TY - JOUR
T1 - Development of an algorithm to identify pregnancy episodes in an integrated health care delivery system
AU - Hornbrook, Mark C.
AU - Whitlock, Evelyn P.
AU - Berg, Cynthia J.
AU - Callaghan, William M.
AU - Bachman, Donald J.
AU - Gold, Rachel
AU - Bruce, F. Carol
AU - Dietz, Patricia M.
AU - Williams, Selvi B.
PY - 2007/4
Y1 - 2007/4
N2 - Objective. To develop and validate a software algorithm to detect pregnancy episodes and maternal morbidities using automated data. Data Sources/Study Setting. Automated records from a large integrated health care delivery system (IHDS), 1998-2001. Study Design. Through complex linkages of multiple automated information sources, the algorithm estimated pregnancy histories. We evaluated the algorithm's accuracy by comparing selected elements of the pregnancy history obtained by the algorithm with the same elements manually abstracted from medical records by trained research staff. Data Collection/Extraction Methods. The algorithm searched for potential pregnancy indicators within diagnosis and procedure codes, as well as laboratory tests, pharmacy dispensings, and imaging procedures associated with pregnancy. Principal Findings. Among 32,847 women with potential pregnancy indicators, we identified 24,680 pregnancies occuring to 21,001 women. Percent agreement between the algorithm and medical records review on pregnancy outcome, gestational age, and pregnancy outcome date ranged from 91 percent to 98 percent. The validation results were used to refine the algorithm. Conclusions. This pregnancy episode grouper algorithm takes advantage of databases readily available in IHDS, and has important applications for health system management and clinical care. It can be used in other settings for ongoing surveillance and research on pregnancy outcomes, pregnancy-related morbidities, costs, and care patterns.
AB - Objective. To develop and validate a software algorithm to detect pregnancy episodes and maternal morbidities using automated data. Data Sources/Study Setting. Automated records from a large integrated health care delivery system (IHDS), 1998-2001. Study Design. Through complex linkages of multiple automated information sources, the algorithm estimated pregnancy histories. We evaluated the algorithm's accuracy by comparing selected elements of the pregnancy history obtained by the algorithm with the same elements manually abstracted from medical records by trained research staff. Data Collection/Extraction Methods. The algorithm searched for potential pregnancy indicators within diagnosis and procedure codes, as well as laboratory tests, pharmacy dispensings, and imaging procedures associated with pregnancy. Principal Findings. Among 32,847 women with potential pregnancy indicators, we identified 24,680 pregnancies occuring to 21,001 women. Percent agreement between the algorithm and medical records review on pregnancy outcome, gestational age, and pregnancy outcome date ranged from 91 percent to 98 percent. The validation results were used to refine the algorithm. Conclusions. This pregnancy episode grouper algorithm takes advantage of databases readily available in IHDS, and has important applications for health system management and clinical care. It can be used in other settings for ongoing surveillance and research on pregnancy outcomes, pregnancy-related morbidities, costs, and care patterns.
KW - Episode grouper software
KW - Maternal morbidities
KW - Pregnancy
KW - Research methods
KW - Validity studies
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U2 - 10.1111/j.1475-6773.2006.00635.x
DO - 10.1111/j.1475-6773.2006.00635.x
M3 - Article
C2 - 17362224
AN - SCOPUS:33947096048
SN - 0017-9124
VL - 42
SP - 908
EP - 927
JO - Health Services Research
JF - Health Services Research
IS - 2
ER -