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Neural Networks for Clinical Order Decision Support.

Jonathan X Wang^1,2, Delaney K Sullivan^3,2, Adam J Wells^4,2

¹Biomedical Informatics, Stanford University School of Medicine, Stanford, CA, USA.

AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science

|July 2, 2019

Summary

This study introduces an automated physician order decision support system using a feed-forward neural network. The model enhances clinical decision-making by predicting physician orders from electronic health records, improving accuracy over manual methods.

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Area of Science:

* Medical Informatics
* Artificial Intelligence in Healthcare
* Clinical Decision Support Systems

Background:

* Growing medical literature and data complexity challenge consistent, high-quality clinical decisions.
* Existing clinical decision support relies on manually-curated order sets, which have limitations.

Purpose of the Study:

* To develop and evaluate an automated decision support model for physician orders in inpatient care.
* To improve the accuracy and efficiency of clinical decision-making using electronic health record (EHR) data.

Main Methods:

* Development of a feed-forward neural network model.
* Data extraction and mining from Electronic Health Records (EHR) to capture patient status.
* Prediction of physician clinical orders within a 24-hour timeframe.

Main Results:

* The feed-forward model achieved precision of 0.41, recall of 0.61, and AUROC of 0.80.
* This performance surpasses the benchmark of manually-curated order sets (precision: 0.21, recall: 0.48, AUROC: 0.75).

Conclusions:

* The automated system offers a scalable and robust approach to clinical decision support.
* This AI-driven tool has the potential to enhance the quality and consistency of medical decisions.