Welcome to the content for my poster presentation from the Texas Nurses Association’s 2023 Annual Conference.

Title: A data-driven, care-centric approach to nurse staffing.

Background:

Nurse managers often allocate excessive non-productive time during schedule creation, resulting in overtime & understaffing issues. Discussions with nursing leaders revealed a lack of understanding of how much non-patient care time could be utilized during the scheduling process while preserving the resources needed for patient care.

Methods:

To optimize resource allocation when creating nursing schedules, a simple mathematical modeling approach that considers required patient care resources & various factors that prevent employees from participating in patient care, including orientation, leave, & other scenarios, can determine the maximum amount of replacement (non-productive) time that can be scheduled each week while ensuring the necessary resources for patient care are preserved. This approach can help nurse managers make more informed scheduling decisions, maximize resource utilization & deepen the understanding of their budget. Leveraging care-centric modeling and the expertise of informatics nurses can help to facilitate increased communication and collaboration between nursing and finance.^[1]Begley, R., P. F. Cipriano, and T. Nelson. “The business of caring: promoting optimal allocation of nursing resources, 2020.” Healthcare Financial Management Association. … Continue reading

Discussion

Nurse Staffing Information Structures

Interrelated mathematical & informational layers can be used to describe the budgeting, scheduling, & staffing processes. Resource variances in the Budget & Position Control layers will flow through to the Schedule & Staffing layers. The resources available in the staffing layer heavily influence patient outcomes & a variety of quality, safety, & financial metrics.

Moving from bottom to top in the model, the Budget layer is the foundation and determines the total resources required to provide the desired level of care for an expected volume of patients.^[2]Jones, C., Finkler, S. A., Kovner, C. T., & Mose, J. (2018). Financial Management for Nurse Managers and Executives (5th ed.). Saunders.

The Position Control layer subdivides the FTE totals into individual positions. Filled positions in the Position Control layer populate the Schedule Layer.

The HR Information layer is an informational layer that may influence decisions in the Schedule and Staffing layers. Things such as licensure, certification, special training, experience, being multilingual, etc. can influence assignments in the Staffing and Scheduling layers. For instance, nurses trained as charge nurses can be scheduled for charge nurse shifts. In the Staffing Layer, an employee who speaks the same language as a patient who does not speak English can be assigned to care for that patient.

In the Schedule layer, employees are assigned to one of two states: independently participating in patient care or not independently participating in patient care.

Employees independently participating in patient care in the Schedule layer become the primary input for patient care in the Staffing layer. Adjustments in the staffing layer may be made due to available resources, patient acuity, patient volume, and other factors.

The delivery of patient care through the Staffing layer heavily influences the Outcomes layer, which includes patient outcomes and a variety of quality, safety, productivity, and financial metrics.

Resource variances in one layer can have ripple effects throughout the other layers and impact the outcomes layer. Later, we’ll examine how by moving through the model from top to bottom, we can uncover additional insights that can open the door to evidence-based management in nursing care delivery.

Care-Centric Modeling

Care-Centric Modeling is a resource allocation model that considers the resources required for patient care, orientation, leave, & other scenarios that prevent employees from independently participating in patient care. By deducting these variables from the available resources, we can determine the number of hours that can be allocated each week for non-patient care shifts when creating future schedules while reserving the resources needed for patient care.

To get started with care-centric modeling for nursing schedules, you need 3 pieces of information:

1: Your filled FTEs by skill
2: Your budgeted patient care (productive) FTEs.
3: The status of employees in those FTEs, such as orientation, leave, or any other status that would prevent you from scheduling them on your unit in a patient care capacity.

We’re going to reserve our Patient Care FTEs by subtracting them from our Filled FTEs first.

While orientees may count towards your productivity, they are unable to cover for a fully productive employee when they are out on PTO. So we must deduct our orientation FTEs.

Deduct your Leave and Other FTEs that, for whatever reason, are unable to participate independently in patient care on your unit. A recent example of an “Other FTE” would be an employee temporarily assigned to a covid unit for a period of time. While the employee may still be on your position control, they are not available for scheduling on your unit

What you’re left with is your Available Replacement FTEs. Multiply this value times 40 hours per week, and this is how much you can spend per week on non-patient care shifts when creating schedules. Spending more than this value will create unfilled shifts at the bedside.

If I didn’t explain the formula any further, you could take this formula back to your facility, and by entering different values into the formula, you would begin gaining insights into your budget, your scheduling capacity, and how resource variances impact your ability to provide your employees with time off and education.

To better understand how this formula looks, let’s go through a few scenarios.

In the above scenarios, scenario #1 represents what our mock nursing unit would look like if it was fully hired with no one on orientation or leave. With 15% of total FTEs available for replacement (non-productive) time, we would have 180 available replacement hours per week that could be allocated to non-patient care shifts while leaving the 25.50 FTEs needed for our baseline scheduling for patient care. The fallacy of nursing budgets is that nursing units rarely have all of the resources needed to provide the desired levels of patient care, time off, and education

Scenario #2 introduces a modest 7.5% vacancy rate. Performing the care-centric modeling calculation reveals that our available replacement has been cut in half from 180 hours per week to 90. We last saw average RN vacancy rates in the mid-7 percent range in 2015.^[3]Nursing Solutions, Inc. (2023). 2023 NSI National Health Care Retention & RN Staffing Report.

Just prior to the start of the pandemic in 2019, average RN vacancy rates had crept up to 8.0%.^[4]Nursing Solutions, Inc. (2019). 2023 NSI National Health Care Retention & RN Staffing Report. with further increases to 9.0% in 2020^[5]Nursing Solutions, Inc. (2020). 2023 NSI National Health Care Retention & RN Staffing Report. and 9.9% in 2021.^[6]Nursing Solutions, Inc. (2021). 2023 NSI National Health Care Retention & RN Staffing Report.

In scenario #3, our 10% vacancy rate has further reduced our Available Replacement Hours to only 60 per week – 1/3 of our budgeted total. Our mock unit’s nurse manager has five 12-hour shifts that they can schedule for non-patient care shifts each week. Scheduling more non-patient care shifts than there are Available Replacement Hours to cover will create unfilled shifts at the bedside.

Scenario #3 represents the best-case scenario for a 10% vacancy rate, as we have no employees on orientation or leave. When considering average weekly call-in hours of one 12-hour shift per day, we can anticipate a shortage of two 12-hour shifts per week.

In Scenario #4, adding three 0.9 FTE employees on orientation and one 0.9 FTE employee on leave results in a shortfall of one 12-hour shift every day when creating future schedules. After considering the impact of average weekly call-in hours, our mock unit will be short two 12-hour shifts on every day of the schedule. These shifts would need to be filled with overtime, travel resources, or floats. This unit also has no resources to provide coverage for time off and education, and every non-patient care shift scheduled will create another unfilled shift at the bedside.

Scenario #5 shows our mock unit with the average RN vacancy rate of 15.7% which was reported by Nursing Solutions, Inc. earlier this year.^[7]Nursing Solutions, Inc. (2023). 2023 NSI National Health Care Retention & RN Staffing Report. … Continue reading You should notice that 25.29 Filled FTEs is less than the 25.50 FTEs needed for patient care.

As we saw in the scenarios we just reviewed, as the resources available to provide time off and education decrease, our capacity for delivering safe and effective care decreases.

By understanding the mathematics of nursing care delivery between the budget and outcomes, we can better understand the impact of resource variances on our capacity to provide patient care, time off, and education.

We can also use the data from the calculations to facilitate conversations with administration and finance regarding resource needs.

If, as a nurse manager, you’re being asked to explain your overtime utilization, poor quality outcomes, and high staff turnover, being able to respond with more than “I don’t have enough staff.” can help you better advocate for the resources you need.

Bringing data that clearly shows that not only do you not have enough resources for your baseline staffing, but you also have no resources to provide employees with time off or education, and every time off request or other non-patient care shift scheduled will create another unfilled shift at the bedside is a much more compelling and objective argument. You may also be able to include quality and safety metrics that are sensitive to staffing levels, such as falls^[8]Kim, J., Kim, S., Park, J., & Lee, E. (2019). Multilevel factors influencing falls of patients in hospital: The impact of nurse staffing. Journal of nursing management, 27(5), 1011-1019. and pressure injuries^[9]Alanazi, Faisal Khalaf, et al. “Safety culture, quality of care, missed care, nurse staffing and their impact on pressure injuries: A cross-sectional multi-source … Continue reading.

Evidence-Based Management

If we revisit our Nurse Staffing Information Structures model and read it from top to bottom, now that we understand the care-centric modeling concepts, we can discover additional insights that can open the door to evidence-based management and tie decisions made by executive leadership to outcomes.

If we have an increase in adverse events, such as falls, we can work backward through the model for potential contributors to the problem.

So why did our falls increase?

Because we were significantly understaffed and were unable to respond to call lights in a timely manner.

Why were we significantly understaffed?

It could be that our patient volume was much higher than expected. It could also be that our manager did a poor job of allocating resources. They may have significantly overspent on their non-patient care shifts when creating schedules.

But the problem could be further down the chain.

If there are too many vacancies in the Position Control layer, HR and Finance are largely responsible for recruiting and filling those positions.

At the Budget Layer, if your facility is using the flawed nursing budget process^[10]Ward, W. J., Jr. (2015). Health care budgeting and financial management, 2nd edition (2nd ed.). Praeger., that is the responsibility of Finance. Likewise, if finance significantly underestimated the expected patient volume, the budget will not contain the resources to cover the higher actual volume adequately.

The impact of decisions or mistakes made by administration, HR, and finance at the budget and position control layers will flow through the Schedule and Staffing layers and impact the Outcomes layer.

Through this model, we should be able open the door to evidence-based management in nursing care delivery operations and link decisions made by executive leaders to patient outcomes.

Effective Workforce Management Teams

Informatics nurses leading multidisciplinary teams comprised of the nurse manager & representatives from finance, human resources, & quality could help guide the use, synthesis, & analysis of care-centric modeling, finance, quality, & position control data to understand better the factors impacting nursing care delivery & outcomes.

This effective workforce management team could then provide assessments & recommendations to leadership on the source of problems & suggestions for mitigating the issues.

This team’s work could help facilitate communication & collaboration between nursing & finance. By evaluating the data in FTEs, hours, & shifts, the analysis can act as a Rosetta Stone with the FTEs that finance prefers & the hours & shifts that make more sense to nursing.

Calculating this data for all skills on all units & aggregating it for executive leadership would provide powerful insights & assessments into the facility’s capacity for patient care, time off, & education.

This is a low-overhead, high-impact process. The work can easily be accomplished with Excel & the expertise of the team members.

This approach represents an opportunity to optimize resource utilization while improving the quality of patient care & healthcare work environments. The informatics nurse can play a crucial role in leading these efforts, utilizing their expertise in systems & data analysis & their ability to bridge the gap between nursing & finance. By working collaboratively, the team can develop, recommend, implement, & evaluate strategies that could improve staffing, recruitment & retention, reduce burnout, enhance patient outcomes, & improve the cost-effectiveness of nursing care delivery.

CONCLUSION: Healthcare leaders & professional organizations must facilitate more debate, discussion, understanding, & research around the mathematics of nursing care delivery so we can more accurately determine our nursing resource needs. By accurately assessing the shortfall, we can determine the effectiveness of proposed solutions, such as virtual nursing^[11]Lindgren, Lisa BSN, MBA, RN, CEN, NE-C. Utilizing telehealth to enhance nursing care and reduce burnout. Nursing Made Incredibly Easy! 21(1):p 41-43, January/February 2023. | DOI: … Continue reading, robotics, artificial intelligence^[12]Liesbet Van Bulck and others, Applications of artificial intelligence for nursing: has a new era arrived?, European Journal of Cardiovascular Nursing, Volume 22, Issue 3, March 2023, Pages … Continue reading & telesitting^[13]Ergai, Awatef PhD; Spiva, LeeAnna PhD, RN; Thurman, Susan DNP, RN; Hatfield, Marianne DNP, RN, CENP; McCollum, Meriel PhD, RN; Holmes, Mona MSN, APRN, ACCNS-AG. The Effectiveness of Remote … Continue reading, to close the resource gap & make better-informed decisions on the optimal allocation of available resources.

With their expertise in nursing & data & systems analysis, informatics nurses can play a crucial role in facilitating communication, collaboration, & education between nursing, finance, human resources, & quality improvement.^[14]McGonigle, Dee, and Kathleen Mastrian. Nursing informatics and the foundation of knowledge. Jones & Bartlett Learning, 2021. They can also guide a more data-driven, care-centric, evidence-based, holistic approach to nursing finance & workforce management.

Nursing possesses the expertise to solve this problem, & we can look to our roots for inspiration. Florence Nightingale’s groundbreaking use of data during the Crimean War was critical in establishing nursing as a scientific profession, saving innumerable lives, & transforming healthcare worldwide.^[15]Gill, Christopher J., and Gillian C. Gill. “Nightingale in Scutari: her legacy reexamined.” Clinical infectious diseases 40.12 (2005): 1799-1805.

To address current staffing challenges, we can follow in Nightingale’s footsteps by leveraging the power of data to understand our nursing workforce better, driving changes that lead to more effective workforce management. Additionally, we can provide simple, data-driven tools & education to empower nursing leaders to better advocate for their patients & staff while ensuring the optimal allocation of available nursing resources, resulting in healthier working environments & high-quality patient care. By adopting this approach, informatics nurses and nursing leaders can drive positive changes in nursing finance & workforce management that improve healthcare for all.