Medical image display monitors require quality control to maintain diagnostic accuracy, for which the method is defined in the guidelines provided by the Japan Medical Imaging and Radiological Systems Industries Association. In contrast, more medical institutions recently build their hospital information system terminals on servers and desktop virtual infrastructure. With the screen processed in the virtual server, transmitted to, and displayed on the client PC, the technology to realize desktop virtualization differs greatly from the conventional client-server system in the screen display method. The influence on image diagnosis, and the quality control method of the medical image display monitor in the hospital information system built in such an environment are unknown. This study compared the client-server system introduced in many facilities with the VDI method and the RDP method of desktop virtualization systems, the subject of this evaluation, on the correctness of images displayed on the medical image display monitor, based on the evaluation method specified in the guidelines. As a result, no difference was found in the results of the evaluation using the quality control method specified in the guidelines, suggesting that there is no impact on image diagnosis even in medical institutions with desktop virtualization introduced. It was also clarified that the quality control of medical monitors can be handled in the conventional way.

　The standardization of health check-up data elements, value sets, units, clinical rulesets, conducting the data validation at a stage close to the data source, and ensuring data calibration opportunities by managing information provenance is essential for the success of the health policy. The Japan Council for management and evaluation of health care is currently developing the standard health check-up data format as the standard that manages medical check-up data elements, categorical result value sets, standardized unit representations, standard ranges, and the result value correlations an integrated manner. In this study, we designed the FHIR implementation guide based on this standard, which may contribute to a higher quality of health check-up data than previous standards. We have developed the database for controlled terminology and validation logic and designed the method to generate the machine-processable implementation guide automatically. The result shows that the suggested approach improve the quality of the implementation guide that ensures comprehensive interoperability and data quality. On the other hand, the issues for harmonization with the JLAC10 standard code were identified.

　Since the medical plan is promoting the clarification of medical institutions that are responsible for outpatient services to utilize medical resources, we analyzed the appropriateness of the placement of advanced medical devices in the conceptual area by using open data and the population center of gravity. The distribution of advanced medical devices in the conceptual area was grasped by center-of-gravity analysis using the number of installed advanced medical devices and the number of tests performed by each medical institution in the functional reporting of hospital beds and the medical function information provision system, and a comparative analysis of the distance and direction between the treatment rate and the center of gravity of the population receiving treatment based on population distribution was conducted. The distribution of advanced medical devices and the number of tests per device did not correspond to the center of gravity of the population receiving care. In addition to this, it was found that by conducting simulations in which the number of tests and locations of advanced medical equipment are varied, the optimal layout for the region can be obtained, and this should be used to promote the installation and renewal of equipment in the entire region.

　A proposal was made to address research issues regarding “A Sentiment Analysis of Tweets about Fertility Treatment: Investigating the Impact of Changing Subsides on Social Media,” which was published in Volume 41, Issue 4 of this journal. Although significant results were obtained in this research, the validity and reproducibility seemed to be diminished due to the inclusion of bot tweet, the lack of consideration of retweets, and the unclear source of tweets. In order to overcome these problems, I propose to use exclusion criteria using the “source” data of tweets, sensitivity analysis weighted by the number of retweets, and specifying the source of tweets.