scholarly journals ADDIS: A decision support system for evidence-based medicine

2013 ◽  
Vol 55 (2) ◽  
pp. 459-475 ◽  
Author(s):  
Gert van Valkenhoef ◽  
Tommi Tervonen ◽  
Tijs Zwinkels ◽  
Bert de Brock ◽  
Hans Hillege
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Evidence Based Medicine ◽  
Evidence Based ◽  
Based Medicine

Developing an Evidence-Based Clinical Decision-Support System to Enhance Prosthetic Prescription

2021 ◽  
Vol 10 (1) ◽  
pp. 121-125
Author(s):  
Pratima Saravanan ◽  
Jessica Menold
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Gait Analysis ◽  
Support System ◽  
Patient Specific ◽  
Evidence Based ◽  
Extensive Literature ◽  
Analysis Tool ◽  
Care Providers

With the rapid increase in the global amputee population, there is a clear need to assist amputee care providers with their decision-making during the prosthetic prescription process. To achieve this, an evidence-based decision support system that encompasses existing literature, current decision-making strategies employed by amputee care providers and patient-specific factors is proposed. Based on an extensive literature review combined with natural language processing and expert survey, the factors influencing the current decision-making of amputee care providers in prosthetic prescription were identified. Following that, the decision-making strategies employed by expert and novice prosthetists were captured and analyzed. Finally, a fundamental understanding of the effect gait analysis has on the decision-making strategies of prosthetists was studied. Findings from this work lay the foundation for developing a real-time decision support system integrated with a portable gait analysis tool to enhance prescription processes. This is critical in the low-income countries where there is a scarcity of amputee care providers and resources for an appropriate prescription.

Evaluation of a computational decision support system for molecularly targeted treatment planning by the clinical outcome data of the randomized trial SHIVA01.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 3642-3642
Author(s):  
Anna Dirner ◽  
Robert Doczi ◽  
Peter Filotas ◽  
Barbara Vodicska ◽  
Edit Varkondi ◽  
...  
Keyword(s):  
Decision Support ◽  
Targeted Therapy ◽  
Decision Support System ◽  
Support System ◽  
Clinical Decision Making ◽  
Real Life ◽  
Molecular Profile ◽  
Evidence Based ◽  
Precision Oncology ◽  
Molecular Profiles

3642 Background: Precision oncology requires the identification of individual molecular pathomechanisms to find optimal personalized treatment strategies for every cancer patient. Incorporation of complex molecular information into routine clinical practice remains a significant challenge due to the lack of a reproducible, standardized process of clinical decision making. Methods: To provide a standardized process for molecular interpretation, we develop a precision oncology decision support system, the Realtime Oncology Molecular Treatment Calculator (MTC). MTC is a rule-based medical knowledge engine that dynamically aggregates and ranks relevant scientific and clinical evidence using currently 26,000 evidence-based associations and reproducible algorithm scoring of drivers, molecular targets to match molecular alterations to efficient therapies. To validate this novel method and system, we used data of the SHIVA01 trial of molecularly targeted therapy (Lancet Oncol 2015 16:1324-34). Molecular profiles of participants were uploaded to MTC and aggregated evidence level (AEL) values of associated targeted treatments were calculated, including those used in the SHIVA01 trial. Results: The MTC output provided a prioritized list of drugs associated with the driver alterations in the patient molecular profile, where ranking is based on AEL values. Of 113 patients who received targeted therapy with available clinical best response data, disease control was experienced in 63 cases (PR: 5, SD: 58), while disease progression occurred in 50 cases. The average AEL score for the therapies applied was significantly higher in the responsive group than in the non-responsive group (1512 and 614, respectively (p = 0.049)). In 94 cases, drugs other than those used for therapy were ranked higher by the MTC. The average AEL difference between the top-ranked and the used drugs was in an inverse correlation with clinical response, i.e. smaller differences associated with a better outcome. Conclusions: Results indicate that the aggregation of evidence-based tumor-driver-target-drug associations using standardized mathematical algorithms of this computational tool is a promising novel approach to improve clinical decisions in precision oncology. Further validation based on the results of other targeted clinical trials and real-life data using more detailed molecular profiles is warranted to explore the full clinical potential of this novel medical solution.

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