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NURS FPX 4040 Assessment 4 Informatics and Nursing Sensitive Quality Indicators

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    NURS FPX 4040 Assessment 4 Informatics and Nursing Sensitive Quality Indicators

    Student Name

    Capella University

    NURS-FPX4040 Managing Health Information and Technology

    Prof. Name


    Informatics and Nursing Sensitive Quality Indicator

    Greetings, I am —-, and I work as a registered nurse at Valley Hospital. First, I congratulate and welcome you all on your induction day. This audio tutorial will give you brief and concise knowledge of informatics and nursing-sensitive quality indicators. I will discuss these indicators and their role in nursing practices. Then, I will discuss a specific nursing-sensitive indicator, its use by healthcare organizations, and evidence-based practices for nurses to enhance patient safety. Additionally, you will also learn how this information on quality indicators is collected and disseminated across the organization.  Let’s brace ourselves and delve into this topic together.

    Nursing-Sensitive Quality Indicator

    The American Nurses Association (ANA) established a comprehensive and concise database of the National Database of Nursing-Sensitive Quality Indicators (NDNQI) in 1998 to gather and analyze data relevant to nursing-sensitive quality indicators (NSQI). These NSQI are measures to analyze the nursing care quality and its influence on patient health outcomes. These measuring areas are benchmarks for evaluating nurses’ performance and planning prospective improvements based on the results.

    The NSQI metrics are classified into three categories: structure (nurse education), process (pain management), and outcomes (patient satisfaction, job satisfaction, and nurse turnover) (Oner et al., 2020). Based on these three classes, various quality indicators can be utilized to evaluate nurses’ performance. By identifying areas of concern, hospitals can improve the quality of care nurses provide. This can include policy changes, promoting nurse staffing standards, and advocating for patient care improvements.

    The Chosen Quality Indicators and its Importance

    The nursing-sensitive quality indicator that I have chosen to discuss in detail is hospital readmission rates. Hospital readmission rates refer to the rate at which patients are readmitted to the hospital shortly after discharge. Hospital readmission rates can reflect the quality of care and care transitions. It is essential to monitor this quality indicator as it can inform healthcare organizations about the quality of care being provided to patients. When the hospital readmission rates are higher, patients may not receive appropriate post-discharge care or coordinated care.

    With escalating hospital readmission rates, the quality of care can be compromised due to disruptions during treatment plans or medication regimens. Moreover, patient safety is further impacted as higher hospital readmissions expose patients to additional risks, such as hospital-acquired infections and adverse events that may take place during subsequent hospital stays. Furthermore, stress and discomfort associated with multiple hospitalizations can also compromise patient safety and overall well-being. Besides, higher rates of hospital readmissions pose various implications, such as added costs to patients and organizations, strained healthcare systems due to increased burden on limited resources, and decreased patient satisfaction (Yeo et al., 2019).

    NURS FPX 4040 Assessment 4 Informatics and Nursing Sensitive Quality Indicators

    New nurses must have adequate knowledge of this quality indicator when providing patient care. They can reduce hospital readmission rates by providing practical, coordinated care to patients before discharge or during care transitions. This will reduce the chances of hospital readmissions as patients are already receiving the right quality of care and showing improved clinical outcomes. If the nurses do not acknowledge this quality indicator, the patient may receive compromised quality of care, increasing the hospital readmission rates. This will ultimately impact nurses as they must provide further care treatments to patients, leading to enhanced burnout among nurses and job dissatisfaction. Therefore, nurses must consider this quality indicator while providing patient care to prevent future hospital readmission rates (Meddings et al., 2023).

    Role of Interdisciplinary Team in Data Management

    Let’s delve into the interdisciplinary team’s role in data management, encompassing collection and analysis. I consulted with our hospital administrator to gain a deeper understanding of data collection and management at Valley Hospital. The interview taught me that our organization utilizes Electronic Health Records (EHRs) to gather data on quality indicators like hospital readmission rates. Various interdisciplinary team members include nurses, IT personnel, Quality control department personnel, and hospital administration.

    The nurses are primarily responsible for accurately reporting data relevant to readmissions, such as discharge diagnoses, follow-up appointments, and patient demographics. Accurate documentation is crucial for estimating hospital readmission rates and analyzing critical areas for improvement. The accurately documented data on hospital readmission rates will enable the proper planning to prevent readmissions by providing high-quality care before patients’ discharge.

    The IT personnel analyze and disseminate these data by creating dashboards to share with quality improvement department leaders and healthcare administration. The dashboards are analyzed monthly, and interdisciplinary team meetings are conducted. These quality improvement meetings bring together nurses, quality improvement leaders, and healthcare administrators. Their focus is to discuss observed patterns and trends and devise prospective plans to enhance patient safety and the quality of care. The interdisciplinary team plays a crucial role in data collection as a collaboration of nurses and IT personnel in maintaining and using EHR reduces the chances of errors and inaccurate data. This leads to accurate analysis and driving improvements needed in healthcare organizations to improve organizational performance in reducing hospital readmission rates. 

    Use of Nursing Sensitive Quality Indicators by Healthcare Organizations

    Healthcare organizations use nursing-sensitive quality indicators to ensure the quality of care delivered to patients is up to mark and evaluate their position in improving patients’ clinical health outcomes. Healthcare organizations can use hospital readmission rates to enhance patient safety, patient care outcomes, and organizational performance reports. For instance, patient safety is enhanced when nurses closely monitor the health status of discharged patients to ensure they do not require further care treatments at hospitals and reduce the risk of hospital-acquired infections on readmissions.

    Moreover, by utilizing the hospital readmission rates, healthcare professionals analyze whether the care transitions are smoothly accomplished through effective care coordination, medical reconciliation, and patient education before discharge. Lower hospital readmission rates show successful care transitions with enhanced patient safety (Kripalani et al., 2019). By analyzing hospital readmission rates and their root causes, nurses can identify areas for improvement and implement evidence-based interventions to enhance patient care outcomes, such as by ensuring appropriate follow-up care and adherence to treatment plans and reducing medication errors or other adverse events.

    Additionally, considering the hospital readmission rates, healthcare professionals can devise quality improvement plans based on root causes, leading to improved patient care outcomes. Lastly, healthcare organizations can enhance organizational performance reports by using organizational data on hospital readmission rates and comparing them with national or standard benchmarks. This will lead to finding areas for improvement, setting realistic goals for reducing readmissions, and improving organizational performance (Barbieri et al., 2020).

    Evidence-Based Practice Guidelines for Nurses

    Nursing-sensitive quality indicators like hospital readmission rates can guide nurses on various practice guidelines by establishing evidence-based interventions. Multiple evidence-based practices are established due to higher hospital readmission rates, which nurses can implement while using patient care technologies. A study by Romero-Brufau et al., 2020 shows that an artificial intelligence-based clinical decision support tool is developed and implemented due to increased hospital readmission rates.

    Nurses can use this tool that extracts patients’ health data from EHR and combines them with nonclinical data to generate a report for each patient with a high risk of acquiring hospital readmission status within 30 days. This is coupled with multidisciplinary team interventions for high-risk patients to reduce hospital readmission rates by providing patient-centered and coordinated care through effective healthcare technologies. The study showed that hospital readmission rates were diminished post-implementation of the AI-based CDS system, resulting in improved patient safety, health outcomes, and patient satisfaction.

    Another evidence-based strategy that emerged from high hospital readmission rates was implementing care coordination while using patient care technologies such as EHRs and educating patients about their health and medical treatments. Moreover, the study suggests monitoring readmission rates and involving multidisciplinary rounding teams to improve hospital operations and patient interactions. Lastly, the strategies involve creating a vision and mission among healthcare staff to provide high-quality care treatments while using patient care technologies to enhance patient safety. This will lead to better health outcomes and high patient satisfaction, as hospital readmission rates will decline (Warchol et al., 2019). 


    To conclude, we covered nursing-sensitive quality indicators and how they were enacted. Later, I discussed the chosen nursing-sensitive quality indicator of hospital readmission rates and their importance in healthcare organizations. Moreover, I discussed data collection on hospital readmission rates in Valley Hospital and how it is managed through interdisciplinary team collaboration.

    Additionally, I discussed how the hospital can use hospital readmission rates as a nursing-sensitive quality indicator to enhance patient safety and patient care outcomes and improve organizational performance reports. Lastly, I discussed evidence-based strategies like the AI-based CDS system and multidisciplinary team approach that emerged due to higher hospital readmission rates. These strategies are essential for nurses to implement while using patient care technologies to improve patient safety, patient care outcomes, and satisfaction.


    Barbieri, S., Kemp, J., Perez-Concha, O., Kotwal, S., Gallagher, M., Ritchie, A., & Jorm, L. (2020). Benchmarking deep learning architectures for predicting readmission to the ICU and describing patients-at-risk. Scientific Reports, 10(1). 

    Kripalani, S., Chen, G., Ciampa, P., Theobald, C., Cao, A., McBride, M., Dittus, R. S., & Speroff, T. (2019). A transition care coordinator model reduces hospital readmissions and costs. Contemporary Clinical Trials, 81, 55–61. 

    Meddings, J., Gibbons, J. B., Reale, B. K., Banerjee, M., Norton, E. C., & Bynum, J. P. W. (2023). The impact of nurse practitioner care and accountable care organization assignment on skilled nursing services and hospital readmissions. Medical Care, 36(6). 

    NURS FPX 4040 Assessment 4 Informatics and Nursing Sensitive Quality Indicators

    Oner, B., Zengul, F. D., Oner, N., Ivankova, N. V., Karadag, A., & Patrician, P. A. (2020). Nursing‐sensitive indicators for nursing care: A systematic review (1997–2017). Nursing Open, 8(3). 

    Romero-Brufau, S., Wyatt, K. D., Boyum, P., Mickelson, M., Moore, M., & Cognetta-Rieke, C. (2020). Implementation of artificial intelligence-based clinical decision support to reduce hospital readmissions at a regional hospital. Applied Clinical Informatics, 11(04), 570–577. 

    Warchol, S. J., Monestime, J. P., Mayer, R. W., & Chien, W.-W. (2019). Strategies to reduce hospital readmission rates in a non-medicaid-expansion state. Perspectives in Health Information Management, 16(\). 

    NURS FPX 4040 Assessment 4 Informatics and Nursing Sensitive Quality Indicators

    Yeo, I., Cheung, J. W., Feldman, D. N., Amin, N., Chae, J., Wong, S. C., & Kim, L. K. (2019). Assessment of hospital readmission rates, risk factors, and causes after cardiac arrest. JAMA Network Open, 2(9), e1912208.