EHR Database and Data Management for Obesity

EHR Database and Data Management

Electronic health record (EHR) has, in the recent past, emerged as a crucial element in the management of patient data/information. The emergence of this crucial element is fueled by the increased measures by policymakers, researchers, healthcare providers and professionals, patients, and health insurers to enhance the delivery of healthcare services, particularly enhanced management of patient information. The adoption of electronic health records in the modern healthcare setting is attributable to their numerous benefits in comparison to the conventional ways of managing patient data. However, the use of EHR in the clinical setting requires developing suitable databases and utilizing appropriate data management processes. This paper discusses EHR database and data management for obesity, which is a public health concern.
Brief Description of the Patient Problem
An example of a clinically-based patient problem that would benefit from the use of a database management approach is obesity. Obesity is a multi-factorial condition that is characterized by increasing BMI, which is linked to increase in risk of mortality and disease burden (Wood et al., 2012). This patient problem has developed to reach epidemic levels in the United States since it affects approximately two-thirds of adults in the country. The condition is associated with greater risk for a series of co-morbidities and increased life expenditures in healthcare. Given its prevalence, healthcare professionals have found that dietary modification and physical activity are the most suitable prevention and treatment approaches.
Since obesity has reached epidemic levels and is associated with severe negative impacts, it could benefit from the use of a database management approach. The use of this approach requires documenting critical information that would help the patient in managing the condition. Some of the information required for the patient to manage obesity includes patient demographics, problem list i.e. existing or historical medical data on co-morbidities, hospital visits, medication prescription orders, procedures, social history, laboratory test results, and list of medication (Wood et al., 2012). This information is crucial because it enables the healthcare provider to effectively assess the severity of the patient’s condition and the required treatment approaches for managing obesity.
Incorporating the Database and Healthcare Provider
As shown in the above discussion, the management of obesity requires the healthcare provider to access and manage various kinds of information relating to the patient. The diverse nature of this information implies that the healthcare provider requires a suitable framework for documenting and assessing patient data. The use of a database management approach would help in effective management of patient information relating to this condition. The database and healthcare provider can be effectively incorporated into this approach to help enhance patient outcomes. This would entail creating an electronic health record for healthcare providers to identify, assess, and treat patients with this condition. The database is designed to include various subsets including demographic data, health risks and status, administrative data, patient medical history, existing medical management, and outcomes data. The database includes necessary reminders for the clinician and is integrated into the clinical decision support system (Agency for Healthcare Research and Quality, 2015).
Structured and Unstructured Data
The electronic health database will contain structured and unstructured data that will help in the clinical decision making process. According to Abhyankar et al. (2014), structured data refers to coded data like diagnosis codes and laboratory results whereas unstructured data refers to information included in a clinician’s notes. The structured data that will be pulled or extracted from this electronic health record include demographic data, administrative data, health risks and status, and patient medical history. Demographic data refers to the patient’s personal information such as gender, nationality, name, race and/or ethnicity, address, marital status, immediate family members, area of residence, and emergency information. On the other hand, administrative data will include health insurance facts like insurer, membership, and eligibility. Health risks and status will include dietary and physical information data, lifestyle, behavior, and genetic factors. Patient medical history will include previous and current medical problems and their treatment or management approaches.
The unstructured data in this electronic health record will include clinician’s notes regarding current medical management and outcomes. For instance, when the clinician measures weight and/or BMI, he/she will be required to provide a note on the variables/metrics utilized to carry out the measurements. Additionally, the clinician will provide information regarding patients’ response to previous or current medication in his/her notes.
These identified data entities will have significant relationships with each other to help in the clinical decision making process. The relationships between these data entities or attributes to help in managing this condition are as shown below…
Figure 1: Database Concept Map

Gender
Age
Nationality
Name
Ethnicity/Race
Marital Status
Patient
Health Risks & Status
Medical History
Current Medical Management
Outcomes
Insurance Data


In conclusion, electronic health records provide a suitable mechanism for management of patient information, which is crucial towards promoting better patient outcomes. Through a database management approach, electronic health records provide a suitable framework for healthcare providers to access and manage patient information in a clinical setting. As shown in the example of management of obesity, EHRs are used to store different kinds of patient information, which is easily accessible and utilized in the clinical setting. These systems are effective when incorporated in the clinical decision support systems in a healthcare facility.

References
Abhyankar et al. (2014, January 2). Combining Structured and Unstructured Data to Identify a Cohort of ICU Patients Who Received Dialysis. Journal of the American Medical Informatics Association, 21(5), 801-807.
Agency for Healthcare Research and Quantity. (2015). Use of Electronic Health Records for Addressing Overweight and Obesity in Primary Care (Massachusetts). Retrieved September 25, 2017, from https://healthit.ahrq.gov/ahrq-funded-projects/use-electronic-health-records-addressing-overweight-and-obesity-primary-care
Wood et al. (2012, May 28). An Electronic Health Record-Enabled Obesity Database. BMC Medical Informatics and Decision Making, 12(45). Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3508953/