scholarly journals Computational Constraint Models for Decision Support and Holistic Solution Design

Author(s):  
Carmen Gervet
2020 ◽  
Vol 27 (2) ◽  
pp. e100124
Author(s):  
Ann Wales

This short report shares learning from the research and development phase of the national decision support programme in NHS Scotland. It outlines how the programme has adopted an outcomes-focused approach which has guided critical decisions on solution design, engagement of policy sponsors, clinical and management leaders, implementation and evaluation approach, technical architecture and technology development. It discusses how this outcomes-led approach positions decision support as catalyst for a learning health and care system that continuously refreshes the healthcare knowledge base through new insights generated by evaluating impact and outcomes.


Author(s):  
Richard Threlkeld ◽  
Lirim Ashiku ◽  
Casey Canfield ◽  
Daniel B. Shank ◽  
Mark A. Schnitzler ◽  
...  

Abstract Purpose of Review A transdisciplinary systems approach to the design of an artificial intelligence (AI) decision support system can more effectively address the limitations of AI systems. By incorporating stakeholder input early in the process, the final product is more likely to improve decision-making and effectively reduce kidney discard. Recent Findings Kidney discard is a complex problem that will require increased coordination between transplant stakeholders. An AI decision support system has significant potential, but there are challenges associated with overfitting, poor explainability, and inadequate trust. A transdisciplinary approach provides a holistic perspective that incorporates expertise from engineering, social science, and transplant healthcare. A systems approach leverages techniques for visualizing the system architecture to support solution design from multiple perspectives. Summary Developing a systems-based approach to AI decision support involves engaging in a cycle of documenting the system architecture, identifying pain points, developing prototypes, and validating the system. Early efforts have focused on describing process issues to prioritize tasks that would benefit from AI support.


2013 ◽  
Vol 46 (2) ◽  
pp. 52
Author(s):  
CHRISTOPHER NOTTE ◽  
NEIL SKOLNIK

Author(s):  
William Elm ◽  
Scott Potter ◽  
James Tittle ◽  
David Woods ◽  
Justin Grossman ◽  
...  

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