SuperOrder

Provider order recommendation system for outpatient clinics

Yi Shan Sung, Ronald W. Dravenstott, Jonathan David Darer, Priyantha D. Devapriya, Soundar Kumara

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
JournalHealth Informatics Journal
DOIs
StatePublished - Jan 1 2019

Fingerprint

Rural Health
Electronic Health Records
Health Facilities
Ambulatory Care Facilities
Appointments and Schedules
Outpatients
Delivery of Health Care
Lung
Machine Learning

Keywords

  • clinical decision model
  • machine learning
  • network analytics
  • order recommendation system
  • outpatient clinics

ASJC Scopus subject areas

  • Health Informatics

Cite this

SuperOrder : Provider order recommendation system for outpatient clinics. / Sung, Yi Shan; Dravenstott, Ronald W.; Darer, Jonathan David; Devapriya, Priyantha D.; Kumara, Soundar.

In: Health Informatics Journal, 01.01.2019.

Research output: Contribution to journalArticle

Sung, Yi Shan ; Dravenstott, Ronald W. ; Darer, Jonathan David ; Devapriya, Priyantha D. ; Kumara, Soundar. / SuperOrder : Provider order recommendation system for outpatient clinics. In: Health Informatics Journal. 2019.
@article{83f7520ff1f844ad9c474f92acfe8b0b,
title = "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.",
keywords = "clinical decision model, machine learning, network analytics, order recommendation system, outpatient clinics",
author = "Sung, {Yi Shan} and Dravenstott, {Ronald W.} and Darer, {Jonathan David} and Devapriya, {Priyantha D.} and Soundar Kumara",
year = "2019",
month = "1",
day = "1",
doi = "10.1177/1460458219857383",
language = "English (US)",
journal = "Health Informatics Journal",
issn = "1460-4582",
publisher = "SAGE Publications Ltd",

}

TY - JOUR

T1 - SuperOrder

T2 - Provider order recommendation system for outpatient clinics

AU - Sung, Yi Shan

AU - Dravenstott, Ronald W.

AU - Darer, Jonathan David

AU - Devapriya, Priyantha D.

AU - Kumara, Soundar

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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.

AB - 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.

KW - clinical decision model

KW - machine learning

KW - network analytics

KW - order recommendation system

KW - outpatient clinics

UR - http://www.scopus.com/inward/record.url?scp=85068590919&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85068590919&partnerID=8YFLogxK

U2 - 10.1177/1460458219857383

DO - 10.1177/1460458219857383

M3 - Article

JO - Health Informatics Journal

JF - Health Informatics Journal

SN - 1460-4582

ER -