Implementation of a scalable, web-based, automated clinical decision support risk-prediction tool for chronic kidney disease using C-CDA and application programming interfaces.
Implementation of a scalable, web-based, automated clinical decision support risk-prediction tool for chronic kidney disease using C-CDA and application programming interfaces.
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DOI：
10.1093/jamia/ocx065
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发表时间：
2017-11-01
期刊：
影响因子：
--
通讯作者：
Wright A
中科院分区：
文献类型：
--
作者：
Samal L;D'Amore JD;Bates DW;Wright A
Clinical decision support tools for risk prediction are readily available, but typically require workflow interruptions and manual data entry so are rarely used. Due to new data interoperability standards for electronic health records (EHRs), other options are available. As a clinical case study, we sought to build a scalable, web-based system that would automate calculation of kidney failure risk and display clinical decision support to users in primary care practices. We developed a single-page application, web server, database, and application programming interface to calculate and display kidney failure risk. Data were extracted from the EHR using the Consolidated Clinical Document Architecture interoperability standard for Continuity of Care Documents (CCDs). EHR users were presented with a noninterruptive alert on the patient’s summary screen and a hyperlink to details and recommendations provided through a web application. Clinic schedules and CCDs were retrieved using existing application programming interfaces to the EHR, and we provided a clinical decision support hyperlink to the EHR as a service. We debugged a series of terminology and technical issues. The application was validated with data from 255 patients and subsequently deployed to 10 primary care clinics where, over the course of 1 year, 569 533 CCD documents were processed. We validated the use of interoperable documents and open-source components to develop a low-cost tool for automated clinical decision support. Since Consolidated Clinical Document Architecture–based data extraction extends to any certified EHR, this demonstrates a successful modular approach to clinical decision support.
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影响因子：
3.8
作者：
Chipman, Jonathan;Drohan, Brian;Blackford, Amanda;Parmigiani, Giovanni;Hughes, Kevin;Bosinoff, Phil
通讯作者：
Bosinoff, Phil
DOI：
10.1136/amiajnl-2014-002883
发表时间：
2014-11-01
影响因子：
6.4
作者：
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DOI：
10.1136/amiajnl-2013-001990
发表时间：
2014-02-01
影响因子：
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DOI：
10.1001/jama.2015.18202
发表时间：
2016-01-12
期刊：
JAMA
影响因子：
--
作者：
Tangri N;Grams ME;Levey AS;Coresh J;Appel LJ;Astor BC;Chodick G;Collins AJ;Djurdjev O;Elley CR;Evans M;Garg AX;Hallan SI;Inker LA;Ito S;Jee SH;Kovesdy CP;Kronenberg F;Heerspink HJ;Marks A;Nadkarni GN;Navaneethan SD;Nelson RG;Titze S;Sarnak MJ;Stengel B;Woodward M;Iseki K;CKD Prognosis Consortium
通讯作者：
CKD Prognosis Consortium
影响因子：
4.4
作者：
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