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Essay Available:
Pages:
4 pages/≈1100 words
Sources:
Check Instructions
Style:
APA
Subject:
Health, Medicine, Nursing
Type:
Research Paper
Language:
English (U.S.)
Document:
MS Word
Date:
Total cost:
$ 25.06
Topic:

EHR Database and Data Management

Research Paper Instructions:

As a DNP-prepared nurse, you may be called upon to assist in the design of a clinical database for your organization. This assignment requires you to integrate a clinical problem with data technologies to better understand the components as well as how those components can lead to better clinical outcomes.
General Guidelines:
Use the following information to ensure successful completion of the assignment:
• This assignment uses a rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion.
• Doctoral learners are required to use APA style for their writing assignments. The APA Style Guide is located in the Student Success Center.
• Use primary sources published within the last 5 years. Provide citations and references for all sources used.
• Refer to the examples in the topic resources for health care database examples.
• You are required to submit this assignment to LopesWrite Zero plagerism
Directions:
For this assignment, write a 1,100-word paper in which you: Please do not exceed 1,100
1. Select a clinically based patient problem in which using a database management approach provides clear benefit potential.
2. Consider how a hypothetical database could be created to assist with this clinically based patient problem. Identify and describe the data needed to manage this patient problem using information from the electronic health record (EHR).
3. Include a brief description of the patient problem that incorporates information needed to manage the specific problem. Describe what information is required for the patient to manage the condition and how the database and health care provider can be incorporated into the approach for better health outcomes.
4. Describe each entity (data or attribute) that will be pulled from the EHR as either structured or unstructured and provide an operational definition for each. Structured data is more easily searchable and specifically defined. For example, structured data can be placed in a drop-down menu like hair color: brown, black, grey, salt and pepper, blonde, platinum, etc. Unstructured data is data that would be included in a nurse's notes. An operational definition is how a researcher or informatics specialist decides to measure a variable. For example, when the nurses enter height into the EHR, do they enter height as measured in inches or centimeters or in feet and inches?
5. Provide a complete description of data entities (the objects for which you seek information, e.g., patients) and their relationships to the attributes collected for each entity (data collected for each entity, e.g., gender, birthdate, first name, last name) that apply to the hypothetical database. You can use a concept map similar to the "Database Concept Map" resource, to help you describe the relationships between each entity and its attributes.

Research Paper Sample Content Preview:

EHR Database and Data Management
Name
Institution
Due Date
EHR Database and Data Management
Introduction
Congestive heart failure is a chronic disease that demands high management costs. The condition continues to be a problem due to a lack of consistency in managing the disease based on established guidelines. The condition occurs as a result of the heart failing to pump blood, resulting in symptoms such as weight gain and swelling of dependent limbs, usually accompanied by shortness of breath effectively. The build-up of the fluid within the heart occurs in the scenario where kidneys no longer excrete water and salt. Such a situation leads to an increase in blood volume alongside excess fluid that leaks into the body tissues. At times, cases of CHF can become very lethal to the patient (Westphal et al., 2018). Notably, there is a gap in care between cardiologists and general practitioners in CHF patients' care, leading to unnecessary readmissions of patients with CHF conditions (Rusli et al., 2020). Therefore, CHF is recognized as a leading chronic health disease prevalent amongst the elderly, providing more consistent management as per established guidelines. Shafie et al. (2019) assert that the possibility of reducing the mean annual cost of CHF with an assurance of efficient clinical management leads to the prevention of the condition. This paper provides a discussion on data required for CHF patients' management that ensures adherence to established care guidelines—also incorporating information and identifying data required for CHF patients' management and discussion of structures and unstructured data including the rationale.
Creation of hypothetical database needed to manage Congestive Heart Failure
Acute heart failure is one of the most prevalent and costly hospital admissions for those over 65 years of age. However, early assessment, the aspect of monitoring the associated pharmacological, and established guideline treatment are key in providing efficient and cost-effective care (Rusli et al., 2020). The creation of a database is key in monitoring the effective implementation of evidence-based guidelines in CHF management within the electronic medical record (EMR). The application of EMR as a source of data requires both structured and unstructured data types for the management of CHF patients.
The application of remote monitoring systems is deemed appropriate in managing patients suffering from CHF, which could lead to a reduction in death rates and re-hospitalizations, including multiple systematic reviews. The quality improvement primer considers six initial steps that focus on improving CHF care. This provides for ways through which patients like Mr. X could be kept well and out of the hospital. Firstly, the aspect of acknowledging the improvement required and setting of a culture that necessitates improvement. Then there is the setting of a Quality Improvement team, understanding the local problem, generation of improvement strategies that relate to the local problem, monitoring, testing, refining improvements, and finally, data analysis and interpretation. The premier in this study applies the use of hypothetical data to highlight the principles of QI (Westphal et al., 2018).
Hypothet...
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