No abstract

In most Japanese hospitals, different nurses handle the pre-assigned nursing cares in different ways, which directly affect the quality of nursing cares. To our knowledge, there has been less attention on ensuring that nurses provide nursing cares in a timely and accurate fashion. Consequently, in this paper, considering the similarities to the traditional job shop scheduling problems, we will model the daily nursing care scheduling problems and propose an efficient scheduling method based on simulated annealing algorithm. By iteratively local searching based on simulated annealing: (1) permutating the tasks from one nurse to another and (2) permutating the sub tasks handled by a nurse from its original position to another new one, the proposed method is evaluated to be applicable to the nursing care scheduling problems (providing comprehensive, coordinated and cost effective nursing cares to patients).

The purpose of this study is to evaluate a dynamic scheduling-based nursing support system for nurses working in acute care. Due to unpredictable occurrences such as random disturbances from patients and the variability of processing times in nursing care, inpatient nursing in practical environments is complex. In this study, we implement a nursing support system in a series of laboratory experiments under simulated conditions. In the laboratory experiments, clinical nurses are asked to perform assigned nursing tasks and simulated patients are used to make the environment realistic. Our results show that, compared to the nurses' performance based on their own procedures (rules of action), the dynamic scheduling method resulted in an average improvement of 71% in terms of earliness or tardiness of care. The proposed dynamic scheduling-based nursing support system is proven to be highly applicable to nursing work in practical nursing care environments.

We present a system used in the term recognition competition, one of the subtasks covered by the NTCIR tmrec group, and we evaluate its term recognition results. We regard that terms are lexical items, characteristic of a field, which have the following three features: (1) they appear frequently in documents of the target field; (2) they are not common words in the target field; and (3) they appear less frequently in the corpora of other fields. Our system uses corpora from different fields and uses these features to recognize terms. We then analyze the differences between our term list and the manual candidates list produced by the NTCIR tmrec group. In this article we identify features that are important for automatic term recognition. Furthermore, through comparative experiments based on manual candidates, we establish the importance of indices in extracting a term list.

Nursing, with the primary mission to provide quality services to patients, is accompanied by a serial of activities like patients assessment, outcomes identification for patients. In general, there has been less attention on ensuring that nurses provide nursing cares in a timely and accurate fashion. Consequently, in this paper, considering the similarity to the traditional job shop scheduling problems, we will model the daily nursing care scheduling problems and propose an efficient scheduling method based on simulated annealing algorithm. By the comparison of several nursing care plans obtained by the dispatching-rule based methods (which have been recognized to be the implementation of human thoughts), the proposed method is evaluated to be applicable to the nursing care scheduling problems (providing comprehensive, coordinated and cost effective nursing cares to patients).

To shorten the notoriously long waits for service in hospitals in Japan and to improve efficiency, we propose a scheduling algorithm with a 2-layer local search based on simulated annealing -- permutating (switching) (i) tasks among nurses and (ii) subtasks on each nurse. The scheduling algorithm generates a solution initializing our proposed dynamic scheduling to iteratively generate new, feasible schedules based on the scheduling algorithm to accommodate interruptions while preventing nurses' work hours from increasing. To verify the effectiveness of our proposed scheduling, we executed a set of nursing scheduling problems taken from those actually observed and focused on those that featuring frequent interruptions.

Nursing, with the primary mission to provide quality services to patients up to 24 hours a day, is accompanied by a serial of activities like patients' assessment, outcomes identification, etc. As we know, hospitals with lower nurse staffing levels tend to have higher rates of poor patient outcomes. However, increasing staffing levels is not yet an easy task. Therefore, an online nursing scheduling system is practically mandatory to instruct nurses' actions for high-quality nursing cares. In general, major difficulties contributing to propose an effective scheduling system include the nurse action rules and necessary reference information. In this respect, we illustrate the ways of nurses to handle their works from the viewpoint of scheduling in this paper. By hypothetically modelling the action rules as some traditional dispatching rules, we analyse the difference of nursing staffing levels (especially between nurse experts and novices). Implementing a set of traditional dispatching rules on several actual nursing cares, this paper concludes the closest ones to nurses' action rules. It also concludes that nurse experts consider the preparation tasks more, and handle them with a slack time.

No abstract