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J Am Med Inform Assoc. 2017 Jan;24(1):24-29. doi: 10.1093/jamia/ocw014. Epub 2016 Mar 28.

Discharge recommendation based on a novel technique of homeostatic analysis.

Author information

1
Dascena Inc., Hayward, CA, USA.
2
Department of Emergency Medicine, University of California San Francisco, San Francisco, CA, USA.
3
Kaiser Permanente South San Francisco Medical Center, South San Francisco, CA, USA.
4
Dascena Inc., Hayward, CA, USA ritankar@dascena.com.

Abstract

OBJECTIVE:

We propose a computational framework for integrating diverse patient measurements into an aggregate health score and applying it to patient stability prediction.

MATERIALS AND METHODS:

We mapped retrospective patient data from the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) II clinical database into a discrete multidimensional space, which was searched for measurement combinations and trends relevant to patient outcomes of interest. Patient trajectories through this space were then used to make outcome predictions. As a case study, we built AutoTriage, a patient stability prediction tool to be used for discharge recommendation.

RESULTS:

AutoTriage correctly identified 3 times as many stabilizing patients as existing tools and achieved an accuracy of 92.9% (95% CI: 91.6-93.9%), while maintaining 94.5% specificity. Analysis of AutoTriage parameters revealed that interdependencies between risk factors comprised the majority of each patient stability score.

DISCUSSION:

AutoTriage demonstrated an improvement in the sensitivity of existing stability prediction tools, while considering patient safety upon discharge. The relative contributions of risk factors indicated that time-series trends and measurement interdependencies are most important to stability prediction.

CONCLUSION:

Our results motivate the application of multidimensional analysis to other clinical problems and highlight the importance of risk factor trends and interdependencies in outcome prediction.

KEYWORDS:

clinical decision support systems; computer-assisted diagnosis; length of stay; medical informatics; patient discharge

PMID:
27026611
DOI:
10.1093/jamia/ocw014
[Indexed for MEDLINE]

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