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Predicting Patient Acuity from Electronic Patient Records

Elina Kontio, Antti Airola, Tapio Pahikkala, Heljä Lundgren-Laine, Kristiina Junttila, Heikki Korvenranta, Tapio Salakoski, Sanna Salanterä, Predicting Patient Acuity from Electronic Patient Records. Journal of Biomedical Informatics 51, 35–40, 2014.

http://dx.doi.org/10.1016/j.jbi.2014.04.001

Abstract:

Background

The ability to predict acuity (patients’ care needs), would provide a powerful tool for health care managers to allocate resources. Such estimations and predictions for the care process can be produced from the vast amounts of healthcare data using information technology and computational intelligence techniques. Tactical decision-making and resource allocation may also be supported with different mathematical optimization models.

Methods

This study was conducted with a data set comprising electronic nursing narratives and the associated Oulu Patient Classification (OPCq) acuity. A mathematical model for the automated assignment of patient acuity scores was utilized and evaluated with the pre-processed data from 23,528 electronic patient records. The methods to predict patient’s acuity were based on linguistic pre-processing, vector-space text modeling, and regularized least-squares regression.

Results

The experimental results show that it is possible to obtain accurate predictions about patient acuity scores for the coming day based on the assigned scores and nursing notes from the previous day. Making same-day predictions leads to even better results, as access to the nursing notes for the same day boosts the predictive performance. Furthermore, textual nursing notes allow for more accurate predictions than previous acuity scores. The best results are achieved by combining both of these information sources. The developed model achieves a concordance index of 0.821 when predicting the patient acuity scores for the following day, given the scores and text recorded on the previous day.

Conclusions

By applying language technology to electronic patient documents it is possible to accurately predict the value of the acuity scores of the coming day based on the previous daýs assigned scores and nursing notes.

BibTeX entry:

@ARTICLE{jKoAiPaLuJuKoSaSa14a,
  title = {Predicting Patient Acuity from Electronic Patient Records},
  author = {Kontio, Elina and Airola, Antti and Pahikkala, Tapio and Lundgren-Laine, Heljä and Junttila, Kristiina and Korvenranta, Heikki and Salakoski, Tapio and Salanterä, Sanna},
  journal = {Journal of Biomedical Informatics},
  volume = {51},
  pages = {35–40},
  year = {2014},
  keywords = {Patient acuity, Patient classification system, Electronic patient record, Machine learning},
}

Belongs to TUCS Research Unit(s): Algorithmics and Computational Intelligence Group (ACI)

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