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 | |
| 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 | United States |
| City | Park City |
| Period | 8/23/17 → 8/26/17 |
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ASJC Scopus subject areas
- Health Informatics
Cite this
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 proceeding › Conference contribution
}
TY - GEN
T1 - Using Unstructured Data to Identify Readmitted Patients
AU - Rastegar-Mojarad, Majid
AU - Lovely, Jenna K.
AU - Pankratz, Joshua
AU - Sohn, Sunghwan
AU - Ihrke, Donna M.
AU - Merchea, Amit
AU - Larson, David
AU - Liu, Hongfang D
PY - 2017/9/8
Y1 - 2017/9/8
N2 - 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.
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.
UR - http://www.scopus.com/inward/record.url?scp=85032359856&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85032359856&partnerID=8YFLogxK
U2 - 10.1109/ICHI.2017.99
DO - 10.1109/ICHI.2017.99
M3 - Conference contribution
SP - 1
EP - 4
BT - Proceedings - 2017 IEEE International Conference on Healthcare Informatics, ICHI 2017
PB - Institute of Electrical and Electronics Engineers Inc.
ER -