Abstract
The paper is devoted to rehabilitation patient scheduling and its significance for clinical practice. The study employs a literature review as the primary data collection and analysis tool. The purpose of the research is the evaluation of different algorithm-based scheduling models, their features, advantages, and flaws. The findings reveal that the application of automatic planning systems support the increase in service and work efficiency and facilitates the achievement of the positive patient and economic outcomes. The reviewed dynamic programming and deterministic linear programming models can both be effective in rehabilitation hospitals. However, the linear model computation can be readjusted to meet particular situations, needs, and constraints and it is less time-consuming than DP exploration.
Introduction
The purpose of rehabilitation divisions’ work is the maintenance and recovery of patients’ health, frequently right after surgery or other interventions of acute conditions. The largest part of rehabilitation inpatient populations stays in hospitals for some weeks and receive various treatments that involve the implementation of numerous resources (staff, equipment, medicines, time, etc.). The number of rendered services and treatments varies from one setting to another depending on size and specialization. Similarly, the demand for them differs in distinct communities. However, despite potential dissemblance in supply and demand, rehabilitation divisions play an essential role in health systems due to the very nature of their functions.
Most of the therapeutic rehabilitation processes are carried out with the assistance of personnel and are usually fixed in duration. Therefore, the quality, accuracy, and efficiency of scheduling practices are critical to the patient, staff, and economic outcomes within such settings. Based on the importance of this issue, in the given paper, we aim to review and evaluate the evidence about the major rehabilitation patient scheduling principles collected from the recent scholarly articles. Through the analysis of findings regarding various hospital scheduling systems, we will attempt to generate some recommendations aimed to improve adverse situations within the context of the discussion.
Literature Review
Problem Statement
According to Wang, Hung, and Yen (2016), hospital punctuality, regularity in the use of treatment equipment by rehabilitates, and wholehearted health practitioners’ support are the key success factors in rehabilitation intervention. Each nurse, therapist, physician, and other clinical staff members all equally responsible for the effective treatment provision and adaptability to the course of intervention and particular hospital conditions. Providers should thus be allocated with sufficient time to meet every patient’s needs. Additionally, practitioners’ patient communication schedules should be aligned with the therapeutic time that takes the major part of the inpatient stay at the unit.
Because most of the rehabilitated should participate in several types of therapy per day, the coordination of different activities at the organizational level may be inhibited and result in delays, increased waiting time, and overall patient dissatisfaction. For this reason, rehabilitation patient scheduling requires an integrated approach. However, the researchers claim that many hospitals continue to use paper-based records that do not support communication between physicians and therapists and are associated with a significant level of human error and uncertainty inherent with manually developed plans (Wang et al., 2016). Therefore, to increase the accuracy and efficiency of scheduling and consequently improve patient satisfaction, hospitals need to implement automatic resource and process management systems.
Scheduling Models
It is observed that automatic planning systems increase the quality of reporting and forecasting, and their implementation may ultimately lead to greater service effectiveness in various hospital settings (Baesler, Gatica, & Correa, 2015). Depending on the particular interests and needs of organizations and their patients (e.g., revenue, service reliability, etc.), different resource and process management models and scheduling algorithms can be employed. For instance, Baester et al. (2015) suggest simulated annealing as the method for searching for an optimal scheduling solution. The given tool helps evaluate the efficiency of a designed schedule by creating a simulation model based on a set of initial conditions (e.g., number of patients and procedures).
The simulation tool measures the overall level performance that can be achieved in a particular situation and automatically generates a new improved version of the schedule. It is observed that simulation annealing can help to increase planning efficiency by 18% no matter how large a patient batch may be (Baester et al., 2015). According to the research findings, the given model is associated with better scheduling results than such traditional scheduling task models as First-In, First-Out (FIFO), and Shortest/Longest Processing Time. Although the study by Baester et al. (2015) reviews the model implementation in the surgery department, it is possible to say that despite the apparent difference of rehabilitation divisions’ clinical processes, the given search and optimization method can be employed in both types of the settings because the resources used there, as well as the general objectives that surgical and rehabilitation care providers pursue, are similar to a large extent.
The optimal implementation of available resources facilitated workflow, and a high level of commitment to clients/patients are considered the primary objective of scheduling science (Abbasian, Nosratabadi, & Fazlollahtabar, 2015). To attain the given aims, the adjustment of multiple performance factors and their control through adequate scheduling is required. For the achievement of a greater level of operational flexibility, the researchers recommend using dynamic programming (DP) models as they perfectly suit dynamic work environments, such as manufacturing enterprises and hospitals (Abbasian et al., 2015).
The dynamic genetic algorithm (GA) proposed by the authors combines numerous variables and parameters such as several job operations, available equipment and time, the earliest and latest time of completion, etc., and allows selecting the best possible combination through the development of initial population, crossover, and mutation (Abbasian et al., 2015). GA can be adjusted to solve any general scheduling problem or sub-problem. However, when using GA, it is necessary to balance between exploration and exploitation of effective solutions. It means that in case one implements elitism strategy, some measures to compensate for its influence should be undertaken. These measures include the increase of mutation probability (Abbasian et al., 2015).
Simulation-based and genetic optimization methods are commonly used methods of DP. According to Wang and Fung (2015), they are powerful, yet it is difficult to handle DP computation because it refers to some sophisticated characteristics such as initialization, exploration, exploitation, etc. To reduce the number of variables in the DP model, the researchers suggest a decomposition-based algorithm meant to deconstruct it into a few linear sub-elements (Wang & Fung, 2015). In this way, the correlation of the revenue issues analysis with patient preferences, the consideration of which is core to patient satisfaction improvement efforts, becomes facilitated. To do so, in the reformulated DP model meant to estimate maximal revenue per a patient appointment, the separate therapy/appointment periods should be relaxed to stay continual while patient choice variants are supposed to be deterministic (Wang & Fung, 2015). Through the collection of choice values and consideration of patient interests, hospitals can meet the demand, increase patient satisfaction, and, at the same time, cut costs.
Discussion
It is possible to say that manually performed scheduling operations, both technology-mediated and paper-based, require significant employee efforts. Moreover, planning processes and resources according to the FIFO model may not be effective at all. Manually constructed schedules are characterized by a lack of flexibility. It is difficult to make changes in them without compromising either organizational cost efficiency or patient satisfaction. The reviewed DP and decomposition-based scheduling algorithms may help to solve the mentioned problems and increase control over resources and processes in the short and long terms.
Automatic DP and LP electronic scheduling systems are less time-consuming than manual planning and more efficient in many ways. Both DP and LP models support the development of a systematic approach to organizational resource management. Moreover, they provide a significant number of alternative solutions and can help to address different patient groups distinctly, i.e., consider their financial capabilities and interests and optimize organizational revenues and rehabilitates’ satisfaction accordingly.
The literature review reveals that the dynamic simulation-based and genetic algorithms are appropriate to solve randomly determined clinical scheduling issues because the implementation of exploration and search methods associated with them is very practical. The given models allow a realistic representation of problems and the inclusion of numerous variables such as therapeutic processes, availability of beds, equipment, or other resources. Although these methods can both be efficiently implemented within a rehabilitation setting, the search in the genetic population-based method requires more time and effort to generate appropriate scheduling solutions. Therefore, the implementation of GA can be especially difficult in large settings where extensive amounts of data should be explored.
The deterministic LP model analyzed by Wang and Fung (2015) involves fewer variables and, in this way, it is easier to compute. Furthermore, the decomposition-based algorithm may help address all constraints of the monolithic scheduling models. It also allows modification and consideration of some additional aspects of the planning process. The decomposition of the DP model leads to the loss of a few variables (e.g., particular treatments and activities depending on the designed algorithm) and, to some extent, this loss can be noticeable. However, the decreased LP-related computation effort regardless of the size of patient flow indicates the greater suitability of the given system to rehabilitation settings.
Conclusion
The conducted review of the literature helped to comprehend the key features of various programming models that can be implemented for resource and patient planning within rehabilitation units. The discussed problems associated with the given type of settings have some specific characteristics that emphasize the importance of using automatic or semi-automatic scheduling programs for the optimization of patient flow and workflow. It is possible to say that the implementation of decomposition-based algorithms and models integrating linear processes are more appropriate for rehabilitation settings due to the reduced level of computation efforts and time. It can also be suggested to implement algorithm-based models rather than problem-specific ones because they can be easily readjusted to particular situations and organizational needs.
Algebraic scheduling models are characterized by a high level of flexibility. Therefore, the implementation of this kind of system can help to transform the adopted resource management practices and processes. The automatic scheduling systems generate advantages at the individual patient or care provider level and the larger organizational level. They substantially contribute to the growth of patient satisfaction, enhancement of operational efficiency including the efficiency of scheduling task performance, and have the potential to reduce costs as a result of the decreased waiting time and human errors.
References
Abbasian, M., Nosratabadi, H. E., & Fazlollahtabar, H. (2015). Applying an intelligent dynamic genetic algorithm for solving a multi-objective flexible job shop scheduling problem with maintenance considerations. Journal of Applied & Computational Mathematics, 04(04). Web.
Baesler, F., Gatica, J., & Correa, R. (2015). Simulation optimisation for operating room scheduling. International Journal of Simulation Modelling (IJSIMM), 14(2), 215-226. Web.
Wang, C., Hung, L., & Yen, N. (2016). Using RFID positioning technology to construct an automatic rehabilitation scheduling mechanism. Journal of Medical Systems, 40(1), 1-12. Web.
Wang, J., & Fung, R. Y. (2015). Dynamic appointment scheduling with patient preferences and choices. Industrial Management & Data Systems, 115(4), 700-717. Web.