SuperOrder: Provider order recommendation system for outpatient clinics.

Abstract:

:This study aims at developing SuperOrder, an order recommendation system for outpatient clinics. Using the electronic health record data available at midnight, SuperOrder predicts the order contents for each upcoming appointment on a daily basis. A two-level prediction framework is proposed. At the base-level, the predictions are produced by aggregating three machine learning methods. The meta-level predictions are generated by integrating the base-level predictions with the order co-occurrence network. We used the retrospective data between 1 April 2014 and 31 March 2015 in pulmonary clinics from five hospital sites within a large rural health care facility in Pennsylvania to test the feasibility. With a decrease of 6 per cent in the precision, the improvement of the recall at the meta-level is approximately 20 per cent from the base-level. This demonstrates that the proposed order co-occurrence network helps in increasing the performance of order predictions. The implementation will bring a more effective and efficient way to place outpatient orders.

journal_name

Health Informatics J

authors

Sung YS,Dravenstott RW,Darer JD,Devapriya PD,Kumara S

doi

10.1177/1460458219857383

subject

Has Abstract

pub_date

2020-06-01 00:00:00

pages

999-1016

issue

2

eissn

1460-4582

issn

1741-2811

journal_volume

26

pub_type

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