Model
Digital Document
Publisher
Florida Atlantic University
Description
Recent federal legislation has incentivized hospitals to focus on quality of patient
care. A primary metric of care quality is patient readmissions. Many methods exist to
statistically identify patients most likely to require hospital readmission. Correct
identification of high-risk patients allows hospitals to intelligently utilize limited resources
in mitigating hospital readmissions. However, these methods have seen little practical
adoption in the clinical setting. This research attempts to identify the many open research
questions that have impeded widespread adoption of predictive hospital readmission
systems.
Current systems often rely on structured data extracted from health records systems.
This data can be expensive and time consuming to extract. Unstructured clinical notes are
agnostic to the underlying records system and would decouple the predictive analytics
system from the underlying records system. However, additional concerns in clinical
natural language processing must be addressed before such a system can be implemented. Current systems often perform poorly using standard statistical measures.
Misclassification cost of patient readmissions has yet to be addressed and there currently
exists a gap between current readmission system evaluation metrics and those most
appropriate in the clinical setting. Additionally, data availability for localized model
creation has yet to be addressed by the research community. Large research hospitals may
have sufficient data to build models, but many others do not. Simply combining data from
many hospitals often results in a model which performs worse than using data from a single
hospital.
Current systems often produce a binary readmission classification. However,
patients are often readmitted for differing reasons than index admission. There exists little
research into predicting primary cause of readmission. Furthermore, co-occurring evidence
discovery of clinical terms with primary diagnosis has seen only simplistic methods
applied.
This research addresses these concerns to increase adoption of predictive hospital
readmission systems.
care. A primary metric of care quality is patient readmissions. Many methods exist to
statistically identify patients most likely to require hospital readmission. Correct
identification of high-risk patients allows hospitals to intelligently utilize limited resources
in mitigating hospital readmissions. However, these methods have seen little practical
adoption in the clinical setting. This research attempts to identify the many open research
questions that have impeded widespread adoption of predictive hospital readmission
systems.
Current systems often rely on structured data extracted from health records systems.
This data can be expensive and time consuming to extract. Unstructured clinical notes are
agnostic to the underlying records system and would decouple the predictive analytics
system from the underlying records system. However, additional concerns in clinical
natural language processing must be addressed before such a system can be implemented. Current systems often perform poorly using standard statistical measures.
Misclassification cost of patient readmissions has yet to be addressed and there currently
exists a gap between current readmission system evaluation metrics and those most
appropriate in the clinical setting. Additionally, data availability for localized model
creation has yet to be addressed by the research community. Large research hospitals may
have sufficient data to build models, but many others do not. Simply combining data from
many hospitals often results in a model which performs worse than using data from a single
hospital.
Current systems often produce a binary readmission classification. However,
patients are often readmitted for differing reasons than index admission. There exists little
research into predicting primary cause of readmission. Furthermore, co-occurring evidence
discovery of clinical terms with primary diagnosis has seen only simplistic methods
applied.
This research addresses these concerns to increase adoption of predictive hospital
readmission systems.
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