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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.<\/jats:p>","DOI":"10.1177\/1460458219857383","type":"journal-article","created":{"date-parts":[[2019,7,3]],"date-time":"2019-07-03T04:03:18Z","timestamp":1562126598000},"page":"999-1016","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["SuperOrder: Provider order recommendation system for outpatient clinics"],"prefix":"10.1177","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4072-6351","authenticated-orcid":false,"given":"Yi-Shan","family":"Sung","sequence":"first","affiliation":[{"name":"University of Arkansas for Medical Sciences, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ronald W","family":"Dravenstott","sequence":"additional","affiliation":[{"name":"UNT Health Science Center, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jonathan 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