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http://scihub22266oqcxt.onion/ C5333329!5333329!28269916     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=28269916&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 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       AMIA+Annu+Symp+Proc 2016 ; 2016 (ä): 1189-98 Nephropedia Template TP {{tp|p=28269916|t=2016. Automating Guidelines for Clinical Decision Support: Knowledge Engineering and Implementation |pdf=|usr=}}{{28269916}} gab.com Text Automating Guidelines for Clinical Decision Support: Knowledge Engineering and Implementation JO: AMIA Annu Symp Proc http://www.kidney.de/pget.php?&tw=1&pmid=28269916 #FOAvip As utilization of clinical decision support (CDS) increases, it is important to continue the development and refinement of methods to accurately translate the intention of clinical practice guidelines (CPG) into a computable form. In this study, we validate and extend the 13 steps that Shiffman et al.5 identified for translating CPG knowledge for use in CDS. During an implementation project of ATHENA-CDS, we encoded complex CPG recommendations for five common chronic conditions for integration into an existing clinical dashboard. Major decisions made during the implementation process were recorded and categorized according to the 13 steps. During the implementation period, we categorized 119 decisions and identified 8 new categories required to complete the project. We provide details on an updated model that outlines all of the steps used to translate CPG knowledge into a CDS integrated with existing health information technology. Twit Text FOAVip Automating Guidelines for Clinical Decision Support: Knowledge Engineering and Implementation ????AMIA Annu Symp Proc http://www.kidney.de/pget.php?&tw=1&pmid=28269916 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28269916 #FOAvip English Wikipedia <ref>{{Cite pmid|28269916}}</ref> Automating Guidelines for Clinical Decision Support: Knowledge Engineering and Implementation #MMPMID28269916 Tso GJ; Tu SW; Oshiro C; Martins S; Ashcraft M; Yuen KW; Wang D; Robinson A; Heidenreich PA; Goldstein MK AMIA Annu Symp Proc 2016[]; 2016 (ä): 1189-98 PMID28269916show ga As utilization of clinical decision support (CDS) increases, it is important to continue the development and refinement of methods to accurately translate the intention of clinical practice guidelines (CPG) into a computable form. In this study, we validate and extend the 13 steps that Shiffman et al.5 identified for translating CPG knowledge for use in CDS. During an implementation project of ATHENA-CDS, we encoded complex CPG recommendations for five common chronic conditions for integration into an existing clinical dashboard. Major decisions made during the implementation process were recorded and categorized according to the 13 steps. During the implementation period, we categorized 119 decisions and identified 8 new categories required to complete the project. We provide details on an updated model that outlines all of the steps used to translate CPG knowledge into a CDS integrated with existing health information technology. äAutomating Guidelines for Clinical Decision Support: Knowledge Enginee(epmc).pdf DeepDyve Pubget Overpricing