Clinical interventions for certain disease largely depends on the decision-making based on the data of the patient. The invention of the Electronic Health Record (EHR) system has helped in solving such errors that have usually resulted in health hazards in an attempt to treat prevalent conditions. The advancement of the EHR system to the level where other digital database systems such as the Computerized Physician Order Entry (CPOE) and the Clinical Decision Support System (CDSS) are incorporated has contributed to better decision making and an improved health care quality (Morosetti et al., 2013). Chronic Kidney Disease, also branded as CKD, is one disease that necessitates for a careful intervention grounded on a patient’s clinical data as well as the hospital’s data (Collins et al., 2015). Certain decisions could be fatal without consideration of the patient’s data, and that is why it is recommended to adopt the digital database systems so that the intervention of the CKD is effective and improves the patient experience. Thus, it is under this backdrop that the essay examines the data management system for patients with Chronic Kidney Disease.
The adoption of the EHR database has helped in the management and the treatment of Chronic Kidney Disease. The data that can be stored and is readily available in the management of the Chronic Kidney Disease include mineral metabolism, patient’s comorbidities, electrolyte balance, medication history, allergies, anemia, and the glomerular filtration rate. According to a study carried out by Morosetti et al. (2013), the lack of attention towards Chronic Kidney Disease related illnesses during therapies by non-nephrology expert results in inadequate treatment and referrals hence an increased chance of deaths and the advancement of the disease. Such crucial data could easily be obtained from the digital databases such as the CPOE and CDSS embedded in the EHR database (Lazarus et al., 2016). During a therapeutic intervention on a patient with CKD, the nephrologist or any general practitioner could easily rely on the databases in making decisions that would improve the quality and efficiency of decisions made.
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The success of the treatment of the CKD disease depends on the management of the other related complications and historical data. Other such conditions may contribute to cardiovascular issues and/or death (Macdougall et al., 2016). These are what have been referred to as the comorbidities and they include diabetes, hypertension, anemia, as well as blood sugar. In addition, electrolyte balance, medication history, allergies, anemia and the glomerular filtration rate are amongst the records that have certain complications to the nephrology therapy and can result in fatalities and even deaths (Lazarus et al., 2016). For instance, patients with diabetes and Chronic Kidney Disease are more likely to die of the disease than those without diabetes. Thus, the databases can help the nephrologist and general practitioners to detect a patient’s health records and make sound judgments on how to approach both problems (Macdougall et al., 2016). The medical intervention of a patient with CKD and diabetes at the same time may not be the same as the other with CKD alone.
All these data (patient’s medical history, allergies, and comorbidities) are unstructured data. This is because there are a million of databases within the EHR. As such, the data stored in the electronic record can only be recognized through a search and match of the patient’s information. The data stored within the electronic health record database is obtained from various healthcare facilities, departments, and government databases. It is only through the help of the CPOE and the CDSS that the data can be converted to structured data types which can then be easily identified and used in decision making (Adler-Milstein et al., 2015). Thus, the data within the EHR database is unstructured due to its volume. Nevertheless, with embedded systems such as the CPOE and CDSS, they can easily be obtained and assist in decision making for interventions in CKD patients.
Because of the volume of data within the EHR database, there is a need to have them organized in both structured and unstructured data. The data on patient’s age, medical history, and city and population segments are stored in the form of structured data while the data on comorbidities, electrolyte balance, glomerular absorption rate as well as allergic reactions may be stored as unstructured data (Garfield et al., 2018). The reason for the unstructured data is that most of the patients with CKD have such conditions as stated above which are in high volumes than in the structured data. According to Adler-Milstein et al. (2015), structured data can easily be obtained while unstructured data cannot be easily retrieved, and this can lead to delays when making clinical decisions. Structured data as applied in patients with CKD is useful as it allows for easier identification of the intervention to undertake for the patients.
The data within the database could easily be accessed by the nephrologists whenever needed. The data organization through both the structured and unstructured types would allow for easy identification of the decision to make (Garfield et al., 2018). Additionally, the availability of relevant data for the Chronic Kidney Disease patients would also help the nurses to avoid medication errors. The data in the electronic health record system that can also be retrieved by the CPOE and CDSS interrelate and the doctors can learn on the decision to make (Frisse et al., 2016). Certain conditions may necessitate for a different intervention from other conditions in a CKD patient. Data of patients can be merged by the database and assist in making sound judgment and decisions on the best therapy to undertake. This is helpful as some of the cases may result in fatalities.
As a conclusive remark, the Electronic Health Record system has helped in data storage which can easily be retrieved to manage the Chronic Kidney Disease patients. The invention of the EHR system has helped in solving such errors that have usually resulted in health hazards to treat the Chronic Kidney Diseases. With the help of the CPOE and the CDSS, nephrologists and the general health practitioners can easily make sound judgments that result in quality care and positive patient experience. Storage of the data within the database is in two types, structured and unstructured data. However, they are all interrelated and contribute to the success of the therapies for Chronic Kidney Patients. Some of the CKD patients have numerous other related diseases that may necessitate for better intervention, an intervention only possible through the availability of the patient’s data.
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