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10.1055/s-0041-1723024 http://scihub22266oqcxt.onion/10.1055/s-0041-1723024 33694142!7946598!33694142     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33694142&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\33694142.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Appl+Clin+Inform 2021 ; 12 (1): 170-178 Nephropedia Template TP {{tp|p=33694142|t=2021. Extracting Medical Information from Paper COVID-19 Assessment Forms |pdf=|usr=}}{{33694142}} gab.com Text Extracting Medical Information from Paper COVID-19 Assessment Forms JO: Appl Clin Inform http://www.kidney.de/pget.php?&tw=1&pmid=33694142 #FOAvip OBJECTIVE: This study examines the validity of optical mark recognition, a novel user interface, and crowdsourced data validation to rapidly digitize and extract data from paper COVID-19 assessment forms at a large medical center. METHODS: An optical mark recognition/optical character recognition (OMR/OCR) system was developed to identify fields that were selected on 2,814 paper assessment forms, each with 141 fields which were used to assess potential COVID-19 infections. A novel user interface (UI) displayed mirrored forms showing the scanned assessment forms with OMR results superimposed on the left and an editable web form on the right to improve ease of data validation. Crowdsourced participants validated the results of the OMR system. Overall error rate and time taken to validate were calculated. A subset of forms was validated by multiple participants to calculate agreement between participants. RESULTS: The OMR/OCR tools correctly extracted data from scanned forms fields with an average accuracy of 70% and median accuracy of 78% when the OMR/OCR results were compared with the results from crowd validation. Scanned forms were crowd-validated at a mean rate of 157 seconds per document and a volume of approximately 108 documents per day. A randomly selected subset of documents was reviewed by multiple participants, producing an interobserver agreement of 97% for documents when narrative-text fields were included and 98% when only Boolean and multiple-choice fields were considered. CONCLUSION: Due to the COVID-19 pandemic, it may be challenging for health care workers wearing personal protective equipment to interact with electronic health records. The combination of OMR/OCR technology, a novel UI, and crowdsourcing data-validation processes allowed for the efficient extraction of a large volume of paper medical documents produced during the COVID-19 pandemic. Twit Text FOAVip Extracting Medical Information from Paper COVID-19 Assessment Forms ????Appl Clin Inform http://www.kidney.de/pget.php?&tw=1&pmid=33694142 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33694142 #FOAvip English Wikipedia <ref>{{Cite pmid|33694142}}</ref> Extracting Medical Information from Paper COVID-19 Assessment Forms #MMPMID33694142 White-Dzuro CG; Schultz JD; Ye C; Coco JR; Myers JM; Shackelford C; Rosenbloom ST; Fabbri D Appl Clin Inform 2021[Jan]; 12 (1): 170-178 PMID33694142show ga OBJECTIVE: This study examines the validity of optical mark recognition, a novel user interface, and crowdsourced data validation to rapidly digitize and extract data from paper COVID-19 assessment forms at a large medical center. METHODS: An optical mark recognition/optical character recognition (OMR/OCR) system was developed to identify fields that were selected on 2,814 paper assessment forms, each with 141 fields which were used to assess potential COVID-19 infections. A novel user interface (UI) displayed mirrored forms showing the scanned assessment forms with OMR results superimposed on the left and an editable web form on the right to improve ease of data validation. Crowdsourced participants validated the results of the OMR system. Overall error rate and time taken to validate were calculated. A subset of forms was validated by multiple participants to calculate agreement between participants. RESULTS: The OMR/OCR tools correctly extracted data from scanned forms fields with an average accuracy of 70% and median accuracy of 78% when the OMR/OCR results were compared with the results from crowd validation. Scanned forms were crowd-validated at a mean rate of 157 seconds per document and a volume of approximately 108 documents per day. A randomly selected subset of documents was reviewed by multiple participants, producing an interobserver agreement of 97% for documents when narrative-text fields were included and 98% when only Boolean and multiple-choice fields were considered. CONCLUSION: Due to the COVID-19 pandemic, it may be challenging for health care workers wearing personal protective equipment to interact with electronic health records. The combination of OMR/OCR technology, a novel UI, and crowdsourcing data-validation processes allowed for the efficient extraction of a large volume of paper medical documents produced during the COVID-19 pandemic. |*Health Information Exchange[MESH] |*Information Storage and Retrieval[MESH] |COVID-19/*diagnosis[MESH] |Crowdsourcing[MESH] |Humans[MESH] |Physicians[MESH]Extracting Medical Information from Paper COVID-19 Assessment Forms (epmc).pdf DeepDyve Pubget Overpricing