DNP 805 EHR Database and Data Management

Grand Canyon University DNP 805 EHR Database and Data Management– Step-By-Step Guide

This guide will demonstrate how to complete the Grand Canyon University DNP 805 EHR Database and Data Management  assignment based on general principles of academic writing. Here, we will show you the A, B, Cs of completing an academic paper, irrespective of the instructions. After guiding you through what to do, the guide will leave one or two sample essays at the end to highlight the various sections discussed below.

How to Research and Prepare for DNP 805 EHR Database and Data Management     

Whether one passes or fails an academic assignment such as the Grand Canyon University NUR 550 Benchmark – Evidence-Based Practice Project: Literature Review depends on the preparation done beforehand. The first thing to do once you receive an assignment is to quickly skim through the requirements. Once that is done, start going through the instructions one by one to clearly understand what the instructor wants. The most important thing here is to understand the required format—whether it is APA, MLA, Chicago, etc.

After understanding the requirements of the paper, the next phase is to gather relevant materials. The first place to start the research process is the weekly resources. Go through the resources provided in the instructions to determine which ones fit the assignment. After reviewing the provided resources, use the university library to search for additional resources. After gathering sufficient and necessary resources, you are now ready to start drafting your paper.

How to Write the Introduction for DNP 805 EHR Database and Data Management     

The introduction for the Grand Canyon University DNP 805 EHR Database and Data Management  is where you tell the instructor what your paper will encompass. In three to four statements, highlight the important points that will form the basis of your paper. Here, you can include statistics to show the importance of the topic you will be discussing. At the end of the introduction, write a clear purpose statement outlining what exactly will be contained in the paper. This statement will start with “The purpose of this paper…” and then proceed to outline the various sections of the instructions.

How to Write the Body for DNP 805 EHR Database and Data Management     

After the introduction, move into the main part of the DNP 805 EHR Database and Data Management assignment, which is the body. Given that the paper you will be writing is not experimental, the way you organize the headings and subheadings of your paper is critically important. In some cases, you might have to use more subheadings to properly organize the assignment. The organization will depend on the rubric provided. Carefully examine the rubric, as it will contain all the detailed requirements of the assignment. Sometimes, the rubric will have information that the normal instructions lack.

Another important factor to consider at this point is how to do citations. In-text citations are fundamental as they support the arguments and points you make in the paper. At this point, the resources gathered at the beginning will come in handy. Integrating the ideas of the authors with your own will ensure that you produce a comprehensive paper. Also, follow the given citation format. In most cases, APA 7 is the preferred format for nursing assignments.

How to Write the Conclusion for DNP 805 EHR Database and Data Management     

After completing the main sections, write the conclusion of your paper. The conclusion is a summary of the main points you made in your paper. However, you need to rewrite the points and not simply copy and paste them. By restating the points from each subheading, you will provide a nuanced overview of the assignment to the reader.

How to Format the References List for DNP 805 EHR Database and Data Management     

The very last part of your paper involves listing the sources used in your paper. These sources should be listed in alphabetical order and double-spaced. Additionally, use a hanging indent for each source that appears in this list. Lastly, only the sources cited within the body of the paper should appear here.

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Sample Answer for DNP 805 EHR Database and Data Management

EHR Database and Data Management 

Databases in the medical field provide a suitable framework for collecting, analyzing, and monitoring vital health information such as tests, expenditures, invoicing and transactions, patient information, etc. These records must be stored private from the general public while being widely available to health care providers who utilize them to save lives (Pastorino et al., 2019). This paper seeks to describe how a database can be used to diagnose chronic obstructive pulmonary disease (COPD) early diagnosis. 

Clinically Based Patient Problem 

COPD is a prevalent long-term condition marked by acute respiratory cough and shortness of breath, coughing, and sputum secretion. COPD is typically caused by prolonged exposure to hazardous chemicals and pollutants (Agarwal et al., 2022). Smoking accounts for approximately 85 percent of patients with COPD (Asamoah-Boaheng et al., 2022). Smoking is the leading cause of respiratory injury and asthma. COPD may also be caused by smoke inhalation from fuel combustion (Choi & Rhee, 2020). If the evidence is in the person’s private files, the caregiver may ignore it. When the problem exacerbates, non-smokers are usually diagnosed with COPD (Choi & Rhee, 2020). Exacerbation is defined by deteriorating health problems such as increasing dyspnea, continuous sneezing, and a change in the color of the sputum (Holmes & Murdoch, 2017). Exacerbations individuals incur higher healthcare expenditures, and certain drugs used in therapy, such as corticosteroids, have long-term negative consequences (Asamoah-Boaheng et al., 2022). COPD might also be caused by genetic anomalies, including severe hereditary impairment of alpha-1 antitrypsin (AATD) (Asamoah-Boaheng et al., 2022), which could be overlooked in large amounts of data. 

Individuals have one of two phenotypes that vary in intensity: acute emphysema or bronchiolitis. COPD must be evaluated in individuals who have difficulty breathing, sputum secretion, or persistent cough (Agarwal et al., 2022). Nevertheless, there are various other diagnoses for COPD, such as anaemia, lung cancer, persistent asthma, etc. COPD is often associated with concurrent chronic conditions such as diabetes and obesity, both of which produce various COPD-related symptoms such as cough and shortness of breath (Choi & Rhee, 2020). This postpones the diagnosis of COPD. The best technique to verify COPD in a person is high-quality spirometry. Spirometry is recommended when COPD is detected, and for a non-smoker with associated conditions, spirometry may be performed when the disease has progressed. The slow symptom onset further distinguishes COPD, so an individual may fail to detect dyspnoea despite having chronic coughing, causing COPD diagnosis to be delayed. 

Early diagnosis is important in establishing the appropriate treatment course considering the individual’s severity and phenotype. Early detection has been found to enhance treatment experience by lowering the incidence and frequency of flare-ups, lowering treatment costs, and preventing long-term adverse effects associated with pharmacological treatment (Asamoah-Boaheng et al., 2022). Hospitals also avoid wasting money owing to erroneous treatment (Choi & Rhee, 2020). 

Conceptual Database Design 

The healthcare database is the backbone of the electronic health record, holding a wealth of organized and unstructured user data (Pastorino et al., 2019). The database material will only be relevant in the earlier detection and successful management of COPD if the unorganized data is analyzed to provide data that can be put inside the predefined metadata. The intended patient result in building the healthcare database will be the early identification of COPD by giving the provider access to patient health information from the database, which will then be utilized to detect COPD. The database shall contain comprehensive and accurate information received from or entered at the various hospital settings where the person has been treated. The data must also be accurately evaluated and structured, making it easier to remove differential diagnoses and do COPD confirmation spirometry. 

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The data items needed to construct the database to facilitate early diagnosis of COPD are identified and categorized as unstructured or structured during the conceptual design stage. Whereas structured data from the EHR may be directly filled into the system, unstructured data from caregivers’ and doctors’ notes are processed using natural language technologies to make data sensible within the environment. The relational database will include a healthcare-specific guideline and a language processing technology. The natural language-specific guideline deconstructs unstructured format input to enable NLP creators to execute specialized natural language processing inspections, including detecting the presence of vague terms. The healthcare-specific benchmark will then seek the relevant healthcare words and record the proper information in the correct fields depending on the context of the NPL transcribers. 

Thus far, the database executes conventional database operations. The registry will include a unique COPD risk area to aid early detection. Whenever health records are processed, all past respiratory illnesses will be recorded under the COPD risk area. This section will be filled with indicators such as a persistent cough, breathlessness, and sputum secretion. The space will be filled with debris and hazardous gases from the patient’s surroundings. COPD is distinguished by the slow onset of symptoms that may or may not occur concurrently. Initially, an individual may describe dyspnoea without other symptoms, which may be missed and linked to a concurrent condition. Nevertheless, the data will be saved in the database so that when the person experiences a new symptom, such as mucus production, and the nurse brings up their record, the COPD risk area will be updated with all the identified symptoms thus far leading to early diagnosis. If the facility has a CDSS connected to the database, the CDSS program will notify the physician of a possible COPD diagnosis. 

Attributes and Data Entities 

1. Patient Details 

This entity is a personal identification; it is connected to all the other properties which provide this person’s profile in the medical setting. All of its characteristics are organized, and these traits are objective. 

Attributes 

• Name (Varchar) 
• Gender (Boolean) 
• Age (Int) 
• Address (Varchar) 

2. Health Status 

The entity represents the patient’s medical history as evaluated by the physician. Because the physician’s assessment and perception of the consequences of the patient’s condition may differ, all of the qualities are unstructured. 

Attributes 

• BMI (Int) 
• Height (Int) 
• Weight (Int) 
• Blood Pressure (Int) 

Because data submitted into the database is not primarily utilized for the initial COPD diagnosis, the entities described below will include more than the given characteristics. On the other hand, the features indicated must be included in the COPD risk section if the risk exists. 

4. Genetics 

The entity has qualities that describe physical and genetic anomalies that may impact the likelihood of COPD. 

Attributes 

• Alpha-1 Antitrypsin Dysfunction (Boolean) 

3. Environment  

Identifies the patient’s regular surroundings, which may raise COPD risk. 

• Second-hand smoking inhalation (Boolean) 
• Use of biofuels (Boolean) 
• Work-related toxic contact (Boolean) 

6. Medical history  

Specifies the health history and updates the COPD risk field with factors that elevate the chance of COPD. 

• Smoking history (Varchar) 
• Smoking Frequency (Varchar) 
• Respiratory diseases (Varchar) 

5. Signs and Symptoms 

This entity explains the patient’s present symptoms and the rationale for the hospitalization. Every symptom is regarded as a separate feature. The following symptoms will be included in the COPD risk field:  

• Severe cough 
• Sputum secretion 
• Dyspnoea 

The logical data model architecture concerns data modeling, which specifies the link between the objects. Figure 1 below illustrates the general conceptual map for the proposed database. 

Figure 1: Conceptual map 

Conclusion 

The significance of a database in medical diagnosis cannot be ignored. It is critical for physicians, caregivers, and executive management to have timely and error-free access to detailed patient data. Hospital services rely on the efficiency, accuracy, and effectiveness of medical databases to ensure timely diagnosis and treatment, as in the case of COPD. 

References 

Agarwal, A. K., Raja, A., & Brown, B. D. (2022). Chronic Obstructive Pulmonary Disease. In StatPearls. StatPearls Publishing. http://www.ncbi.nlm.nih.gov/books/NBK559281/ 

Asamoah-Boaheng, M., Farrell, J., Osei Bonsu, K., & Midodzi, W. K. (2022). Examining risk factors accelerating time-to-chronic obstructive pulmonary disease (Copd) diagnosis among asthma patients. COPD: Journal of Chronic Obstructive Pulmonary Disease, 19(1), 47–56. https://doi.org/10.1080/15412555.2021.2024159 

Choi, J. Y., & Rhee, C. K. (2020). Diagnosis and treatment of early chronic obstructive lung disease(Copd). Journal of Clinical Medicine, 9(11), 3426. https://doi.org/10.3390/jcm9113426 

Pastorino, R., De Vito, C., Migliara, G., Glocker, K., Binenbaum, I., Ricciardi, W., & Boccia, S. (2019). Benefits and challenges of Big Data in healthcare: An overview of the European initiatives. European Journal of Public Health, 29(Supplement_3), 23–27. https://doi.org/10.1093/eurpub/ckz168 

DNP 805 Evaluation of Healthcare Technology Sample

Evaluation of Healthcare Technology 

Nursing practice has formed a critical part of patient care in the past years and at present by adapting to the ever-changing patient care landscape. There has been an increased call for better and improved patient care making the stakeholders explore options that can result in better results. One such strategy that has gained attention and momentum is the technology and technological applications (Wu & Luo, 2019). Technology has largely been used in the health care sector to enhance disease diagnosis, prevention, treatment, and management. The implication is that patient care, treatment, and disease management have greatly improved over the years, preventing several death cases. With continued innovation and invention, more and better technological applications are likely to enter the patient care environment to make the processes even better. As such, the purpose of this paper is to explore a technology that has been studied in this course, explore the measurement and evaluation of its elements, and the user interfaces evaluation. In addition, this write-up will explore the determination of the functionality of the technology and any possible improvements. 

The Identified Technology 

Several technologies and technological applications have been explored in this course. Therefore, the chosen technology to be addressed in this assignment is assistive technologies for older adults. These are technologies that have been invented to help older individuals have better outcomes by preventing falls, using alarms to detect their movements, and ensuring that they are safe from harm, among others. Among such assistive technologies for older individuals are smart homes (Pirzada et al., 2018). Smart homes are created in such a way that the home environment contains devices that help in monitoring the individuals. Such monitoring improves their independence and enhances their life quality. 

The Elements Measurement and Evaluation 

As earlier indicated, smart homes improve older adults’ independence and quality of life. Therefore, a smart home has various elements, for example, smart devices. Smart devices are used to enhance smart home functionality. These devices include device switches, plugs, lighting, speakers, cameras, appliances, accessories, and sensors. The evaluation of these smart home elements is undertaken by considering how best they enhance the smart home functionality (Pal et al., 2018). The gateway is one of such elements. A gateway is a hub, controller, or bridge that connects with smart devices like WiFi.   

Evaluation can also be done based on how well the elements are capable of communicating with the smart devices and controlling activities. Among the most common gateways are WiFi, IR, Zigbee, Ethernet, Z-wave, and Bluetooth. The other element is user communication strategies, including the use of smartphones, voice assistants, sensors, and automation. Servers are also used in storing and retrieving information which may be evaluated by how well and efficiently they help the user to retrieve information (Pal et al., 2018). Some types include Home Assistant, Amazon Server, and Apple Home Kit. 

Evaluation of the User-Technology Interface 

The user interface or human-machine interface is one of the factors that have to consider when designing a technology to be used by human beings. It refers to an information exchange platform between the technology system and the user. The smart home also has a user interface with elements that can be evaluated. One of them is easy to learn and use (Pal et al., 2018). This refers to whether the users can easily learn and operate the smart home functionalities. The next one is its operation which may entail how easy it is to remember. User satisfaction is another important element, and it measures how levels of satisfaction the technology bring to the users, which in this case is the level of satisfaction regarding the use of smart homes. Error frequency and magnitude are other elements to be considered. It refers to the number of errors that occur and how serious the errors are.  Interactive efficiency is another important element. This element refers to the application of technology to fulfill specific tasks. 

Assessment For Functionality 

Smart homes play a critical role in older individuals’ health and life as they promote better health outcomes and better living. The implication is the smart homes’ user interface should be formulated to help fulfill the safety needs and perform particular tasks as needed. To enhance the interaction, various interfaces can be used. One of them is input controls such as buttons, text fields, checkboxes, radio buttons, dropdown lists, list boxes, toggles, and date fields (Hargreaves & Wilson, 2017). Navigation components are also critical, and they include breadcrumb, slider, the pagination search field, tags, and icons. In addition, there are informational components such as tooltips, icons, progress bar, notifications, message boxes, and modal windows. 

Assessing these elements is important as they help in determining the functionality. For example, one can tell the technicality of a user interface by looking at the input controls. Smart homes are majorly formulated for elderly individuals; therefore, the user interface needs to be as simple as possible. The optimization of smart home use for better results may mean that older adults have to navigate the functionalities to achieve specific tasks. The implication is that elements used for navigation, such as icons, should be representative and clear to enhance their ability to navigate and interpret the system (Hargreaves & Wilson, 2017). Complex navigation bars the user from using some of the best functionalities which could have been of good use. Therefore, their use is limited, or they are not used at all. 

Informational components like the tooltips, progress bar, and message boxes, among others, can also be at the center of assessment. Therefore, their design should also be as simple as possible for better manipulation. For example, in the case of an error message, especially a technical error, the user should be able to effortlessly trace the message in the message box and appropriately act on the same (Hargreaves & Wilson, 2017). Such is only possible with a simple design. However, in cases where the informational components have complexities, then the user may miss critical system warnings, notifications, and messages, which can lead to poor functions. 

Suggestions for Improvement 

Even though the discussed smart homes improve the elderly’s health outcomes, some of its components and elements can be adjusted or improved to make them better. The elements that can be improved include smart devices such as switches, plugs, lighting, speakers, cameras, appliances, accessories, and sensors. One of the possible improvements is procuring quality smart devices that will have few technical issues or errors (Demiris et al., 2017). The end result is enhanced functionality of the system. The gateway is a major component of the smart home; hence it needs to be of high quality and not error-prone. Therefore, it should be acquired from trusted suppliers. In addition, security is key in such systems. Therefore, the devices used as part of the system, such as smartphones, need to have security enhancements to bar potential access by unauthorized individuals. 

Conclusion 

Smart homes are one of the most common assistive technologies for older individuals who usually have more health complications, compromised gait and balance, and reduced mobility. Therefore, this assignment has explored smart homes and their components. The components need to be efficient to improve the functionality. In addition, the user interface should be simply built to enable elderly individuals to navigate the system and perform specific tasks. 

References 

Demiris, G., van Hoof, J., & Wouters, E. (2017). Handbook of smart homes, health care, and well-being. Springer International Publishing. 

Hargreaves, T., & Wilson, C. (2017). Smart homes and their users. Cham: Springer International Publishing 

Pal, D., Funilkul, S., Charoenkitkarn, N., & Kanthamanon, P. (2018). Internet-of-things and smart homes for elderly healthcare: An end-user perspective. IEEE Access, 6, 10483-10496. https://doi.org/10.1109/ACCESS.2018.2808472 

Pirzada, P., White, N., & Wilde, A. (2018, April). Sensors in smart homes for independent living of the elderly. In 2018 5th International Multi-Topic ICT Conference (IMTIC) (pp. 1-8). IEEE. https://doi.org/10.1109/IMTIC.2018.8467234 

Wu, M., & Luo, J. (2019). Wearable technology applications in healthcare: a literature review. Online J. Nurs. Inform, 23(3). https://www.himss.org/resources/wearable-technology-applications-healthcare-literature-review