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

Healthcare delivery is becoming more advanced and sophisticated with the use of technology. The field of bioinformatics helps to incorporate technology with data to improve healthcare delivery and use its benefits of it. The healthcare field is specifically focusing on bioinformatics to use policies and practices to improve decision-making and care delivery. The recent COVID Pandemic has affected almost the world. It causes acute respiratory infection. It is necessary to understand the causes of the infection to reduce and prevent it. The preventive interventions require a significant amount of data from the patients to be analyzed to understand what increases the spread of the infection (Meng et al., 2020). It has been found that patients with multiple serious diseases are more prone to this infection. 

The use of Best Practice Advisory Alert (BPA) and Clinical Decision Support (CDS) has helped millions of people to live healthy life. Many healthcare settings use CDS in the form of BPA to send alerts to patients about their medical conditions (Baumgart, 2020). Electronic Health Record (EHR) has enabled healthcare professionals to use the patients’ data and take needful actions based on it. The alerts by BPA are provided in the form of a pop-up where the patients are regularly reminded about their treatments. The use of BPA allows healthcare professionals to keep the patients up to date regarding their therapies which is not only beneficial for the patients but also helps the hospitals to reduce the hospitals’ readmission rates.  

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 consists of several actions and approaches. In a review carried out by Winkley et al. (2020), the self-management of Type 2 Diabetes refers to the activities and actions of healthcare professionals and nurses, and stakeholders to treat and control the condition. Since millions of US citizens have type 2 diabetes, patients must know how to monitor their health adequately. This presentation explores various aspects of diabetes self-management systems in healthcare organizations, such as testing blood sugar (glucose), using a balanced meal plan for patients, and helping them with regular exercise plans (Agarwal et al., 2019). This study describes how and why we are monitoring the outcomes of type 2 diabetes to improve care.

Why and How to Measure for a Specific Quality Outcome

           Since more than 500 million people in the US have type 2 diabetes, measuring this specific outcome is vital for helping patients with diabetes to learn self-management skills through diabetes self-management education (Adam, 2018). For example, the DSMES program will provide educational and learning support to patients to control their disease. These learning outcomes aim to help community members gain more awareness of self-management skills and adopt positive self-management behaviors. Moreover, the Chronic Disease Management system CDMS is a vital program to help such people manage their lower blood sugar (glucose) levels and also reduce complications. These measures are vital for improving the life quality of patients and can also help hospitals reduce their healthcare costs (Agarwal et al., 2019). Moreover, outcome measures are vital standards that help to establish a patient baseline. 

Benchmarks Associated with that Outcome

           The benchmarks are related to type 2 diabetes state according to the American Diabetes Association, most people in the United States suffering from this disease have an acceptance rate of below 7% as a benchmark (van Smoorenburg et al., 2019). Moreover, more strong emphasis is placed on reducing patients’ weight by up to 15% based on the efficacy of drugs and medications (Apovian et al., 2018). Furthermore, the patient mortality rate is 5%, which is relatively high, and that is due to poor hospital care quality.

Evaluate Data Measures and Data Trending

           There are a few data measures and trends which need to be considered for this evaluation of this specific line of service. For example, the following data measures are apparent from the evidence available such as:

• Early deaths of patients
• Shortened life spans of patients
• Regarding type 2 diabetes readmission in the US, the readmission rate is almost 25%
• The lower the education and awareness of the population, the higher the chances of the disease
• People who are highly educated are less likely to be diagnosed (Wu, 2019).
• The risk of type 2 diabetes in Hispanic and black Americans is higher than in others

Interpretation of the Data related to the Benchmarks

           The incidence rate of Type 2 diabetes in many Western countries has constantly increased over the past four decades (Winkley et al., 2020). Sadly, this trend has not been reduced significantly in the current decade. In several middle-aged and baby boomers, the type 2 diabetes incidence rate has decreased in recent years. This implies that the younger population has developed a greater risk of catching this disease in the past ten years. 

Moreover, several measures are available for type 2 diabetes, such as the value of blood sugar levels less than 140 mg/dL (van Smoorenburg, 2019). If the level is higher than this level, it is abnormal or higher than usual. Moreover, if the reading is higher than 200 mg/dL, the rate between 140 and 200 shows more people are likely to suffer from diabetes. This raises the importance and the value of diabetes type 2 self-management programs and can reduce readmission rates.

Data Spreadsheet

The World Health Organization reveals that diabetes mellitus represents a substantial global health challenge for healthcare professionals. Between the 1980s and 2015, the adult population suffering from this disease doubled from 4.7 to 8.5% (Agarwal et al., 2019). According to the American Diabetes Association ADA, the following statistics and type 2 diabetes figures and stats are crucial to consider in the datasheet. Diabetes has been the seventh most prominent cause of death in the USA since 2019, with almost 87,647 death certificates (Adam, 2018). The following datasheet shows facts for different races of Americans suffering from higher and lower rates of diabetes due to their education and racial preferences and issues. 

Conclusion

The above data analysis of type 2 diabetes self-management shows a deep relationship between individuals’ education levels and diabetes disease in the United States. Behavioral self-management is crucial for nurses and patients to reduce the prevalence of a higher rate of diabetes. The data evidence shows that many countires, including the US, have a diabetes diagnosis rate that is steady growth due to lower education of patients and racial differences. 

References 

Adam, L., O’Connor, C., & Garcia, A. C. (2018). Evaluating the impact of diabetes self-management education methods on knowledge, Attitudes and Behaviours 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 (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

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 are dedicated to enhancing care delivery to improve patient outcomes, with a significant emphasis on prioritizing and ensuring patient safety. In the United States, falls represent the primary cause of unintentional mortality among individuals aged 65 and older (CDC, 2020), leading to approximately 2.8 million elderly individuals seeking emergency room treatment each year (CDC, 2020). Various factors contribute to the heightened risk of falls in this population, including confusion, mobility limitations, and urgent toileting needs, occurring both within hospital environments and in the community (LeLaurin & Shorr, 2019).

In hospitals, an estimated 700,000 to 1 million patients experience falls annually, with an incidence rate ranging from 3.5 to 9.5 falls per 1,000 bed days (LeLaurin & Shorr, 2019). A study by Galet et al. (2018) involving 931 patients found that 633 individuals were at the highest risk of falls due to mental or physical impairments and incontinence. A single fall can extend a patient’s hospital stay significantly.

To address the risk of falls, OhioHealth’s informatics team created the Schmid tool (Lee et al., 2019), designed to identify high-risk individuals and implement effective preventive strategies. The Schmid tool evaluates multiple factors, including mobility, mental status, toileting abilities, fall history, and current medications. The goal of this study is to assess the effectiveness of the Schmid tool in enhancing patient safety and overall healthcare outcomes by integrating data with informatics models.

Introduction

Each year, around 2.8 million adults seek emergency care for fall-related injuries (LeLaurin & Shorr, 2019). Hospitalized patients are particularly vulnerable, with between 700,000 and 1 million falls occurring annually (LeLaurin & Shorr, 2019). These falls often lead to extended hospital stays, contributing to increased healthcare costs.

The Schmid tool serves as a means to identify patients at high risk of falls by analyzing factors such as mobility, mental status, toileting abilities, fall history, and medications. Assessing the effectiveness of the Schmid tool is crucial for improving 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 domains: mobility, cognition, toileting abilities, and medication use (Amundsen et al., 2020). The mobility domain comprises four subcategories: mobile (0), mobile with assistance (1), unstable (1b), and immobile (0a). Cognition is evaluated as alert (0), occasionally confused (1a), always confused (1b), or unresponsive (0b). The toileting abilities are classified as completely independent (0a), independent with frequency (1a), requiring assistance (1b), or incontinent (1c). Finally, medication usage is categorized into several types, including anticonvulsants (1a), psychotropics (1b), tranquilizers (1c), hypnotics (1d), or none (0) (Amundsen et al., 2020).

Literature Review

Despite a gradual decline, falls occurring in hospitals continue to be a major concern for healthcare facilities, representing a leading cause of patient harm. Patients affected by falls often experience increased rates of injury and mortality, which adversely affects their quality of life. Simultaneously, healthcare providers encounter rising costs due to extended hospital stays and increased medical care needs. Since 2008, Medicare and Medicaid have stopped covering fall-related injuries for hospitalization reimbursement (LeLaurin & Shorr, 2019). Consequently, hospitals must take proactive measures to reduce patient falls due to the significant financial burden they impose.

Recent studies reveal a troubling trend of readmissions among older patients suffering from traumatic injuries, such as falls, underscoring the necessity for robust social support systems and fall prevention strategies for the elderly (Galet et al., 2018). Falls remain the primary cause of injury and mortality for individuals aged 65 and older in the United States (CDC, 2020), highlighting the urgent need for effective fall prevention initiatives.

Conclusion

The comprehensive approach detailed in this study illustrates the potential to decrease the incidence of falls within hospitals. Prior research has established falls as a leading cause of death in the United States. By integrating the informatics model in developing the Schmid tool for quality improvement, this study has observed a noteworthy reduction in the frequency of falls.

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.
• CDC. (2020). Falls among older adults: An overview. Centers for Disease Control and Prevention. https://www.cdc.gov/homeandrecreationalsafety/falls/adultfalls.html
• 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.

NURS FPX 6414 Assessment 1 Conference Poster Presentation

• 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.