Childhood Obesity Data Management

Childhood Obesity: Data Management

Childhood obesity is one of the most alarming health issues the US society is facing. Wright et al. (2016) state that approximately 30% of American children are overweight or obese. It has been acknowledged that obesity in children persists in their later life (Manios et al., 2013). This health condition is associated with the development of such serious disorders as diabetes mellitus, cardiovascular diseases, depression, and so on. Healthcare professionals utilize various strategies to address the issue.

The use of electronic health records (EHR) is regarded as one of the effective ways to treat obesity in this population (Cochran & Baus, 2015). The researchers claim that the use of EHR can be instrumental in diagnosing and developing treatment plans. These systems also help avoid medical errors. This paper includes a brief analysis of an effective EHR database that can be used when treating childhood obesity.

This database should include some basic personal data including the name, age, address, and so on. Such details are very common for the existing databases. However, it is essential to expand the scope of the information by introducing such entities as community details. Address details cannot be enough as healthcare professionals are often unaware of the peculiarities of all communities while demographic data can help in understanding patients’ health conditions.

It is widely known that socioeconomic factors have a considerable effect on people’s health in general and childhood obesity in particular (Knai & McKee, 2010). Patients’ cultural peculiarities and socioeconomic status affect the ways healthy diets are seen and maintained. For instance, some populations (for example, Hispanic Americans) believe that there should be no restrictions regarding food, so body weight gain is regarded as something positive. Some communities can be characterized by the prevalence of the Latino or African American groups, which can shed light on these people’s health behaviors. Therefore, it is necessary to add such entities as ethnic peculiarities of the community, socioeconomic status, and the like. It is also necessary to include some data concerning the resources available in the community.

Furthermore, such details as gender, exact age, body weight, height, and so on are also quite common for EHR databases. The height should be noted in feet and inches. This precision is essential for the estimation of body weight gain. Patients’ weight can be measured in pounds. These measurements are widely used in the USA, and the vast majority of healthcare professionals understand them. As far as possible improvements in the existing EHR, body weight gain can also be traced in some systems, but it can be poorly presented (Manios et al., 2013).

It can be beneficial to develop an EHR system where body weight gain can be presented in the form of a chart. Healthcare professionals can note a patient’s human body weight during just about every check-up while typically the system will make the chart of which will help approximate the progress or perhaps increasing risks. This kind of information needs to be presented in body size index (BMI). Files concerning patients’ actual activity should in addition be one involving the entities. The best measurement is several hours per week. Many of these qualitative data seeing that the information with regards to patients’ lifestyle have to also be offer. Healthcare professionals can certainly provide brief paperwork on the subject. Hobbies and hobbies can be known.

An efficient data management technique also need to include many data concerning patients’ previous treatment. Typically the use of EHR is helpful while healthcare professionals can certainly note and find the application of different styles of medication. In terms of treating childhood unhealthy weight, it has recently been acknowledged how the work with of antibiotics with early stages involving a lot more associated using increased body mass get in childhood (Bailey et al., 2014). Therefore, it is usually significant to develop surgery that highlights the application of antibiotics throughout patients’ health history. Health-related professionals should always be able to begin to see the exact periods or perhaps dates when drugs were prescribed. Consequently , healthcare practitioners have to pay specific awareness of such records.

Some EHR systems include really basic information on the subject of patients’ parents. Even so, parental peculiarities, seeing that well as their very own views, play a crucial role in typically the advancement their children’s health behaviors and even medical conditions (Wright ain al., 2016). Intended for example, Wright ain al. (2016) be aware that parents are likely to underestimate the outcomes of their children’s body weight prove future health. Fathers and mothers never regard their very own children as fat or overweight though they believe of which excessive weight will be linked to many health issues. Seeing that has been stated above, cultural peculiarities and ethnicity will be important variables.

Therefore, typically the possible EHR technique should include typically the corresponding entities. This sort of data as parents’ ethnicity, thoughts about their very own children’s weight, their very own opinions concerning unhealthy weight and so in needs to be noted. Manios et al. (2013) stress that this sort of information as mother’s education and health and fitness behaviors (especially smoking) needs to be included while these factors will be influential predictors involving childhood obesity.

Finally, that is necessary to be able to add that the info mentioned above have to be presented inside of a drop-down food selection. As many specifics as is possible should turn out to be given in this type as it can help healthcare pros to enter files quickly. Such files as gender, grow older, weight, body fat gain, lifestyle, diet program, and also other details can easily be presented throughout drop-down menus. In addition, such qualitative files as parental alternatives can be presented in this kind. IT specialists can easily use the types mentioned consist of experiments.

Typically the CORE measurement designed by Manios ain al. (2013) may be applied. The work with of drop-down food selection is also linked to fewer medical problems. The development involving such systems can require the work with of the present data and selected research. It is certainly essential to accumulate demographic data relevant to local communities. Yet , it is in addition vital to feature place for notes and even qualitative data. Intended for example, Wright ain al. (2016) check out parental views in youngsters health situations, and the types used by typically the researchers can always be utilized. The offered database will incorporate several categories (see Appendix A).

In realization, it is potential to be aware that the effective EHR databases will include quantitative and qualitative files revealing the peculiarities of patients and even their parents. This sort of details as sexuality, age, body mass, racial, height, parental training, health habits, and even opinion needs to be involved. The system really should have a drop-down food selection that will support reduce the time required to complete typically the database and lower the number involving medical errors. Many entities will nonetheless be given inside of are brief paperwork. The introduction of the fresh database requires typically the collaboration between health-related professionals plus it professionnals. The EHR technique will also will need secondary research geared towards revealing the present health trends and even cultural peculiarities involving people moving into typically the community.

References

Bailey, L., Forrest, C., Zhang, S., Richards, T., Livshits, A., & DeRusso, P. (2014). Connection of antibiotics throughout infancy with early on childhood obesity. JAMA Pediatrics, 168 (11), 1063-1069.

Cochran, J., & Baus, A. (2015). Developing interventions intended for overweight and fat children using electronic digital health records files . Online Log of Nursing Informatics, 19 (1). Web.

Knai, C., & McKee, M. (2010). Tackling childhood unhealthy weight: The importance involving understanding the situation. Log of Public Health and fitness, 32 (4), 506-511.

Manios, Y., Birbilis, M., Moschonis, Grams., Birbilis, G., Mougios, V., Lionis, G., & Chrousos, Grams. (2013). Childhood unhealthy weight risk evaluation structured on perinatal components and family sociodemographic characteristics: CORE listing. Euro Journal of Pediatrics, 172 (4), 551-555.

Wright, D., Lustroso, P., Dawson-Hahn, Elizabeth., Christakis, D., Haaland, W., & Basu, A. (2016). Parent predictions and awareness regarding long-term the child years obesity-related health threats. Academic Pediatrics, 16 (5), 475-481.

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