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Using Predictive Analytics to Improve Healthcare Outcomes


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helpful action plans were created based on these inquiries. However, the data is now also looked at in terms of meaningful questions such as “Why do falls happen more often in the bathrooms at night?” “How is rounding conducted at night?” and “How do staff members interact with the patient during rounding?” When decisions about what to measure are made using a process that includes discussion of a wide and relevant array of real‐world influences, your subsequent measurements will produce results that allow people to improve operations and design safer care delivery processes. These discussions, which included people from the staff councils and leadership quality councils, helped take the conversation deeper, from unit‐based to broad systems of care. They encouraged examination of how workflow happens, consideration of the various staffing roles and skill mix, and the question we ask every time: “Can it be done better?”

      Once the data analyst and PSI RNs were in place, the organization made sure resources were available for them to do deep dives as a team. As each unit has its own culture and specific service line, the PSI RNs and the staff members of individual units came together to review the data reports from the analyst and discuss the “whys” and “hows.” They came up with responsive interventions that were specifically designed for their patient population, since what works in a pediatric unit, for example, may not work in an intensive care unit. This grassroots approach made sense to the team and led to greater staff buy‐in. It is largely staff‐to‐staff discussions, not manager‐to‐staff mandates, that create effective planning to meet the unit‐specific goals of excellence and positive patient outcomes.

       It is largely staff‐to‐staff discussions, not manager‐to‐staff mandates, that create effective planning to meet the unit‐specific goals of excellence and positive patient outcomes.

      The PSI RNs provided context and clarity, helping managers and staff members see how policies, standards, regulations, and best practice come together to define quality care. This support for the nurse manager helped facilitate the change to a high‐quality/high‐reliability culture on the units, ensuring that each unit was delivering high‐quality, evidenced‐based care around the clock. This work can be overwhelming in organizations where there is a clinical staff of 150 nurses in large units, such as in the emergency department. The addition of the PSI RNs provided a unique mediating and educating role which served to support both staff members and managers in establishing standards of quality that were consistent with policy.

      Nurses have always been a driving force in quality. Whether at the chief nurse officer's level or leading the organization's quality improvement department, nursing is charged with providing and ensuring excellent patient care. However, application of the quality data is interdisciplinary by nature, as each discipline's care delivery impacts the others. In our organization, after the addition of a data analyst and the PSI RNs, people in other disciplines observed that nursing had made significant sustainable improvements in care and outcomes. They started to ask for consultation from nursing in the proper use of predictive analytics and its application for operational refinement and outcomes improvement. Leaders in radiology asked nursing to teach their staff members how to examine the data and how to use the same brainstorming techniques the nursing staff had used to develop action plans.

       Although to the products and purchasing team it was just a color, and the yellow caps were less expensive than the green caps, they had no idea the impact the change in cap color would make on our infection control practices.

      There are two key success factors that I would recommend to organizations pursuing the sort of quality improvement work outlined in this chapter. They are (a) hiring nurses with MSN and BSN degrees to work as PSI RNs, and (b) using recognition as an incentive for the good work done on quality improvement projects.

      Education Levels of PSI RNs

      Advanced practice nurses and clinical staff members with baccalaureate or master's‐level education have been taught to be consistent in asking the “why” and “how” questions that need to be asked in the data analysis process. When the question arises: “Did we look at this specific factor?” these highly prepared clinicians tap into their knowledge of the subject and ask the necessary questions to get at meaningful, actionable data that can be used to improve care outcomes. Leaders need to advocate for nurses who are hired as PSI RNs and into other performance improvement positions, to be MSN and BSN level nurses, and they must support nurses at all levels of preparation in seeking continuing education in areas such as statistics, research, ethics, and practice standards. (Fun fact: Our PhD prepared data analyst was once an associate nurse.) This emphasis will promote sustainability and encourage a culture of innovation.

      Recognition of Excellence