Patient acuity is defined as the categorization of patients according to an assessment of their nursing care requirements (Harper & McCully, 2007). Patient acuity or classification systems have been utilized since the 1960s. They started as cumbersome tools to tally tasks, procedures, and characteristics of patients with the primary intent of determining appropriate staffing. By the 1990s, the systems in place were being easily manipulated and were not controlling staffing or costs. Nurses were able to skew the results in favor of increasing the number of staff. As a result, patient classification systems were redesigned to control nurse decisions by dictating staffing through forms, lists of nursing tasks, labor standards, and point systems. They eliminated professional judgment even when the criteria were outdated, incomplete, or irrelevant (Shaha & Bush, 1996).

Time-based tools are generally task-based instruments that prescribe the time it should take to complete a discrete nursing task. For example, a tool may allot 15 minutes to put in a Foley catheter. However, the actual time this task takes depends on the patient: it might take 5 minutes for an experienced nurse to place a Foley on a man with an uncomplicated urethra; or it might take an inexperienced nurse 45 minutes to place a catheter in a obese woman with a hip fracture, and this nurse will also require help from other staff.

Other tools are based on DRGs (diagnostic related groups), to account for work among different types of patients. As previously stated, actual workload within a DRG can vary up to 40%; most tools do not account for multiple DRGs, such as the patient who has pneumonia, diabetes, and COPD after a hip fracture (Laport, Sermeus, Vanden Boer, & Van Herck, 2008).

Very few tools look at individual patient characteristics as a method to determine staffing. The tools that do are often complicated, do not account for every patient scenario, and may lack reliability and validity. They also tend to be subjective, based on the nurses’ perceptions rather than objective patient characteristics.

Traditionally, nursing tasks have been the least complex metric by which to measure workload. Wickens (2002) identified that certain tasks, depending on their structural dimensions, cannot be completed at the same time. O’Brien-Pallas et al. (1997) explain that the time it takes to complete an individual nursing task is dependent on specific patient characteristics and work environment, which includes the physical unit layout and availability of resources. By measuring tasks, and the time it takes to complete them, such staffing tools narrowly define nursing work as what can be accomplished in a specific time period and not necessarily what should be accomplished (Needleman, 1996). Using this concept, Hoi et al. (2010) developed a workload measurement system using performance predictions to observe and collect average times for common nursing tasks on inpatient units. Using regression modeling, they developed an average intensity per unit to demonstrate how many nurses are needed each shift.