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 aim to improve patient outcomes by prioritizing and maintaining patient safety, particularly concerning falls, which are the leading cause of unintentional mortality among individuals aged 65 and older in the United States. Approximately 2.8 million elderly individuals seek emergency room treatment annually due to falls. Several factors contribute to this risk, including confusion, mobility limitations, and urgent toileting needs. In hospital settings, between 700,000 and 1 million patients experience falls each year, with an incidence rate ranging from 3.5 to 9.5 falls per 1000 bed days. A study identified 633 individuals at the highest risk of falls due to mental or physical impairments and incontinence. To mitigate this risk, OhioHealth’s informatics team developed the Schmid tool to identify high-risk individuals and implement preventive measures. This study aims to evaluate the Schmid tool’s effectiveness in enhancing patient safety and healthcare outcomes. Introduction Annually, approximately 2.8 million adults seek emergency department care for fall-related injuries, while hospitalized patients face a significant risk of falling, contributing to extended hospital stays and increased healthcare costs. The Schmid tool is utilized to identify high-risk patients by considering mobility, mental status, toileting abilities, history of falls, and medications. Evaluating its effectiveness is crucial for enhancing patient safety and healthcare outcomes. Analyzing the Use of the Informatics Model The Schmid fall risk scale categorizes a patient’s fall risk into mobility, cognition, toileting abilities, and medication usage. Mobility includes categories such as mobile, mobile with assistance, unstable, and immobile. Cognition is assessed as alert, occasionally confused, always confused, or unresponsive. Toileting abilities range from completely independent to incontinent, while medication usage is categorized into various medications or none. Literature Review In-hospital falls remain a significant concern despite a gradual decline, leading to increased injury and fatality rates and rising healthcare costs. Preventive measures are essential due to Medicare and Medicaid no longer covering fall-related injuries for hospitalization reimbursement. Recent studies highlight the need for social support networks and fall prevention initiatives for older patients, emphasizing the importance of fall prevention strategies. Conclusion This study demonstrates a comprehensive approach to reducing hospital falls, observing a significant reduction in fall incidence through the development and utilization of the Schmid tool. Falls remain a leading cause of death in the United States, but employing informatics models for quality improvement shows promise in mitigating this risk.
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 Toolkit for Bioinformatics in Health Security In light of the COVID-19 virus, heightened concerns about health security have emerged, particularly among individuals who visited hospitals during the outbreak and feared contracting the virus in the healthcare setting (Wu et al., 2020). Rapid identification and treatment of COVID-19 infections are crucial for enhancing people’s sense of security. Health Information Technology, including Clinical Decision Support Systems (CDSS) and Best Practice Advisory (BPA) alerts, plays a vital role in achieving this objective (Wu et al., 2020). This paper aims to provide a comprehensive toolkit for the implementation of CDSS and BPA alerts. Evidence-Based Policy for Healthcare The burden imposed by the COVID-19 pandemic has escalated the workload for healthcare workers and significantly inflated healthcare costs. Failure to control the spread of the illness could lead to substantial challenges for patients, care providers, and health systems due to a shortage of medical professionals and equipment (Moulaei, 2022). Monitoring early signs of COVID-19 infections is essential, and the optimized use of CDSS can assist physicians in making informed decisions, resulting in quicker and more accurate diagnoses and outbreak containment (Moulaei, 2022). In the realm of health information technology, the Affordable Care Act mandates healthcare providers to adopt and fully utilize 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 crucial for a learning health system capable of navigating the complex healthcare landscape. Integrated clinical decision support technologies, such as Best Practice Advisory (BPA) alerts, enhance clinical decision-making by providing relevant information to clinicians (Fry, 2021). Guidelines for Effective Policy Implementation Successful 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). Regular meetings involving physicians, nurses, hospital administrators, nurse informaticists, and information technology specialists should be conducted to develop an efficient CDSS and BPA alert system. These meetings aim to improve technology user-friendliness, minimize errors, and provide training on efficient technology usage (Akhloufi et al., 2022). Following meetings and training sessions, the implementation planning may commence, with the development team defining project goals. Collaboration with system vendors is essential for effective technology integration, with vendors potentially introducing a beta version or minimum viable product for testing and 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 necessitates buy-in from all relevant stakeholders. Healthcare organizations can educate their staff on maximizing technology potential through weekly training sessions, seminars, and webinars, while also addressing staff concerns (Lukowski et al., 2020). Classroom-based team training interventions and simulation have been shown to be beneficial for assessing technical competence and addressing training gaps in healthcare technology use (Bienstock & Heuer, 2022). Monitor Data to Evaluate Outcomes After successfully implementing CDSS and BPA alert systems, evaluating their impact on COVID-19 patient outcomes is crucial. The potential of the CDSS system 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 the 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 in Action 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). Process: Before and After CDSS Implementation Metric Before CDSS Implementation After CDSS Implementation 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 negatives 3-4 false negatives Conclusion This study explored the feasibility of utilizing CDSS systems in the administration and management of COVID-19. The CDSS system’s ability to swiftly diagnose COVID-19 patients assists 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). https://doi.org/10.1186/s12911-022-01860-3 Bienstock, J., & Heuer, A. (2022). A review on the evolution of simulation-based training to help build a safer future. Medicine, 101(25), e29503. https://doi.org/10.1097/MD.0000000000029503 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. https://doi.org/10.4037/aacnacc2021252 NURS FPX 6414 Assessment 3 Tool Kit for Bioinformatics 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). https://doi.org/10.1093/eurheartj/ehab724.3054 Karthikeyan, A., Garg, A., Vinod, P. K., & Priyakumar, U. D. (2021). Machine learning-based Clinical Decision Support System for early COVID-19 mortality prediction. Frontiers in Public Health, 9. https://doi.org/10.3389/fpubh.2021.626697 Lukowski, F., Baum, M., & Mohr, S. (2020). Technology, tasks and training – Evidence on the provision of employer-provided training in times of technological change in Germany. Studies in Continuing Education, 1–22. https://doi.org/10.1080/0158037x.2020.1759525 Moulaei, K. (2022). Diagnosing, managing, and controlling COVID-19 using Clinical Decision Support systems: A study to introduce CDSS applications. Journal of Biomedical Physics and Engineering,
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: Type 2 Diabetes (T2D) Self-Management Initiatives Type 2 Diabetes (T2D) self-management encompasses various actions and strategies. Winkley et al. (2020) define T2D self-management as the collaborative efforts of healthcare professionals, nurses, and stakeholders to address and regulate the condition affecting millions of US citizens. Given the prevalence of type 2 diabetes, it is crucial for patients to acquire proper self-monitoring skills. This presentation explores key aspects of diabetes self-management within healthcare organizations, including blood sugar testing, balanced meal planning, and the promotion of regular exercise for patients (Agarwal et al., 2019). The study aims to elucidate the significance of monitoring type 2 diabetes outcomes to enhance patient care. Rationale for and Methodology of Outcome Measurement With over 500 million people in the US diagnosed with type 2 diabetes, measuring specific outcomes is essential to assist patients in acquiring self-management skills through diabetes self-management education (Adam, 2018). Programs like DSMES provide educational support to help patients control their disease, fostering community awareness and positive self-management behaviors. Additionally, the Chronic Disease Management System (CDMS) plays a pivotal role in managing blood sugar levels and reducing complications, ultimately enhancing patient quality of life and reducing healthcare costs (Agarwal et al., 2019). Outcome measures serve as crucial standards for establishing patient baselines. Benchmark Criteria According to the American Diabetes Association, the benchmark for acceptable blood sugar levels in the United States is below 7% (van Smoorenburg et al., 2019). Emphasis is also placed on achieving up to a 15% reduction in patient weight, based on the efficacy of medications (Apovian et al., 2018). Unfortunately, a 5% patient mortality rate underscores the need for improved hospital care quality. Evaluation of Data Measures and Trends Several data measures and trends warrant consideration in evaluating this specific line of service. Noteworthy data measures include early patient deaths, shortened lifespans, a 25% readmission rate for type 2 diabetes in the US, and the inverse relationship between education levels and disease prevalence. Additionally, the risk of type 2 diabetes is higher in Hispanic and Black Americans compared to other populations (Wu, 2019). Data Interpretation in Relation to Benchmarks The incidence rate of type 2 diabetes has steadily increased in Western countries over the past four decades, with a particular surge in the younger population in the last decade (Winkley et al., 2020). Blood sugar levels below 140 mg/dL are considered normal, while levels exceeding 200 mg/dL indicate a higher likelihood of diabetes. This underscores the importance of type 2 diabetes self-management programs in reducing readmission rates. Data Spreadsheet Diabetes mellitus poses a significant global health challenge, with the adult population affected doubling from 4.7% to 8.5% between the 1980s and 2015 (Agarwal et al., 2019). Notably, diabetes has been the seventh leading cause of death in the USA since 2019, with almost 87,647 death certificates (Adam, 2018). The datasheet provides crucial statistics on type 2 diabetes across different races in the US, considering education and racial disparities. NURS FPX 6414 Assessment 2 Proposal to Administration Conclusion The analysis of type 2 diabetes self-management data reveals a profound correlation between individuals’ education levels and diabetes prevalence in the United States. Behavioral self-management is pivotal for both nurses and patients in mitigating the higher rates of diabetes. The evidence suggests a steady increase in diabetes diagnoses in the US, influenced by lower patient education 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. https://doi.org/10.1016/j.jcjd.2017.11.003 Agarwal, P., Mukerji, G., Desveaux, L., Ivers, N. M., Bhattacharyya, O., Hensel, J. M., … 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). Patient’s 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., … 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. 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