DNP 805 EHR Database and Data Management SAMPLE

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

This guide will demonstrate how to complete the Grand Canyon University DNP 805 EHR Database and Data Management SAMPLE  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 SAMPLE     

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 SAMPLE     

The introduction for the Grand Canyon University DNP 805 EHR Database and Data Management SAMPLE  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 SAMPLE     

After the introduction, move into the main part of the DNP 805 EHR Database and Data Management SAMPLE 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 SAMPLE     

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 SAMPLE     

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 SAMPLE

Better clinical outcomes and patient satisfaction are the most important things that every clinician looks forward to. As a doctor of nursing practice prepared nurse, one can be called upon in assisting with the designing of a clinical database of their organization for a better clinical outcome. Therefore,  the purpose of  paper is to,  “identify a patient clinical problem in which using a database management approach provides clear benefit potential, Identifying the data needed to manage this patient problem using information from the electronic health record (EHR), identifying whether the data is structured or unstructured and providing a complete description of the structured and unstructured data from the EHR that are needed to organize a hypothetical database” (GCU, 2017). 

The Clinical problem 

Predicting Sepsis Risk and mortality is a clinical problem that can be managed by data from EHRs to provide clear benefit potential.  According  to Miller, (2016), “Physicians are forever recording information about their patients. They take vital signs, order lab tests and imaging, prescribe medications, check boxes to define patients’ diagnoses for billing purposes, and write or dictate narrative descriptions of each patient’s status.”  All this data is found in structured and unstructured data.  “The widespread adoption of electronic health records by US health care providers is motivating a rapid growth in the use of predictive models to guide clinical decisions, to identify patients at high risk of future events (e.g., 30-day readmission), and to detect disease early, among other applications” (Dey, et al., 2016). This data, structured or unstructured is used in  predicting sepsis in its early stages, which has been found to be one of the “leading cause of death and hospitalization in the United States” (Dey, et al., 2016).   

According  to Desautels, et al. (2016), “Sepsis is defined as a systemic inflammatory response syndrome (SIRS) due to infection.”  They go on to explain that, “Sepsis, severe sepsis, and septic shock are umbrella terms for a broad and complex variety of disorders characterized by a dysregulated host response to infectious insult and because of the heterogeneous nature of possible infectious insults and the diversity of host response, these disorders have long been difficult for physicians to recognize and diagnose” (Desautels et al, 2016). The criteria for SIRS is having, “Temperature >38°C or <36°C, Heart Rate >90 bpm, Respiratory Rate >20 Breaths Per Minute, or Arterial carbon dioxide tension <32 mm Hg (equivalent to 4.3 kPa) and White Blood Cell Count >11 or <4 (×109 cells), or 10% immature (band) forms” (Desautels et al, 2016).  Also measuring three elements,  “lactate level, blood pressure and respiratory rate can pinpoint the likelihood that a patient will die from the disease”. There are several bedside scoring systems that can help nurses and doctors to predict sepsis so that an early intervention helps to prevent morbidity and mortality in these patients. Some of these bedside scoring systems are: 

• “InSight 
•  qSOFA (quick SOFA) 
•  Sequential Organ Failure Assessment (SOFA) score   
•  Modified Early Warning Score (MEWS)  
•  Simplified Acute Physiology Score (SAPS II)  
• Systemic Inflammatory Response Syndrome (SIRS) criteria” (Desautels et al, 2016).   
• AutoTriage “AutoTriage is designed to detect imbalances in homeostasis through the analysis of correlations between patient vital signs and clinical measurements over time. AutoTriage is designed to continuously sample and analyze patient measurement correlations automatically, and be able to alert clinicians to a deteriorating patient’s state” (Calvert J., et al., 2016).  

Structured and Unstructured Data 

According  to Dey, et al., (2016), “Copious longitudinal structured and unstructured data are captured by EHRs to characterize the patient’s demographic (e.g., age, sex, address), health and treatment status, diagnoses, lab test results, and medication orders”. They go on to explain that, “As much as 80% of the EHR data is thought to be in unstructured form and to effectively use EHR data it is important to understand how the data comes to be” (Dey, et al., 2016).  

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Structured data are objective data or hard data that are populated in the EHR, such as vital signs, laboratory values, patient demographics, dates, names, diagnosis codes, identification numbers, and “specific words and short phrases that are often presented in an easy-to-use user point-and-click interface via drop-down boxes with options to select the item of interest” (GCU, 2014). So therefore  in predicting sepsis these data play a vital role, especially the vital signs and laboratory results.  

On the other hand, unstructured data, are data points “that are typically “free text” in progress notes and care plans, comments embedded in the flow sheet, and the like”(GCU, 2014). These comments and notes often contain valuable information that expands upon the structured data and that can provide beneficial input for a database designed to map to patient care, but unfortunately, unstructured data are not easily captured in the somewhat inelastic programming processes of a computer system. That is where innovative and user-based database design comes into play” (GCU, 2014). “Unstructured data is the information that typically requires a human touch to read, capture and interpret properly. It includes machine-written and handwritten information on unstructured paper forms, audio voice dictations, email messages and attachments, and typed transcriptions–to name a few” (DataMark, 2013)  

Description of data relationships that apply to the hypothetical database. 

According to Shortliffe  & Cimino, (2014), “Data provide the basis for categorizing the problems a patient may be having or for identifying subgroups within a population of patients. They also help a physician to decide what additional information is needed and what actions should be taken to gain a greater understanding of a patient’s problem or most effectively to treat the problem that has been diagnosed” There is a need for accurate prediction of mortality risk and patient deterioration in the acute care units.  Advanced warning of patient deterioration is crucial for timely medical intervention and patient management, and accurate risk assessment aids in allocating the limited resources in these acute care units. Clinical Decision Support Systems (CDSS) have been used in these acute care units for predicting patient outcome and to score the severity of patient condition. The vast  majority of prediction models currently in use are based on aggregate baseline patient characteristics. These systems usually rely on a weighted linear combination of features, “such as age, type of admission, and vital sign measurements. However, the most commonly used CDSS such as the Modified Early Warning Score (MEWS), the Sequential Organ Failure Assessment (SOFA), and the Simplified Acute Physiology Score (SAPS II), have suboptimal specificity and sensitivity when applied to patient mortality prediction” (Calvert J., et al., 2016) . These CDSS assessments assume that risk factors are independent from one another, and, therefore, they are not sensitive to the underlying complex homeostatic physiologies of patients. Additionally, they do not account for variations in individual patient physiologies and trends in patient information. The increasing prevalence of EHR has evidence based practice providing a great opportunity to extract clinically relevant patient vital signs and laboratory results for increased predictive value in patient outcome (Calvert J., et al., 2016).  

Conclusion 

As a doctor of nursing practice prepared nurse, called upon in assisting with the designing of a clinical database of an organization for a better clinical outcome, is very important. In this paper it has been shown that there are bedside scoring systems that can predict sepsis at an early stage, leading to a beneficial outcome for both the patient and the clinician. The data can be obtained from both structured and unstructured data. According to McCann, (2014), “Septicemia is currently responsible for the deaths of 36,000 people each year, according to data from the Centers for Disease Control and Prevention, making it the No. 11 leading cause of death in the U.S.”  He goes on to explain that, “Officials estimate average sepsis mortality rates to be more than 16 percent nationwide. In addition to the human death toll, the disease also costs the industry a pretty penny financially. It persists as the No. 1 most expensive hospital condition, costing more than $20 billion annually” (McCann, 2014). So having a designed database in the EHRs that can help clinicians predict this deadly clinical problem is very important.  

Resourses 

Calvert J., Mao Q., Hoffman J.L., Jay M., Desautels T., Mohamadlou H., Chettipally U., Das R.  

(2016) . Using electronic health record collected clinical variables to predict medical intensive care unit mortality Annals of Medicine and Surgery,  11 , pp. 52-57. 

DataMark, (2013)Unstructured Data in Electronic Health Record Systems: Challenges and  

Solutions http://insights.datamark.net/white-papers/unstructured-data-in-electronic-health-record-systems-challenges-and-solutions 

Desautels, T., Calvert, J., Hoffman, J., Jay, M., Kerem, Y., Shieh, L., … Das, R. (2016).  

Prediction of Sepsis in the Intensive Care Unit With Minimal Electronic Health Record Data: A Machine Learning Approach. JMIR Medical Informatics, 4(3), e28. http://doi.org/10.2196/medinform.5909 

Dey, S., Wang, Y., Byrd, R. J., Ng, K., Steinhubl, S. R., deFilippi, C., & Stewart, W. F. (2016).  

Characterizing Physicians Practice Phenotype from Unstructured Electronic Health Records. AMIA Annual Symposium Proceedings, 2016, 514–523. 

Grand Canyon University(GCU). (2014). Lecture notes. Retrieved on 3/25/17 from  

Grand Canyon University(GCU). (2017), EHR Database and Data Management 

https://lc grad3.gcu.edu/learningPlatform/user/users.html?operation=loggedIn#/learningPlatform/user/users.html?operation=studentHome&classId=b8ffe6a2-0942-40fc-b88c-88fc1ee76d19& 

McCann, E (2014). Analytics project slashes sepsis deaths. Retrieved from  

http://www.healthcareitnews.com/news/data-analytics-strategy-slashes-sepsis-death-rates 

Miller, K. (2016) Learning from Patients’ Health Records  

http://biomedicalcomputationreview.org/content/learning-patients’-health-records Shortliffe, E., H. & Cimino, J., J. (2014). Biomedical informatics: Computer applications in  

health care and biomedicine (health informatics) (4th ed.). New York: NY: Springer Science + Business Media. ISBN-13: 9781447144748 

DNP 805 EHR Database and Data Management FINAL Sample

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. 

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. 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. 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. 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. 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