JMIRxMed JMIRx Med JMIRx Med 2563-6316 JMIR Publications Toronto, Canada v2i3e31551 10.2196/31551 Peer-Review Report Peer-Review Report Peer Review of “Finding Potential Adverse Events in the Unstructured Text of Electronic Health Care Records: Development of the Shakespeare Method” Meinert Edward Yu Haiyan PhD 1 https://orcid.org/0000-0002-6473-8552 Center for Data and Decision Sciences Chongqing University of Posts and Telecommunications Chongqing China Corresponding Author: Haiyan Yu yuhy@cqupt.edu.cn Jul-Sep 2021 11 8 2021 2 3 e31551 24 6 2021 24 6 2021 ©Haiyan Yu. Originally published in JMIRx Med (https://med.jmirx.org), 11.08.2021. 2021

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 https://www.medrxiv.org/content/10.1101/2021.01.05.21249239v1 https://preprints.jmir.org/preprint/27017 https://med.jmirx.org/2021/3/e31568/ https://med.jmirx.org/2021/3/e27017/

This is a peer-review report submitted for the paper “Finding Potential Adverse Events in the Unstructured Text of Electronic Health Care Records: Development of the Shakespeare Method”

Round 1 Review General Comments

This paper [1] investigated the new and increasing rates of adverse events (AEs) in unstructured text in electronic health records (EHRs). The topic is interesting. The authors used the Shakespeare method to identify attributed and unattributed potential AEs with EHRs. This method would be a useful supplement to AE reporting and surveillance. Although I believe that the topic of the study is very relevant, I have some concerns related to the theoretical background of the study. Specific major and minor comments are listed below.

 Specific Comments Major Comments

What is the accuracy of the new method, the Shakespeare method, for identifying attributed and unattributed potential AEs? The previous study showed the process of this method in the literature [2]. This paper did not mention the accuracy of the new method.

 Minor Comments

Too many keywords. I would suggest that the authors reduce some of the keywords.

In the “Conclusions” subsection, I would suggest the paragraphs be reorganized to improve them.

 Abbreviations AE

adverse event

 EHR

electronic health record

None declared.

 Bright RA Dowdy K Rankin SK Blok SV Palmer LAM Bright SJ Finding Potential Adverse Events in the Unstructured Text of Electronic Health Care Records: Development of the Shakespeare Method JMIRx Med 2021 8 11 2 3 e27017 https://med.jmirx.org/2021/3/e27017/ 10.2196/27017 Bright RA Rankin SK Dowdy K Blok SV Bright SJ Palmer LAM Potential Blood Transfusion Adverse Events Can be Found in Unstructured Text in Electronic Health Records using the'Shakespeare Method medRxiv Preprint published on January 6, 2021 https://www.medrxiv.org/content/10.1101/2021.01.05.21249239v1.full.pdf+html 10.1101/2021.01.12.21249674