Excessive staff workload is at the root of the current talent crisis in healthcare. High workload leads to fatigue, increased medical errors, burnout, absenteeism, and turnover among nurses and others. We've been warned about these issues for years, but little has been done to improve workload management in healthcare. Understaffing seems to be the norm, and the costs of nurse absenteeism and overtime are approaching $1 billion each. This is not sustainable.
The simple effort to cut budgets and reduce healthcare costs has placed immense pressure on frontline nurses who are trying to make up the difference. Years of budgetary restrictions and the associated increased workload have pushed nurses and other frontline staff to their limits and they are leaving the profession in droves. It seems the healthcare system is purposefully hemorrhaging talent by creating unsustainable workloads. We need better workload management.
Figure 1: The “Better Work for Better Care” framework, illustrating how health system design choices determine staff workload levels. These can have negative effects on nurses in terms of fatigue, which in turn negatively impacts patients (in this case missed care), leading to poorer outcomes and increased system costs in the long term.
Efforts to move to private for-profit health care are not helping. Increasing shareholder value in health care can only really be achieved by raising fees, adding unnecessary services and tests, or further squeezing employees in the pursuit of “efficiencies.” This only exacerbates the problem of excessive workload. The U.S. for-profit health care system costs easily twice as much as most other countries: $12,318 per person per year compared to $5,905 in Canada (both in U.S. dollars). At the same time, the U.S. for-profit system produces one of the worst health outcomes in the world.
For-profit healthcare has proven to be a disaster in itself: if anything, it leads to worse working conditions, a continued exodus of staff, and patients suffering.
Whether the choice is between expensive and ineffective commercial healthcare or equal public healthcare, the workload of frontline health care workers must be managed. Currently, the Canadian health care system lacks objective tools to quantify the workload of nurses and social worker (PSW) workers. This lack of support is the reason why nurses are assigned to work as long as 14 hours in a 12-hour shift (Figure 1).
Figure 2: Results of a simulation model revealing excessive time demands placed on nurses in a medical-surgical unit for a 12-hour shift with and without breaks based on the facility’s standard hours and frequencies obtained from the GRASP system (from Neumann et al., 2023).
Workload management is complex and needs to go beyond the nurse-patient ratio approach discussed. In developing a simulation tool that can quantify the time demands on nurses and the impact on patients, we have attempted to address key drivers of workload, including patient severity and dependency, the design of the built environment, the location of assigned patient beds, the placement of equipment and medications, the care routines and practice policies in place, and the requirements of infection control routines. A uniform patient assignment ignores the severity and dependency of individual patients, which can lead to some nurses being overloaded and unable to care for patients. This is a significant quality issue that threatens patient safety and increases mortality.
The modeling approach we propose can help explain changes in daily workload, as well as understand the impact of proposed system changes. Understanding how much care might be lost due to additional data entry work required by new technologies such as electronic medical records is an important example. It is also important to predict the impact of outbreak or pandemic scenarios on both nurse workload and the amount of care they can provide.
By creating a virtual mirror of the care delivery process, the impact of each system design choice can be tested individually or in combination. Analyzing the current state of the system reveals things like staff time utilization, distance walked measurements, remaining care at the end of a shift, “wait time” for a task (e.g., patient call) to be completed, etc. These metrics can be explored in the future configuration of care units that will be required. Changes in the mix of care tasks, patient needs, work routines (e.g., nurse-to-patient ratios), rearrangements of supplies and equipment, etc., all of these impacts can be quantified quickly and accurately. For example, if we include education and emotional support in our care practice, how will adding these tasks affect shift-level workload?
Figure 3: Schematic of the inputs and outputs of the basic simulation model used to track task times and performance patterns across a shift as specified by the inputs.
Models that quantify workload are not a panacea, but they can provide insight into the impact of workload of current or planned operational scenarios on nurses' time demands. There should be no question about the capacity and ability to adapt care to meet the individual care needs of patients… if there is enough time to provide that care. This is the current issue that requires evidence-based decision-making, from policy level to the bedside.
We presented this approach to professional nursing associations, unions, national and state nursing leaders, practitioners, and health system administrators and received significant interest and support, indicating a recognition of the need for an evidence-based approach to workload management in health care and widespread support for this type of objective workload measurement approach that can aid in appropriate and safe staffing.
Our modeling work revealed that during the COVID pandemic, biomechanical loads were significantly lower in charge nurses caring for COVID-positive patients despite increased time demands. We attribute this counterintuitive finding to the need to don personal protective equipment upon entering each room. When all five patients in the simulated nurse's care were infected with COVID, half of the 12-hour shift was spent on infection control tasks, which are less physically taxing than most care tasks. These measurements demonstrate how physical demands can decrease even as increased patient care demands accumulate time demands and psychological pressures.
This scenario illustrates how modeling tools can be used to understand the complex drivers of workload. The need for such quantitative measures in practice remains. Evidence-based workload measures are a potential antidote to the nursing profession's long-standing and oft-repeated backlash against complaints and warnings. Policymakers and governments may be tempted to ignore the qualitative reports from these experts, as it becomes difficult to maintain a “just do your job” attitude when faced with objective evidence that the promised health services cannot be delivered with available resources.
It is not enough to tell staff to “add data entry to your routine” when their routine already includes more work hours than they can fit into a shift. This is healthcare, not button manufacturing. You cannot just come back tomorrow and start the machine again. This is typical “magical thinking” by policymakers.
Quantitative workload management is the antidote to magical thinking. It is unacceptable to think that asking nurses to work harder or faster is appropriate workload management. The consequences of this approach are evident in staff departures, shortages and dissatisfaction. They are also evident in declining quality of care.
Excessive workload and the associated loss of capacity undermine efforts towards the fundamentals of care: providing a person-centred approach to care that delivers better outcomes for patients. Without considering all aspects of patient care as part of the workload, quality of care remains elusive. Appropriate performance standards, backed by evidence-based workload quantification, are the way out of the trap of “magical thinking”. So start using them!
