1. Sherrod, Dennis EdD, RN
  2. McKesson, Tori BSN, RN
  3. Mumford, Mechelle BSN, RN, CMSRN

Article Content

Today's complex healthcare systems allow little to no margin for error, and nurse managers throughout the nation vigilantly monitor unit benchmarks to ensure delivery of quality, accessible, and cost-effective services. As healthcare organizations rapidly develop informatics and electronic systems, data become readily available. Operational and data analysis is a primary leadership skill for nurse managers.1 In constant pursuit of process improvement and quality, nurse managers must equip themselves with data analysis and data-driven decision-making skills.


What's data-driven decision making?

Data are actually nothing more than sets of numbers and/or words but with appropriate analysis, data become information that provides the basis for informed decisions. Essentially, data provide information that guides the majority of nurse manager decisions. Data-driven decision making is a systematic process of collecting, analyzing, and synthesizing data; making a judgment about the data; and then making a decision based on the knowledge derived from your judgment in order to improve patient outcomes. Whereas evidence-based practice applies to research findings that may have been completed in the last month, year, or longer, data-driven decision making is action oriented and may involve making a decision for tomorrow based on today's outcomes. Action knowledge informs different types of decisions, which may include appraising goal setting, progress, or accomplishment; assessing whether individual or group needs are met; determining whether resources need to be allocated and/or reallocated; and evaluating the effectiveness of processes and practices.2


Examples of data-driven decisions that nurse managers make include assigning unit and shift staffing levels based on patient acuity rates, increasing focus on infection control measures based on patient infection figures, and a heightened concentration on service delivery based on patient satisfaction survey results. As demand for quality care grows, you'll need to develop competencies and skills that allow you to identify issues influencing delivery of care, determine what data are needed to provide further understanding, and analyze internal and/or external data for effective decision making. If the findings validate a need for change or unit improvement, you'll need to formulate a plan for how to implement the change and apply it.


Healthcare is an industry in which collecting data is extremely important for growth and change. Analyzing data resulting from your unit outcomes, discovering strategies to tackle the issue, and formulating a plan for improvement will assist you in making better clinical decisions. These administrative skills are a permanent component of the process for ensuring continuity and quality of care delivered to patients.


Keeping staff informed of metric results, benchmarks, and unit outcome measurements such as patient satisfaction scores and complaints is helpful for increasing staff buy-in to improve processes in the unit. But staff nurses also need to add data analysis and data-driven decision-making skills as part of their direct-care competencies. Complex, unique, and ever-changing patient demands require rapid response and frequent adjustments. As a unit leader, you're the critical link for completing the circle of using outcome data to improve patient care. Thus, information and knowledge resulting from data assist healthcare personnel to develop strategies to improve performance and patient-care quality. Given that healthcare is ever-changing and evolving, it's the responsibility of you and your staff to familiarize yourselves with data analysis and data-driven decision-making processes in order to make more informed decisions.


Needed competencies and skills

Of course, data-driven decision-making competencies and skills require knowledge and understanding of your unit processes, the data available in your unit, statistical terms and principles, and quality improvement (QI) models. A degree in statistics isn't required, but you'll need a basic understanding of common statistical terms, as well as data collection, analysis, and reporting functions. Expertise in the following areas will greatly assist your data analysis and decision making.


Identify data needed for decision making in your unit. Assess whether the data are readily accessible for analysis and if not, identify a strategy for making them available. You might choose to utilize standardized metrics, dashboards, or measurement tools that can be adapted to your unit, but you'll need to make sure the required data are collected. More commonly, healthcare systems suffer from the collection of too much data rather than too little.


Identify benchmarks and performance targets. Compare unit benchmarks inside and outside your hospital. This process allows you to identify the key quality indicators used, such as patient satisfaction scores, infection control rates, and staffing needs. Benchmarking can assist you to adapt best practices in areas of quality, clinical, operational, and financial areas of performance.


Demonstrate a basic knowledge of essential statistical concepts and how to interpret findings, and know the limitations and power of analysis methods. An understanding of basic statistical terms, such as mean, median, mode, percentage, normal distribution, and level of significance, is helpful to determine whether analysis results are meaningful. It's pertinent when reviewing your results to fully understand what they really mean. You want to make sure you're critically evaluating all data findings to determine if they warrant action.


Deconstruct complex processes into their component parts for analysis.3 When looking at unit issues, view each component individually. For example, an increase in hospital-acquired infections in your unit doesn't actually identify the cause. Experience and expertise assist you to decide if you should encourage staff to become more vigilant about hand washing, gowning, and other infection precautions or if you should initiate a separate analysis to identify a root cause. After examining the components, you can better evaluate overall patient-care delivery.


Recognize processes as part of the care delivery system and how issues interrelate. Patient satisfaction scores have become the gold standard for determining quality care. Although patient satisfaction outcomes are important, you may need to identify a number of processes that influence patient care in your unit. Some of the areas we know influence quality care include pain management, staff interactions with patients, and timely and accurate medication administration. Unacceptable patient satisfaction scores don't identify a specific area for improvement, but review and analysis of processes that influence patient outcomes can help you decide where you need to focus your unit's improvement actions.


Know how to run various analyses commonly used for decision making in your unit and whether they have the correct data required. Learning how to calculate and measure data can be overwhelming if you aren't familiar with the process. Attending seminars on data collection and basic analysis techniques can be a positive and beneficial experience. Access to a computer and spreadsheet program allows for manipulation of many basic analysis operations.


Draw reasonable and proper inferences about quality and performance from the results of your analyses. After your data are calculated and findings are determined, it's time to make an informed decision. Knowledge and understanding of your data and analysis methods allow you to confidently communicate findings and decisions to your staff.


Incorporation into the QI process

A first step in any QI process is to identify and define the problem. After the problem is defined, exploring possible causes will assist with the data that need to be collected and examined. Upon data analysis, if the findings warrant a need for improvement, implement a change to correct the process, then monitor and measure the improvement. Monitoring trends on a month-to-month basis can be helpful. For example, minimizing catheter-related bloodstream infections can be assisted through constantly monitoring and analyzing the number of blood-related infections in your unit. This method can help determine if the unit or individuals need more education on how to correctly access catheters and/or obtain blood specimens.


A second step is informing staff members of trends-whether it's a negative or a positive deflection-and whether findings are acceptable. Sometimes called the communication period, it's an essential element for correcting quality problems. If staff members don't know what's going on, they can't be held accountable for correcting the problem. After you make data-driven decisions, you must communicate them to staff. During the communication process, staff members should be allowed to discuss and help identify necessary steps for improvement. For example, ways to decrease catheter-related bloodstream infections might include the following: always wash hands and don gloves before touching or accessing a catheter, always clean the catheter port thoroughly with alcohol and/or iodine (facility protocols may vary) before obtaining specimens, and maintain aseptic technique when changing a dressing at the catheter site.4


A third step involves leading by example. You should remain up to date on metrics, dashboards, and measurement tools being used in your unit. If staff members have questions related to data and analysis methods, it's your responsibility and obligation to teach and guide them. Developing a culture of data-driven decision makers in your unit can assist you in achieving and maintaining QI goals. The Joint Commission standards require a focus on "actual performance and actual outcomes." The Joint Commission also mandates facilities to identify and implement improvements based on data related to outcomes and decisions to change or improve systems that are data driven.5


Get prepared!!

As QI processes focus more specifically on patient outcomes, demand for nurse manager competencies in data-driven decision making will surely grow. As your career progresses, data-driven decision-making skills will play a more prominent role in ensuring the delivery of quality, accessible, and cost-effective services. Begin preparing yourself today for data-driven decision making.




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4. O'Grady NP, Alexander M, Dellinger EP, et al. Guidelines for the prevention of intravascular catheter-related infections. MMWRRecomm Rep. 2002;51(RR10):1-29 [Context Link]


5. Parsons EC, Capka MB. Building a successful risk-based competency assessment model. AORN J. 1997;66(6):1065-1071. [Context Link]