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Talat Ashraf Catch It Presentation
1. CATCH-IT JOURNAL CLUB WRIGHT, A., SITTIG, D. F., ASH, J.S., SHARMA, S., PANG, J. E., AND MIDDLETON, B. (2009). CLINICAL DECISION SUPPORT CAPABILITIES OF COMMERCIALLY-AVAILABLE CLINICAL INFORMATION SYSTEMS. JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION, 16(5), 637 – 644. Presented by: Talat Ashraf HAD 5726 Master of Health Informatics University of Toronto Date: November 2, 2009 11/2/2009 CATCH-IT Journal Club
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3. ABOUT THE PAPER 11/2/2009 CATCH-IT Journal Club Partner HealthCare System is a Boston based integrated healthcare system that includes primary care, specialty care, community hospitals, academic medical centers, and other health-related entities (2). Partners is an important entity for this research since it is related to several important aspects of this research, such as the taxonomy that is used.
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7. ADAM WRIGHT’S NETWORK OF RESEARCH 11/2/2009 CATCH-IT Journal Club Source: Biomed Experts (4)
8. SITTIG’S NETWORK OF RESEARCH 11/2/2009 CATCH-IT Journal Club Source: Biomed Experts (4)
9. ASH’S NETWORK OF RESEARCH 11/2/2009 CATCH-IT Journal Club Source: Biomed Experts (4)
10. PRIOR RESEARCH BY AUTHORS 11/2/2009 CATCH-IT Journal Club Authors Article Year Main Findings Wright A , Bates DW, Middleton B , Hongsermeier T, Kashyap V, Thomas SM, Sittig DF . Creating and sharing clinical decision support content with Web 2.0: Issues and examples. 2009 evaluate the potential of Web 2.0 technologies to enable collaborative development and sharing of CDSS through the lens of three case studies; analyzing technical, legal and organizational issues for developers, consumers and organizers of clinical decision support content in Web 2.0 Wright A , & Sittig D F A Four-Phase Model of the Evolution of Clinical Decision Support Architectures 2008 The authors review the history of evolution of CDSS from 1959 to present and propose a 4 phase architecture model for how the mechanism of integration of CDSS into Clinical Information systems have evolved, beginning with stand alone systems through sophisticated levels of integration Wright A, Sittig D F. A framework and model for evaluating clinical decision support architectures. 2008 Develop a four-phase model for evaluating architectures for clinical decision support that focuses on: defining a set of desirable features for a decision support architecture; building a proof-of-concept prototype; demonstrating that the architecture is useful by showing that it can be integrated with existing decision support systems and comparing its coverage to that of other architectures.
11. PRIOR RESEARCH BY AUTHORS 11/2/2009 CATCH-IT Journal Club Authors Article Year Main Findings Wright A ; Goldberg H; Hongsermeier T; Middleton B A Description and Functional Taxonomy of Rule-based Decision Support Content at a Large Integrated Delivery Network 2007 Developed a functional taxonomy of rule-based clinical decision support along four axes: Trigger, input data elements, interventions and offered choices Sittig DF , Wright A , Osheroff JA, Middleton B , Teich JM, Ash JS , Campbell E, Bates DW. Grand challenges in clinical decision support. 2007 Discusses the ten grand challenges in the development of CDS applications based on an iterative consensus building. The ten grand challenges include i mprovement of HCI, dissemination of best practices in CDS design, development and implementation, creation of internet accessible CDS repositories, and so on.
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18. TAXONOMY USED 11/2/2009 CATCH-IT Journal Club Source: Wright et al. (7) Axes Explanation Example of Rule Triggers Event that cause decision support rule to be invoked When penicillin allergy is entered; check medications list Input Data Data elements used by a rule to make inferences If entered ‘female’ as gender; only show options related to female such as mammogram, pregnancy test etc Interventions Possible actions a decision support module can take If physician wants to override an alert; it asks the physician to provide a reason Offered Choices Provide the users with flexibility and offer them choices Allow users to change the dosage of drug
What can we derive from this? This is not an indicator of the quality of research by Adam Wright and other co-authors However, it tells the following Exposure into research Co-authors Research exposure of co-authors Innovative method of using social networking concepts in the area of research Possible candidate for Research 2.0 ?
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Based on comprehensive analysis of the clinical decision support knowledge base in use at Partners HealthCare system
(Certification Commission for Health Information Technology) Different certification criteria for different type of system
There were some ambiguities in the methods that the authors undertook. The authors did not mention what systems were picked initially when first contacted the companies and their customers. Also how were these 9 systems shortlisted, especially because of the generally positive response received from the vendor organizations. Authors did not mention who did they speak with. Did they speak with technical people of did they speak with sales representatives. These questions are important because users don’t have a complete knowledge of the capabilities of the system and depending on whom they asked there can be biases. Claudia and James
As mentioned before the features were evaluated along 4 axes… The results were presented pseudonomously in order to protect the confidentiality of vendors and sensitivity of product capabilities.
Main Results
We’ll discuss this table again
Limitations as identified by the authors
There are a few methodological issues that are identified by me and the class comments in the blog. First, I was curious about the acceptance of the functional taxonomy they have used. On further research I found that there are 5 articles referencing to this taxonomy, out of the 5; 4 are referenced by the original authors of this study. Only 1 study cited this research in 2 instances but did not refer to the taxonomy information itself…it basically referenced other information of the paper.