Category: NURS-FPX6414

NURS FPX 6414 Assessment 3 Tool Kit for Bioinformatics Name Capella university NURS-FPX 6414 Advancing Health Care Through Data Mining Prof. Name Date Executive Summary The incorporation of technology in healthcare has significantly advanced medical practices, with bioinformatics playing a pivotal role in enhancing healthcare services. By leveraging technology and data, bioinformatics supports improved decision-making, policy formulation, and the effective implementation of healthcare practices. The COVID-19 pandemic, characterized by widespread acute respiratory infections, underscored the necessity of understanding disease transmission and prevention strategies. Analyzing large datasets of patient information has become essential in identifying risk factors that contribute to the spread of infections (Meng et al., 2020). Research findings indicate that individuals with multiple severe health conditions are at a higher risk of contracting COVID-19, further emphasizing the importance of bioinformatics in identifying trends, refining interventions, and improving overall healthcare outcomes. NURS FPX 6414 Assessment 3: Tool Kit for Bioinformatics The advancement of healthcare technology has introduced systems such as Best Practice Advisory (BPA) alerts and Clinical Decision Support (CDS) tools, both of which significantly contribute to enhancing patient health outcomes. Many healthcare facilities implement CDS tools like BPA to provide timely alerts regarding patients’ medical conditions (Baumgart, 2020). The integration of Electronic Health Records (EHR) allows healthcare providers to access patient information efficiently, enabling informed decision-making. BPA alerts, often delivered through pop-up notifications, serve as reminders for patients to adhere to their treatment plans, ensuring continuity of care. This proactive approach benefits both patients and healthcare institutions by reducing hospital readmission rates. These technological interventions highlight the importance of digital solutions in optimizing healthcare efficiency and improving patient well-being. NURS FPX 6414 Assessment 3: Tool Kit for Bioinformatics Category Description References Technology in Healthcare The integration of bioinformatics enhances decision-making, policy formulation, and healthcare delivery. Meng et al., 2020 Impact of COVID-19 The pandemic highlighted the necessity of data-driven analysis for understanding disease spread and prevention. Meng et al., 2020 Use of BPA and CDS BPA and CDS systems contribute to improved patient health outcomes by sending alerts and reducing hospital readmissions. Baumgart, 2020 References Baumgart, D. C. (2020). Digital advantage in the COVID-19 response: Perspective from Canada’s largest integrated digitalized healthcare system. NPJ Digital Medicine, 3(1). https://doi.org/10.1038/s41746-020-00326-y NURS FPX 6414 Assessment 3 Tool Kit for Bioinformatics Meng, L., Dong, D., Li, L., Niu, M., Bai, Y., Wang, M., Qiu, X., Zha, Y., & Tian, J. (2020). A deep learning prognosis model help alert for COVID-19 patients at high-risk of death: A multi-center study. IEEE Journal of Biomedical and Health Informatics, 24(12), 3576–3584. https://doi.org/10.1109/JBHI.2020.3034296

NURS FPX 6414 Assessment 2 Proposal to Administration Name Capella university NURS-FPX 6414 Advancing Health Care Through Data Mining Prof. Name Date Proposal to Administration Type 2 Diabetes (T2D) self-management encompasses various strategies and interventions aimed at improving patient outcomes. According to Winkley et al. (2020), self-management involves healthcare professionals, nurses, and stakeholders working collaboratively to control and treat the condition. Given the high prevalence of Type 2 Diabetes in the United States, it is essential for patients to develop skills for effective health monitoring. This proposal explores key elements of diabetes self-management in healthcare organizations, including regular blood sugar monitoring, dietary planning, and exercise regimens (Agarwal et al., 2019). By implementing structured self-management programs, healthcare providers can enhance patient education and improve diabetes care outcomes. Measuring and Benchmarking Type 2 Diabetes Outcomes Since over 500 million people in the U.S. have Type 2 Diabetes, evaluating specific quality outcomes is critical to enhancing patient self-management skills through Diabetes Self-Management Education and Support (DSMES) programs (Adam, 2018). These programs offer structured learning opportunities to improve patient awareness and promote adherence to positive self-care behaviors. Additionally, the Chronic Disease Management System (CDMS) plays a crucial role in controlling blood glucose levels and minimizing complications. Measuring these outcomes allows for improved quality of life and reduced healthcare costs (Agarwal et al., 2019). Outcome measures also provide essential baseline data for evaluating patient progress and treatment efficacy. The American Diabetes Association (ADA) has established key benchmarks for managing Type 2 Diabetes. The primary benchmark includes maintaining HbA1c levels below 7% for optimal disease control (van Smoorenburg et al., 2019). Additionally, weight management is emphasized, with a recommended reduction of at least 15% through pharmacological and lifestyle interventions (Apovian et al., 2018). The patient mortality rate remains a critical concern, currently standing at 5%, which underscores the need for improved healthcare quality and diabetes management strategies. Data Measures and Trends in Type 2 Diabetes Several data metrics and trends provide insight into the current state of Type 2 Diabetes management. These include: Increased early mortality rates among diabetes patients. Reduced life expectancy due to diabetes-related complications. A high hospital readmission rate of approximately 25% in the U.S. Lower levels of diabetes education correlate with higher disease prevalence. Individuals with higher educational attainment have a reduced risk of Type 2 Diabetes (Wu, 2019). Minority populations, particularly Hispanic and Black Americans, face a higher risk of developing Type 2 Diabetes. The incidence of Type 2 Diabetes has steadily risen over the past four decades in Western countries (Winkley et al., 2020). While middle-aged adults and older generations have experienced some decline in diabetes prevalence, younger populations face an increasing risk. The standard blood glucose benchmark is set at less than 140 mg/dL, with levels above 200 mg/dL indicating a significant risk for diabetes progression (van Smoorenburg, 2019). These findings highlight the critical need for comprehensive self-management programs to reduce hospital readmissions and improve patient outcomes. Data Analysis and Implications According to the World Health Organization, diabetes mellitus presents a significant global health burden. Between the 1980s and 2015, the prevalence of diabetes in adults nearly doubled from 4.7% to 8.5% (Agarwal et al., 2019). Data from the American Diabetes Association (ADA) further indicate that diabetes has been the seventh leading cause of death in the U.S. since 2019, with approximately 87,647 diabetes-related deaths recorded (Adam, 2018). The following table summarizes key findings on racial disparities, education levels, and diabetes prevalence in the U.S.: Table 1: Type 2 Diabetes Self-Management Data Trends Key Factors Findings Sources Diabetes prevalence Over 500 million people in the U.S. have Type 2 Diabetes. Adam (2018) HbA1c benchmark Optimal HbA1c level: below 7%. van Smoorenburg et al. (2019) Weight management goal Patients should aim for a 15% reduction. Apovian et al. (2018) Hospital readmission rate Approximately 25% for diabetes patients. Wu (2019) Mortality rate 5% of diabetes patients die due to poor care quality. Agarwal et al. (2019) Racial disparities Hispanic and Black Americans face higher risks. Wu (2019) Education impact Lower education correlates with higher diabetes rates. Winkley et al. (2020) Conclusion The data analysis emphasizes the strong correlation between education levels and diabetes prevalence in the United States. Implementing behavioral self-management programs is crucial for reducing diabetes-related complications and hospital readmissions. Current trends indicate a steady increase in Type 2 Diabetes diagnoses, primarily influenced by education gaps and racial disparities. Addressing these challenges through structured diabetes self-management interventions can significantly improve patient outcomes and overall healthcare efficiency. References Adam, L., O’Connor, C., & Garcia, A. C. (2018). Evaluating the impact of diabetes self-management education methods on knowledge, attitudes, and behaviors of adult patients with Type 2 Diabetes Mellitus. Canadian Journal of Diabetes, 42(5), 470–477.e2. https://doi.org/10.1016/j.jcjd.2017.11.003 Agarwal, P., Mukerji, G., Desveaux, L., Ivers, N. M., Bhattacharyya, O., Hensel, J. M., Shaw, J., Bouck, Z., Jamieson, T., Onabajo, N., Cooper, M., Marani, H., Jeffs, L., & Bhatia, R. S. (2019). Mobile app for improved self-management of Type 2 Diabetes: Multicenter pragmatic randomized controlled trial. JMIR mHealth and uHealth, 7(1), e10321. https://doi.org/10.2196/10321 Apovian, C. M., Okemah, J., & O’Neil, P. M. (2018). Body weight considerations in the management of Type 2 Diabetes. Advances in Therapy, 36(1), 44–58. https://doi.org/10.1007/s12325-018-0824-8 van Smoorenburg, A. N., Hertroijs, D. F. L., Dekkers, T., Elissen, A. M. J., & Melles, M. (2019). Patients’ perspective on self-management: Type 2 Diabetes in daily life. BMC Health Services Research, 19(1), 605. https://doi.org/10.1186/s12913-019-4384-7 NURS FPX 6414 Assessment 2 Proposal to Administration Winkley, K., Upsher, R., Stahl, D., Pollard, D., Kasera, A., Brennan, A., Heller, S., & Ismail, K. (2020). Psychological interventions to improve self-management of Type 1 and Type 2 Diabetes: A systematic review. Health Technology Assessment, 24(28), 1–232. https://doi.org/10.3310/hta24280 Wu, F. L., Tai, H. C., & Sun, J. C. (2019). Self-management experience of middle-aged and older adults with Type 2 Diabetes: A qualitative study. Asian Nursing Research, 13(3), 209–215. https://doi.org/10.1016/j.anr.2019.06.002 NURS FPX 6414 Assessment 2 Proposal to Administration

NURS FPX 6414 Assessment 1 Conference Poster Presentation Name Capella university NURS-FPX 6414 Advancing Health Care Through Data Mining Prof. Name Date Abstract Healthcare professionals consistently work toward improving patient care and ensuring safety, with particular attention to fall prevention. Falls are a significant cause of unintentional injuries and fatalities among individuals aged 65 and older in the United States, contributing to approximately 2.8 million emergency room visits annually (CDC, 2020). Several risk factors, including cognitive impairment, reduced mobility, and urgent toileting needs, contribute to falls in both hospital and community settings (LeLaurin & Shorr, 2019). In hospital settings, between 700,000 and 1 million falls occur each year, with an incidence rate of 3.5 to 9.5 falls per 1,000 bed days (LeLaurin & Shorr, 2019). A study by Galet et al. (2018) involving 931 patients indicated that 633 were at an elevated risk of falling due to cognitive dysfunction, mobility impairments, and incontinence. A single fall can lead to extended hospital stays, increased healthcare costs, and poorer patient outcomes. To mitigate fall risks, OhioHealth’s informatics team developed the Schmid tool, a structured assessment that identifies patients at high risk of falling and facilitates targeted interventions (Lee et al., 2019). The tool evaluates key factors such as mobility, cognitive function, toileting needs, fall history, and medication use. This study explores the effectiveness of the Schmid tool in improving patient safety and overall healthcare outcomes by leveraging informatics-driven solutions. Introduction Falls represent a significant public health concern, especially among hospitalized patients. Each year, approximately 2.8 million adults require emergency medical care due to fall-related injuries (LeLaurin & Shorr, 2019). In hospital settings, falls result in prolonged hospitalizations and increased medical expenses, with an annual occurrence of 700,000 to 1 million falls (LeLaurin & Shorr, 2019). Given the substantial impact of falls on patient safety and healthcare costs, effective fall prevention strategies are imperative. The Schmid tool is a widely used assessment method designed to identify patients at an elevated risk of falling. It evaluates critical factors such as mobility, cognitive function, toileting abilities, medication use, and fall history. Assessing the effectiveness of this tool is essential to enhancing fall prevention strategies and improving patient care outcomes. Analyzing the Use of the Informatics Model The Schmid fall risk assessment tool categorizes patients based on four primary domains: mobility, cognitive function, toileting ability, and medication use (Amundsen et al., 2020). Each domain includes specific subcategories that enable healthcare professionals to determine patients requiring additional fall prevention measures. The mobility domain assesses a patient’s ability to move independently, ranging from fully mobile to completely immobile. Cognitive function is evaluated based on alertness, occasional confusion, persistent disorientation, or unresponsiveness. Similarly, toileting ability is classified from independent function to complete incontinence. Lastly, medication use is assessed based on drug classifications, including anticonvulsants, psychotropics, tranquilizers, and hypnotics, all of which may increase fall risk (Amundsen et al., 2020). Literature Review Despite advances in fall prevention strategies, falls continue to present challenges for healthcare institutions. Falls are a leading cause of injury, disability, and mortality among older adults, significantly affecting their quality of life. Moreover, hospitals face financial burdens due to increased healthcare costs and prolonged hospital stays. Since 2008, Medicare and Medicaid have ceased reimbursement for fall-related injuries, emphasizing the importance of implementing effective fall prevention measures (LeLaurin & Shorr, 2019). Research highlights the growing concern regarding hospital readmissions among elderly patients who have suffered fall-related injuries, reinforcing the need for robust fall prevention strategies and social support systems (Galet et al., 2018). Falls remain the primary cause of injury-related deaths among individuals aged 65 and older in the United States, necessitating the use of evidence-based interventions such as the Schmid tool (CDC, 2020). Conclusion The study findings emphasize the importance of integrating structured fall prevention tools in hospital settings. Falls remain a significant contributor to injury and mortality, particularly among elderly patients. By adopting informatics-driven solutions such as the Schmid tool, healthcare institutions can reduce fall incidents, improve patient safety, and enhance overall healthcare outcomes. Schmid Fall Risk Assessment Criteria Category Assessment Criteria Description Mobility Mobile (0) Fully independent with no mobility assistance required. Mobile with assistance (1) Requires caregiver or assistive device for movement. Unstable (1b) Experiences balance issues and is at risk of falling. Immobile (0a) Unable to move independently, requiring full assistance. Cognition Alert (0) Fully aware, oriented, and responsive. Occasionally confused (1a) Experiences intermittent disorientation or forgetfulness. Always confused (1b) Consistently disoriented and requires supervision. Unresponsive (0b) Unable to respond to stimuli or interact meaningfully. Toileting Abilities Completely independent (0a) Manages toileting without assistance. Independent with frequency (1a) Requires frequent restroom visits but manages independently. Requires assistance (1b) Needs caregiver help for toileting. Incontinent (1c) Unable to control bladder or bowel function. Medication Use Anticonvulsants (1a) Uses seizure medications, which may increase fall risk. Psychotropics (1b) Takes medications affecting mental state and cognition. Tranquilizers (1c) Uses sedative medications that may cause dizziness. Hypnotics (1d) Takes sleep-inducing medications that could impair balance. None (0) No medications contributing to fall risk. References Amundsen, T., O’Reilly, P., & Kverneland, T. (2020). Assessing the effectiveness of the Schmid tool in fall risk management. Journal of Healthcare Informatics Research, 4(2), 75-88. Centers for Disease Control and Prevention (CDC). (2020). Falls among older adults: An overview. Centers for Disease Control and Prevention. https://www.cdc.gov/homeandrecreationalsafety/falls/adultfalls.html NURS FPX 6414 Assessment 1 Conference Poster Presentation Galet, C., Kelly, C., & DeCicco, T. (2018). Understanding the impact of falls in elderly populations: A focus on hospital readmissions. Journal of Elderly Care, 12(3), 213-222. Lee, K., Spangler, D., & Clark, T. (2019). Utilizing the Schmid tool for fall prevention: A case study from OhioHealth. Nursing Informatics, 45(1), 33-40. LeLaurin, J., & Shorr, R. (2019). Patient falls in hospitals: A review of the literature. Journal of Patient Safety, 15(4), 233-239.