Knowledge management system for clinical decision support — Application in cardiology

Author(s):  
Pavle Kostic ◽  
Zorana Vasiljevic ◽  
Sinisa Pavlovic ◽  
Ivica Milosavljevic ◽  
Jelica Grujic Milanovic ◽  
...  
2013 ◽  
Vol 59 (1) ◽  
pp. 45-53 ◽  
Author(s):  
Brian E. Dixon ◽  
Linas Simonaitis ◽  
Howard S. Goldberg ◽  
Marilyn D. Paterno ◽  
Molly Schaeffer ◽  
...  

2020 ◽  
Vol 27 (5) ◽  
pp. 726-737 ◽  
Author(s):  
Jeffrey Lam Shin Cheung ◽  
Natalie Paolucci ◽  
Courtney Price ◽  
Jenna Sykes ◽  
Samir Gupta ◽  
...  

Abstract Objective Computerized clinical decision support systems (CCDSSs) promise improvements in care quality; however, uptake is often suboptimal. We sought to characterize system use, its predictors, and user feedback for the Electronic Asthma Management System (eAMS)—an electronic medical record system–integrated, point-of-care CCDSS for asthma—and applied the GUIDES checklist as a framework to identify areas for improvement. Materials and Methods The eAMS was tested in a 1-year prospective cohort study across 3 Ontario primary care sites. We recorded system usage by clinicians and patient characteristics through system logs and chart reviews. We created multivariable models to identify predictors of (1) CCDSS opening and (2) creation of a self-management asthma action plan (AAP) (final CCDSS step). Electronic questionnaires captured user feedback. Results Over 1 year, 490 asthma patients saw 121 clinicians. The CCDSS was opened in 205 of 1033 (19.8%) visits and an AAP created in 121 of 1033 (11.7%) visits. Multivariable predictors of opening the CCDSS and producing an AAP included clinic site, having physician-diagnosed asthma, and presenting with an asthma- or respiratory-related complaint. The system usability scale score was 66.3 ± 16.5 (maximum 100). Reported usage barriers included time and system accessibility. Discussion The eAMS was used in a minority of asthma patient visits. Varying workflows and cultures across clinics, physician beliefs regarding asthma diagnosis, and relevance of the clinical complaint influenced uptake. Conclusions Considering our findings in the context of the GUIDES checklist helped to identify improvements to drive uptake and provides lessons relevant to CCDSS design across diseases.


2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Noam H. Arzt ◽  
Maiko Minami ◽  
Daryl Chertcoff ◽  
Janet Hui

ObjectiveTo discuss how clinical decision support (CDS) for electronic case reporting (eCR) will evolve over time to provide multiple deployment modelsIntroductionAs the knowledge required to support case reporting evolves from unstructured to more structured and standardized formats, it becomes suitable for electronic clinical decision support (CDS). CDS for case reporting confronts two challenges: a) While EHRs are moving toward local CDS capabilities, it will take several years for EHR systems to consistently support this capability; and b) public health-related CDS knowledge, such as Zika infection detection and reporting rules, may differ from jurisdiction to jurisdiction. Therefore, there is an ongoing need to manage reporting rules in a distributed manner. Similarly, there is a need for more decentralized models of CDS execution to overcome some of the disadvantages of centralized deployment and to leverage local CDS capabilities as they become available over the next several years.MethodsThe Reportable Condition Knowledge Management System (RCKMS) is a project funded by the CDC, through the Council of State and Territorial Epidemiologists (CSTE), to develop a tool that allows jurisdictions to author rules that define whether a patient is reportable for certain conditions. RCKMS includes a Decision Support Service (DSS) that runs the jurisdictions’ rules and determines if a patient is reportable, for which condition(s) and to which jurisdiction(s). RCKMS currently plays a significant role in the broader Digital Bridge project that has been working to provide structure and governance around the national planning and implementation effort of eCR. RCKMS is currently a centralized CDS service that can be accessed by EHRs until they all have local CDS capabilities; and a knowledge authoring environment that allows ongoing distributed rule authoring. RCKMS supports the strategy for public health knowledge management, and it will evolve over time to provide the systems and services to satisfy short-, mid-, and long-term public health CDS requirements. In addition, RCKMS will comply with emerging technical standards that support this work.ResultsRCKMS is currently being deployed as a single, central, national service on the APHL Informatics Messaging Services (AIMS) platform, which is operated and maintained by Association of Public Health Laboratories (APHL). The AIMS platform connects directly with reporters and provides a routing and validation service for incoming and outgoing messages. Two distributed CDS scenarios for decentralized eCR models have been identified. In the first scenario, the Decision Support Service component of the RCKMS software is installed within a clinical organization (as it would be in a centralized service) and executed locally. The second distributed CDS scenario for eCR involves the distribution of the Reporting Specification rules without the software. In this scenario, local electronic health record (EHR) implementations would be required to consume the Reporting Specifications and utilize them in a CDS capability within their EHR.ConclusionsIt is expected that given the diversity of organizations, systems, and architectures in the United States that multiple deployment scenarios for CDS for eCR will be simultaneously deployed for the foreseeable future. It cannot be stressed enough, however, that in all scenarios – centralized and distributed – there must be a centralized and uniform authoring of the Reporting Specification rules, since the specifications themselves originate from public health through a centralized process and must be administered nationally through a well-established process. It is also essential that all sites have all the rules available to them, since there may be multiple jurisdictions whose rules and reporting is required, with the determination of jurisdiction(s) based on where a patient lives and where they receive care.  


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