Using Unstructured Data to Identify Readmitted Patients

Majid Rastegar-Mojarad; Jenna K. Lovely; Joshua Pankratz; Sunghwan Sohn; Donna M. Ihrke; Amit Merchea; David Larson; Hongfang D Liu

doi:10.1109/ICHI.2017.99

Using Unstructured Data to Identify Readmitted Patients

Majid Rastegar-Mojarad, Jenna K. Lovely, Joshua Pankratz, Sunghwan Sohn, Donna M. Ihrke, Amit Merchea, David Larson, Hongfang D Liu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

Abstract

Readmission rate is a quality metric for hospitals. The electronic medical record is the main source to identify readmitted patients and calculating readmission rates. Difficulties remain in identifying patients readmitted to a facility different than the one performing the procedure. In this study, we assessed the impact of using unstructured data in detecting readmission within 30 days of surgery. We implemented two rule-based systems to recognize any mention of readmission in follow-up phone call conversions. We evaluated our systems on datasets from two hospitals. Our evaluation showed using unstructured data, in addition to structured data, increased sensitivity in the both dataset, from 53 to 81 and 66 to 87 percent.

Original language	English (US)
Title of host publication	Proceedings - 2017 IEEE International Conference on Healthcare Informatics, ICHI 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-4
Number of pages	4
ISBN (Electronic)	9781509048816
DOIs	https://doi.org/10.1109/ICHI.2017.99
State	Published - Sep 8 2017
Event	5th IEEE International Conference on Healthcare Informatics, ICHI 2017 - Park City, United States Duration: Aug 23 2017 → Aug 26 2017

Other

Other	5th IEEE International Conference on Healthcare Informatics, ICHI 2017
Country/Territory	United States
City	Park City
Period	8/23/17 → 8/26/17

ASJC Scopus subject areas

Health Informatics

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10.1109/ICHI.2017.99

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Rastegar-Mojarad, M., Lovely, J. K., Pankratz, J., Sohn, S., Ihrke, D. M., Merchea, A., Larson, D., & Liu, H. D. (2017). Using Unstructured Data to Identify Readmitted Patients. In Proceedings - 2017 IEEE International Conference on Healthcare Informatics, ICHI 2017 (pp. 1-4). [8031124] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICHI.2017.99

Using Unstructured Data to Identify Readmitted Patients. / Rastegar-Mojarad, Majid; Lovely, Jenna K.; Pankratz, Joshua et al.

Proceedings - 2017 IEEE International Conference on Healthcare Informatics, ICHI 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1-4 8031124.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Rastegar-Mojarad, M, Lovely, JK, Pankratz, J, Sohn, S, Ihrke, DM, Merchea, A, Larson, D & Liu, HD 2017, Using Unstructured Data to Identify Readmitted Patients. in Proceedings - 2017 IEEE International Conference on Healthcare Informatics, ICHI 2017., 8031124, Institute of Electrical and Electronics Engineers Inc., pp. 1-4, 5th IEEE International Conference on Healthcare Informatics, ICHI 2017, Park City, United States, 8/23/17. https://doi.org/10.1109/ICHI.2017.99

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AB - Readmission rate is a quality metric for hospitals. The electronic medical record is the main source to identify readmitted patients and calculating readmission rates. Difficulties remain in identifying patients readmitted to a facility different than the one performing the procedure. In this study, we assessed the impact of using unstructured data in detecting readmission within 30 days of surgery. We implemented two rule-based systems to recognize any mention of readmission in follow-up phone call conversions. We evaluated our systems on datasets from two hospitals. Our evaluation showed using unstructured data, in addition to structured data, increased sensitivity in the both dataset, from 53 to 81 and 66 to 87 percent.

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Using Unstructured Data to Identify Readmitted Patients

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