- 著者
- TAKENORI INOMATA JAEMYOUNG SUNG ALAN YEE AKIRA MURAKAMI YUICHI OKUMURA KEN NAGINO KENTA FUJIO YASUTSUGU AKASAKI AKIE MIDORIKAWA-INOMATA ATSUKO EGUCHI KEIICHI FUJIMOTO TIANXIANG HUANG YUKI MOROOKA MARIA MIURA HURRAMHON SHOKIROVA KUNIHIKO HIROSAWA MIZU OHNO HIROYUKI KOBAYASHI
- 出版者
- The Juntendo Medical Society
- 雑誌
- 順天堂醫事雑誌 (ISSN:21879737)
- 巻号頁・発行日
- pp.JMJ22-0032-R, (Released:2023-01-26)
- 参考文献数
- 63
- 被引用文献数
- 1
During the 5th Science, Technology, and Innovation Basic Plan, the Japanese government proposed a novel societal concept -Society 5.0- that promoted a healthcare system characterized by its capability to provide unintrusive, predictive, longitudinal care through the integration of cyber and physical space. The role of Society 5.0 in managing our quality of vision will become more important in the modern digitalized and aging society, both of which are known risk factors for developing dry eye. Dry eye is the most common ocular surface disease encountered in Japan with symptoms including increased dryness, eye discomfort, and decreased visual acuity. Owing to its complexity, implementation of P4 (predictive, preventive, personalized, participatory) medicine in managing dry eye requires a comprehensive understanding of its pathology, as well as a strategy to visualize and stratify its risk factors. Using DryEyeRhythm®, a mobile health (mHealth) smartphone software (app), we established a route to collect holistic medical big data on dry eye, such as the subjective symptoms and lifestyle data for each individual. The studies to date aided in determining the risk factors for severe dry eye, the association between major depressive disorder and dry eye exacerbation, eye drop treatment adherence, app-based stratification algorithms based on symptomology, blink detection biosensoring as a dry eye-related digital phenotype, and effectiveness of app-based dry eye diagnosis support compared to traditional methods. These results contribute to elucidating disease pathophysiology and promoting preventive and effective measures to counteract dry eye through mHealth.
- 著者
- TAKENORI INOMATA JAEMYOUNG SUNG ALAN YEE AKIRA MURAKAMI YUICHI OKUMURA KEN NAGINO KENTA FUJIO YASUTSUGU AKASAKI AKIE MIDORIKAWA-INOMATA ATSUKO EGUCHI KEIICHI FUJIMOTO TIANXIANG HUANG YUKI MOROOKA MARIA MIURA HURRAMHON SHOKIROVA KUNIHIKO HIROSAWA MIZU OHNO HIROYUKI KOBAYASHI
- 出版者
- The Juntendo Medical Society
- 雑誌
- 順天堂醫事雑誌 (ISSN:21879737)
- 巻号頁・発行日
- vol.69, no.1, pp.2-13, 2023 (Released:2023-02-28)
- 参考文献数
- 63
- 被引用文献数
- 1
During the 5th Science, Technology, and Innovation Basic Plan, the Japanese government proposed a novel societal concept -Society 5.0- that promoted a healthcare system characterized by its capability to provide unintrusive, predictive, longitudinal care through the integration of cyber and physical space. The role of Society 5.0 in managing our quality of vision will become more important in the modern digitalized and aging society, both of which are known risk factors for developing dry eye. Dry eye is the most common ocular surface disease encountered in Japan with symptoms including increased dryness, eye discomfort, and decreased visual acuity. Owing to its complexity, implementation of P4 (predictive, preventive, personalized, participatory) medicine in managing dry eye requires a comprehensive understanding of its pathology, as well as a strategy to visualize and stratify its risk factors. Using DryEyeRhythm®, a mobile health (mHealth) smartphone software (app), we established a route to collect holistic medical big data on dry eye, such as the subjective symptoms and lifestyle data for each individual. The studies to date aided in determining the risk factors for severe dry eye, the association between major depressive disorder and dry eye exacerbation, eye drop treatment adherence, app-based stratification algorithms based on symptomology, blink detection biosensoring as a dry eye-related digital phenotype, and effectiveness of app-based dry eye diagnosis support compared to traditional methods. These results contribute to elucidating disease pathophysiology and promoting preventive and effective measures to counteract dry eye through mHealth.
- 著者
- Kenji Momo Takeo Yasu Seiichiro Kuroda Sonoe Higashino Eiko Mitsugi Hiromasa Ishimaru Kazumi Goto Atsuko Eguchi Kuniyoshi Sato Masahiro Matsumoto Takashi Shiga Hideki Kobayashi Reisuke Seki Mikako Nakano Yoshiki Yashiro Takuya Nagata Hiroshi Yamazaki Shou Ishida Naoki Watanabe Mihoko Tagomori Noboru Sotoishi Daisuke Sato Kengo Kuroda Dai Harada Hitoshi Nagasawa Takashi Kawakubo Yuta Miyazawa Kyoko Aoyagi Sachiko Kanauchi Kiyoshi Okuyama Satoshi Kohsaka Kohtaro Ono Yoshiyasu Terayama Hiroshi Matsuzawa Mikio Shirota
- 出版者
- The Pharmaceutical Society of Japan
- 雑誌
- Biological and Pharmaceutical Bulletin (ISSN:09186158)
- 巻号頁・発行日
- vol.45, no.10, pp.1489-1494, 2022-10-01 (Released:2022-10-01)
- 参考文献数
- 19
- 被引用文献数
- 1
The aim of this study was to determine the proportion of near-miss dispensing errors in hospital pharmacies in Japan. A prospective multi-center observational study was conducted between December 2018 and March 2019. The primary objective was to determine the proportion of near-miss dispensing errors in hospital pharmacy departments. The secondary objective was to determine the predictive factors for near-miss dispensing errors using multiple logistic regression analysis. The study was approved by the ethical committee at The Institute of Medical Sciences, University of Tokyo, Japan. A multi-center prospective observational study was conducted in 20 hospitals comprising 8862 beds. Across the 20 hospitals, we assessed data from 553 pharmacists and 53039 prescriptions. A near-miss dispensing error proportion of 0.87% (n = 461) was observed in the study. We found predictive factors for dispensing errors in day-time shifts: a higher number of drugs in a prescription, higher number of quantified drugs, such as liquid or powder formula, in a prescription, and higher number of topical agents in a prescription; but we did not observe for career experience level for clinical pharmacists. For night-time and weekend shifts, we observed a negative correlation of near-miss dispensing errors with clinical pharmacist experience level. We found an overall incidence of near-miss dispensing errors of 0.87%. Predictive factors for errors in night-time and weekend shifts was inexperienced pharmacists. We recommended that pharmacy managers should consider education or improved work flow to avoid near-miss dispensing errors by younger pharmacists, especially those working night or weekend shifts.
- 著者
- TAKENORI INOMATA JAEMYOUNG SUNG MASAHIRO NAKAMURA MASAO IWAGAMI YUICHI OKUMURA KENTA FUJIO YASUTSUGU AKASAKI KEIICHI FUJIMOTO AI YANAGAWA AKIE MIDORIKAWA-INOMATA KEN NAGINO ATSUKO EGUCHI HURRRAMHON SHOKIROVA JUN ZHU MARIA MIURA MIZU KUWAHARA KUNIHIKO HIROSAWA TIANXING HUANG YUKI MOROOKA AKIRA MURAKAMI
- 出版者
- The Juntendo Medical Society
- 雑誌
- 順天堂醫事雑誌 (ISSN:21879737)
- 巻号頁・発行日
- vol.67, no.6, pp.519-529, 2021 (Released:2021-12-31)
- 参考文献数
- 67
- 被引用文献数
- 9
Society 5.0, a visionary human-centered societal model, fuels economic development and resolves long-standing social problems. The model establishes a technological foundation and social contract to integrate cyberspace into the physical (real) space fully. The medical infrastructure outlined by the model envisions a healthcare paradigm that revolves around preventative, lifelong patient- and population- centered care that functions seamlessly within one's daily life. In satisfying this goal, cross-hierarchical integrative data-driven biological research has received attention due to medical big data and artificial intelligence (AI) technologies, capable of highly accurate and rapid data analysis. However, the collection of big data has been a bottleneck, and the capability of AI analysis is not being utilized to its full potential. In solving this obstacle, we explore mobile health (mHealth) and multi-omics as two rich sources of medical big data. Additionally, we discuss the implications of cross-hierarchical integrative analysis that encompasses all levels of cellular function, from intracellular molecular dynamics to end-phenotypes. This is to understand ocular disease pathology and implement the pillars of P4 (predictive, personalized, preventative, participatory) medicine toward human-centered healthcare. Here, we discuss notable studies in utilizing mHealth to stratify subjective symptoms, presentations of dry eye disease, and employing multi-omics machine learning targeted at elucidating immunologic mechanisms of corneal allograft rejection and ocular inflammation. We also discuss the role of cross-hierarchical integrative data-driven research in promoting future-oriented healthcare envisioned by the Society 5.0 plan.