The following research was performed to establish an engineering knowledge management strategy for improving knowledge sharing concerning risk prediction models for hospital readmissions. The health care industry has been met with numerous approaches toward reducing preventable hospital readmissions; a recognized quality of care metric. Risk prediction models are increasingly being seen as a data-driven readmission reduction tactic that proactively inform health care teams of a patient's risk for readmission by using a sophisticated blend of data derived from electronic health records and other pertinent sources.
In an industry where effective communication is critical to the deployment, execution, and sustainment of delivering quality health care to patients, validated methods contributing to the improvement of communication processes are generously accepted and encouraged. Application of an engineering knowledge management strategy was identified as a novel approach to guiding the flow of knowledge regarding the use and interpretation of risk prediction models for readmissions throughout health care teams. An engineering management research method was linked to the process of developing a knowledge management strategy and ultimately resulted in a pilot (trial) to realize the impact of improved knowledge capturing and sharing activities on using risk prediction models as a readmission reduction tactic. Overall, application of this praxis is intended to be performed by health care consultants and industrial/systems engineers involved in deploying readmission-based risk prediction models in hospitals.
|Advisor:||Young, Stuart, Bersson, Thomas|
|Commitee:||Bersson, Thomas, Budge, Jeffrey, Etemadi, Amirhossein, Malalla, Ebrahim, Young, Stuart|
|School:||The George Washington University|
|School Location:||United States -- District of Columbia|
|Source:||DAI-B 79/12(E), Dissertation Abstracts International|
|Keywords:||Engineering management, Industrial engineering, Predictive analytics, Readmission, Risk prediction, Systems engineering|
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