MIHARI project, a preceding study of MID-NET, adverse event detection database of Ministry Health of Japan-Validation study of the signal detection of adverse events of drugs using export data from EMR and medical claim data.

We studied the effectiveness of the direct data collection from electronic medical records (EMR) when it is used for monitoring adverse drug events and also detection of already known adverse events. In this study, medical claim data and SS-MIX2 standardized storage data were used to identify four d...

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Autores principales:	Hiroshi Watanabe, Kiyoteru Takenouchi, Michio Kimura
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8d2e6be4694942dcb60b5a1ed61be1d6
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spelling	oai:doaj.org-article:8d2e6be4694942dcb60b5a1ed61be1d62021-12-02T20:04:41ZMIHARI project, a preceding study of MID-NET, adverse event detection database of Ministry Health of Japan-Validation study of the signal detection of adverse events of drugs using export data from EMR and medical claim data.1932-620310.1371/journal.pone.0255863https://doaj.org/article/8d2e6be4694942dcb60b5a1ed61be1d62021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0255863https://doaj.org/toc/1932-6203We studied the effectiveness of the direct data collection from electronic medical records (EMR) when it is used for monitoring adverse drug events and also detection of already known adverse events. In this study, medical claim data and SS-MIX2 standardized storage data were used to identify four diseases (diabetes, dyslipidemia, hyperthyroidism, and acute renal failure) and the validity of the outcome definitions was evaluated by calculating positive predictive values (PPV). The maximum positive predictive value (PPV) for diabetes based on medical claim data was 40.7% and that based on prescription data from SS-MIX2 Standardized Storage was 44.7%. The PPV for dyslipidemia was 50% or higher under either of the conditions. The PPV for hyperthyroidism based on disease name data alone was 20-30%, but exceeded 60% when prescription data was included in the evaluation. Acute renal failure was evaluated using information from medical records in addition to the data. The PPV for acute renal failure based on the data of disease names and laboratory examination results was slightly higher at 53.7% and increased to 80-90% when patients who previously had a high serum creatinine (Cre) level were excluded. When defining a disease, it is important to include the condition specific to the disease; furthermore, it is very useful if laboratory examination results are also included. Therefore, the inclusion of laboratory examination results in the definitions, as in the present study, was considered very useful for the analysis of multi-center SS-MIX2 standardized storage data.Hiroshi WatanabeKiyoteru TakenouchiMichio KimuraPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 9, p e0255863 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hiroshi Watanabe Kiyoteru Takenouchi Michio Kimura MIHARI project, a preceding study of MID-NET, adverse event detection database of Ministry Health of Japan-Validation study of the signal detection of adverse events of drugs using export data from EMR and medical claim data.
description	We studied the effectiveness of the direct data collection from electronic medical records (EMR) when it is used for monitoring adverse drug events and also detection of already known adverse events. In this study, medical claim data and SS-MIX2 standardized storage data were used to identify four diseases (diabetes, dyslipidemia, hyperthyroidism, and acute renal failure) and the validity of the outcome definitions was evaluated by calculating positive predictive values (PPV). The maximum positive predictive value (PPV) for diabetes based on medical claim data was 40.7% and that based on prescription data from SS-MIX2 Standardized Storage was 44.7%. The PPV for dyslipidemia was 50% or higher under either of the conditions. The PPV for hyperthyroidism based on disease name data alone was 20-30%, but exceeded 60% when prescription data was included in the evaluation. Acute renal failure was evaluated using information from medical records in addition to the data. The PPV for acute renal failure based on the data of disease names and laboratory examination results was slightly higher at 53.7% and increased to 80-90% when patients who previously had a high serum creatinine (Cre) level were excluded. When defining a disease, it is important to include the condition specific to the disease; furthermore, it is very useful if laboratory examination results are also included. Therefore, the inclusion of laboratory examination results in the definitions, as in the present study, was considered very useful for the analysis of multi-center SS-MIX2 standardized storage data.
format	article
author	Hiroshi Watanabe Kiyoteru Takenouchi Michio Kimura
author_facet	Hiroshi Watanabe Kiyoteru Takenouchi Michio Kimura
author_sort	Hiroshi Watanabe
title	MIHARI project, a preceding study of MID-NET, adverse event detection database of Ministry Health of Japan-Validation study of the signal detection of adverse events of drugs using export data from EMR and medical claim data.
title_short	MIHARI project, a preceding study of MID-NET, adverse event detection database of Ministry Health of Japan-Validation study of the signal detection of adverse events of drugs using export data from EMR and medical claim data.
title_full	MIHARI project, a preceding study of MID-NET, adverse event detection database of Ministry Health of Japan-Validation study of the signal detection of adverse events of drugs using export data from EMR and medical claim data.
title_fullStr	MIHARI project, a preceding study of MID-NET, adverse event detection database of Ministry Health of Japan-Validation study of the signal detection of adverse events of drugs using export data from EMR and medical claim data.
title_full_unstemmed	MIHARI project, a preceding study of MID-NET, adverse event detection database of Ministry Health of Japan-Validation study of the signal detection of adverse events of drugs using export data from EMR and medical claim data.
title_sort	mihari project, a preceding study of mid-net, adverse event detection database of ministry health of japan-validation study of the signal detection of adverse events of drugs using export data from emr and medical claim data.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/8d2e6be4694942dcb60b5a1ed61be1d6
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MIHARI project, a preceding study of MID-NET, adverse event detection database of Ministry Health of Japan-Validation study of the signal detection of adverse events of drugs using export data from EMR and medical claim data.

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