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INOMATA TAKENORI, SUNG JAEMYOUNG, OKUMURA YUICHI, NAGINO KEN, MIDORIKAWA-INOMATA AKIE, EGUCHI ATSUKO, HIROSAWA KUNIHIKO, AKASAKI YASUTSUGU, HUANG TIANXIANG, MOROOKA YUKI, KOBAYASHI HIROYUKI, NAKAO SHINTARO. A Medical Paradigm Shift in Society 5.0: Implementation of a Smartphone App-based Dry Eye Diagnosis Assistance Software as a Medical Device. JUNTENDO IJI ZASSHI = JUNTENDO MEDICAL JOURNAL 2024; 70:332-338. [PMID: 39545226 PMCID: PMC11560333 DOI: 10.14789/jmj.jmj24-0018-p] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2024] [Accepted: 07/01/2024] [Indexed: 11/17/2024]
Abstract
Society 5.0, proposed as part of the 5th Science and Technology Basic Plan by Japan's National Institute of Advanced Industrial Science and Technology, is a human-centered society where cyberspace and physical space are integrated, to resolve social challenges and promote economic growth. In Society 5.0, medicine will undergo extensive digital transformation (DX), and digital health technology is expected to expand markedly, becoming part of routine clinical practice. Prompt diagnosis of dry eye disease (DED) and uninterrupted monitoring of such patients with healthcare barriers is currently an unmet need. DX of DED evaluation and management can boost the current quality of DED care. Software as Medical Devices (SaMDs), i.e., software programs developed through evidence-based research to provide diagnostic, therapeutic, and preventive services, and particularly medical devices based on smartphone applications (apps), have attracted attention. We have striven to actualize the DX of ophthalmic care and evaluation, denoted by our ongoing development of SaMDs to assist DED diagnosis. To illustrate healthcare using the Internet of Medical Things, we here present the research and development process of our smartphone app-based SaMD for DED diagnosis assistance.
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Affiliation(s)
- TAKENORI INOMATA
- Corresponding author: Takenori Inomata, Juntendo University Graduate School of Medicine, Department of Ophthalmology, 2-1-1 Hongo, Bunkyo-ku, Tokyo 113-8431, Japan, TEL: +81-3-5802-1228 FAX: +81-3-5689-0394 E-mail: , 52nd Health Topics for Tokyoites “The Frontier of Healthcare: Artificial Intelligence and Data Science” 〔Held on Feb. 17, 2024〕
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Nagino K, Okumura Y, Akasaki Y, Fujio K, Huang T, Sung J, Midorikawa-Inomata A, Fujimoto K, Eguchi A, Hurramhon S, Yee A, Miura M, Ohno M, Hirosawa K, Morooka Y, Murakami A, Kobayashi H, Inomata T. Smartphone App-Based and Paper-Based Patient-Reported Outcomes Using a Disease-Specific Questionnaire for Dry Eye Disease: Randomized Crossover Equivalence Study. J Med Internet Res 2023; 25:e42638. [PMID: 37535409 PMCID: PMC10436120 DOI: 10.2196/42638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 03/22/2023] [Accepted: 07/12/2023] [Indexed: 08/04/2023] Open
Abstract
BACKGROUND Using traditional patient-reported outcomes (PROs), such as paper-based questionnaires, is cumbersome in the era of web-based medical consultation and telemedicine. Electronic PROs may reduce the burden on patients if implemented widely. Considering promising reports of DryEyeRhythm, our in-house mHealth smartphone app for investigating dry eye disease (DED) and the electronic and paper-based Ocular Surface Disease Index (OSDI) should be evaluated and compared to determine their equivalency. OBJECTIVE The purpose of this study is to assess the equivalence between smartphone app-based and paper-based questionnaires for DED. METHODS This prospective, nonblinded, randomized crossover study enrolled 34 participants between April 2022 and June 2022 at a university hospital in Japan. The participants were allocated randomly into 2 groups in a 1:1 ratio. The paper-app group initially responded to the paper-based Japanese version of the OSDI (J-OSDI), followed by the app-based J-OSDI. The app-paper group responded to similar questionnaires but in reverse order. We performed an equivalence test based on minimal clinically important differences to assess the equivalence of the J-OSDI total scores between the 2 platforms (paper-based vs app-based). A 95% CI of the mean difference between the J-OSDI total scores within the ±7.0 range between the 2 platforms indicated equivalence. The internal consistency and agreement of the app-based J-OSDI were assessed with Cronbach α coefficients and intraclass correlation coefficient values. RESULTS A total of 33 participants were included in this study. The total scores for the app- and paper-based J-OSDI indicated satisfactory equivalence per our study definition (mean difference 1.8, 95% CI -1.4 to 5.0). Moreover, the app-based J-OSDI total score demonstrated good internal consistency and agreement (Cronbach α=.958; intraclass correlation=0.919; 95% CI 0.842 to 0.959) and was significantly correlated with its paper-based counterpart (Pearson correlation=0.932, P<.001). CONCLUSIONS This study demonstrated the equivalence of PROs between the app- and paper-based J-OSDI. Implementing the app-based J-OSDI in various scenarios, including telehealth, may have implications for the early diagnosis of DED and longitudinal monitoring of PROs.
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Affiliation(s)
- Ken Nagino
- Department of Hospital Administration, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Yuichi Okumura
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Yasutsugu Akasaki
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
- AI Incubation Farm, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Kenta Fujio
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Tianxiang Huang
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Jaemyoung Sung
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Akie Midorikawa-Inomata
- Department of Hospital Administration, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Keiichi Fujimoto
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Atsuko Eguchi
- Department of Hospital Administration, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Shokirova Hurramhon
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Alan Yee
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Maria Miura
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Mizu Ohno
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Kunihiko Hirosawa
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Yuki Morooka
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Akira Murakami
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Hiroyuki Kobayashi
- Department of Hospital Administration, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Takenori Inomata
- Department of Hospital Administration, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Digital Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan
- AI Incubation Farm, Juntendo University Graduate School of Medicine, Tokyo, Japan
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