Literature DB >> 31080195

Miyagi Medical and Welfare Information Network: A Backup System for Patient Clinical Information after the Great East Japan Earthquake and Tsunami.

Keisuke Ido1, Naoki Nakamura2, Masaharu Nakayama1,2,3.   

Abstract

On March 11, 2011, the Great East Japan Earthquake and ensuing tsunami that hit the northeastern coastal region of Japan caused about 18,000 casualties and destroyed numerous buildings. Additionally, many medical facilities were damaged and patient medical records lost. In order to maintain patient clinical information, a prefectural medical network system, the Miyagi Medical and Welfare Information Network (MMWIN), began providing backup data storage services in 2013 for hospitals, clinics, pharmacies, and other care facilities as a precaution for upcoming disasters. This system also facilitates the sharing of clinical information trans-institutionally as long as patients provide consent for this. In the present study, we examined the development of the MMWIN and its efficiency during the 5 years from its launch, and identified general problems to maintain such a backup system. At the end of 2018, the system contained backup data from more than 11 million patients with more than 420 million data items; more than 900 facilities were MMWIN users, and the number of patients consenting to sharing their clinical information reached 90,000. The use of the system has become widespread and the accumulating data should be utilized for research in the future. Maintaining a balance between income and cost is critical to make this project independent from local government subsidies.

Entities:  

Keywords:  backup; clinical information; data sharing; disaster; electronic health records

Mesh:

Year:  2019        PMID: 31080195     DOI: 10.1620/tjem.248.19

Source DB:  PubMed          Journal:  Tohoku J Exp Med        ISSN: 0040-8727            Impact factor:   1.848


  8 in total

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Journal:  JMA J       Date:  2022-03-11

2.  Postsurgical functional outcome prediction model using deep learning framework (Prediction One, Sony Network Communications Inc.) for hypertensive intracerebral hemorrhage.

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3.  Health Information Exchange between Specialists and General Practitioners Benefits Rural Patients.

Authors:  Masaharu Nakayama; Ryusuke Inoue; Satoshi Miyata; Hiroaki Shimizu
Journal:  Appl Clin Inform       Date:  2021-06-09       Impact factor: 2.762

4.  Easily created prediction model using deep learning software (Prediction One, Sony Network Communications Inc.) for subarachnoid hemorrhage outcomes from small dataset at admission.

Authors:  Masahito Katsuki; Yukinari Kakizawa; Akihiro Nishikawa; Yasunaga Yamamoto; Toshiya Uchiyama
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5.  Preliminary development of a prediction model for daily stroke occurrences based on meteorological and calendar information using deep learning framework (Prediction One; Sony Network Communications Inc., Japan).

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6.  Assessment of Information Sharing on Adverse Drug Reactions by Community Pharmacies with Other Medical Institutions.

Authors:  Daisuke Kikuchi; Taku Obara; Aoi Noda; Gen Oyanagi; Mami Ishikuro; Kouji Okada; Nariyasu Mano
Journal:  Pharmacy (Basel)       Date:  2022-02-05

Review 7.  From pharmacogenetics to pharmaco-omics: Milestones and future directions.

Authors:  Chiara Auwerx; Marie C Sadler; Alexandre Reymond; Zoltán Kutalik
Journal:  HGG Adv       Date:  2022-03-16

8.  Hemodialysis Record Sharing: Solution for Work Burden Reduction and Disaster Preparedness.

Authors:  Keisuke Ido; Mariko Miyazaki; Masaharu Nakayama
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  8 in total

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