TY - GEN
T1 - Clinical decision support for colonoscopy surveillance using natural language processing
AU - Wagholikar, Kavishwar
AU - Sohn, Sunghwan
AU - Wu, Stephen
AU - Kaggal, Vinod
AU - Buehler, Sheila
AU - Greenes, Robert
AU - Wu, Tsung Teh
AU - Larson, David
AU - Liu, Hongfang
AU - Chaudhry, Rajeev
AU - Boardman, Lisa
PY - 2012/12/1
Y1 - 2012/12/1
N2 - Colorectal cancer is the second leading cause of cancer-related deaths in the United States. However, 41% of patients do not receive adequate screening, since the surveillance guidelines for colonoscopy are complex and are not easily recalled by health care providers. As a potential solution, we developed a guideline based clinical decision support system (CDSS) that can interpret relevant freetext reports including indications, pathology and procedure notes. The CDSS was evaluated by comparing its recommendations with those of a gastroenterologist for a test set of 53 patients. The CDSS made the optimal recommendation in 48 cases, and helped the gastroenterologist revise the recommendation in 3 cases. We performed an error analysis for the 5 failure cases, and subsequently were able to modify the CDSS to output the correct recommendation for all the test cases. Results indicate that the system has a high potential for clinical deployment, but further evaluation and optimization is required. Limitations of our study are that the study was conducted at a single institution and with a single expert, and the evaluation did not include rare decision scenarios. Overall our work demonstrates the utility of natural language processing to enhance clinical decision support.
AB - Colorectal cancer is the second leading cause of cancer-related deaths in the United States. However, 41% of patients do not receive adequate screening, since the surveillance guidelines for colonoscopy are complex and are not easily recalled by health care providers. As a potential solution, we developed a guideline based clinical decision support system (CDSS) that can interpret relevant freetext reports including indications, pathology and procedure notes. The CDSS was evaluated by comparing its recommendations with those of a gastroenterologist for a test set of 53 patients. The CDSS made the optimal recommendation in 48 cases, and helped the gastroenterologist revise the recommendation in 3 cases. We performed an error analysis for the 5 failure cases, and subsequently were able to modify the CDSS to output the correct recommendation for all the test cases. Results indicate that the system has a high potential for clinical deployment, but further evaluation and optimization is required. Limitations of our study are that the study was conducted at a single institution and with a single expert, and the evaluation did not include rare decision scenarios. Overall our work demonstrates the utility of natural language processing to enhance clinical decision support.
KW - clinical decision support
KW - colonoscopy
KW - colorectal cancer
KW - natural language processing
UR - http://www.scopus.com/inward/record.url?scp=84871949244&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84871949244&partnerID=8YFLogxK
U2 - 10.1109/HISB.2012.11
DO - 10.1109/HISB.2012.11
M3 - Conference contribution
AN - SCOPUS:84871949244
SN - 9780769549217
T3 - Proceedings - 2012 IEEE 2nd Conference on Healthcare Informatics, Imaging and Systems Biology, HISB 2012
SP - 12
EP - 21
BT - Proceedings - 2012 IEEE 2nd Conference on Healthcare Informatics, Imaging and Systems Biology, HISB 2012
T2 - 2012 IEEE 2nd Conference on Healthcare Informatics, Imaging and Systems Biology, HISB 2012
Y2 - 27 September 2012 through 28 September 2012
ER -