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10.1093/jamia/ocab054 http://scihub22266oqcxt.onion/10.1093/jamia/ocab054 34051088!8194878!34051088     free     free     free Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 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\34051088.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       J+Am+Med+Inform+Assoc 2021 ; 28 (8): 1765-1776 Nephropedia Template TP {{tp|p=34051088|t=2021. Privacy-protecting, reliable response data discovery using COVID-19 patient observations |pdf=|usr=}}{{34051088}} gab.com Text Privacy-protecting, reliable response data discovery using COVID-19 patient observations JO: J Am Med Inform Assoc http://www.kidney.de/pget.php?&tw=1&pmid=34051088 #FOAvip OBJECTIVE: To utilize, in an individual and institutional privacy-preserving manner, electronic health record (EHR) data from 202 hospitals by analyzing answers to COVID-19-related questions and posting these answers online. MATERIALS AND METHODS: We developed a distributed, federated network of 12 health systems that harmonized their EHRs and submitted aggregate answers to consortia questions posted at https://www.covid19questions.org. Our consortium developed processes and implemented distributed algorithms to produce answers to a variety of questions. We were able to generate counts, descriptive statistics, and build a multivariate, iterative regression model without centralizing individual-level data. RESULTS: Our public website contains answers to various clinical questions, a web form for users to ask questions in natural language, and a list of items that are currently pending responses. The results show, for example, that patients who were taking angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers, within the year before admission, had lower unadjusted in-hospital mortality rates. We also showed that, when adjusted for, age, sex, and ethnicity were not significantly associated with mortality. We demonstrated that it is possible to answer questions about COVID-19 using EHR data from systems that have different policies and must follow various regulations, without moving data out of their health systems. DISCUSSION AND CONCLUSIONS: We present an alternative or a complement to centralized COVID-19 registries of EHR data. We can use multivariate distributed logistic regression on observations recorded in the process of care to generate results without transferring individual-level data outside the health systems. Twit Text FOAVip Privacy-protecting, reliable response data discovery using COVID-19 patient observations ????J Am Med Inform Assoc http://www.kidney.de/pget.php?&tw=1&pmid=34051088 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34051088 #FOAvip English Wikipedia <ref>{{Cite pmid|34051088}}</ref> Privacy-protecting, reliable response data discovery using COVID-19 patient observations #MMPMID34051088 Kim J; Neumann L; Paul P; Day ME; Aratow M; Bell DS; Doctor JN; Hinske LC; Jiang X; Kim KK; Matheny ME; Meeker D; Pletcher MJ; Schilling LM; SooHoo S; Xu H; Zheng K; Ohno-Machado L J Am Med Inform Assoc 2021[Jul]; 28 (8): 1765-1776 PMID34051088show ga OBJECTIVE: To utilize, in an individual and institutional privacy-preserving manner, electronic health record (EHR) data from 202 hospitals by analyzing answers to COVID-19-related questions and posting these answers online. MATERIALS AND METHODS: We developed a distributed, federated network of 12 health systems that harmonized their EHRs and submitted aggregate answers to consortia questions posted at https://www.covid19questions.org. Our consortium developed processes and implemented distributed algorithms to produce answers to a variety of questions. We were able to generate counts, descriptive statistics, and build a multivariate, iterative regression model without centralizing individual-level data. RESULTS: Our public website contains answers to various clinical questions, a web form for users to ask questions in natural language, and a list of items that are currently pending responses. The results show, for example, that patients who were taking angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers, within the year before admission, had lower unadjusted in-hospital mortality rates. We also showed that, when adjusted for, age, sex, and ethnicity were not significantly associated with mortality. We demonstrated that it is possible to answer questions about COVID-19 using EHR data from systems that have different policies and must follow various regulations, without moving data out of their health systems. DISCUSSION AND CONCLUSIONS: We present an alternative or a complement to centralized COVID-19 registries of EHR data. We can use multivariate distributed logistic regression on observations recorded in the process of care to generate results without transferring individual-level data outside the health systems. |*Algorithms[MESH] |*COVID-19[MESH] |*Computer Communication Networks[MESH] |*Confidentiality[MESH] |*Electronic Health Records[MESH] |*Natural Language Processing[MESH] |Common Data Elements[MESH] |Female[MESH] |Humans[MESH] |Information Storage and Retrieval/*methods[MESH] |Logistic Models[MESH] |Male[MESH]Privacy-protecting, reliable response data discovery using COVID-19 pa(epmc).pdf DeepDyve Pubget Overpricing