Abstract
Nursing managers are faced with rising turnover and shortages of qualified nursing staff. At the same time they are under increased pressure to simultaneously increase patient care and satisfaction while reducing costs. In this study, we examine the impact of centralizing scheduling decisions across departments in a hospital. By pooling nurses from multiple units and scheduling them in one model, improved costs and reduced overtime result. Reduced overtime improves schedules for nurses. Improved satisfaction levels can positively impact turnover rates among nurses. Our results show that by using a centralized model, nursing managers in hospitals can improve the desirability of nurse schedules by approximately 34% and reduce overtime by approximately 80% while simultaneously reducing costs by just under 11%.
Keywords
Workforce scheduling; Health service; Nursing
1. Introduction
Studies show that the availability of qualified and available nursing staff continues to be well below the needs of the healthcare system. It is projected that by 2020 the gap between supply and demand for qualified nurses will expand to 36% (Biviano et al. [11]). Although there are significant shortfalls in the education pipeline for nurses, there are also considerable challenges with retaining the current nursing workforce. Nursing managers continue to struggle with high turnover levels. The average nurse turnover rate in the United States was 15.5% in 2005 (Bernhard Hodes Group [10]). A recent survey of registered nurses revealed that over one-third intend to leave their position within a year (Aiken et al. [2] and [3]). Nurses cite many reasons for leaving their positions. Gerson and Oliver [19] found scheduling policies to be an important reason why nurses leave the profession. Nurses desire more influence in their work hours (Holtom and O'Neill [21]). High workload, stress, and an increased proportion of non-patient duties also lead to nurse dissatisfaction. In this paper, we propose a nurse scheduling model that assists managers in achieving more desirable schedules (less overtime) while simultaneously reducing wage cost. A model that accomplishes both of these goals is a “win–win” for nurses and healthcare administrators.
In the current hospital nursing environment, it is common for each unit within the hospital to schedule its assigned nurses independently, based on the nurses availabilities and hospital policies. This decentralized scheduling practice compartmentalizes nurses based on the unit manager to whom they report. Within their unit, hospital nurses are commonly called upon to work mandatory overtime. This contributes to less desirable schedules and additional costs for the hospital. In fact, mandatory overtime has caught the attention of many state legislatures who have enacted laws that limit overtime that can be assigned. Currently 16 US states have mandatory overtime limits and several more are considering the issue with legislation (nursingworld.org [28]). One benefit of the model presented in this paper is that a substantial reduction in overtime is possible by centralizing scheduling decisions across departments. Centralized scheduling is particularly attractive when the units being scheduled are general medical/surgical type units with similar nurse training requirements so that cross-training is minimized. In this scenario, many duties are similar from one unit to the next enabling the use of cross-trained employees across multiple units, or cross-utilization.
This paper contributes to the literature by investigating how centrally scheduling cross-trained nurses across multiple units in a hospital can be used to reduce costs and improve nurse satisfaction. To provide further motivation for our approach, we base our investigation on real nurse data from two medium to large hospitals in the United States. These hospitals are particularly conducive to cross-utilization after making efforts to standardize procedures across many of their units. Results of the study show how centralized nurse scheduling in these hospitals improves the desirability of nurse schedules by approximately 34% and reduces overtime by approximately 80% while simultaneously reducing costs by just under 11%. In addition, our solution methodology includes a new linearization of service constraints which also provides contribution to the nurse scheduling literature. These service constraints and their linearization are discussed in 5.1 and 5.2.
The remainder of the paper is organized as follows. Next, we briefly discuss relevant literature. We then provide details of the hospitals with which we worked. The centralized nurse scheduling model is then presented followed by details of our experimental design. Next, results of the experimental study are presented and their implications discussed. In the final section we note limitations of our study and provide some concluding remarks.
2. Literature review
Various aspec