DNP 805 Assignment EHR Database and Data Management Essay

DNP 805 Assignment EHR Database and Data Management Essay

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

 

This guide will demonstrate how to complete the DNP 805 Assignment EHR Database and Data Management Essay 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 Assignment EHR Database and Data Management Essay                                   

 

Whether one passes or fails an academic assignment such as the Grand Canyon University   DNP 805 Assignment EHR Database and Data Management Essay 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 Assignment EHR Database and Data Management Essay                                   

 

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

 

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

 

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 Assignment EHR Database and Data Management Essay                                   

 

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 Assignment EHR Database and Data Management Essay

According to medical studies in regards to cancer treatment, Prostate cancer is regarded as the most common illness among men (Hernandez-Boussard, Blayney & Brooks, 2020). Recently diagnosed men encounter complex therapy choices, each with various risks of obtained patient-centered outcomes such as urinary and erectile dysfunction. In these present times, care providers and patients find it difficult to contrast the trade-offs among patient-centered results across various treatments since the experimental evidence about these trade-offs does not exist (Hernandez-Boussard, Blayney & Brooks, 2020). According to experts, this is because patient-centered outcomes are not consistently recorded in computable formats.  For healthcare institutions to enhance cancer care and accuracy of data, evidence recorded in computable forms should be placed in the hands of the clinicians and patients through a Web-based tool (Hernandez-Boussard, Blayney & Brooks, 2020).  There are three significant innovative measures proposed in this article concerning the management of cancer patient’s data.

Select a family to complete a family health assessment. (The family cannot be your own.)

Before interviewing the family, develop three open-ended, family-focused questions for each of the following health patterns:

1. Values, Health Perception
2. Nutrition
3. Sleep/Rest
4. Elimination
5. Activity/Exercise
6. Cognitive
7. Sensory-Perception
8. Self-Perception
9. Role Relationship
10. Sexuality
11. Coping

The first proposed approach endorses the development of an EHR prostate cancer database that will create an opportunity for clinical information to be analyzed alongside diagnostic details (Hernandez-Boussard, Blayney & Brooks, 2020).  The second approach creates new ontological representations of quality metrics that are public and reliable across the EHR programs. The third proposed approach involves gathering a robust data information mining workflow that expands on modern methods by centering on ontology-based dictionaries to interpret the free text (Hernandez-Boussard, Blayney & Brooks, 2020). Combining these three innovative approaches will uniquely allow both clinicians and patients to use current EHRs to understand the trade-offs among patient-centered outcomes across various treatments adequately.

Utilizing EHR to measure and enhance prostate cancer treatment is essential since it creates room for healthcare facilities to share necessary information (Hernandez-Boussard, Blayney & Brooks, 2020).  Through the EHR, it easy to develop the building blocks desirable to recognize quality metric information in EHRs. It is of the essence to create an EHR database, map quality metrics to medical vocabularies, and develop electronic quality metric phenotypes. The EHR program creates a web-based tool that integrates the empirical evidence and clinical characteristics to evaluate patient personalized risk prediction that assists care providers and patients in selecting a treatment option (Hernandez-Boussard, Blayney & Brooks, 2020). These options provide the best-anticipated quality of care given the significance they assign to each patient-centered outcome. Making use of EHR will assist in addressing a crucial gap in evidence for prostate cancer therapy and research by offering care providers and patients practical evidence desirable to contrast the trade-offs among patient-centered outcomes across various treatments.

Prostate cancer is a complex illness, and existing therapies have associated risks of patient-centered outcomes, although no definite evidence exists on the variation of hazards across treatments (Hernandez-Boussard, Blayney & Brooks, 2020). Therefore, this article’s proposal develops measures to gather patient-centered outcomes recorded in EHRs and a risk evaluation tool to estimate the personalized hazards of issues across therapies. Such data will assist policymakers and healthcare staff in enhancing a patient’s healthcare experience and results (Hernandez-Boussard, Blayney & Brooks, 2020). Moreover, Electronic Health Records helps clinicians by making it easy to gather and analyze information regarding patients in a meaning full manner that keeps track of patients over time and recognize trends associated with cancer. According to studies concerning cancer diagnosis and therapy, using EHRs is associated with enormously higher healthcare quality for cancer (all types of cancer) (Hernandez-Boussard, Blayney & Brooks, 2020). The EHRs are essential since it provides information without difficulty keeps records of treatment and enables individuals to assess knowledge better.

In the current healthcare organization, no one is engaged in any unit of healthcare delivery, or planning can fail to recognize immense changes in the perspective of data management (Magyar, 2017). This article reviews examples of healthcare databases used in healthcare organizations to enhance patient care concerning cancer therapy (Magyar, 2017). For one to comprehend the range of EHRs that the healthcare department might access and why there might be a concern in regards to the protection of individual data, care providers are obliged to consider various factors of EHRs data management such as comprehensiveness (Magyar, 2017). According to healthcare studies, comprehensiveness portrays the completeness of data of patient’s healthcare experiences and data pertinent to an individual patient (Magyar, 2017). Comprehensiveness incorporates the amount of data care providers have regarding patients both for each personal experience with the healthcare department as well as treatment procedures. Data that is comprehensive incorporates demographic information, organizational data, health risks, and conditions, patient therapeutic history, existing management of health status, and result statistics (Magyar, 2017).

Demographic information

Demographic information consists of statistics including age, race, gender, national origin, marital standing, address of dwelling, names of direct relatives and other details concerning direct relatives, and alternative data (Magyar, 2017). Moreover, demographic data in the EHR also include information regarding employment status and employers, education level, and some indicator of socioeconomic rank.

Administrative Information

Administrative data incorporates information concerning health insurance such as membership and admissibility, dual coverage, and obligatory copayments and deductibles for a provided benefits bundle (Fox, Aggarwal, Whelton & Johnson, 2018). Administrative data commonly recognize care providers with an exclusive identifier and probably offer extra specific data. These may comprise the kind of physician, physician specialty, and culture of the institution.

Health risks and Health status data

Health risks data reflects the lifestyle and characters of an individual. For instance, in cases of cancer patients, the care provider might ask if the individual uses tobacco products or regularly participates in strenuous activities (Magyar, 2017). Health risk data also includes information about genetic factors and family history, such as whether a person has first-degree relatives with a significant class of cancer.

Health status information is generally and often reported by individuals themselves. Health condition data reflects factors of health such as physical status, emotional and mental actuality, intellectual functioning, communal and role functioning, and observations of an individual’s health in the past, current, and future and contrasted with that of an individual’s peers. Health conditions and quality of life measures are commonly considered outcomes of healthcare (Fox et al., 2018). Still, evaluators and researchers also require such information to record their analysis of the mix of patients and the range of severity of health status.

Patient Therapeutic History

Patient medicinal history incorporates information on previous health check encounters, including hospital admissions, surgical processes, pregnancies, and live births (Fox et al., 2018). It contains data on past medical issues and probably the family history of events such as intoxication or parental separation (Magyar, 2017). Additionally, although such information is essential for quality care, they may be vital for case-mix and severity alteration.

Existing Medical Management

Existing medical management includes information in regards to the gratification of experience procedures and parts of the patient file (Magyar, 2017). Such data might replicate health screening, existing health issues, and diagnosis, treatment processes conducted, laboratory tests performed, and counseling offered.

Outcomes information

Outcomes data includes a range of choices of procedures of the effects of health care and the outcome of different health issues across the spectrum, from mortality to increased stages of performance and wellbeing (Fox et al., 2018).  Outcomes data reflect healthcare occasions such as readmission to healthcare institutions or unplanned difficulties and side effects of care. Consequently, outcome data often incorporates measures of satisfaction with patient care.  Results evaluated weeks or months after therapy procedures, and by information straight from individuals or immediate relatives, are desirable. However, such data appear to be the least commonly found in the secondary record (Magyar,2017). The EHRs program manages and presents the historical and existing test result in suitable healthcare providers. Through this, healthcare professions can review the patient’s information with the ability to filter and compare the outcome. Additionally, the EHR system allows physicians to manage patient records electronically and store them for future references.

Conclusion

According to medical studies, the more inclusive the EHR is, the more present and probably more sensitive data regarding patients is likely to be. The comprehensiveness of the Electronic Health Records has a significant correlation with concerns regarding confidentiality and privacy. One of the most significant approaches to ensure individuals have complete advantage of the benefits of EHRs and enhance quality care, preventive cancer care, and patient outcome is to attain meaningful use. By healthcare institutions achieving meaningful use, they can obtain benefits beyond monetary enticements. Over the past years, approximately every significant healthcare institute invested majorly in computerization. These technological advances, such as EHRs, are allowing care providers to present a faster and more efficient patient outcome.

References

Magyar, G. (2017). Blockchain: Solving the privacy and research availability tradeoff for EHR data: A new disruptive technology in health data management. In 2017 IEEE 30th Neumann Colloquium (NC) (pp. 000135-000140). IEEE.

Hernandez-Boussard, T., Blayney, D. W., & Brooks, J. D. (2020). Leveraging digital data to inform and improve quality cancer care.

Fox, F., Aggarwal, V. R., Whelton, H., & Johnson, O. (2018, June). A data quality framework for process mining of electronic health record data. In 2018 IEEE International Conference on Healthcare Informatics (ICHI) (pp. 12-21). IEEE.

DNP 805 Assignment EHR Database and Data Management Essay

Assessment Description

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. A link to the LopesWrite technical support articles is located in Class Resources if you need assistance.
• Learners will submit this assignment using the assignment dropbox in the learning management system. In addition, learners must upload this deliverable to the Learner Dissertation Page (LDP) in the DNP PI Workspace for later use.

Directions:

For this assignment, write a 1,000-1,250 word paper in which you:

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.

Sample Answer 2 for DNP 805 Assignment EHR Database and Data Management Essay

Patient Clinical Problem

Adult patients undergoing colon cancer surgery who are prone to surgical site infections are the clinical issue of focus. Individuals undergoing colorectal cancer surgery had an elevated risk of morbidity (20%-40%) and mortality (2%), mostly due to postoperative surgical site infections, according to Li et al. (2018). According to reports, the risk of postoperative infections in patients undergoing colon surgery has grown by more than 25%. (Grundmeier et al., 2019; PSNet, 2019). Infections, particularly hospital acquired infections (HAIs), increase the length of stay in health facilities, increase susceptibility to other illnesses, and raise medical costs. Providers and organizations can use creative ways, such as the creation of EHR databases, to manage data to prevent and minimize the occurrence of surgical site infections.

The adoption of information technology and reforms aimed at expanding access to healthcare services has led to the generation and accumulation of huge amounts of data by health care systems and organizations from multiple sources. The meaningful use incentive for utilization of the electronic health records (EHRs) has created vast and efficient data storage and databases that healthcare systems and providers can use to manage some of the common conditions affecting populations across the care continuum (Kruse et al., 2018). The purpose of this assignment is to discuss HER databases and management of data by focusing on a patient problem of surgical site infection. The paper explains incorporation of the information required to manage the issue, data required so that the database can manage the condition and enhance patient outcomes. The paper also describes entities that will be pulled from the EHRs and their relationships that the hypothetical database.

Patient Clinical Problem

The clinical issue of focus entails adult patients undergoing surgical procedure for colorectal cancer and susceptible to surgical site infections. According to Li et al. (2018), individuals undergoing colorectal cancer surgery have increased risk for morbidity (20%-40%) and mortality about 2% caused primarily by postoperative surgical site infections. Reports suggest that the rate of surgical infections has increased by over 25% in patients having a colon surgery (Grundmeier et al., 2019; PSNet, 2019). The implication is that infections, especially hospital acquired infections (HAIs) lead to increased length of stay in health facilities, increases susceptibility to other conditions and leads to a rise in medical cost. Imperatively, providers and organizations can leverage innovative approaches like development of EHRs databases to manage data and prevent and reduce the rate of surgical site infections.

Hypothetical EHR Database and Data Required

EHRs are digital forms of patient health information (PHI) and include personal contact data, patient medical history, allergies and treatment plans as well as test and diagnostic results. The benefits of EHRs include improving positive patient outcome and population health, organization and analysis of patient information, and enhancing clinical efficiency through better workflow, timelines, and quality of care. EHRs data can be deployed to create and internally authenticate a data-driven standard to detect at risk patients (Kruse et al., 2018). It also helps in clinical decision support to effectively identify patient at increased risk for surgical site infections. A hospital data management system incorporate all data associated with the facility in an organized manner and is a critical component of EHRs system to enhance interoperability and decision making among providers and patients.

Incorporating Information to Manage the Problem

The EHRs is widely used to gather patient health information including all their details based on the system requirement and are fed into the database. To retrieve information from such a system, one needs to write queries statement with all the criteria needed for its development. The system will offer a multi-tasking functionality by recording patient details while also taking the on duty staff details. The hypothetical database will be created to help colorectal cancer patients. The database will require information collected in the EHRs to input these details automatically. The criteria for data that will be pulled in into the database would include all patients with colon surgical procedure. The additional data would include start and end time of the procedure, room time, gender of the patient, past medical history, weight, family medical history, place of residence, any allergies and previous procedures. The database will also contain the time of medication administration, especially the administration of preoperative antibiotics (De Simone et al., 2018). The goal of these details is to ensure that preoperative antibiotics are administered 30 minutes before the set time for surgical incision.

Patient Problem Incorporating Information Needed

The patient issue being addressed is surgical site infection after a surgical procedure for adult patients with colon or rectal cancer. Surgical site infections (SSIs) after a colorectal procedure are a prevalent issue that impact patient safety and quality care outcomes with numerous reports asserting that close to 25% of such patients get these infections (Coccolini et al., 2018). These infections present a potentially preventable source of mortality, morbidity and resource use in healthcare (Kethman et al., 2019). SSIs are being used increasingly to measure a health care quality status and the main focus of Hospital Acquired Condition Reduction Program (HACRP). The HACRP is a pay-for-performance model that lowers payment to the bottom 25% worst-performing entities in management of surgical site infections and other types of HAIs. The program is focused on enhancing value-based care and paying providers for quality delivered and not quantity.

The pulled information into the database to manage the condition will entail gender, weight and patient past medical history. The database will also have family medical and health history to help in making effective clinical decisions. The data will help providers to determine the susceptibility or risk of the patient for surgical site infections. By pulling these patients into the database using specific information, the providers will ascertain the patients at higher risk for SSI, and give them antibiotics and treat them prophylactically to reduce their vulnerability to infections at the surgical site (Gerson et al., 2019). The implication is that this approach will lead to better health outcomes for the patients.

Data Entities Description

The EHRs implementation leads to collection of vast quantities of data; both structured and unstructured, that providers and facilities need to use to make decisions. Structure data comprises of patient demographics, medications, allergy and vitals, and family history. Structured information pulled in the EHR is easy to evaluate and complete identification of patients at risk for SSIs. Unstructured data comprises of information that does not have a definite model or structural framework. These include medical notes, faxed laboratory results, x-ray images and even patient phone calls (Assale et al., 2019). The information helps clinicians to figure out and obtain more accurate information about a patient’s overall risk for SSIs.

The development of this database will entail pulling data from multiple sources; either as structured or unstructured. However, the database will have mainly structured data as it involves the use of medications before the surgical procedure. The database will have check boxed for past and family medical history, patient age, and patient weight that will be document in pounds. The system will have a drop down for gender with options like male, female and non-binary. Upon the completion of the surgical procedure, the information would be pulled into the database together with the documentation in the operating room done by the anesthesiologist. These would include procedure start and finish time, and time they administered preoperative antibiotic.

The operating surgeon will assess demographics and surgical information for accuracy. The anesthesiologist will assess and validate data on aspects like gender, weight, and body mass index (BMI for accuracy. The implication is that the database will store all associated data that is needed by the facility to make critical decisions to address the issue of SSIs. The system is designed for recording basic details for any facility to reduce SSIs. The current system in most facilities has details about patient ID, name, and address. However, this database will store all information in a structured manner so that the user can easily navigate it based on the system’s requirements.

Conclusion

Through the use of structured data as mentioned in the assignment, facilities can identify with enhanced accuracy, the susceptibility of patients to SSIs. Further, based on this information, they can prophetically medicate them before they develop surgical site infections. SSIs are a significant cause of morbidity, mortality and are associated with not only increased length of stay but also increased rates of readmissions, high costs, and poor patient outcomes. The implication is that there is need to implement practices that will lower the incidences of associated complications and enhance patient safety, quality, and clinical outcomes.

References

Assale, M., Dui, L. G., Cina, A., Seveso, A., & Cabitza, F. (2019). The Revival of the Notes

Field: Leveraging the Unstructured Content in Electronic Health Records. Frontiers in

medicine, 6, 66. https://doi.org/10.3389/fmed.2019.00066

Coccolini, F., Improta, M., Cicuttin, E., Catena, F., Sartelli, M., Bova, R., … & Chiarugi, M.

(2021). Surgical site infection prevention and management in immunocompromised patients: a systematic review of the literature. World Journal of Emergency Surgery, 16(1), 1-13. https://doi.org/10.1186/s13017-021-00375-y

De Simone, B., Sartelli, M., Coccolini, F., Ball, C. G., Brambillasca, P., Chiarugi, M., … &

Catena, F. (2020). Intraoperative surgical site infection control and prevention: a position paper and future addendum to WSES intra-abdominal infections guidelines. World journal of emergency surgery, 15(1), 1-23. DOI: https://doi.org/10.1186/s13017-020-0288-4

Gerson, L., Barton, J., Monaco, C., & Baro, L. (2019). Using EMR to Implement and Track

Compliance of a Unique Colon Bundle That Reduced Surgical Site Infection in Colorectal Surgery: A Single Institution Review. https://digitalcommons.pcom.edu/research_day/research_day_PA_2019/researchPA2019/20/

Grundmeier, R. W., Xiao, R., Ross, R. K., Ramos, M. J., Karavite, D. J., Michel, J. J., … &

Coffin, S. E. (2018). Identifying surgical site infections in electronic health data using predictive models. Journal of the American Medical Informatics Association, 25(9), 1160-1166. https://doi.org/10.1093/jamia/ocy075

Kethman, W., Shelton, E., Kin, C., Morris, A., & Shelton, A. (2019). Effects of colorectal

surgery classification on reported postoperative surgical site infections. Journal of SurgicalResearch, 236, 340-344. https://doi.org/10.1016/j.jss.2018.12.005.

Kruse, C. S., Stein, A., Thomas, H., & Kaur, H. (2018). The use of Electronic Health Records to

Support Population Health: A Systematic Review of the Literature. Journal of medical

systems, 42(11), 214. https://doi.org/10.1007/s10916-018-1075-

Liu, L., Liu, L., Liang, L., Zhu, Z., Wan, X., Dai, H., & Huang, Q. (2018). Impact of

preoperative anemia on perioperative outcomes in patients undergoing elective colorectal surgery.Gastroenterology Research & Practice, 1-7. https://doi.org/10.1155/2018/2417028.

Patient Safety Network (PSNet) (2019 September 7). Surgical Site Infections.

https://psnet.ahrq.gov/primer/surgical-site-infections

Sample Answer 3 for DNP 805 Assignment EHR Database and Data Management Essay

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. 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.

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

 

EHR Database and Data Management – Rubric

Collapse All EHR Database And Data Management – RubricCollapse All

Selection of Clinically-Based Patient Problem

10 points

Criteria Description

Selection of Clinically-Based Patient Problem

5. Excellent

10 points

Patient problem selection indicates clear benefit potential by using a database management approach. Discussion is insightful and forward-thinking. Information presented is from current scholarly sources.

4. Good

9.2 points

Patient problem selection indicates clear benefit potential by using a database management approach. Discussion is convincing. Information presented is from scholarly though dated sources.

3. Satisfactory

8.8 points

Patient problem selection indicates clear benefit potential by using a database management approach, but the connection is perfunctory.

2. Less Than Satisfactory

8 points

Patient problem selection indicates some benefit potential by using a database management approach, but the connection is marginal or incomplete.

1. Unsatisfactory

0 points

Patient problem selection does not provide clear benefit potential by using a database management approach.

Identification of Data Needed to Manage the Patient Problem Using Information From the Electronic He

10 points

Criteria Description

Identification of Data Needed to Manage the Patient Problem Using Information From the Electronic Health Record (EHR)

Read Also:  DNP 805 Topic 3 Assignment Using CPOE and CDSS

5. Excellent

10 points

Identification of data needed to manage the patient problem using information from the electronic health record (EHR) is present in full. Discussion is convincing and defines specific elements. Discussion is insightful and forward-thinking. Information presented is from current scholarly sources.

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4. Good

9.2 points

Identification of data needed to manage the patient problem using information from the electronic health record (EHR) is present in full. Discussion is convincing and defines specific elements. Information presented is from scholarly though dated sources.

3. Satisfactory

8.8 points

Identification of data needed to manage the patient problem using information from the electronic health record (EHR) is present but at a perfunctory level.

2. Less Than Satisfactory

8 points

Identification of data needed to manage the patient problem using information from the electronic health record (EHR) is marginal or incomplete.

1. Unsatisfactory

0 points

Identification of data needed to manage the patient problem using information from the electronic health record (EHR) is not present.

Patient Problem Description Incorporates Information Needed to Manage the Problem

15 points

Criteria Description

Patient Problem Description Incorporates Information Needed to Manage the Problem

5. Excellent

15 points

Patient problem description that incorporates information needed to manage the problem is present in full. Discussion is convincing and defines specific elements. Discussion is insightful and forward-thinking. Information presented is from current scholarly sources.

4. Good

13.8 points

Patient problem description that incorporates information needed to manage the problem is present in full. Discussion is convincing and defines specific elements. Information presented is from scholarly though dated sources.

3. Satisfactory

13.2 points

Patient problem description that incorporates information needed to manage the problem is present but at a perfunctory level.

2. Less Than Satisfactory

12 points

Patient problem description that incorporates information needed to manage the problem is marginal or incomplete.

1. Unsatisfactory

0 points

Patient problem description that incorporates information needed to manage the problem is not present.

Identification of EHR-Supplied Data as Structured or Unstructured With Explanation

10 points

Criteria Description

Identification of EHR-Supplied Data as Structured or Unstructured With Explanation

5. Excellent

10 points

Identification of EHR-supplied data as structured or unstructured is present with a thorough explanation. Discussion is insightful and forward-thinking. Information presented is from current scholarly sources.

4. Good

9.2 points

Identification of EHR-supplied data as structured or unstructured is present with a thorough explanation. Information presented is from scholarly though dated sources.

3. Satisfactory

8.8 points

Identification of EHR-supplied data as structured or unstructured is present with an explanation but is rendered at a perfunctory level.

2. Less Than Satisfactory

8 points

Identification of EHR-supplied data as structured or unstructured is present with or without an explanation but is marginal or incomplete.

1. Unsatisfactory

0 points

Identification of EHR-supplied data as structured or unstructured with an explanation is not present.

Structured and Unstructured Data From the EHR That Are Needed to Organize a Hypothetical Database

10 points

Criteria Description

Structured and Unstructured Data From the EHR That Are Needed to Organize a Hypothetical Database

5. Excellent

10 points

Structured and unstructured data from the EHR needed to organize a hypothetical database are described in full. A discussion of the rationale behind the design development is convincing and defines specific elements. Discussion is insightful and forward-thinking. Information presented is from current scholarly sources.

4. Good

9.2 points

Structured and unstructured data from the EHR needed to organize a hypothetical database are described in full. Discussion is convincing and defines specific elements. Information presented is from scholarly though dated sources.

3. Satisfactory

8.8 points

Structured and unstructured data from the EHR needed to organize a hypothetical database are described, but description is rendered at a perfunctory level.

2. Less Than Satisfactory

8 points

Structured and unstructured data from the EHR needed to organize a hypothetical database are described, but description is marginal or incomplete.

1. Unsatisfactory

0 points

Structured and unstructured data from the EHR needed to organize a hypothetical database are not described.

Data Relationships That Apply to the Hypothetical Database

15 points

Criteria Description

Data Relationships That Apply to the Hypothetical Database

5. Excellent

15 points

Data relationships that apply to the hypothetical database are not described in full. Discussion is convincing and defines specific elements. Discussion is insightful and forward-thinking. Information presented is from current scholarly sources.

4. Good

13.8 points

Data relationships that apply to the hypothetical database are not described in full. Discussion is convincing and defines specific elements. Information presented is from scholarly though dated sources.

3. Satisfactory

13.2 points

Data relationships that apply to the hypothetical database are described, but description is rendered at a perfunctory level.

2. Less Than Satisfactory

12 points

Data relationships that apply to the hypothetical database are described, but description is marginal or incomplete.

1. Unsatisfactory

0 points

Data relationships that apply to the hypothetical database are not described.

Thesis Development and Purpose

7 points

Criteria Description

Thesis Development and Purpose

5. Excellent

7 points

Thesis is comprehensive and contains the essence of the paper. Thesis statement makes the purpose of the paper clear.

4. Good

6.44 points

Thesis is clear and forecasts the development of the paper. Thesis is descriptive and reflective of the arguments and appropriate to the purpose.

3. Satisfactory

6.16 points

Thesis is apparent and appropriate to purpose.

2. Less Than Satisfactory

5.6 points

Thesis is insufficiently developed or vague. Purpose is not clear.

1. Unsatisfactory

0 points

Paper lacks any discernible overall purpose or organizing claim.

Argument Logic and Construction

8 points

Criteria Description

Argument Logic and Construction

5. Excellent

8 points

Clear and convincing argument that presents a persuasive claim in a distinctive and compelling manner. All sources are authoritative.

4. Good

7.36 points

Argument shows logical progressions. Techniques of argumentation are evident. There is a smooth progression of claims from introduction to conclusion. Most sources are authoritative.

3. Satisfactory

7.04 points

Argument is orderly, but may have a few inconsistencies. The argument presents minimal justification of claims. Argument logically, but not thoroughly, supports the purpose. Sources used are credible. Introduction and conclusion bracket the thesis.

2. Less Than Satisfactory

6.4 points

Sufficient justification of claims is lacking. Argument lacks consistent unity. There are obvious flaws in the logic. Some sources have questionable credibility.

1. Unsatisfactory

0 points

Statement of purpose is not justified by the conclusion. The conclusion does not support the claim made. Argument is incoherent and uses noncredible sources.

Mechanics of Writing (includes spelling, punctuation, grammar, language use)

5 points

Criteria Description

Mechanics of Writing (includes spelling, punctuation, grammar, language use)

5. Excellent

5 points

Writer is clearly in command of standard, written, academic English.

4. Good

4.6 points

Prose is largely free of mechanical errors, although a few may be present. The writer uses a variety of effective sentence structures and figures of speech.

3. Satisfactory

4.4 points

Some mechanical errors or typos are present, but they are not overly distracting to the reader. Correct and varied sentence structure and audience-appropriate language are employed.

2. Less Than Satisfactory

4 points

Frequent and repetitive mechanical errors distract the reader. Inconsistencies in language choice (register) or word choice are present. Sentence structure is correct but not varied.

1. Unsatisfactory

0 points

Surface errors are pervasive enough that they impede communication of meaning. Inappropriate word choice or sentence construction is used.

Paper Format (Use of appropriate style for the major and assignment)

5 points

Criteria Description

Paper Format (Use of appropriate style for the major and assignment)

5. Excellent

5 points

All format elements are correct.

4. Good

4.6 points

Template is fully used; There are virtually no errors in formatting style.

3. Satisfactory

4.4 points

Template is used, and formatting is correct, although some minor errors may be present.

2. Less Than Satisfactory

4 points

Template is used, but some elements are missing or mistaken; lack of control with formatting is apparent.

1. Unsatisfactory

0 points

Template is not used appropriately or documentation format is rarely followed correctly.

Documentation of Sources

5 points

Criteria Description

Documentation of Sources (citations, footnotes, references, bibliography, etc., as appropriate to assignment and style)

5. Excellent

5 points

Sources are completely and correctly documented, as appropriate to assignment and style, and format is free of error.

4. Good

4.6 points

Sources are documented, as appropriate to assignment and style, and format is mostly correct.

3. Satisfactory

4.4 points

Sources are documented, as appropriate to assignment and style, although some formatting errors may be present.

2. Less Than Satisfactory

4 points

Documentation of sources is inconsistent or incorrect, as appropriate to assignment and style, with numerous formatting errors.

1. Unsatisfactory

0 points

Sources are not documented.

Total 100 points