DNP Project Implementation of Predictive Analytics to Improve Quality, Safety, and Fiscal Management of an Acute Care Organization.

DNP Project Implementation of Predictive Analytics to Improve Quality, Safety, and Fiscal Management of an Acute Care Organization.

The author’s DNP project focuses on implementing predictive analytics for nurse staffing to enhance the quality and safety of patient care and fiscal management in the acute care setting. Nurse staffing models have historically been based on a fixed staffing grid or prior census data, which fail to account for key factors that determine staff workload and the level of care patients require. Anticipated admissions and discharges, as well as the anticipated workload, are factors to consider when making staffing decisions using predictive analytics to determine staffing needs based on data that best reflect patient and staff needs.

Elevating the quality and safety of care is paramount to improving patient and staff outcomes. Establishing systems to track and measure key quality indicators, reduce practice variation, and promote a culture of safety are among the clinical leader’s key responsibilities (Bemker & Whitehead, 2024). The predictive model used in this project helps anticipate future staffing needs, thereby reducing the risk of compromised care due to inadequate staffing. Using a model that incorporates patient acuity, anticipated admissions and discharges, and Medical Usefulness Scale (MUSE) scores in lieu of census information enables nurse managers to make accurate, data-driven staffing decisions with confidence. The information gathered from the staffing model enables the organization to identify and track key indicators of adverse effects of reduced-quality care, such as missing care, inequitable workload distribution, staff fatigue, and decreased patient and staff satisfaction and outcomes (Bemker & Whitehead, 2024).

Another critical measure that will be improved with the use of a staffing model that predicts patient needs is patient safety. Studies have demonstrated that when there are adequate numbers of nurses on staff, patients receive better care. This includes fewer instances of medication errors, patient falls, and healthcare-associated infections. In addition, there is less missed care for patients and decreased patient mortality when there are adequate numbers of nurses on staff (Needleman et al., 2011). The use of a predictive model will enable the nurse manager to make staffing decisions earlier and timelier, anticipate and staff for patient needs, and ensure equitable distribution of the workforce. The model will also enable the nurse manager to continuously monitor patients for signs of deterioration and communicate with staff about patient needs. This type of proactive approach to patient care is what is expected of high-reliability organizations (Institute for Healthcare Improvement [IHI], 2024). These organizations are designed to anticipate and prevent errors rather than respond to them after they occur.

Strategic financial management in healthcare organizations is key in maintaining quality of care while achieving organizational goals. There are initial expenses for technology, education for all involved, and organizational data structures required for predictive analytics. However, the long-term financial benefits of reduced overtime, reduced reliance on expensive agency nurses, increased productivity, improved staff retention, and the prevention of costly adverse events will far exceed the costs of implementing and sustaining a process for using predictive analytics to make staffing decisions. There is substantial evidence from the literature that having patients cared for by an adequate number of qualified nurses has resulted in, and will continue to result in, the best patient outcomes, decreased incidence of preventable complications, and reduced nursing turnover (Lasater et al., 2021).

Next steps are necessary to continue improving the organization through predictive staffing. First, various metrics predicted to be sensitive to nursing will need to be incorporated into the organization’s strategic planning. This data needs to be displayed on the organization’s quality dashboards, which are reviewed by executive leadership across the organization (Bemker & Whitehead, 2024).

An interdisciplinary governance group will monitor and evaluate the use of predictive analytics for nurse staffing on an ongoing basis. Members of this group should consist of the following: nurse leaders, frontline nurses, finance, quality improvement, information technology, and data analysts. The governance group will assist in monitoring, evaluating, and making changes as necessary to ensure predictive analytics supports optimal patient care as the patient population and organization change (Bemker & Whitehead, 2024).

Third, data will continue to be used for determining the appropriate number of nurses needed to care for patients. The data gathered by the staffing model will be used by the DNP project team members, consisting of the Nurse Leaders and the Frontline Nurses, in conjunction with their clinical expertise, to make decisions about patient care and to make daily staffing changes as needed. The use of data for staffing will undergo PDSA cycles to identify areas for improvement and make changes as needed to ensure patients receive the best possible care (Institute for Healthcare Improvement [IHI], 2024).

4. Sustain Return on Investment- Organizations must assess the financial return on investment in predictive staffing models. The model should demonstrate both clinical and financial benefits to the organization. Clinical benefits may include reduced missed care and improved patient outcomes. Financial benefits may include reduced overtime, decreased need for agency staff, improved productivity, and improved nursing staff retention. These benefits, and others, demonstrate the long-term quality improvement and fiscal management strategy predictive analytics can provide (Lasater et al., 2021).

In summary, the use of predictive analytics for nurse staffing decisions is a sound leadership approach. It not only enhances the quality of care for patients, safety of patients and employees, and the fiscal management of organizations, but it also provides a framework and a tool for nurse leaders to be proactive in ensuring an adequate and effective workforce in the face of predicted workforce shortages and increasing complexity of patient needs. Such innovative leadership requires a commitment to quality care, patient safety, and fiscal responsibility from all levels of the organization. An interdisciplinary team of experts, including nurse leaders, information technology staff, finance specialists, data analysts, and quality improvement specialists, must work together on an ongoing basis to evaluate the outcomes of data-driven decisions made using predictive analytics. The tool and process must be embedded into the decision-making process of all levels of the organization, and the data used to evaluate and improve all processes. And the tool and process must be a strategic priority for the organization to continue improving processes and patient care.

Bemker, M., & Whitehead, D. (2024).Advancing organizations by exemplary nursing leadership. Destech Publications.

Institute for Healthcare Improvement. (2024). The IHI Triple Aim and Quintuple Aim.https://www.ihi.orgLinks to an external site.

Lasater, K. B., Aiken, L. H., Sloane, D. M., French, R., Martin, B., Reneau, K., Alexander, M., & McHugh, M. D. (2021). Chronic hospital nurse understaffing meets COVID-19: An observational study.BMJ Quality & Safety, 30(8), 639–647.https://doi.org/10.1136/bmjqs-2020-011512Links to an external site.

Needleman, J., Buerhaus, P., Pankratz, V. S., Leibson, C. L., Stevens, S. R., & Harris, M. (2011). Nurse staffing and inpatient hospital mortality.New England Journal of Medicine, 364(11), 1037–1045.https://doi.org/10.1056/NEJMsa1001025Links to an external site.