DNP 805 Assignment EHR Database and Data Management
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:
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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.
- 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:
- Select a clinically based patient problem in which using a database management approach provides clear benefit potential.
- 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).
In the realm of cancer treatment, prostate cancer stands out as the most common ailment affecting men (Hernandez-Boussard, Blayney & Brooks, 2020). Men recently diagnosed with prostate cancer face complex therapeutic decisions, each with unique risks that can affect patient-centered outcomes, such as urinary and erectile dysfunction.
Today, healthcare providers and patients encounter challenges in comparing the trade-offs among patient-centered outcomes across different treatments due to a lack of experimental evidence on these trade-offs (Hernandez-Boussard, Blayney & Brooks, 2020). Experts attribute this gap to inconsistent recording of patient-centered outcomes in computable formats. To improve cancer care and data accuracy, this article proposes three innovative measures for managing cancer patient data, including the use of online nursing papers that can assist in research and understanding treatment options.
The first proposed strategy suggests creating an electronic health record (EHR) prostate cancer database. This database would allow for the analysis of clinical information alongside diagnostic details, aiding in the preparation of nursing assignments and research papers that help healthcare providers understand treatment implications (Hernandez-Boussard, Blayney & Brooks, 2020).
The second approach involves developing new ontological representations of quality metrics that are publicly accessible and consistent across various EHR systems, potentially leading to the creation of custom nursing papers focused on quality measures. The third strategy includes implementing a robust data mining workflow that builds on current methods, utilizing ontology-based dictionaries to interpret free text (Hernandez-Boussard, Blayney & Brooks, 2020). Integrating these three innovative approaches will empower both clinicians and patients to utilize existing EHRs for a comprehensive understanding of the trade-offs among patient-centered outcomes across diverse treatments.
Utilizing EHRs to measure and enhance prostate cancer treatment is crucial, as it enables the sharing of essential information among healthcare facilities (Hernandez-Boussard, Blayney & Brooks, 2020). The EHR lays the groundwork for recognizing quality metric information, allowing for the creation of an EHR database, mapping of quality metrics to medical vocabularies, and developing electronic quality metric phenotypes. This EHR program can construct a web-based tool that integrates empirical evidence and clinical characteristics to assess personalized risk predictions for patients, aiding both care providers and patients in selecting the best treatment options, often discussed in top nursing essays and research papers (Hernandez-Boussard, Blayney & Brooks, 2020). These options prioritize the best-anticipated quality of care, taking into account the significance of each patient-centered outcome. Leveraging EHRs will help bridge a critical gap in evidence for prostate cancer therapy and nursing research, providing practical evidence for care providers and patients to effectively compare trade-offs among outcomes across various treatments.
Prostate cancer is a complex illness that introduces various risks to patient-centered outcomes, with no conclusive evidence on the variations of hazards across treatments (Hernandez-Boussard, Blayney & Brooks, 2020). This article outlines measures to collect patient-centered outcomes recorded in Electronic Health Records (EHRs) and a risk assessment tool for estimating personalized hazards across therapies. This data aims to assist policymakers and healthcare staff in enhancing the patient experience and outcomes (Hernandez-Boussard, Blayney & Brooks, 2020). Additionally, EHRs play a vital role in assisting clinicians by making information gathering and analysis more effective, enabling the tracking of patients over time and identifying trends related to cancer. According to studies on cancer diagnosis and therapy, EHRs are linked to significantly higher healthcare quality for various types of cancer (Hernandez-Boussard, Blayney & Brooks, 2020). They are essential in providing accessible information, maintaining treatment records, and allowing individuals to evaluate knowledge more effectively, which can be beneficial for nursing assignments.
In today’s healthcare landscape, those involved in healthcare delivery or planning cannot overlook the substantial changes in data management perspectives (Magyar, 2017). This article reviews examples of healthcare databases used in organizations to enhance patient care in cancer therapy (Magyar, 2017). To understand the range of EHRs available in the healthcare sector and address concerns about individual data protection, care providers must consider various aspects of EHR data management, including comprehensiveness (Magyar, 2017). Comprehensive data includes demographic information, organizational data, health risks and conditions, patient therapeutic history, existing health status management, and outcome statistics (Magyar, 2017).
Demographic Information:
Demographic information includes statistics such as age, race, gender, national origin, marital status, address, names of direct relatives, and other relevant details (Magyar, 2017). It also encompasses employment status, education level, and indicators of socioeconomic status.
Administrative Information:
Administrative data involves health insurance details, membership, eligibility, dual coverage, copayments, deductibles, and specific identifiers for care providers (Fox et al., 2018).
Health Risks and Health Status Data:
Health risks data includes lifestyle and behavioral factors, such as tobacco use and physical activity (Magyar, 2017). Health status information reflects the overall physical, emotional, and mental well-being of an individual.
Patient Therapeutic History:
Patient medical history consists of information on previous healthcare encounters, hospital admissions, surgical procedures, pregnancies, and live births (Fox et al., 2018).
Existing Medical Management:
Existing medical management includes details about treatment satisfaction, procedures performed, laboratory tests conducted, and counseling provided (Magyar, 2017).
Outcomes Information:
Outcomes data covers a range of measures indicating the effects of healthcare, from mortality to improved performance and well-being (Fox et al., 2018). This includes readmission rates, unplanned complications, side effects, and patient satisfaction measures.
The EHR program manages both historical and current test results, enabling healthcare providers to efficiently review patient information, filter, and compare outcomes. EHRs allow physicians to electronically manage and store patient records for future reference, which can inform custom nursing papers and online nursing essays.
Conclusion:
In healthcare, the comprehensiveness of EHRs directly relates to concerns about confidentiality and privacy. Achieving meaningful use of EHRs is crucial for ensuring that individuals fully benefit from these systems, enhancing quality care, preventive cancer care, and patient outcomes. Technological advances, such as EHRs, are transforming healthcare delivery, providing faster and more efficient patient outcomes. This shift also opens avenues for online nursing research, including the creation of the best nursing papers and top nursing essays, which can further contribute to the field.
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.
Clinical Problem:
The focus of this clinical problem is on adult patients undergoing colon cancer surgery who are susceptible to surgical site infections. Colorectal cancer surgery presents a significant risk of morbidity (20%-40%) and mortality (2%), primarily attributed to postoperative surgical site infections (Li et al., 2018). Recent reports indicate a concerning increase of over 25% in the risk of postoperative complications in patients undergoing colon surgery (Grundmeier et al., 2019; PSNet, 2019). These infections, particularly hospital-acquired infections (HAIs), contribute to prolonged hospital stays, heightened vulnerability to other illnesses, and escalated medical costs. To address and mitigate the occurrence of surgical site infections, healthcare providers and organizations can employ innovative approaches, such as the establishment of Electronic Health Record (EHR) databases, for effective EHR data management.
Understanding Surgical Site Infections in Colon Cancer Surgery
Dey et al. (2016) highlight that Electronic Health Records (EHRs) capture extensive longitudinal structured and unstructured data, encompassing patient demographic details (e.g., age, sex, address), health and treatment status, diagnoses, lab test results, and medication orders. They note that up to 80% of EHR data is often in unstructured form, emphasizing the importance of understanding the data’s origin for effective use (Dey et al., 2016).
The Role of EHR in Managing Patient Data
Structured Data: Structured data in EHRs are objective and easily searchable, including vital signs, laboratory values, patient demographics, dates, names, diagnosis codes, identification numbers, and specific words presented in user-friendly drop-down boxes (GCU, 2014). These data, such as vital signs and laboratory results, play a crucial role in predicting conditions like sepsis, thereby supporting infection prevention strategies and improving patient care quality.
Unstructured Data: Conversely, unstructured data are typically free-text entries in progress notes, care plans, and comments embedded in flow sheets. These narratives provide valuable additional information beyond structured data and are crucial for a comprehensive understanding of patient care (GCU, 2014). However, capturing unstructured data presents challenges due to the inflexibility of computer systems.
Unstructured data often require human interpretation and can include handwritten information on paper forms, audio voice dictations, email messages, and typed transcriptions (DataMark, 2013). These data may contain rich contextual information but are more complex to process within traditional computer systems.
Importance of Structured and Unstructured Data in Infection Control
For the patient problem of surgical site infections in colon cancer surgery, both structured and unstructured data are vital. Structured data, such as vital signs and laboratory results, are essential for immediate assessment and intervention. Unstructured data, found in nurse’s notes, offer nuanced information that aids in a more comprehensive understanding of the patient’s condition, contributing to better clinical outcomes.
Operational Definitions: Operational definitions for structured data involve clear measurements, like height being recorded in inches or centimeters. Unstructured data, found in nurse’s notes, might include qualitative information about the patient’s overall well-being and responses to treatment.
Database Concept Map: A concept map, similar to the “Database Concept Map,” can illustrate the relationships between entities and their attributes in the hypothetical database. For example, in a Labor and Delivery department, a pre-natal questionnaire may collect structured data like demographic information and unstructured data like comments on mental health status.
Conclusion
In the realm of healthcare technology and databases, the distinction between structured and unstructured data is crucial for providing comprehensive patient care quality. The integration of both types of data ensures that healthcare providers have a holistic view of the patient’s condition, leading to improved decision-making and better health outcomes. By utilizing data analytics in healthcare, organizations can enhance their infection prevention strategies and ultimately reduce the impact of hospital-acquired infections (HAIs).
References
Colvin, C., Baird, P., Easty, T., & Trbovich, P. (2013). Human Factors and Medical IT Systems: Complex Incident Reporting Systems and Multiple IV Infusions.Biomedical Instrumentation & Technology, 47(2), 59-63. https://lopes.idm.oclc.org/login?url=https://www.proquest.com/scholarly-journals/human-factors-medical-systems-complex-incident/docview/1461394405/se-2?accountid=7374
Fellmeth, G., Fazel, M., & Plugge, E. (2017). Migration and perinatal mental health in women from low- and middle-income countries: a systematic review and meta-analysis. BJOG : an international journal of obstetrics and gynaecology, 124(5), 742–752. https://doi.org/10.1111/1471-0528.14184
Attachments DNP-805A-RS-DatabaseConceptMap.do
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