Yuki Yokooka

Forecasting the absolute and relative shortage of physicians in Japan using a system dynamics model approach

BackgroundIn Japan, a shortage of physicians, who serve a key role in healthcare provision, has been pointed out as a major medical issue. The healthcare workforce policy planner should consider future dynamic changes in physician numbers. The purpose of this study was to propose a physician supply forecasting methodology by applying system dynamics modeling to estimate future absolute and relative numbers of physicians.MethodWe constructed a forecasting model using a system dynamics approach. Forecasting the number of physician was performed for all clinical physician and OB/GYN specialists. Moreover, we conducted evaluation of sufficiency for the number of physicians and sensitivity analysis.Result & conclusionAs a result, it was forecast that the number of physicians would increase during 2008–2030 and the shortage would resolve at 2026 for all clinical physicians. However, the shortage would not resolve for the period covered. This suggests a need for measures for reconsidering the allocation system of new entry physicians to resolve maldistribution between medical departments, in addition, for increasing the overall number of clinical physicians.

Quantitative analysis of ontology research articles in the radiologic domain

To investigate the most advanced ontology research in health care and its impact on the radiologic domain, we proposed a concept identification and abstraction technique called “Concept Step”. This technique identifies a MeSH term, medical subject headings used in PubMed, in a sentence and climbs up through its hierarchy to reach an abstract concept. We developed original Java software to implement this technique. We tested it on 2,774 abstracts in health-care ontology research retrieved from MEDLINE on 23 October 2008. The total number of MeSH terms was 112,690. We counted a total of 33 MeSH terms (0.029%) in the radiologic domain. The most frequently occurring term was “radiology”, which occurred 21 times in the article set. Other frequent terms were “magnetic resonance imaging” and “tomography”, the counts of which were 4 and 3, respectively. A pair plot showed no correlation among the MeSH categories “Analytical Diagnostic and Therapeutic Techniques and Equipment”, “Anatomy”, “Biological Sciences”, and “Chemicals and Drugs”. We conclude that ontology research is well established in the biomedical domain, and that further study is required in the radiologic domain.

Development of terminology for mammographic techniques for radiological technologists

We are developing a mammographic ontology to share knowledge of the mammographic domain for radiologic technologists, with the aim of improving mammographic techniques. As a first step in constructing the ontology, we used mammography reference books to establish mammographic terminology for identifying currently available knowledge. This study proceeded in three steps: (1) determination of the domain and scope of the terminology, (2) lexical extraction, and (3) construction of hierarchical structures. We extracted terms mainly from three reference books and constructed the hierarchical structures manually. We compared features of the terms extracted from the three reference books. We constructed a terminology consisting of 440 subclasses grouped into 19 top-level classes: anatomic entity, image quality factor, findings, material, risk, breast, histological classification of breast tumors, role, foreign body, mammographic technique, physics, purpose of mammography examination, explanation of mammography examination, image development, abbreviation, quality control, equipment, interpretation, and evaluation of clinical imaging. The number of terms that occurred in the subclasses varied depending on which reference book was used. We developed a terminology of mammographic techniques for radiologic technologists consisting of 440 terms.

Construction of mammographic examination process ontology using bottom–up hierarchical task analysis

Describing complex mammography examination processes is important for improving the quality of mammograms. It is often difficult for experienced radiologic technologists to explain the process because their techniques depend on their experience and intuition. In our previous study, we analyzed the process using a new bottom–up hierarchical task analysis and identified key components of the process. Leveraging the results of the previous study, the purpose of this study was to construct a mammographic examination process ontology to formally describe the relationships between the process and image evaluation criteria to improve the quality of mammograms. First, we identified and created root classes: task, plan, and clinical image evaluation (CIE). Second, we described an “is-a” relation referring to the result of the previous study and the structure of the CIE. Third, the procedural steps in the ontology were described using the new properties: “isPerformedBefore,” “isPerformedAfter,” and “isPerformedAfterIfNecessary.” Finally, the relationships between tasks and CIEs were described using the “isAffectedBy” property to represent the influence of the process on image quality. In total, there were 219 classes in the ontology. By introducing new properties related to the process flow, a sophisticated mammography examination process could be visualized. In relationships between tasks and CIEs, it became clear that the tasks affecting the evaluation criteria related to positioning were greater in number than those for image quality. We developed a mammographic examination process ontology that makes knowledge explicit for a comprehensive mammography process. Our research will support education and help promote knowledge sharing about mammography examination expertise.

AUTOMATIC ACQUISITION OF CT RADIATION DOSE DATA: USING THE DIAGNOSTIC REFERENCE LEVEL FOR RADIATION DOSE OPTIMIZATION

The present work describes that we try to construct a system that collects dose information that performed CT examination from multiple facilities and unified management. The results of analysis are compared with other National diagnostic reference level (DRL), and the results are fed back to each facility and the cause of the abnormal value is investigated for dose optimization. Medical information collected 139 144 tests from 33 CT devices in 13 facilities. Although the DRL of this study is lower than that of Japan DRL, it was higher than the DRL of each country. When collecting all the examination, it is thought that the variation of the dose due to the error other than the intended imaging site is large. In future, we should continue to collect information in order to DRL renewal and we also think that it is desirable to collect information on physique and detailed scan region as well.

Quantitative evaluation of expression difference in report assignments between nursing and radiologic technology departments

Our purpose in this study was to investigate the expression differences in report assignments between students in nursing and radiologic technology departments. We have known that faculties could identify differences, such as word usage, through grading their students’ assignments. However, there are no reports in the literature dealing with expression differences in vocabulary usage in medical informatics education based on statistical techniques or other quantitative measures. The report assignment asked for students’ opinions in the event that they found a rare case of a disease in a hospital after they graduated from professional school. We processed student report data automatically, and we applied the space vector model and TF/IDF (term frequency/inverse document frequency) scoring to 129 report assignments. The similarity-score distributions among the assignments for these two departments were close to normal. We focused on the sets of terms that occurred exclusively in either department. For terms such as “radiation therapy” or “communication skills” that occurred in the radiologic technology department, the TF/IDF score was 8.01. The same score was obtained for terms such as “privacy guidelines” or “consent of patients” that occurred in the nursing department. These results will help faculties to provide a better education based on identified expression differences from students’ background knowledge.