{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T04:14:32Z","timestamp":1747196072211,"version":"3.40.5"},"reference-count":18,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2021,9,8]],"date-time":"2021-09-08T00:00:00Z","timestamp":1631059200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>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\u201330%, 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\u201390% 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.<\/jats:p>","DOI":"10.1371\/journal.pone.0255863","type":"journal-article","created":{"date-parts":[[2021,9,8]],"date-time":"2021-09-08T17:28:50Z","timestamp":1631122130000},"page":"e0255863","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":1,"title":["MIHARI project, a preceding study of MID-NET, adverse event detection database of Ministry Health of Japan\u2014Validation study of the signal detection of adverse events of drugs using export data from EMR and medical claim data"],"prefix":"10.1371","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5461-066X","authenticated-orcid":true,"given":"Hiroshi","family":"Watanabe","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kiyoteru","family":"Takenouchi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michio","family":"Kimura","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2021,9,8]]},"reference":[{"journal-title":"CDISC.","article-title":"2019. 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Ohe","year":"2017","journal-title":"Transactions of Japanese Society for Medical and Biological Engineering"},{"journal-title":"The utilization and challenges of Japan\u2019s MID-NET (medical information database network) in post-marketing drug safety assessments: A summary of pilot pharmacoepidemiological studies Pharmacoepidemiology and Drug Safety (in press)","author":"Uyama Yoshiaki","key":"pone.0255863.ref014"},{"issue":"10","key":"pone.0255863.ref015","doi-asserted-by":"crossref","first-page":"2170","DOI":"10.1002\/sim.2677","article-title":"Confidence intervals for predictive values with an emphasis to case-control studies","volume":"26","author":"N. D. Mercaldo","year":"2007","journal-title":"Statistics in Medicine"},{"journal-title":"Alternative Methods for Health Outcomes of Interest Validation","year":"2013","author":"T Glen","key":"pone.0255863.ref016"},{"key":"pone.0255863.ref017","doi-asserted-by":"crossref","first-page":"e10072","DOI":"10.1002\/lrh2.10072","article-title":"Development of a new seamless data stream from EMR to EDC system using SS-MIX2 standards applied for observational research in diabetes mellitus","volume":"3","author":"Kiyoteru Takenouchi","year":"2019","journal-title":"Learn Health Sys"},{"issue":"1","key":"pone.0255863.ref018","doi-asserted-by":"crossref","first-page":"23","DOI":"10.3820\/jjpe.18.23","article-title":"Use of Electronic Medical Information Including SS-MIX Data for Drug Safety Measures","volume":"18","author":"Eiko Tada","year":"2013","journal-title":"Japanese Journal of Pharmacoepidemiology"}],"container-title":["PLOS ONE"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0255863","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,8]],"date-time":"2021-09-08T17:29:15Z","timestamp":1631122155000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0255863"}},"subtitle":[],"editor":[{"given":"Jinn-Moon","family":"Yang","sequence":"first","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2021,9,8]]},"references-count":18,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2021,9,8]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0255863","relation":{},"ISSN":["1932-6203"],"issn-type":[{"type":"electronic","value":"1932-6203"}],"subject":[],"published":{"date-parts":[[2021,9,8]]}}}