PMID- 16901760
OWN - NLM
STAT- MEDLINE
DCOM- 20070403
LR  - 20071203
IS  - 1532-0480 (Electronic)
IS  - 1532-0464 (Linking)
VI  - 40
IP  - 2
DP  - 2007 Apr
TI  - Automated identification of adverse events related to central venous catheters.
PG  - 174-82
AB  - Methods for surveillance of adverse events (AEs) in clinical settings are limited 
      by cost, technology, and appropriate data availability. In this study, two 
      methods for semi-automated review of text records within the Veterans 
      Administration database are utilized to identify AEs related to the placement of 
      central venous catheters (CVCs): a Natural Language Processing program and a 
      phrase-matching algorithm. A sample of manually reviewed records were then 
      compared to the results of both methods to assess sensitivity and specificity. 
      The phrase-matching algorithm was found to be a sensitive but relatively 
      non-specific method, whereas a natural language processing system was 
      significantly more specific but less sensitive. Positive predictive values for 
      each method estimated the CVC-associated AE rate at this institution to be 6.4 
      and 6.2%, respectively. Using both methods together results in acceptable 
      sensitivity and specificity (72.0 and 80.1%, respectively). All methods including 
      manual chart review are limited by incomplete or inaccurate clinician 
      documentation. A secondary finding was related to the completeness of 
      administrative data (ICD-9 and CPT codes) used to identify intensive care unit 
      patients in whom a CVC was placed. Administrative data identified less than 11% 
      of patients who had a CVC placed. This suggests that other methods, including 
      automated methods such as phrase matching, may be more sensitive than 
      administrative data in identifying patients with devices. Considerable potential 
      exists for the use of such methods for the identification of patients at risk, AE 
      surveillance, and prevention of AEs through decision support technologies.
FAU - Penz, Janet F E
AU  - Penz JF
AD  - Department of Surgery, University of Utah, Salt Lake City, UT 84148, USA. 
      Janet.penz@hsc.utah.edu <Janet.penz@hsc.utah.edu>
FAU - Wilcox, Adam B
AU  - Wilcox AB
FAU - Hurdle, John F
AU  - Hurdle JF
LA  - eng
GR  - 2R01LM006274-04/LM/NLM NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PT  - Research Support, U.S. Gov't, Non-P.H.S.
DEP - 20060609
PL  - United States
TA  - J Biomed Inform
JT  - Journal of biomedical informatics
JID - 100970413
SB  - IM
MH  - Artificial Intelligence
MH  - Catheterization, Central Venous/*adverse effects
MH  - *Database Management Systems
MH  - Humans
MH  - Information Storage and Retrieval/*methods
MH  - *Medical Errors
MH  - *Medical Records Systems, Computerized
MH  - *Natural Language Processing
MH  - Pattern Recognition, Automated/*methods
EDAT- 2006/08/12 09:00
MHDA- 2007/04/04 09:00
CRDT- 2006/08/12 09:00
PHST- 2006/03/21 00:00 [received]
PHST- 2006/06/06 00:00 [revised]
PHST- 2006/06/06 00:00 [accepted]
PHST- 2006/08/12 09:00 [pubmed]
PHST- 2007/04/04 09:00 [medline]
PHST- 2006/08/12 09:00 [entrez]
AID - S1532-0464(06)00065-7 [pii]
AID - 10.1016/j.jbi.2006.06.003 [doi]
PST - ppublish
SO  - J Biomed Inform. 2007 Apr;40(2):174-82. doi: 10.1016/j.jbi.2006.06.003. Epub 2006 
      Jun 9.