ArticleJournal of the American Medical Informatics Association : JAMIA2017
Implementation of a scalable, web-based, automated clinical decision support risk-prediction tool for chronic kidney disease using C-CDA and application programming interfaces.
Article in Journal of the American Medical Informatics Association : JAMIA, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed, 18 citations in OpenAlex.
- Web application development using multiple regression analysis to predict silicosis risk among stone carvers in Nakhon Ratchasima, Thailand.Journal of occupational medicine and toxicology (London, England) · 2026Article
- Preventing chronic kidney disease and maintaining kidney health: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference.Kidney international · 2025Article
- Development and validation of a nomogram for predicting low Kt/VBMC nephrology · 2025Article
- Revolutionizing Chronic Kidney Disease Management with Machine Learning and Artificial Intelligence.Journal of clinical medicine · 2023Article
- Impact of Kidney Failure Risk Prediction Clinical Decision Support on Monitoring and Referral in Primary Care Management of CKD: A Randomized Pragmatic Clinical Trial.Kidney medicine · 2022Article
- Clinical Implementation of Predictive Models Embedded within Electronic Health Record Systems: A Systematic Review.Informatics (MDPI) · 2020Article
- Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation.Journal of clinical medicine · 2020Article
- Interoperability: What Is It, How Can We Make It Work for Clinicians, and How Should We Measure It in the Future?Health services research · 2018Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
BACKGROUND AND
objectiveClinical 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. MATERIALS AND
methodsWe 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.
resultsWe 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.
conclusionsWe 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.