Phillip October 7, 2023 No Comments

NURS FPX 6414 Assessment 3 Tool Kit for Bioinformatics

NURS FPX 6414 Assessment 3 Tool Kit for Bioinformatics Student Name Capella University NURS-FPX 6414 Advancing Health Care Through Data Mining Prof. Name Date   Tool Kit for Bioinformatics Since the emergence of the COVID-19 virus, concerns have arisen regarding health security, especially among those who visited hospitals during the outbreak and were worried about contracting the virus in the hospital environment (Wu et al., 2020). Early measures such as rapid identification and treatment of COVID-19 infections can enhance people’s sense of security. This objective can be achieved through the use of Health Information Technology, including Clinical Decision Support Systems (CDSS) and Best Practice Advisory (BPA) alerts (Wu et al., 2020). Thus, this paper provides a toolkit for implementing CDSS and BPA alerts. Evidence-Based Policy The burden of the COVID-19 pandemic has increased the workload for healthcare workers and significantly inflated healthcare costs. Consequently, patients, care providers, and health systems could face significant challenges due to the shortage of medical professionals and equipment if the spread of the illness is not effectively controlled (Moulaei, 2022). To effectively treat and prevent the spread of the illness, healthcare providers must closely monitor the early signs of COVID-19 infection. Optimizing the use of CDSS can assist physicians in making more informed decisions about patient diagnoses, treatments, and follow-ups, leading to quicker and more accurate diagnoses and outbreak containment (Moulaei, 2022). NURS FPX 6414 Assessment 3 Tool Kit for Bioinformatics In the medical field, particularly health information technology, the delivery of high-quality, timely treatment has been simplified. The Affordable Care Act mandates healthcare providers to adopt and fully utilize health information technology to improve quality, patient outcomes, and reduce healthcare costs (Fry, 2021). A fully developed electronic health record (EHR) with clinical decision support (CDS) is essential for a learning health system capable of navigating the complex healthcare landscape. Integrated clinical decision support technologies within electronic health records, such as Best Practice Advisory (BPA) alerts, enhance clinical decision-making by providing pertinent information to clinicians (Fry, 2021). Guidelines Effective policy implementation requires the support of key stakeholders. Communicating guiding principles, norms, and policies to the entire healthcare workforce is essential (Akhloufi et al., 2022). Healthcare institutions should conduct regular meetings involving physicians, nurses, hospital administrators, nurse informaticists, and information technology specialists to develop an efficient CDSS and BPA alert system. These meetings aim to improve the technology’s user-friendliness and minimize errors during its use. Additionally, they offer training on efficient technology usage (Akhloufi et al., 2022). Following meetings and training sessions, planning for implementation may commence, with the development team defining project goals. The development team collaborates with system vendors to integrate technology effectively to achieve these goals (Akhloufi et al., 2022). Vendors may introduce a beta version or minimum viable product for healthcare organizations to test and provide feedback, leading to system adjustments tailored to the needs of patients and healthcare professionals (Akhloufi et al., 2022). Practical Recommendations Stakeholders Education Successful technology implementation requires the buy-in of all relevant stakeholders. Healthcare organizations can educate their staff on maximizing technology’s potential by conducting weekly training sessions, seminars, and webinars, while also addressing staff concerns (Lukowski et al., 2020). Studies have shown the benefits of classroom-based team training interventions and simulation for assessing technical competence and addressing training gaps in healthcare technology use (Bienstock & Heuer, 2022). Monitor Data to Evaluate Outcomes After successfully implementing the CDSS and Best Practice Advisory (BPA) alert systems, it is crucial to evaluate their impact on COVID-19 patient outcomes. The CDSS system’s potential to enhance health outcomes through rapid and accurate disease detection can reduce its spread, lower healthcare costs, and increase patient safety (Karthikeyan et al., 2021). Saegerman et al. (2021) demonstrated that the CDSS system facilitated rapid identification of COVID-19 patients, aiding triage efforts in understaffed diagnostic labs during the pandemic. This clinical decision support tool plays a crucial role in managing the pandemic (Saegerman et al., 2021). A Specific Example of Bioinformatics Clinicians can significantly reduce the time required to evaluate patients with COVID-19 symptoms by using a clinical decision support tool for diagnostic assessments (Gavrilov et al., 2021). Effective quarantine of patients with COVID-19 symptoms is essential to prevent further virus spread in healthcare facilities. The CDSS system guides practitioners through a standardized COVID-19 diagnostic workup based on the latest recommendations, streamlining the process (Gavrilov et al., 2021). The integration of CDSS systems with Best Practice Advisory (BPA) alerts offers several advantages, including improved patient and staff safety, rapid virus detection, and time-saving benefits (Gavrilov et al., 2021). NURS FPX 6414 Assessment 3 Tool Kit for Bioinformatics Process Before the implementation of the CDSS system After the implementation of the CDSS system Time to make an accurate diagnosis of COVID-19 1-2 days 5-6 hours Healthcare costs $9500 $2000 Unidentified patients in quarantine 10-20 patients 5 patients False Negative Results 7-8 false negative results 3-4 false negative results NURS FPX 6414 Assessment 3 Tool Kit for Bioinformatics Conclusion This study examined the feasibility of using CDSS systems in the administration and management of COVID-19. The CDSS system’s ability to swiftly diagnose COVID-19 patients aids healthcare professionals in containing its spread, reducing complications, lowering unnecessary treatment costs, shortening diagnostic procedures, and improving clinical performance and patient outcomes. References Akhloufi, H., van der Sijs, H., Melles, D. C., van der Hoeven, C. P., Vogel, M., Mouton, J. W., & Verbon, A. (2022). The development and implementation of a guideline-based clinical decision support system to improve empirical antibiotic prescribing. BMC Medical Informatics and Decision Making, 22(1). Bienstock, J., & Heuer, A. (2022). A review on the evolution of simulation-based training to help build a safer future. Medicine, 101(25), e29503. Fry, C. (2021). Development and evaluation of best practice alerts: Methods to optimize care quality and clinician communication. AACN Advanced Critical Care, 32(4), 468–472. Gavrilov, D., Kuznetsova, T., Gusev, A., Korsakov, N., & Novitskiy, R. (2021). Application of a clinical decision support system to assess the severity of the new coronavirus infection COVID-19. European Heart Journal, 42(Supplement_1).

Phillip October 7, 2023 No Comments

NURS FPX 6414 Assessment 2 Proposal to Administration

NURS FPX 6414 Assessment 2 Proposal to Administration Student Name Capella University NURS-FPX 6414 Advancing Health Care Through Data Mining Prof. Name Date Proposal to Administration Title: Type 2 Diabetes Self-Management in Healthcare Organizations Introduction Type 2 Diabetes (T2D) self-management involves a multifaceted approach encompassing healthcare professionals, nurses, and stakeholders working collaboratively to manage and control the condition (Winkley et al., 2020). With a significant number of individuals in the United States diagnosed with type 2 diabetes, it is imperative that patients acquire the necessary knowledge and skills to monitor their health effectively. This presentation delves into various facets of diabetes self-management systems within healthcare organizations, including blood sugar monitoring, adherence to balanced meal plans, and implementation of regular exercise regimens (Agarwal et al., 2019). The study aims to elucidate the rationale behind and the methods employed in monitoring type 2 diabetes outcomes to enhance patient care. Rationale for Measuring Specific Quality Outcomes Given that over 500 million individuals in the United States grapple with type 2 diabetes, measuring specific outcomes is paramount in facilitating patient education on self-management through diabetes self-management education (Adam, 2018). For instance, Diabetes Self-Management Education and Support (DSMES) programs provide patients with educational resources and support to manage their condition effectively. The objectives of these educational initiatives are to empower community members with increased self-awareness and foster positive self-management behaviors. NURS FPX 6414 Assessment 2 Proposal to Administration Additionally, the Chronic Disease Management System (CDMS) plays a pivotal role in assisting individuals in maintaining lower blood glucose levels, thereby mitigating complications. These measures not only enhance the quality of life for patients but also contribute to cost reduction in healthcare settings (Agarwal et al., 2019). Furthermore, outcome measures serve as essential benchmarks for establishing a patient’s baseline. Benchmarks Associated with Specific Outcomes Benchmarks pertaining to type 2 diabetes, as defined by the American Diabetes Association, suggest that the majority of individuals in the United States with the condition should aim for an acceptance rate of less than 7% (van Smoorenburg et al., 2019). Additionally, a significant emphasis is placed on achieving a weight reduction of up to 15% through the efficacy of medications (Apovian et al., 2018). Furthermore, the patient mortality rate, currently at 5%, remains relatively high, primarily due to suboptimal hospital care quality. Evaluation of Data Measures and Trends Several data measures and trends must be considered when evaluating this particular line of service. Notable data measures, supported by available evidence, include: Early patient mortality rates. Reduced life expectancy of patients. A type 2 diabetes readmission rate of approximately 25% in the United States. A direct correlation between lower education levels and increased disease prevalence. A lower likelihood of diagnosis among highly educated individuals (Wu, 2019). Elevated risk of type 2 diabetes among Hispanic and black Americans compared to other ethnic groups. Interpretation of Data in Relation to Benchmarks The incidence rate of type 2 diabetes has steadily risen in numerous Western countries over the past four decades, with little reduction in the current decade (Winkley et al., 2020). Recent years have witnessed a decline in the incidence rate among middle-aged and baby boomer populations, suggesting an increased risk of the disease among younger individuals over the past decade. Additionally, specific blood sugar level thresholds, such as values exceeding 140 mg/dL, are indicative of abnormal or elevated readings. Levels exceeding 200 mg/dL signify a higher likelihood of diabetes, underscoring the significance of type 2 diabetes self-management programs in reducing readmission rates. NURS FPX 6414 Assessment 2 Proposal to Administration Data Spreadsheet The World Health Organization highlights diabetes mellitus as a substantial global health challenge. Between the 1980s and 2015, the prevalence of diabetes among adults doubled from 4.7% to 8.5% (Agarwal et al., 2019). Notably, diabetes has ranked as the seventh leading cause of death in the USA since 2019, accounting for approximately 87,647 death certificates (Adam, 2018). The following datasheet presents statistics related to type 2 diabetes across different racial demographics in the United States, considering factors such as education and racial disparities. Conclusion The data analysis presented here underscores a strong association between individuals’ education levels and the prevalence of diabetes in the United States. Behavioral self-management is critical for both healthcare professionals and patients to curb the escalating rates of diabetes. The data evidence suggests that diabetes diagnosis rates continue to rise steadily in many countries, including the USA, primarily due to lower patient education levels and racial disparities. 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. 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. 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. NURS FPX 6414 Assessment 2 Proposal to Administration van Smoorenburg, A. N., Hertroijs, D. F. L., Dekkers, T., Elissen, A. M. J., & Melles, M. (2019). Patient’s perspective on self-management: Type 2 diabetes in daily life. BMC Health Services Research, 19(1), 605. 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 (Winchester, England), 24(28), 1–232. 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.

Phillip October 7, 2023 No Comments

NURS FPX 6414 Assessment 1 Conference Poster Presentation

NURS FPX 6414 Assessment 1 Conference Poster Presentation Student Name Capella University NURS-FPX 6414 Advancing Health Care Through Data Mining Prof. Name Date Abstract Healthcare professionals strive to enhance care delivery to improve patient outcomes, with a key focus on prioritizing and maintaining patient safety. Falls are the leading cause of unintentional mortality among individuals aged 65 and older in the United States (CDC, 2020), resulting in approximately 2.8 million elderly individuals seeking emergency room treatment annually (CDC, 2020). Several factors, such as confusion, mobility limitations, and urgent toileting needs, contribute to the increased risk of falls among the elderly, both in and out of hospital settings (LeLaurin & Shorr, 2019). In the hospital, between 700,000 and 1 million patients experience falls each year (LeLaurin & Shorr, 2019), with an incidence rate ranging from 3.5 to 9.5 falls per 1000 bed days (LeLaurin & Shorr, 2019). Galet et al. (2018) conducted a study involving 931 patients, identifying 633 individuals at the highest risk of falls due to mental or physical impairments and incontinence. The occurrence of a single fall can prolong a patient’s hospital stay. To mitigate the risk of falls, OhioHealth’s informatics team developed the Schmid tool (Lee et al., 2019) to identify high-risk individuals and implement appropriate preventive measures. The Schmid tool assesses various factors, including mobility, mental status, toileting abilities, history of falls, and current medications. This study aims to evaluate the Schmid tool’s effectiveness in enhancing patient safety and overall healthcare outcomes by utilizing data in conjunction with informatics models. Introduction Annually, approximately 2.8 million adults seek emergency department care for fall-related injuries (LeLaurin & Shorr, 2019). Hospitalized patients also face a significant risk of falling, with between 700,000 and 1 million falls occurring each year (LeLaurin & Shorr, 2019). Falls contribute to extended hospital stays, leading to increased healthcare costs. The Schmid tool is utilized to identify patients at high risk of falls by considering factors such as mobility, mental status, toileting abilities, history of falls, and medications. Evaluating the Schmid tool’s effectiveness is essential to enhance patient safety and healthcare outcomes. Analyzing the Use of the Informatics Model The Schmid fall risk scale categorizes a patient’s fall risk into four main categories: mobility, cognition, toileting abilities, and medication usage (Amundsen et al., 2020). Mobility includes four subcategories: mobile (0), mobile with assistance (1), unstable (1b), and immobile (0a). Cognition is assessed as alert (0), occasionally confused (1a), always confused (1b), or unresponsive (0b). Toileting abilities are classified as completely independent (0a), independent with frequency (1a), requiring assistance (1b), or incontinent (1c). Finally, medication usage is categorized into various medications such as anticonvulsants (1a), psychotropics (1b), tranquilizers (1c), hypnotics (1d), or none (0) (Amundsen et al., 2020). Literature Review Despite a gradual decline, in-hospital falls remain a significant concern for healthcare institutions, as they are a leading cause of harm to patients. Patients suffer increased injury and fatality rates, leading to a diminished quality of life, while healthcare providers face rising expenses due to prolonged hospital stays and medical care costs. Since 2008, Medicare and Medicaid no longer cover fall-related injuries for hospitalization reimbursement (LeLaurin & Shorr, 2019). Hospitals must take preventive measures to reduce patient falls due to the substantial financial burden they impose. Recent studies indicate an alarming trend of readmissions among older patients with traumatic injuries, such as falls, highlighting the need for social support networks and fall prevention initiatives for the elderly (Galet et al., 2018). Falls are the primary cause of injury and mortality among individuals aged 65 and older in the United States (CDC, 2020), emphasizing the importance of fall prevention strategies. Conclusion The comprehensive approach outlined in this study demonstrates the potential to reduce hospital falls. Previous research has identified falls as the leading cause of death in the United States. By employing the informatics model throughout the development of the Schmid tool for quality improvement, this study observed a significant reduction in the incidence of falls.

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