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 Jjournal_title
Health informatics journalauthors
Sung YS,Dravenstott RW,Darer JD,Devapriya PD,Kumara Sdoi
10.1177/1460458219857383subject
Has Abstractpub_date
2020-06-01 00:00:00pages
999-1016issue
2eissn
1460-4582issn
1741-2811journal_volume
26pub_type
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