DNP 805 Week 4 Assignment EHR Database and Data Management GCU
Details: As a DNP-prepared nurse, you may be called upon to assist in the design of a clinical database for patient outcomes within your organization.
This assignment requires you to integrate a clinical problem with data technologies to better understand the components of data management in healthcare and how those components can lead to better clinical outcomes.
General Guidelines
To ensure successful completion of the assignment, use the following information:
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- 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.
- This assignment requires at least two additional scholarly research sources related to this topic, and at least one in-text citation from each source must be included.
- You are required to submit this assignment to Turnitin. Refer to the directions in the Student Success Center.
Directions
For this assignment, write a 1,000-1,250 word paper in which you:
- Select a clinically-based patient problem where using a database management approach provides clear benefits.
- Identify the data needed to manage this patient problem using information from the Electronic Health Records (EHR).
- Include a brief description of the patient problem that incorporates the necessary information to manage it effectively.
- Identify whether the EHR-supplied data is structured or unstructured, and explain why.
- Provide a complete description of the structured vs. unstructured data in EHR needed to organize a hypothetical database.
- Describe the data relationships in healthcare that apply to the hypothetical database.
Portfolio Practice Hours
It may be possible to earn portfolio practice hours for this case report. Enter the following after the references section of your paper:
Practice Hours Completion Statement DNP-805
I, (INSERT NAME), verify that I have completed (NUMBER OF) clock hours in association with the goals and objectives for this assignment. I have also tracked said practice hours in the Typhon Student Tracking System for verification purposes and will ensure that all approvals are in place from my faculty and practice mentor.
Additional Instructions for the Class
Discussion Questions (DQ)
Initial responses to the DQ should address all components of the questions asked, include a minimum of one scholarly source, and be at least 250 words. Successful responses are substantive (i.e., add something new to the discussion, engage others in the discussion, and present a well-developed idea) and include at least one scholarly source. One or two-sentence responses, simple statements of agreement or “good post,” and off-topic responses will not count as substantive. Substantive responses should be at least 150 words. Incorporating readings from the week (as applicable) into your responses is encouraged.
Weekly Participation
Your initial responses to the mandatory DQ do not count toward participation and are graded separately. In addition to the DQ responses, you must post at least one reply to peers (or me) on three separate days, for a total of three replies. Participation posts do not require a scholarly source/citation unless you cite someone else’s work. Part of your weekly participation includes viewing the weekly announcement and attesting to watching it in the comments. These announcements ensure you understand everything that is due during the week.
APA Format and Writing Quality
Familiarize yourself with APA format and practice using it correctly, as it is essential for most writing assignments in your degree program. Visit the Writing Center in the Student Success Center, under the Resources tab in LoudCloud, for APA paper templates, citation examples, and tips. Points will be deducted for poor use of APA format or absence of APA format (if required). Cite all sources of information! When in doubt, cite the source. Paraphrasing also requires a citation. It is recommended to use the APA Publication Manual, 6th edition.
Use of Direct Quotes
Overutilization of direct quotes in DQs and assignments at the Master’s level is discouraged, and points may be deducted accordingly. As Master’s level students, it is important to critically analyze and interpret information from journal articles and other resources. Simply restating someone else’s words does not demonstrate understanding or critical analysis of the content. It is best to paraphrase content and cite your source.
LopesWrite Policy
For assignments that need to be submitted to LopesWrite, ensure you have received your report and Similarity Index (SI) percentage BEFORE doing a final submit. Once you have your report, review it for grammatical, punctuation, and spelling errors that can easily be fixed. Take the extra minutes to review instead of losing points for these mistakes. Check your similarities: Did you forget to cite something? Did you paraphrase well enough? Is your paper made up of someone else’s thoughts more than your own? Visit the Writing Center in the Student Success Center, under the Resources tab in LoudCloud, for tips on improving your paper and SI score.
Late Policy
The university’s policy on late assignments includes a 10% penalty PER DAY LATE. This also applies to late DQ replies. Please communicate with me if you anticipate needing to submit an assignment late. I am happy to be flexible with advance notice. We may work out an extension based on extenuating circumstances. If you do not communicate with me before submitting an assignment late, the GCU late policy will be enforced. Assignments that are two or more weeks late are not accepted unless previously arranged. No assignments will be accepted after the last day of class. Any assignment submitted after midnight on the last day will not be accepted for grading.
Communication
Communication is crucial. There are multiple ways to connect with me:
- Questions to Instructor Forum: This is a great place to ask course content or assignment questions. If you have a question, there is a good chance one of your peers does as well. This is a public forum for the class.
- Individual Forum: This is a private forum to ask me questions or send me messages. I will check this at least once every 24 hours.
Exploration of Data Relationships in a Hypothetical Database
In the realm of healthcare, data plays a pivotal role in categorizing patient problems and identifying subgroups within populations. Shortliffe & Cimino (2014) emphasize the significance of data in aiding physicians to understand patient issues, determine the need for additional information, and decide on appropriate actions for effective treatment. In the acute care setting, accurately predicting mortality risk and patient deterioration is crucial for timely medical intervention and resource allocation.
Clinical Decision Support Systems (CDSS) are instrumental in predicting patient outcomes and assessing the severity of a patient’s condition in acute care units. However, many existing prediction models rely on aggregate baseline patient characteristics, such as age, type of admission, and vital sign measurements. Commonly used CDSS tools, including the Modified Early Warning Score (MEWS), the Sequential Organ Failure Assessment (SOFA), and the Simplified Acute Physiology Score (SAPS II), exhibit suboptimal specificity and sensitivity in predicting patient mortality (Calvert J., et al., 2016).
These CDSS assessments often assume independence among risk factors, neglecting the intricate homeostatic physiologies of patients and failing to account for variations in individual patient physiologies and trends in patient information. The rise of Electronic Health Records (EHR) presents an opportunity to extract clinically relevant patient vital signs and laboratory results, enhancing the predictive value of patient outcomes (Calvert J., et al., 2016).
Conclusion and Future Directions
For a Doctor of Nursing Practice (DNP) prepared nurse engaged in designing a clinical database for patient outcomes, the importance of accurate prediction in clinical outcomes cannot be overstated. This paper highlights the significance of bedside scoring systems in predicting sepsis early, benefiting both patients and clinicians. The data for such predictions can be derived from both structured and unstructured data in EHR. McCann (2014) underscores the severity of septicemia, noting that it is responsible for 36,000 deaths annually and is the 11th leading cause of death in the U.S. With average sepsis mortality rates exceeding 16 percent nationwide, it remains the most expensive hospital condition, costing over $20 billion annually (McCann, 2014). Thus, the development of a well-designed database within EHRs becomes imperative for clinicians to predict and address this critical clinical problem effectively.
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 https://lcgrad3.gcu.edu/learningPlatform/user/users.html?operation=loggedIn#/learningPlatform/loudBooks/loudbooks.html?viewPage=past&operation=innerPage&topicMaterialId=607e9ca1-cd23-42f1-968b-1788cf9283c0&contentId=ebff44f5-7775-4830-b2f2-3eb94bd12979&
Grand Canyon University(GCU). (2017), EHR Database and Data Management https://lcgrad3.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
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