An Integrative Review of the Use of EHR in Childhood Obesity Identification and Management

Citation

Yabut, L. & Rosenblum, R. (Fall, 2017). An Integrative Review of the Use of EHR in Childhood Obesity Identification and Management. Online Journal of Nursing Informatics (OJNI), 21(3), Available at http://www.himss.org/ojni

Abstract

PURPOSE: Childhood obesity has been an enormous and complex burden to individuals, families, and the healthcare system. This systematic review explores the use of electronic health records (EHR) to identify, prevent, provide treatment, and manage care on children who may be at risk for obesity. 

METHOD: A systematic review was conducted using CINAHL complete, PubMed, and Science Direct on the use of EHR to diagnose childhood obesity.  The review contains published research articles between 2011 and 2016. Nine published articles were reviewed and used for this systematic review.

RESULTS: Using an EHR system increases the identification and documentation of childhood obesity which suggests that a unified electronic system is beneficial to recognizing and managing childhood obesity. 

CONCLUSIONS: By using a EHR system, data may be collected, analyzed and shared seamlessly by healthcare organizations to engage, prevent, and manage childhood obesity in innovative and user-friendly ways.  However, the lack of research on the impact of using EHR to manage childhood obesity remains.  Future studies are needed on the use of EHR that individualizes the patient, provides a cohesive treatment plan, and manages the care of the individual.

Introduction

Childhood obesity in the United States is a disturbing and problematic health issue that often persists into adulthood. According to the Centers of Disease Control and Prevention (CDC), childhood obesity among those aged 2-19 years has been steadily growing in the past 30 years: obesity rates for children aged 6-11 years have doubled from 7% in 1980 to 18% in 2012, and those aged 12-19 tripled from 5% in 1980 to 21% in 2012 (Carroll, Navaneelan, Bryan, & Ogden, 2015).  Currently, nearly 16% or 1 in 6 children are obese (Office of Disease Prevention and Health Promotion, n.d.). Although there has been a slight decline on childhood obesity recently, rates remain high (CDC, 2016).

The healthcare industry is constantly evolving with technology, moving from paper charting to electronic charting by using electronic health records (EHR).  EHR can be used to improve processes, increase efficiency, and individualize each diagnosis to the patient.  Smith, Skow, Bodurtha, & Kinra (2013), indicated that childhood obesity screening and treatment improved with the use of EHR. Using EHR will allow pediatricians to better manage patients with obesity by weight management support (WMS) resources that are provided at the time of visit (Adkilari, Parker, Binns, & Ariza, 2012).  EHR automatically calculates body mass index (BMI) when entering height and weight.  Additionally, adapting to an EHR system can assist in providing follow up and management of those who are at risk of developing obesity or who are obese.  Previously, EHR has not been utilized to identify childhood obesity and engage or apply clinical practice procedures to screen, prevent, and reduce childhood obesity for healthcare providers.  Naureckas et al. (2011), suggested that the use of customized EHR can provide transparency and patient centered care to the most vulnerable populations.  

Purpose

The purpose of this systematic review was to examine published literature regarding the use of EHR to identify, prevent, treat, and manage care of children who may be at risk of becoming obese or overweight. EHR and electronic medical records (EMR) is used interchangeably; for this review, EHR is used.

The following question was considered for this review:

Is there evidence in the literature that indicates the use of an EHR system will assist in identifying, preventing, treating, and managing children diagnosed as obese or overweight?

Background

Body mass index (BMI) is a standard measuring tool used to calculate body fat based on height and weight.  Children with a BMI between the 85th and 95th percentile are considered overweight and children with BMI of > 95 percent are considered obese.  In the United States from 2011-2014, 12.7 million children and adolescents aged 2-19 years were considered overweight or obese (CDC, 2016). Racial and ethnic disparities among Hispanics and non-Hispanic blacks are 21.9% compared to 19.5% respectively (CDC, 2016).  The rate of children and adolescent obesity is lower for non-Hispanic whites and non-Hispanic Asians –  14.7% and 8.6% respectively (CDC, 2016). Taveras et al. (2013) showed that the Hispanic population had higher rates of children and adolescent obesity compared to non-Hispanic groups. Also, in their study on low socioeconomic status, race/ethnicity, and overweight/obese students, Rogers et al., (2015) found an increase in the number of overweight/obese Hispanic students compared to non-Hispanic students.  In the United States, Hispanic children make up the fastest growing population to be diagnosed with obesity at 25 percent compared to 13.6 percent non-Hispanic children (Office of Minority Health, 2016).   

Environmental factors contributing to childhood obesity include physical activities, sedentary lifestyles, nutritional behaviors, and/or socioeconomic status.  For instance, children are driven by bus or car to school instead of walking.  Parents work long hours and home cooked meals are replaced by take-out and fast foods.  Outdoor activities are replaced by the Internet, video games, and screen time.  McMurray et al., (2015) noted there was an increase in sedentary lifestyle and lack of physical activities on the weekend, which promoted obese parents who have obese children.     

Rogers et al. (2015) indicated that low socioeconomic status may impact the rate of childhood obesity.  Tomayko, Flood, Tandias, & Hanrahan (2015), showed that using EHR to identify childhood obesity, race/ethnicity, and socioeconomics was essential to pinpointing specific preventions and interventions at a community level, with a focus on low socioeconomic status.  Koning et al., (2016), noted with a healthy lifestyle such as eating healthier and joining sport activities, there was an improvement on overall weight.  However, those with a low socioeconomic status were at higher risk of unhealthier lifestyle choices and limited access to a healthy lifestyle. 

Additionally, America’s food portion and beverages have increased two to five times more over the years (Let’s Move, 2016).   With this increase, Americans are eating more than what is required, adding more calories, and consuming less nutrient-dense food.  These are not healthy eating habits and may be contributing factors to child and adolescent obesity in the United States.  Watt et al. (2012) found a strong correlation with low income Hispanic and infant obesity with the use of supplemental nutrition assistance program (SNAP/food stamps).  SNAP/food stamps are a resource for low-income families to buy grocery products however, those that use SNAP/food stamps often have unhealthy eating habits which in turn results in less nutrient-dense food being consumed.

Childhood obesity can be prevented within the United States and worldwide.  Reducing childhood obesity may help to prevent long-term, debilitating chronic diseasess seen in adulthood. Chronic diseases such as diabetes mellitus, hypertension, and asthma can be unmanageable and costly in adults. Simple changes such as eating healthy and being physically active may reduce the number of obese and overweight children and future adults.  There are several comorbidities associated with childhood and adolescent obesity which involves cardiovascular, endocrine, gastrointestinal, and psychosocial systems (Carroll, et al., 2015). Halfon, Larson, & Slusser (2013) found high associated risk factors between obesity and psychosocial health among children aged 10-17 years.  Disease risk factors included hypertension, dyslipidemia, diabetes type 2, and liver disease.  Okubo et al. (2016), noted that hospitalized pediatrics patients who were overweight/obese had a high risk of acute asthma exacerbation compared to those that were not overweight/obese.  Also, low self-esteem, poor body image, and depression were some of the psychosocial concerns (Carroll, et al., 2015).  In their study on childhood obesity and self-esteem, Strauss (2000) found a strong correlation between childhood obesity and low self-esteem and negative self-perception.

The healthcare industry faces many financial challenges with the treatment of childhood obesity.  Financial expenses include direct and indirect costs (Cawley, 2010). $14.1 billion of direct expenses consist of prescription drugs, emergency department visits, and outpatient costs, as well as $237.6 million of inpatient costs. Furthermore, greater expense is acquired when obese children become obese adults.  The approximate yearly cost of treating illnesses associated with obesity in adults is $146 billion (Cawley, 2010). Overall, if obesity can be prevented, treated, and managed, children’s quality of life improves, financial burden is minimized, and the number of adults with obesity will be reduced.

Theoretical Framework

The Health Belief Model (HBM) was developed in the 1950s by Godfrey Hochbaum, Irwin Rosenstock, and Stephen Kegels (Hayden, 2009).    The HBM was developed with the goal of helping to forecast public thoughts and behavior on health issues.  The social psychologists created this model while conducting tuberculosis (TB) medical screening which had little success with the public (Hayden, 2009).  Using the HBM to identify and categorize childhood obesity will help narrow the existing gap among pediatricians, communities, schools, and parents in terms of their perception and involvement in addressing obesity among children.  With the use of HBM and an EHR system, essential guidelines and recommendations to prevent, provide treatment, and manage obesity can be embedded into the system. This will prompt healthcare providers to engage with an individual that has been diagnosed with obesity, explain in depth the severity of disease, assess readiness to improve the current condition, and explain the benefits of improving lifestyles to reduce body weight.  Furthermore, using EHR or EMR to track and monitor childhood obesity on prevention measures supports treatment and management of care of those diagnosed.  Below are seven key components from HBM that were used as key concepts in this review:

  • Perceived susceptibility: refers to a person’s awareness of their health that affects their wellbeing, the greater the health risk the more behavior may change immensely to decrease health risks (Hayden, 2009).  For example, there are several health risks associated with childhood obesity such as increased high blood pressure, increased blood sugars, increased breathing issues, or increased joint pain (Carroll, et al., 2015). Pediatricians, nurses, and parents can participate in a child’s condition to modify or improve healthier life styles and decrease potential comorbidities associated with obesity.
  • Perceived seriousness: refers to a person’s belief of the consequences of their health risk or condition (Hayden, 2009).  For example, parents can encourage and help their child understand the importance of living and eating a healthier lifestyle now and in the future to prevent childhood obesity and the comorbidities associated with obesity as they age into adulthood.
  • Perceived benefits: refers to the person’s belief that minimizing their health risk or condition by changing their current behavior is beneficial (Hayden, 2009).  For example, living a healthier lifestyle and eating healthier improves quality of life and longevity, thus eating more vegetables and fruits and increasing outdoor activities is a good start to prevent obesity.
  • Perceived barriers: refers to the person’s ability or readiness and/or obstacles to acclimatize to modify behavior related to their health risk or condition (Hayden, 2009).  For example, using EMR/EHR or HIT as a tool marker to identify childhood obesity early on will reduce healthcare costs associated with adult obesity.
  • Modifying variables: refers to the person’s ethnic background, education level, skills, motivation, and prior experiences which may affect a person’s perception of their health (Hayden, 2009). For example, the use of Salud Con La Familia (Health with the Family) is a culturally customized program to improve healthier living styles and eating habits among Latino-American families (Barkin, Gesell, Poe, Escarfuller, & Tempesti, 2012).
  • Cues to action: refers to personal life events or people that are influential which may influence or change a person’s behavior (Hayden, 2009). For example, a family member was hospitalized due to out of control diabetes and contributing factors included obesity; this may be a significant cue to action for a person to understand and manage the disease better and may encourage them to lose weight.
  • Self-Efficacy: is the belief in an individual’s ability to accomplish something and achieve goals (Hayden, 2009). For example, a parent and child who are both overweight may decide that in one month they want to lose a couple of pounds by doing daily walks, and eating more vegetables and fruits to begin to work towards optimal weight instead of becoming obese.

Childhood obesity has been a challenging condition to prevent, treat, and manage. Comorbidities associated with obesity include cardiovascular disease, diabetes, and psychosocial illness.  Using the Chronic Care Model (CCM) to support prevention, provide treatment, and manage childhood obesity leads to healthier lifestyles and may decrease childhood and adulthood obesity. The CCM was developed by Ed Wagner and the model has six key components: self-management support, delivery system design, decision support, clinical information systems, organization of health care, and community to use as key instruments in the literature review (Jacobson, & Gance-Cleveland, 2010).

Self-management support refers to a person’s ability to understand simple information about their illness/condition with support and involvement of healthcare providers, family, friends, and community (Institute for Healthcare Improvement. (n.d.).  Delivery system design refers to healthcare system involvement along with a multidisciplinary team to provide cohesive and patient-centered care to the patient’s specific needs of their illness/condition. Decision support refers to treatment provided by evidence-based best practices. Healthcare providers are in continuous communication with a referred specialist to provide cohesive and patient-centered care and receive continuing education on the illness/condition. Clinical information systems refer to a seamless, cohesive, and shared database of a patient’s information where all healthcare providers and multidisciplinary team members have access to observe data about a patient’s illness/condition and provide treatment, anticipate problems and monitor progress.  Organization of health care refers to an organization that needs to be involved to improve the care of chronic disease and provide preventative care with the support of a senior management team ((Institute for Healthcare Improvement. (n.d.). Community refers to community involvement to extend the support from the healthcare system to the community in order to improve the overall health of the population and provide alliance with the local community and nation.

The HBM and CCM model complement each other and can be used to prevent, provide treatment, and manage care on childhood obesity. Childhood obesity has become a chronic disease that needs to be managed and cared for closely.  Using HBM and CCM in conjunction will help manage the care and treatment of childhood obesity along with the use EHR or EMR systems.  The HBM is used to assess the patient’s understanding of their illness/condition, identify the severity of an illness/condition, and assess the patient’s readiness to change.  Once that has been established, the CCM can be used to identify resources for the individual, primary care, and community to reduce the progression of the illness/condition.  An EHR or EMR system can incorporate both the HBM and CCM to reduce the number of those diagnosed with childhood obesity.  Specialists and other healthcare organizations can communicate seamlessly with each other by using an EHR or EMR system.   Jacobson & Gance-Cleveland (2010) found that obese children, adolescents and their families require more in-depth education and support to have positive patient outcomes, including self-management skills and engaging in more healthy lifestyle behaviors using the CCM.

Significance of the Project

Childhood obesity is a major health concern that may lead to adulthood obesity with associated comorbidities that may be debilitating to health and decrease quality of life.  Obesity has been a huge health problem to the healthcare system in terms of prevention, treatment, and care management. The American Academy of Pediatrics (AAP) Institute for Healthy Childhood Weight (n.d.) recommended making minor changes such as healthier eating, healthier living, and less screen time early on in life with more parent and pediatrician involvement to prevent a lifetime of complications associated with childhood obesity. The healthcare cost of childhood obesity for outpatient treatment is $14.1 billion and inpatient is $237.6 million in the US alone (Cawley, 2010).

The significance of this systematic review may improve the understanding of the use of EHR to identify the number of children diagnosed as overweight or obese.  Furthermore, having the healthcare industry use a unified EHR could aid in the identification, prevention, treatment, and management of childhood obesity.

Limitations

There are some limitations to this review.  EHR is an innovative method to address chronic disease such as childhood obesity; however, the technology can either be basic or complex with many features depending on the particular system used by a healthcare organization.  For example, when using EHR to extract data to identify potential risks or analyze patient outcomes, one system can have this feature to extract data specifically with demographics, age, race/ethnicity, insurance, ICD-10 codes, while others may not have the option to be so specific.  Thaker et al., (2016), noted that using EHR with a customized template to document and treat obesity from well-child visits may increase awareness, individualize patient care, and further monitor the obesity epidemic at a community level.  Exclusion and inclusion criteria are also a limitation since the study does not include socioeconomic status and health disparities.  Also, limitations to this review are that the analyzed published literature results are between 2011 to 2016, used certain key search words that limited the online database search, and was also limited to English-language articles. Additionally, results using EHR for documentation may be lower or higher on screening and managing care of childhood obesity (Smith et al., 2013). Furthermore, there were time constraints within the study, thus only one person analyzed the articles. 

Classification of Terms

Body mass index (BMI)a measurement tool used to indicate whether a person is over or underweight by dividing weight in kilograms and height meters squared (CDC, 2015).

Electronic Medical Records (EMR) – a digital technology that encompasses all medical information from a medical office visit (HealthIT.gov., 2013). 

Electronic Health Records (EHR) – a digital system that encompasses patients’ medical history. EHR is a comprehensive medical record that collects, retrieves, and manages data digitally.  Also, EHR can be customized for individual use or healthcare specific (HealthIT.gov., 2013). 

Health Information Technology (HIT) – digital technology that incorporates all health and medical information to store, share and evaluate (HealthIT.gov., 2013). 

Meaningful Use – using EHR technology to improve, engage, and preserve quality, safety, and provide best practices to healthcare outcomes.  It involves collaborating cohesively with patients, family, healthcare providers, and public health providers (HealthIT.gov., 2013). 

Obese – refers to BMI calculation above 95Th percentile (CDC, 2015b).

Overweight – refers to BMI calculation between 85th percentile and 95th percentile (CDC, 2015b).

Presumptions

Methodological presumptions of this systematic review include the following: The examination of current literature will provide a synopsis of pertinent information that will engage the use of EHR system, focusing on identifying and diagnosing childhood obesity.  Additionally, the use of an EHR system may offer an innovating method to using the technology to provide patient-centered care, treatment, and best patient outcomes for childhood obesity and associated comorbidities. 

Systematic Methodology

The aim of the data collection process was to conduct a comprehensive examination of current literature from 2011 to 2016 on related topics about EHR, EMR and childhood obesity.  Nine pertinent articles were identified, collected, and used that met the inclusion criteria. A systematic design was deliberately selected for data collection and the process of collection was evidently defined and documented (Kucan, 2011).

Inclusion/Exclusion Criteria

The data included published research articles between 2011 and 2016 that related to EHR, childhood obesity, pediatric, obesity, or overweight.  These articles focused on samples of children between the ages of 2-19 years and were available as full text in English.  There were a total number of 572 relevant articles available in the CINAHL, Science Direct, and PubMed databases. To narrow down the search, each article’s title and abstract was examined to define the purpose of the systematic review. If an article was not excluded by examining the title or the abstract, then an article was examined in full context.  Peer-reviewed articles were considered to ascertain quality and accuracy for this systematic review.  Exclusion criteria were paper charting data on childhood obesity, articles before 2011, and studies that were not conducted in United States. 

Search Approaches

A thorough and meticulous electronic systematic review was done to search the Cumulative Index to Nursing and Allied Health Literature (CINAHL), PubMed, and Science Direct online databases. Key search words used included childhood obesity, overweight, obese, pediatric, electronic medical records (EMR) and electronic health records (EHR). There were a total number of 572 relevant articles on the online database search results. To narrow down the search findings, inclusion and exclusion criteria were applied.  By using the Johns Hopkins Nursing Evidence-Based Practice Model and Guidelines Research Evidence Appraisal Tool, each article was analyzed to meet inclusion criteria and ensure quality (Johns Hopkins Hospital/Johns Hopkins University, 2013).  Permission was attained to use the appraisal tool from The Johns Hopkins Hospital/The Johns Hopkins University. There were nine research articles that met the criteria after a comprehensive systematic electronic literature review (Figure 1). 

Synopsis of Articles

The first article by Bailey, et al., (2013) explored the use of data sharing EHR with various organizations for the purpose of screening, treating, and preventing childhood obesity.  Using data from six outpatient pediatric medical centers’ EHR systems and comparing the information with the National Health and Nutrition Examination Survey (CDC, 2008), the researchers identified the need to share data to monitor and improve quality care among those diagnosed with obesity. Limitations to this study included a low rate of obesity diagnoses found in EHR data, the lack of quality data abstracted from EHR that measures BMI and demographics, and the small sample used from the study on EHR surveillance and childhood obesity.  Nevertheless, the study employed a practical method which highlighted the eloquent use of integrating a multi-organizational EHR database to aid the healthcare provider and public health care system in reducing childhood obesity.

Article two by Bode, Roberts, and Johnson (2013) analyzed EMR data on documentation of overweight and obese adolescents in a military medical setting.  They used the Find-Organize-Clarify-Select-Plan-Do-Check-Act (FOCUS-PDCA) method to conduct their study, which showed an increase in documentation of identification and diagnoses of overweight and obese adolescents based on BMI percentiles and growth chart curves prompted by the EMR system. Limitations of this study were that acute visits or specialty visits – such as asthma exacerbation or injuries – were included instead of well-child check visits only, resident physicians managed patient care rather than fellows and trained staff, and the patient demographics.  Nevertheless, the EMR system showed an increase in the documentation of diagnosing adolescents as overweight and obese in a military medical setting, which suggested that the system is a user-friendly and effortless way to identify and diagnose adolescent obesity.      

Article three by Brady et al. (2016) reviewed the use of EHR data on severe obesity among the pediatric population. Severe obesity was defined with a BMI  >99th percentile. The study focused on pediatric patients aged 6 years and under and revealed low rates of severe obesity identified among the pediatric population studied.  A limitation of this study was that it resulted in providers’ identifying children as overweight, obese and severely obese using ICD-9 codes.  ICD-9 codes are a universal language to classify diseases, injuries, and death for health providers. Though there was a lack of identifying severe obesity from EHR data, this study showed there is a need for secondary and tertiary framework prevention on early onset of overweight and obese patients to prevent impending complications from obesity.   

Article four by Cochran & Baus (2015) examined the use of EHR data on overweight and obese children beyond the “Meaningful Use” requirements from the Centers for Medicare & Medicaid Services (CMS) and focused on the Chronic Disease Electronic Management System (CDEMS) to identify obesity and associated co-morbidities (Centers for Medicare & Medicaid Services [CMS], 2014). Meaningful use was defined as using EHR technology to improve and engage health care providers and patients in their health such as accessing health records online. The U.S government gave incentives to health organizations to implement and use “meaningful use”.  From this retrospective chart review, they found the potential to enhance and incorporate CDEMS in EHR system to use on pediatric populations.  A limitation of this study showed intensive manual labor exacting data on “Meaningful Use” from the EHR.  A benefit to using EHR with CDEMS is that it encouraged health care providers to deliver patient-centered treatment surrounding their patients’ health and co-morbidities.

Article five by Flood et al. (2015) studied EHR use with childhood obesity and community health data on obesity, to determine if EHRs can reduce health care costs and obesity rates locally.  The study showed similar results on obesity rates using the Public Health Information Exchange (PHINEX) database compared to National Health and Nutrition Examination Survey (NHANES).  Additionally, the PHINEX database identified disparities in race and ethnicity.  A few limitations are that the research did not show a unified EHR measurement on obesity from one organization to another, and it failed to explain if staff was properly trained to use EHR to input measurements correctly.  But, the study showed a practical way to use EHR at a local level on childhood obesity involving the community.

Article six by Higgins, McCarville, Kurowski, McEwen, & Tanz, (2014) explored EMR and screening tests related to childhood obesity.  Their study showed screening tests were ordered suitably using an EMR to diagnose obesity. A limitation of this study is that resident physicians conducted the assessment, documented BMI calculations, and graphed growth charts with EMR rather than the attending physicians.  Although the study had small results on screening test associated with childhood obesity, the correlation was appropriate to the diagnosis. A recommendation for this study is that an EMR system with an alert function be used so that health providers can screen, offer counseling and resources, and order associated laboratory tests.  

In the seventh article by Keehbauch et al. (2012), the authors conducted a study evaluating an EMR upgrade and the number of overweight children documented, assessed, and provided with educational interventions in a family medicine resident facility.  Their study showed an increase in documentation with physicians that had training on EMR.  A limitation of this study was the accuracy of documentation on improved BMI or overall improved health on overweight children.  A recommendation noted in this study is that increased documentation be encouraged on obesity by providing training sessions for staff on EMR use before the upgrade.  Furthermore, initiated providers should provide guidelines and recommendations on weight management and risks for patients diagnosed with obesity.

In article eight, Savinon et al. (2012) conducted a study on EMR and clinical recommendations to recognize, prevent, and treat childhood obesity. The study showed a minimal increase compared to paper charts on tailored EMR templates to screen, prevent, and treat childhood obesity.  Although results from the study had minimal changes, the study observed an increase in recommended follow-up appointments as interventions for obesity when compared to paper charts.  An increase in follow-up care appointments for obesity suggested a need to provide preventative care with co-morbidities associated with it.  A limitation noted from the conducted study was that it was completed in a small community setting.

The final ninth article by Thaker et al. (2016) examined the use of EHR with a tailored template to document obesity during well-child visits.  The study showed an increase in obesity documentation with the use of the EHR and tailored obesity template.  One limitation was that a small sample was used to conduct the study and documentation from the visits were not representative of the population.  A recommendation for the study was that a tailored EHR template should be used to increase obesity documentation and support further discussion about prevention methods (Table 1). 

View Table 1

Results/Data Evaluation

Bode, et al., (2013), Higgins, et al., (2014), Savinon, et al., (2012), and Thaker, et al. (2016) showed an increase in documentation of childhood and adolescent obesity with the use of EHR/EMR and measurement of BMI.   Flood et al (2015) showed that the use of EHR or EMR could be used to isolate demographic, race/ethnicity, age, or gender statistics to identify a need for access to healthier living; this included activities or food, cultural needs such as traditions or food selection, status on social-economic needs, or educational needs to the community. Socio-economic status can be detrimental to childhood obesity with minimal access to the playground, healthier food access, or health care providers (Tomayko, et al., 2015).  McDonald et al., (2011) identified that the use of health information technology (HIT) was valuable to pediatricians and their obese pediatric patients.  However, most pediatricians wanted access to user-friendly educational resources for their patients.   It is noted in the scholarly literature there is a lack of use on EHR, as well as a lack of universal protocol for the identification of childhood obesity.  Additionally, there are few parent involvement studies which impacts healthy eating habits, physical activities, and reduction in screen time i.e. video games, leisure computer time, and television (Keehbauch et al., 2012). Studies show that using EHR to identify childhood and adolescent obesity on well-child or routine visits has the potential to increase and further prompt providers to screen in depth and provide counseling to their patients (Thaker, et al., 2016).  Shaikh, et al. (2014) conducted a study using EHR, standardized documentation templates, and clinical decision-support tools to improve childhood obesity screening, prevention, and treatment. This study showed optimal patient outcomes; such as being able to monitor and graph BMI during every office visit to assess effectiveness of treatment on weight management.  The identification and/or diagnosis of childhood obesity has increased with HER use, but additional studies are needed to evaluate reliable patient outcomes. 

Limitations worth noting are that the use of EHR emphasized documentation on overweight or obese diagnosis, however, there was a lack of follow-up care in the treatment plan.  Further studies are needed where EHR have a customized template to guide and encourage providers and patients to better manage obesity and follow-up care (Thaker, et al., 2016).  Most studies address the documentation on obesity when using EHR, but limitations for follow-up care was not intuitive with the diagnosis.

Implications and Recommendations

This systematic review explored the link of childhood obesity to identify, prevent, provide treatment, and manage care with the use of customized EHR. There were several factors that contributed to the lack of utilizing a customized EHR such as various types of EHR systems, training challenges with the new technology, standardized workflow processes, and insufficient time to implement the technology in a comprehensive way.  Collaboration with clinical providers and use of customized EHR for overweight or obese children is essential to improve the workflow process (Savinon et al., 2011). A user-friendly EHR is also suggested to increase efficiency, identify and provide treatment of childhood obesity.  For example, EHR can easily prompt or alert providers when a child’s BMI is above the 85th percentile, less navigation is required to utilize multiple screens, and educational handouts can be easily generated.  Additionally, providing a cohesive and best practice technology system on childhood obesity may engage parents, pediatricians, and the community to address and improve the current status of this rampant health risk.

Conclusion

EHR templates can be customized and standardized to screen, prevent, and outline treatment of childhood and adolescent comorbidities associated with obesity.  Using a personalized childhood overweight and obese screening EHR tool could help to enhance and engage primary care practitioners to identify, categorize, and provide interventions for childhood obesity.  This review suggested using EHR as a screening tool for childhood obesity for diagnosis and delivering follow-up education.

As suggested in Cochran & Baus (2015), customized EHR tailored for working with childhood obesity by calculating BMI, prompting links and handouts to involve patient plan of care, and setting follow-up appointments with continued care, would provide health care practitioners with valuable guidance and encouragement.  Furthermore, by engaging children in healthier habits, providing families with affordable and healthier food options, and increasing physical activity among children, high rates of childhood and adolescent obesity in the United States can be decreased.  Nyberg, et al., (2011) recommended programs to promote healthy life styles aimed towards low-socio-economic populations to prevent overweight/obese children. Involving pediatrics, community, schools, and parents in research studies supports a wider, more comprehensive discussion of childhood obesity rates that are so prevalent at a community level (Tomayko, et al., 2015).  Recommendations from this systematic review suggested that healthcare providers using EHR with customized templates for obesity may increase positive patient outcomes through easy access to handouts for distribution, online access to monitor current health status, or greater access to connect with other providers or specialists.  Studies are still needed to develop unified EHR systems that address the childhood obesity epidemic, as well as the use of recommended guidelines to screen, prevent, and treat childhood obesity using EHR systems.                

References

Adhikari, P. D., Parker, L. A., Binns, H. J., & Ariza, A. J. (2011). Influence of Electronic Health Records and In-office Weight Management Support Resources on Childhood Obesity Care. Clinical Pediatrics, 51(8), 788-792. doi:10.1177/0009922811407182
American Academy of Pediatrics (AAP) Institute for Healthy Childhood Weight (n.d.). Policy Opportunities Tool. Retrieved November 23, 2016, from https://ihcw.aap.org/Pages/popot.aspx

Bailey, L., Milov, D., Kelleher, K., Kahn, M., Beccaro, M., Yu, F., Richards, T., & Forrest, C. (2013). Multi–Institutional Sharing of Electronic Health Record Data to Assess Childhood Obesity. PLoS ONE 8(6): e66192. https://doi.org/10.1371/journal.pone.0066192

Barkin, S. L., Gesell, S. B., Po'e, E. K., Escarfuller, J., & Tempesti, T. (2012). Culturally Tailored, Family-Centered, Behavioral Obesity Intervention for Latino-American Preschool-aged Children. Pediatrics, 130(3), 445-456. doi:10.1542/peds.2011-3762

Bode, D. V., Roberts, T. A., & Johnson, C. (2013). Increased Adolescent Overweight and Obesity Documentation Through a Simple Electronic Medical Record Intervention. Military Medicine, 178(1), 115-118. doi:10.7205/milmed-d-12-00201

Brady, C. C., Thaker, V. V., Lingren, T., Woo, J. G., Kennebeck, S. S., Namjou-Khales, B., Rocha, A., Bickel, J. P., Patibandla, N., Savova, G. K., Solti, I., Holm, I. A., Harley, J. B., Kohane, I.S., & Crimmins, N. A. (2016). Suboptimal Clinical Documentation in Young Children with Severe Obesity at Tertiary Care Centers. International Journal of Pediatrics, 2016, 1-9. doi:10.1155/2016/4068582

Carroll, M. D., Navaneelan, T., Bryan, S., & Ogden, C. (2015, August). Prevalence of Obesity Among Children and Adolescents in the United States and Canada. U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics. Retrieved November 18, 2016, from https://www.cdc.gov/nchs/data/databriefs/db211.pdf

Cawley, J. (2010). The Economics of Childhood Obesity. Health Affairs, 29(3), 364-371. doi:10.1377/hlthaff.2009.0721

Centers for Medicare & Medicaid Services. (2014). 2014 definition stage 1 of meaningful use. Retrieved from http://www.cms.gov/Regulations-and-Guidance/Legislation/EHRIncentivePrograms/Meaningful_Use.html

Centers of Disease Control and Prevention (CDC). (2016, October 10).  Childhood Obesity Facts. Retrieved October 23, 2016, from https://www.cdc.gov/obesity/data/childhood.html

Centers of Disease Control and Prevention (CDC). (2015, May 15). About Child & Teen BMI, Healthy Weight. Retrieved November 23, 2016, from https://www.cdc.gov/healthyweight/assessing/bmi/childrens_bmi/about_childrens_bmi.html

Centers of Disease Control and Prevention (CDC). (2015b). Defining Childhood Obesity. Retrieved November 17, 2016, from http://www.cdc.gov/obesity/childhood/defining.html

Centers of Disease Control and Prevention (CDC). (2008). National Health and Nutrition Examination Survey (NHANES). Retrieved November 23, 2016 from https://www.cdc.gov/nchs/nhanes/index.htm

Cochran, J., & Baus, A. (2015). Developing Interventions for Overweight and Obese Children using Electronic Health Records Data. Online Journal Of Nursing Informatics, 19(1), Retrieved November 23, 2016 from http://www.himss.org/developing-interventions-overweight-and-obese-children-using-electronic-health-records-data

Flood, T. L., Zhao, Y., Tomayko, E. J., Tandias, A., Carrel, A., & Hanrahan, L. P. (2015). Information for CME Credit — Electronic Health Records and Community Health Surveillance of Childhood Obesity. American Journal of Preventive Medicine, 48(2). doi:10.1016/s0749-3797(14)00700-4

Halfon, N., Larson, K., & Slusser, W. (2013). Associations Between Obesity and Comorbid Mental Health, Developmental, and Physical Health Conditions in a Nationally Representative Sample of US Children Aged 10 to 17. Academic Pediatrics, 13(1), 6-13. Hayden, J. (2009). Introduction to health behavior theory. Sudbury, MA: Jones and Bartlett.

HealthIT.gov. (2013). Basics of Health IT. Retrieved November 17, 2016, from https://www.healthit.gov/patients-families/basics-health-it

Higgins, A., Mccarville, M., Kurowski, J., Mcewen, S., & Tanz, R. R. (2014). Diagnosis and Screening of Overweight and Obese Children in a Resident Continuity Clinic. Global Pediatric Health, 1, 1-6. doi:10.1177/2333794x14559396
Institute for Healthcare Improvement. (n.d.). Self-Management Support for People with Chronic Conditions. Retrieved November 23, 2016, from http://www.ihi.org/resources/Pages/Changes/SelfManagement.aspx

Jacobson, D., & Gance-Cleveland, B. (2010). A systematic review of primary healthcare provider education and training using the Chronic Care Model for Childhood Obesity. Obesity Reviews, 12(5). doi:10.1111/j.1467-789x.2010.00789.x

Johns Hopkins Hospital/Johns Hopkins University. (2013.) Johns Hopkins Nursing Evidence-Based Practice Model and Guidelines: Research Evidence Appraisal Tool. Retrieved from https://www.hopkinsmedicine.org/evidence-based-practice/_docs/appendix_g_individual_evidence_summary_tool.pdf

Keehbauch, J., Miguel, G. S., Drapiza, L., Pepe, J., Bogue, R., & Smith-Dixon, A. (2011). Increased Documentation and Management of Pediatric Obesity Following Implementation of an EMR Upgrade and Education. Clinical Pediatrics, 51(1), 31-38. doi:10.1177/0009922811417293

Koning, M., Hoekstra, T., Jong, E. D., Visscher, T. L., Seidell, J. C., & Renders, C. M. (2016). Identifying developmental trajectories of body mass index in childhood using latent class growth (mixture) modelling: associations with dietary, sedentary and physical activity behaviors: a longitudinal study. BMC Public Health, 16(1). doi:10.1186/s12889-016-3757-7

Kucan, L. (2011). Approximating the Practice of Writing the Dissertation Literature Review. Literacy Research and Instruction, 50(3), 229-240. doi:10.1080/19388071.2010.514037

Let’s Move. (2016.). Learn the facts. Retrieved November 18, 2016 from https://letsmove.obamawhitehouse.archives.gov/learn-facts/epidemic-childhood-obesity

Mcdonald, J., Goldman, R. E., O'brien, A., Ayash, C., Mitchell, K., Marshall, R., Simon, S.R., & Taveras, E. M. (2011). Health Information Technology to Guide Pediatric Obesity Management. Clinical Pediatrics, 50(6), 543-549. doi:10.1177/0009922810395131

McMurray, R. G., Berry, D. C., Schwartz, T. A., Hall, E. G., Neal, M. N., Li, S., & Lam, D. (2015). Relationships of physical activity and sedentary time in obese parent-child dyads: A cross-sectional study. BMC Public Health, 16(1). doi:10.1186/s12889-016-2795

Naureckas, S. M., Zweigoron, R., Haverkamp, K. S., Kaleba, E. O., Pohl, S. J., & Ariza, A. J. (2011). Developing an electronic clinical decision support system to promote guideline adherence for healthy weight management and cardiovascular risk reduction in children: A progress update. Translational Behavioral Medicine, 1(1), 103-107. doi:10.1007/s13142-011-0019-1

Nyberg, G., Sundblom, E., Norman, Å, & Elinder, L. S. (2011). A healthy school start - Parental support to promote healthy dietary habits and physical activity in children: Design and evaluation of a cluster-randomised intervention. BMC Public Health, 11(1). doi:10.1186/1471-2458-11-185

Office of Disease Prevention and Health Promotion. (n.d.). Nutrition, Physical Activity, and Obesity. Healthy People 2020. U.S. Department of Health and Human Services Retrieved November 21, 2016, from https://www.healthypeople.gov/2020/leading-health-indicators/2020-lhi-topics/Nutrition-Physical-Activity-and-Obesity

Office of Minority Health. (2016). Obesity and Hispanic Americans. US Department of Health and Human Services. Retrieved October 23, 2016, from https://minorityhealth.hhs.gov/omh/browse.aspx?lvl=4&lvlid=70

Okubo, Y., Nochioka, K., Hataya, H., Sakakibara, H., Terakawa, T., & Testa, M. (2016). Burden of Obesity on Pediatric Inpatients with Acute Asthma Exacerbation in the United States. The Journal of Allergy and Clinical Immunology: In Practice, 4(6), 1227-1231. doi:10.1016/j.jaip.2016.06.004

Rogers, R., Eagle, T. F., Sheetz, A., Woodward, A., Leibowitz, R., Song, M., Eagle, K. A. (2015). The Relationship between Childhood Obesity, Low Socioeconomic Status, and Race/Ethnicity: Lessons from Massachusetts. Childhood Obesity, 11(6), 691-695. doi:10.1089/chi.2015.0029

Saviñon, C., Taylor, J. S., Canty-Mitchell, J., & Blood-Siegfried, J. (2012). Childhood obesity: Can electronic medical records customized with clinical practice guidelines improve screening and diagnosis? Journal of the American Academy of Nurse Practitioners, 24(8), 463-471. doi:10.1111/j.1745-7599.2012.00735.x

Shaikh, U., Berrong, J., Nettiksimmons, J., & Byrd, R. S. (2014). Impact of Electronic Health Record Clinical Decision Support on the Management of Pediatric Obesity. American Journal of Medical Quality, 30(1), 72-80. doi:10.1177/1062860613517926

Smith, A. J., Skow, A., Bodurtha, J., & Kinra, S. (2013, February 4). Health information technology in screening and treatment of child obesity: A systematic review. Pediatrics Official Journal of the American Academy of Pediatrics, 131(3), e894-902. doi:10.1542/peds.2012-2011

Strauss, R. S. (2000). Childhood Obesity and Self-Esteem. Pediatrics, 105(1). doi:10.1542/peds.105.1.e15

Taveras, E. M., Gillman, M. W., Kleinman, K. P., Rich-Edwards, J. W., & Rifas-Shiman, S. L. (2013). Reducing Racial/Ethnic Disparities in Childhood Obesity. JAMA Pediatrics, 167(8), 731. doi:10.1001/jamapediatrics.2013.85

Thaker, V. V., Lee, F., Bottino, C. J., Perry, C. L., Holm, I. A., Hirschhorn, J. N., & Osganian, S. K. (2016). Impact of an Electronic Template on Documentation of Obesity in a Primary Care Clinic. Clinical Pediatrics, 55(12), 1152-1159. doi:10.1177/0009922815621331

Tomayko, E. J., Flood, T. L., Tandias, A., & Hanrahan, L. P. (2015). Linking electronic health records with community-level data to understand childhood obesity risk. Pediatric Obesity, 10(6), 436-441. doi:10.1111/ijpo.12003

Watt, T., Appel, L., Roberts, K., Flores, B., & Morris, S. (2013). Sugar, Stress, and the Supplemental Nutrition Assistance Program: Early Childhood Obesity Risks Among a Clinic-Based Sample of Low-Income Hispanics. Journal Of Community Health, 38(3), 513-520. doi:10.1007/s10900-012-9641-1

Author Bio

My name is Ly Yabut RN, MSN.  I am a graduate from San Jose State University, The Valley Foundation School of Nursing with a Master’s of Science concentration in nursing and California State University, East Bay with a Bachelor of Science in Administration with a concentration on accounting.  I currently work at Palo Alto Medical Foundation as a registered nurse. 

My advisor throughout the SJSU manuscript was Ruth Rosenblum, DNP, RN, PNP-DC, CNS.