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 proceedingConference contribution

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 languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE International Conference on Healthcare Informatics, ICHI 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-4
Number of pages4
ISBN (Electronic)9781509048816
DOIs
StatePublished - Sep 8 2017
Event5th IEEE International Conference on Healthcare Informatics, ICHI 2017 - Park City, United States
Duration: Aug 23 2017Aug 26 2017

Other

Other5th IEEE International Conference on Healthcare Informatics, ICHI 2017
CountryUnited States
CityPark City
Period8/23/178/26/17

Fingerprint

Patient Readmission
Electronic Health Records
Ambulatory Surgical Procedures
Datasets

ASJC Scopus subject areas

  • Health Informatics

Cite this

Rastegar-Mojarad, M., Lovely, J. K., Pankratz, J., Sohn, S., Ihrke, D. M., Merchea, A., ... 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; Sohn, Sunghwan; Ihrke, Donna M.; Merchea, Amit; Larson, David; Liu, Hongfang D.

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 proceedingConference 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
Rastegar-Mojarad M, Lovely JK, Pankratz J, Sohn S, Ihrke DM, Merchea A et al. Using Unstructured Data to Identify Readmitted Patients. In Proceedings - 2017 IEEE International Conference on Healthcare Informatics, ICHI 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1-4. 8031124 https://doi.org/10.1109/ICHI.2017.99
Rastegar-Mojarad, Majid ; Lovely, Jenna K. ; Pankratz, Joshua ; Sohn, Sunghwan ; Ihrke, Donna M. ; Merchea, Amit ; Larson, David ; Liu, Hongfang D. / Using Unstructured Data to Identify Readmitted Patients. Proceedings - 2017 IEEE International Conference on Healthcare Informatics, ICHI 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 1-4
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