IS in Healthcare
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Paper Type
Complete
Paper Number
2042
Description
Unplanned readmissions are a popular factor to determine the quality of healthcare services that can lead to negative financial and reputational ramifications for hospitals. Identifying patients at high-risk of readmission is key to allow for early interventions and proper discharge management. This paper proposes an intelligent clinical decision support system (ICDSS) that incorporates multiple risk prediction models according to the eight surgical groups defined by the Australian Institute of Health and Welfare (AIHW). The goal of this ICDSS is to enable the identification and visualisation of individual patients at high risk of readmission combined with economic factors to support decision-making in hospital discharge. This paper presents the design, prototypical implementation, and evaluation of an ICDSS that offers relevant insights to healthcare providers based on procedure-specific readmission risk prediction models. The results of the evaluation confirm the suitability and effectiveness of the system to support the decision process in patient discharge.
Recommended Citation
Eigner, Isabella and Bodendorf, Freimut, "An Intelligent Clinical Decision Support System to Determine the Optimal Time of Patient Discharge in Hospitals" (2020). ICIS 2020 Proceedings. 14.
https://aisel.aisnet.org/icis2020/is_health/is_health/14
An Intelligent Clinical Decision Support System to Determine the Optimal Time of Patient Discharge in Hospitals
Unplanned readmissions are a popular factor to determine the quality of healthcare services that can lead to negative financial and reputational ramifications for hospitals. Identifying patients at high-risk of readmission is key to allow for early interventions and proper discharge management. This paper proposes an intelligent clinical decision support system (ICDSS) that incorporates multiple risk prediction models according to the eight surgical groups defined by the Australian Institute of Health and Welfare (AIHW). The goal of this ICDSS is to enable the identification and visualisation of individual patients at high risk of readmission combined with economic factors to support decision-making in hospital discharge. This paper presents the design, prototypical implementation, and evaluation of an ICDSS that offers relevant insights to healthcare providers based on procedure-specific readmission risk prediction models. The results of the evaluation confirm the suitability and effectiveness of the system to support the decision process in patient discharge.
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