR functions and not frequency of use. Further studies could investigate the frequency of EHR function use and the effectiveness of these functions to improve clinical care and health outcomes. Conclusion Few EHRs support physician's obesity-related care and there is low level of obesity-related sophistication in EHRs in this study. A total of 17% of EHRs do not capture BMI and 49% of EHRs do not calculate pediatric BMI percentile, both of which are essential to proper obesityrelated care. More work can be done to develop, implement, and promote the effective utilization of obesity-related EHR functions to improve obesity treatment and prevention efforts. Healthcare providers, administrators, and other stakeholders can work with their EHR vendors and healthcare systems to improve availability and quality of obesity-related functions within their EHR. Conflict of interest statement Kayla Bronder receives funding from The CDC Experience, a one-year fellowship in applied epidemiology at CDC made possible by a public/private partnership supported by a grant to the CDC Foundation from External Medical Affairs, Pfizer Inc. Jennifer Foltz, Stephen Onufrak, and Carrie Dooyema have no funding sources or conflicts of interest to disclose. The findings and conclusions in this report are those of the author and do not necessarily represent the official position of the Centers for Disease Control and Prevention.
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Contents lists available at ScienceDirect
Preventive Medicine journal homepage: www.elsevier.com/locate/ypmed
Electronic health records to support obesity-related patient care: Results from a survey of United States physicians Kayla L. Bronder b,⁎, Carrie A. Dooyema a, Stephen J. Onufrak a, Jennifer L. Foltz a,c a b c
Division of Nutrition, Physical Activity, and Obesity, Centers for Disease Control and Prevention, Atlanta, GA, USA The CDC Experience Applied Epidemiology Fellowship, Scientific Education and Professional Development Program Office, Centers for Disease Control and Prevention, Atlanta, GA, USA United States Public Health Service Commissioned Corps, Atlanta, GA, USA
a r t i c l e
i n f o
Available online 5 May 2015 Keywords: Electronic health records Obesity Body mass index
a b s t r a c t Objective. Obesity-related electronic health record functions increase the rates of measuring Body Mass Index, diagnosing obesity, and providing obesity services. This study describes the prevalence of obesity-related electronic health record functions in clinical practice and analyzes characteristics associated with increased obesity-related electronic health record sophistication. Methods. Data were analyzed from DocStyles, a web-based panel survey administered to 1507 primary care providers practicing in the United States in June, 2013. Physicians were asked if their electronic health record has specific obesity-related functions. Logistical regression analyses identified characteristics associated with improved obesity-related electronic health record sophistication. Results. Of the 88% of providers with an electronic health record, 83% of electronic health records calculate Body Mass Index, 52% calculate pediatric Body Mass Index percentile, and 32% flag patients with abnormal Body Mass Index values. Only 36% provide obesity-related decision support and 17% suggest additional resources for obesity-related care. Characteristics associated with having a more sophisticated electronic health record include age ≤45 years old, being a pediatrician or family practitioner, and practicing in a larger, outpatient practice. Conclusions. Few electronic health records optimally supported physician's obesity-related clinical care. The low rates of obesity-related electronic health record functions currently in practice highlight areas to improve the clinical health information technology in primary care practice. More work can be done to develop, implement, and promote the effective utilization of obesity-related electronic health record functions to improve obesity treatment and prevention efforts. Published by Elsevier Inc.
Background and significance The number of people affected by obesity in the United States is high, with an obesity prevalence of 34.9% for adults and 16.9% for youth in 2011–2012 (Ogden et al., 2013). National obesity treatment guidelines suggest annual screening for obesity as standard of care for adults (Moyer, 2012) and children aged 2 years and older (Barlow, 2007). However, it is estimated that among adults and children with clinical obesity, less than 30% of adults (Ma et al., 2009) and only 18% of obese children were diagnosed as obese during their primary care visit (Patel et al., 2010). Additionally, only 37% of obese adults received any obesity counseling (Ma et al., 2009). Several studies have demonstrated that obesity-related electronic health record (EHR) functions can assist providers in the screening and treatment of both adult (Baer et al., 2013) and childhood obesity (Smith et al., 2013). Obesity-related EHR functions increase the rates of assessing body mass index (BMI), diagnosing obesity,
and providing obesity counseling and treatment services (Baer et al., 2013; Adhikari et al., 2012; Bordowitz et al., 2007; Coleman et al., 2012; Keehbauch et al., 2012; Savinon et al., 2012; Ayash et al., 2013). The Health Information Technology for Economic and Clinical Health Act was passed in 2009 to spur adoption and utilization of EHRs in hospitals and outpatient clinics. This legislation includes funding to providers who adopt and use EHRs meeting specific requirements, known as Meaningful Use (MU) standards. Obesity screening was included in the MU standards; in order to receive incentive payments, EHRs are required to calculate and display BMI for adults and plot and display growth charts, including BMI, for children 0–20 years (Centers for Medicare and Medicaid Services HHS, 2012). Physicians have responded positively to the incentives with EHR adoption increasing from 17% in 2006 to 78% in 2013 among outpatient providers (Hsiao and Hing, 2012). Objective
⁎ Corresponding author at: University of Michigan, 1500 E. Medical Center Drive, D3230 MPB, SPC 5718, Ann Arbor, MI 48109-5718. E-mail address: [email protected] (K.L. Bronder).
http://dx.doi.org/10.1016/j.ypmed.2015.04.018 0091-7435/Published by Elsevier Inc.
While obesity-related EHR functions have been shown to help physicians diagnose and treat obesity, little is known about obesity-
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K.L. Bronder et al. / Preventive Medicine 77 (2015) 41–47
related functions available in EHRs in the U.S. The objectives of this study are to analyze EHR prevalence and characteristics of providers and practices with EHRs, to describe specific obesity-related EHR functions currently available in practice, and to identify characteristics associated with increased obesity-related EHR sophistication. Materials and methods
week, zip code to determine geographic region, and financial situation of the majority of their patients. In the 2013 DocStyles survey, physicians were asked “Does your electronic health record (EHR) provide the following supports for obesity-related care? If you have more than one EHR, answer based on your primary outpatient EHR.” Respondents could select all that apply for a series of twelve obesityrelated EHR functions grouped into four categories: screening functions, selfmanagement, decision supports, and additional resources (Fig. 1). Physicians were also allowed to responded “None of these” or “I do not have an EHR”.
Participants and data collection Data were collected through DocStyles, an annual web-based survey of U.S. healthcare providers from June 26th to July 25th, 2013. DocStyles is developed by Porter Novelli (Washington, DC), with input from agencies including the Centers for Disease Control and Prevention, which aid in survey question development. Physicians in the sample were drawn from the World One's Global Medical Panel of over 300,000 U.S. healthcare professionals and were selected to match the American Medical Association's (AMA) master file proportions for age, gender, and region. Eligible providers included those practicing in the U.S. for at least three years; actively seeing patients; and working in an individual, group or hospital-based practice. The study was exempted from IRB approval because no individual identifiers were included in the dataset. A total of 2657 were invited to participate and quotas were set to reach 1000 primary care physicians (internal and family medicine), 250 pediatricians, 250 OB/GYNs, and 250 Nurse Practitioners. A total of 1757 health professionals completed the survey, yielding a response rate of 74.0%. For this analysis Nurse Practitioners were excluded ( n = 250) because they were not asked to complete the survey module regarding EHRs, leaving a final analytical sample of 1507 physicians. When compared to the AMA master file, the sample had a higher proportion of males (71.7% DocStyles, 59.1% AMA) and younger physicians (average age 48.9 years DocStyles, 52.7 years AMA). Survey instrument Each year, a standard set of questions is fielded to all DocStyles survey participants querying basic demographic information of the physician and their practice. Physician demographic characteristics include gender, age, race, specialty, and weight status (calculated by self-reported height and weight). Practice characteristics include practice type, average number of patients seen per
Establishing levels of obesity-related EHR sophistication As shown in Fig. 1, obesity-related EHR sophistication was categorized into three increasingly stricter, overlapping levels of sophistication: Minimal, Basic, and Advanced. Previous studies have analyzed EHR sophistication in general and for women's health related functionality; however no studies have analyzed obesity-related EHR sophistication (DesRoches et al., 2008; Hsiao and Hing, 2014). As no obesity EHR sophistication criteria currently exist, the sophistication criteria developed for this study were based on previous studies of EHR sophistication as well as healthy weight standards (International HLS, 2013; Enterprise ItH, 2013) and practice recommendations (Barlow, 2007; NF K et al., 2007; Medicine Io, 2010). A Minimal Obesity EHR is defined if at a minimum, it calculates BMI and pediatric BMI percentiles, the two obesity-related requirements for MU incentive payments. An EHR is classified as Basic Obesity EHR if at a minimum, it performs the minimal functions plus displays BMI trajectory over time and flags patients with abnormal BMI values. An Advanced Obesity EHR performs all basic functions plus a least one function from each category of self-management, decision support, and additional resources. Statistical analysis Data analysis was performed using SAS Version 9.3. The prevalence of physicians with an EHR, and the various provider and practice characteristics were estimated. Differences between provider and practice characteristics were tested using Chi-squared tests with significance set at p b .05. After testing for confounding between variables, a multivariable logistic regression model was fit to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for the association between having an EHR with provider and practice characteristics. Next, physicians without an EHR were excluded to determine the prevalence of each of the twelve obesity-related EHR functions. Separate variables were then created for
Fig. 1. Survey questions from DocStyles 2013 and definitions of obesity-related sophistication levels for electronic health records (n = 1322).
K.L. Bronder et al. / Preventive Medicine 77 (2015) 41–47
each of the four categories (obesity screening, self-management, decision support, and additional resources) to determine the prevalence of EHRs that had at least one function in each of those categories. We also calculated the prevalence of each obesity function for three different specialty types of providers: pediatricians, primary care providers (PCPs, excluding pediatricians) that see children and PCPs that do not see children. Finally, the prevalence of physicians with an EHR meeting each level of sophistication was determined. Three independent logistic regression models (Minimal, Basic, Advanced) were fit for provider and practice characteristics as correlates of EHR sophistication.
Results Survey respondents were primarily male (71.7%), ≥ 45 years old (62.8%), and White non-Hispanic (66.0%) (Table 1). Nearly all (97.1%) of the physicians worked in an outpatient setting and 68.1% worked in group practices. A total of 87.7% of physicians in our analytic sample reported using an EHR in their main practice setting. Characteristics associated with having an EHR included age less than 45 years, not being a pediatrician, working primarily in the inpatient setting, and seeing 100 or more patients per week (Table 1).
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Table 2 Prevalence of obesity-related functions available in physician's primary outpatient electronic health record, from DocStyles 2013 (n = 1,322). Functions grouped by category
%
No obesity-related functions Obesity screening Automatically calculate BMIa Automatically calculate BMI percentile for pediatric patientsa Display BMI trajectory over time Flag patients with abnormal BMI values (overweight, obese) Self-management Print patient education materials (healthy eating, active living) Create a visit summary (lifestyle goals, obesity care plan) Decision support Prompt for best practices (screening for related conditions, labs, meds) Prompt for obesity prevention counseling Prompt for diagnostic codes for obesity or other co-morbid conditions Additional resources Refer to clinic-based obesity prevention personnel (nutritionist, trainer) Refer to community-based programs or resources Refer to support staff for case management
6.4 83.4 51.8 36.3 31.8 44.3 42.9 26.6 21.2 18.3 11.6 9.4 9.2
BMI, body mass index. a Denotes Meaningful Use BMI criteria.
Obesity-related EHR functions Six percent of EHRs had no obesity-related functions (Table 2). The proportion of EHRs with each of the twelve specific obesity-related
functions ranged from 9 to 83%. A total of 89% of physicians reported that their EHR had at least one obesity screening function (Fig. 2). Specifically, 83% had an EHR that calculated BMI from patient's height
Table 1 Characteristics of all respondents of DocStyles 2013 and frequency and odds of having an electronic health record.
All physicians Provider characteristics Gender Male Female Age (years) ≥45 b45 Race/ethnicity White, non-Hispanic Black, non-Hispanic Hispanic Asian Other Weight category Normal or underweight Overweight Obese Missing Specialty Pediatrician Family/general practitioners Internist Obstetrician/gynecologist Practice characteristics Practice Type Inpatient Individual outpatient Group outpatient Number of patients per week b100 ≥100 Region South Northeast Midwest West Finances of patients Very poor-lower middle class Lower middle class-middle class Middle class-upper middle class Upper middle class-affluent
All respondents
Respondents with an EHR
N (%)
%
1507 (100.0)
87.7
1081 (71.7) 426 (28.3)
88.0 87.1
1.00 0.87 (0.58–1.29)
946 (62.8) 561 (37.2)
83.5 94.8
1.00 2.77 (1.78–4.31)
994 (66.0) 37 (2.5) 67 (4.5) 317 (21.0) 92 (6.1)
84.8 89.2 97.0 93.4 92.4
1.00 2.03 (0.65–6.36) 4.90 (1.15–20.91) 1.94 (1.16–3.23) 1.86 (0.79–4.36)
579 (38.4) 484 (32.1) 121 (8.0) 323 (21.4)
87.6 88.2 86.8 87.6
1.00 1.25 (0.83–1.88) 1.42 (0.76–2.65) 0.91 (0.58–1.42)
250 (16.6) 566 (37.6) 440 (29.2) 251 (16.7)
82.4 87.8 93.2 83.3
1.00 1.92 (1.23–3.01) 3.34 (1.96–5.71) 1.73 (1.04–2.89)
125 (8.3) 356 (23.6) 1026 (68.1)
96.8 75.3 90.9
1.00 0.16 (0.06–0.47) 0.49 (0.17–1.43)
462 (30.7) 1045 (69.3)
84.8 89.0
1.00 1.57 (1.11–2.24)
494 (32.8) 395 (26.2) 331 (22.0) 287 (19.0)
87.0 85.8 88.2 90.9
1.00 0.86 (0.56–1.30) 1.05 (0.66–1.65) 1.46 (0.87–2.44)
234 (15.5) 603 (40.0) 573 (38.0) 97 (6.4)
91.9 87.6 86.4 86.6
1.00 0.66 (0.38–1.17) 0.62 (0.53–1.01) 0.48 (0.22–1.09)
OR (95% CI)
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Physicians with an EHR
No EHR 12%
88%
Obesity-related EHR Adv. Basic 5% 8% sophistication
Minimal 36%
Obesity Screening
Less than Minimal 51% None 11%
89%
Self-Management
None 43%
57%
Decision Support
None 64%
36%
Patient Referral Additional Resources
17%
None 83%
0%
100% with 100% of surveyed an EHR (n=1,322) physicians (n=1,507) Percent of physicians reporting EHR and obesity functions Note: Levels of sophistication are overlapping. Basic EHRs include functions of Minimal EHRs. Advanced EHRs include functions of Basic EHRs. EHR, electronic health record; Adv., Advanced
Fig. 2. Physicians with an electronic health record (EHR), prevalence of obesity-related sophistication, and obesity-related functions, DocStyles 2013 (n = 1322).
and weight. Fewer EHRs calculated pediatric BMI percentile (52%), displayed a patient's BMI trajectory over time (36%), or flagged a patient's BMI as abnormal if the patient is overweight or obese (32%). Of the physicians that see children (both pediatricians and PCPs that see children) 61% have an EHR that automatically calculates BMI percentile. Only 57% of physicians have EHRs with at least one selfmanagement support function, 36% of physicians reporting that their EHRs had at least one type of obesity-related decision support function and only 17% of EHRs have at least one type of additional resources function. Sophistication of obesity-related EHRs Of the physicians with an EHR, 49% had a Minimal Obesity EHR or better, indicating that their EHR meets the BMI requirements for Meaningful Use incentive payments. Only 13% had a Basic Obesity EHR or better, and 5% had an Advanced Obesity EHR (Table 3). Based on the independent logistic regression results for Minimal, Basic, and Advanced, controlling for provider and practice characteristics, physicians who were b 45 years old were more likely than older physicians to have a Minimal or Basic Obesity EHR or better. Family practitioners, internists, and OB/GYNs were less likely to have a Minimal Obesity EHR than pediatricians. Similarly, internists and OB/GYNS were less likely to have a Basic Obesity EHR than pediatricians. When analyzing the prevalence of the obesity-related functions by provider type, only 37% of pediatricians reported having an EHR that creates visit summaries. PCPs that see children had 1.45 (95% CI 1.1–2.1) higher odds of having an EHR that creates visit summaries. Finally, only 20% of pediatricians have an EHR that prompts for best practices. PCPs that see children had 1.59 (95% CI 1.1–2.4) higher odds of having an EHR that prompts for best practices and PCPs that do not see children had 1.65 (95% CI 1.1–2.6) higher odds. Providers of Asian ethnicity and providers seeing ≥100 patients per week were more likely to have an Advanced Obesity EHR. No differences were seen in any model for physician's weight status, region, or financial situation of the patient population. Discussion In this study 88% of providers reported having an EHR, similar to other nationally representative data showing EHR adoption rates among outpatient providers reached 78% in 2013 (Hsiao and Hing,
2012). We found that younger physicians and group-based practices have higher EHR adoption rates than older physicians and individual practices, also consistent with nationally representative studies (Hsiao et al., 2013; Leu et al., 2012; Decker et al., 2012). No differences in EHR adoption rates were found among geographic region or financial situation of the patient population in this or a previous nationally representative survey (Hsiao et al., 2013). This is the first study, to our knowledge, to evaluate obesity-related EHR functions in the U.S. The obesity related functionality of EHRs varied greatly ranging from 6% of EHRs having no obesity-related functions to 83% of the EHRs automatically calculating BMI. Fewer than half of all EHRs automatically calculate BMI and pediatric BMI percentile, which are the two Meaningful Use BMI criteria necessary to receive incentive payments. A 2011 national survey of office-based physicians, reported that 76% of physicians with EHRs said that their EHR meets all of the MU criteria (King et al., 2014). This disparity may indicate that while many physicians believe that their EHRs meet the MU criteria, many of these EHRs could be lacking the BMI or BMI percentile calculation functions. Approximately half of EHRs provide obesity-related self-management support. More sophisticated EHR functions such as self-management supports may empower patients to take control of their health behaviors to improve health outcomes (Dietz et al., 2007), however more research on this aspect is needed. Self-management support is a multifaceted approach that educates the patient about obesity, engages them in a healthy weight loss and management plan collaboratively developed with providers, and builds the patient's sense of confidence to take control of their weight (McGowan, 2012). Individually tailored patient education handouts have the potential to improve health behaviors, especially for nutrition education (Enwald and Huotari, 2010). Similarly, a visit summary allows the patient to take home their collaborative care plan to use as a reference and share with other health care providers. This sense of engagement and control in health care is essential to successful selfmanagement (Dietz et al., 2007). Despite health information technology advances, less than half of EHRs provide any type of printable patient education material or create a visit summary for patients. As such, there is a need to develop and implement obesity-related self-management supports within EHRs and evaluate the ability of EHRs to support obesity management on a large scale. Less than one out of every five EHRs provide any type of additional resources support to link patients to further obesity management.
K.L. Bronder et al. / Preventive Medicine 77 (2015) 41–47
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Table 3 Prevalence and characteristics of physicians with an Electronic Health Record (n = 1,322) meeting each level of obesity-related EHR sophistication or better. Minimal % All physicians with EHR (n = 1322) Provider characteristics Gender Male Female Age (years) ≥45 b45 Race/ethnicity White, non-Hispanic Black, non-Hispanic Hispanic Asian Other Weight category Normal or underweight Overweight Obese Missing Specialty Pediatrician Family/general practitioners Internists Obstetrician/Gynecologist Practice characteristics Practice type Inpatient Individual outpatient Group outpatient Number of patients per week b100 ≥100 Region South Northeast Midwest West Finances of patients Very poor-lower middle class Lower middle class-middle class Middle class-upper middle class Upper middle class-affluent
Basic OR (95% CI)
48.5
%
Advanced OR (95% CI)
13.3
%
OR (95% CI)
4.8
45.5 56.1
1.00 1.29 (0.98–1.71)
12.3 15.9
1.00 1.14 (0.78–1.66)
4.5 5.4
1.00 1.23 (0.68–2.23)
46.0 52.3
1.00 1.50 (1.16–1.94)
11.3 16.4
1.00 1.59 (1.12–2.24)
4.6 5.1
1.00 1.05 (0.60–1.82)
50.5 60.6 40.0 43.2 48.2
1.00 1.46 (0.68–3.15) 0.67 (0.38–1.17) 0.81 (0.60–1.10) 0.94 (0.58–1.55)
12.0 9.1 18.5 16.2 14.1
1.00 0.77 (0.23–2.63) 1.75 (0.88–3.49) 1.49 (1.00–2.23) 1.13 (0.58–2.23)
3.4 3.0 6.2 8.5 4.7
1.00 0.93 (0.12–7.22) 2.00 (0.66–6.05) 2.79 (1.54–5.06) 1.48 (0.49–4.47)
51.1 47.5 51.4 44.2
1.00 0.94 (0.70–1.25) 1.05 (0.66–1.68) 0.79 (0.58–1.09)
12.8 11.9 16.2 15.2
1.00 1.04 (0.68–1.58) 1.56 (0.84–2.88) 1.22 (0.79–1.88)
4.7 5.2 3.8 4.6
1.00 1.28 (0.68–2.42) 1.05 (0.34–3.24) 0.94 (0.46–1.90)
70.4 61.2 29.8 33.5
1.00 0.61 (0.42–0.88) 0.18 (0.13–0.27) 0.19 (0.12–0.29)
18.9 17.1 7.6 10.1
1.00 0.82 (0.53–1.29) 0.31 (0.18–0.53) 0.46 (0.26–0.83)
5.8 5.4 3.9 3.8
1.00 0.86 (0.41–1.79) 0.51 (0.23–1.15) 0.62 (0.24–1.59)
26.5 45.2 52.3
1.00 2.52 (1.47–4.33) 2.83 (1.74–4.59)
9.9 13.1 13.8
1.00 1.33 (0.63–2.83) 1.23 (0.63–2.42)
5.0 5.6 4.5
1.00 1.00 (0.34–2.94) 0.76 (0.29–1.99)
47.7 48.8
1.00 1.07 (0.82–1.40)
11.0 14.3
1.00 1.42 (0.96–2.09)
2.6 5.7
1.00 2.28 (1.11–4.64)
47.0 48.1 50.3 49.4
1.00 1.22 (0.89–1.67) 1.91 (0.86–1.66) 1.14 (0.81–1.60)
12.3 13.3 13.7 14.6
1.00 1.22 (0.78–1.90) 1.21 (0.76–1.91) 1.24 (0.78–1.97)
4.4 5.0 4.8 5.0
1.00 1.21 (0.21–2.42) 1.25 (0.60–2.60) 1.12 (0.53–2.35)
51.2 45.8 50.5 46.4
1.00 0.92 (0.65–1.34) 1.12 (0.78–1.60) 1.11 (0.64–1.93)
13.0 12.3 14.1 15.5
1.00 1.11 (0.38–1.82) 1.29 (0.78–2.13) 1.29 (0.78–2.13)
4.7 2.8 5.9 4.8
1.00 0.93 (0.42–2.08) 1.44 (0.66–3.15) 1.08 (0.32–3.70)
Note: Rows do not sum to 100% because categories are not mutually exclusive. OR, odds ratio; CI, confidence interval; EHR, electronic health record.
Linking patients with obesity treatment personnel such as nurses and dieticians can build a team of providers for the patient (Brown and Psarou, 2008; Pritchard et al., 1999). Team-based care provides added layers of support and follow-up for patients as they make difficult lifestyle changes to achieve and manage their healthy weight (Richman et al., 1996). Furthermore, connecting patients with community-based resources such as weight management groups, farmers markets, and parks can help strengthen the patient's social capital and may help support healthful behavior change (Brand et al., 2014). An EHR could assist a provider by generating lists of resources in the local community to provide to patients and link patients with local resources by sending electronic referrals to help ensure care coordination. Additional resources functions are under-represented in EHRs despite the potential to improve patient care and health outcomes. Basic obesity screening is a central component of practice for healthcare professionals, especially primary care specialties. This survey indicated that among physicians with EHR, internists and OB/GYNs were less likely than pediatricians to have a Basic Obesity EHR to facilitate obesity screening. This is driven by both the absence of the EHR's ability to calculate BMI percentile for children and to display BMI trajectory over time. Many internists and OBGYNs see patients younger than 20 years olds who need a gender
and age-specific growth charts to document BMI percentile and accurately diagnose childhood obesity. Of relevance for internists, there is a well-established association between obesity and multiple co-morbidities (Guh et al., 2009) and the health benefits of even small amounts of weight loss in adults are well documented (Hakala et al., 2000a,b; Lavie et al., 2009; Stenius-Aarniala et al., 2000; Wing et al., 2011). For OB-GYNs, obesity is associated with difficultly achieving and maintaining a pregnancy, gestational diabetes, as well as increased use of health-care during pregnancy (Chu et al., 2008; Maheshwari et al., 2007). The dangers of obesity during pregnancy emphasize the need to achieve a healthy weight before and during pregnancy which is essential to the health of mother and child (Chu et al., 2007; Heslehurst et al., 2008; Khan, 2012). It is well documented that weight management interventions during pregnancy can improve maternal and infant health outcomes without evidence of harm (Thangaratinam et al., 2012). While pediatricians are more likely to have a Basic Obesity EHR, they were less likely than other providers to have certain advanced the EHR functions of creating visit summaries or prompting for best practices. Obesity screening should be done at every well child visit (Moyer, 2012), and for children with a BMI greater than the 85th percentile for their gender and age, further treatment is indicated
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based national guidelines (Barlow, 2007). Pediatricians' EHRs could prompt for best practices based on a patient's BMI, gender, and age to remind providers on the next evidence based steps for treatment or recommended laboratory screening. Additionally, visit summaries could assist in care coordination and remind patients and families of goals set during the patient visit. For all provider types, targeted and overall efforts to improve the availability of EHR obesity functions could improve the diagnosis and treatment of obesity for multiple patient populations. This study is subject to several limitations. First, the DocStyles survey draws its sample from health professionals across the U.S. to match the American Medical Association Master file on demographic characteristics. While not nationally representative, the data matches national trends where data exist. Second, the data are self-reported, thus obesity-related functions might be available in a physician's EHR without their awareness. Gaps in perceived and actual functionality can identify areas for training to maximize EHR care supports. Similarly, providers were not asked the name of the specific EHR system they use. Finally, providers were asked about available EHR functions and not frequency of use. Further studies could investigate the frequency of EHR function use and the effectiveness of these functions to improve clinical care and health outcomes. Conclusion Few EHRs support physician's obesity-related care and there is low level of obesity-related sophistication in EHRs in this study. A total of 17% of EHRs do not capture BMI and 49% of EHRs do not calculate pediatric BMI percentile, both of which are essential to proper obesityrelated care. More work can be done to develop, implement, and promote the effective utilization of obesity-related EHR functions to improve obesity treatment and prevention efforts. Healthcare providers, administrators, and other stakeholders can work with their EHR vendors and healthcare systems to improve availability and quality of obesity-related functions within their EHR. Conflict of interest statement Kayla Bronder receives funding from The CDC Experience, a one-year fellowship in applied epidemiology at CDC made possible by a public/private partnership supported by a grant to the CDC Foundation from External Medical Affairs, Pfizer Inc. Jennifer Foltz, Stephen Onufrak, and Carrie Dooyema have no funding sources or conflicts of interest to disclose. The findings and conclusions in this report are those of the author and do not necessarily represent the official position of the Centers for Disease Control and Prevention.
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