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 

Sample Answer 2 for DNP 805 EHR Database and Data Management SAMPLE

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