طبقه بندی جدیدی از خدمات بالینی کادر پزشکی برای بازسازی بهینه جریان کار،کار در بخش جراحی: استفاده از تحلیل طیفی و تجزیه و تحلیل رابطه توالی

In order to optimize the job workflow of medical staff, clinical job workflow was investigated from the viewpoint of its periodicity and the strength of causal association among jobs. Time-motion study for the staff at a surgical ward was carried out. To detect the periodicity of the occurrence of each job element, its frequency histogram was determined, and the discrete Fourier transformation was applied. For the analysis on the strength of the relationship among the job-sequence, the sequence relational analysis was developed, which was the expansion of the relation analysis to the sequence process. The job elements were classified into five incident patterns based on the periodicity of each element and into three patterns based on the association with other job elements. Based on time-motion study data, job workflow patterns of medical staff’ were clarified based on the incident pattern of the job elements and the association with other job elements.

How to plan an efficient, effective, and safe hospital job workflow is a question of worldwide significance in hospital administration (Murray, 2002 and Robert, 1998). Despite the number of reports discussing time-motion study in the medical field, as well as mathematical approaches, workflow continues to remain a major concern for hospital managers and medical staff (Harauchi et al., 1999, Hollingsworth et al., 1993, Ishii et al., 2002 and Misener et al., 1987). In order to discuss the medical job workflow rationally, it is necessary to understand the actual job workflow quantitatively, modify the obtained information using computational analysis, measure each job element of the medical staff, and clarify the frequencies of actual medical job elements. In this study, we propose a new classification of medical staff responsibilities, for optimal reconstruction of job workflow, using spectrum analysis and sequence relational analysis based on a series of time-motion data in a surgical ward in Japan. The classification has two viewpoints, the periodicity of job elements and the strength of interdependency among the job elements.

The job element classification of data obtained by a time-motion study was carried out, and a specified database structure and coding format were developed. Using the data, the occurrence periodicity of job elements was investigated, and five groups were detected. The relationships among the job elements were evaluated by RMS value, and three classifications were found.