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Am J Perinatol
DOI: 10.1055/a-2405-3703
Original Article

Identifying Elective Induction of Labor Among a Diverse Pregnant Population from Electronic Health Records within a Large Integrated Healthcare System

Fagen Xie 
1   Research & Evaluation, Kaiser Permanente Southern California, Pasadena, United States (Ringgold ID: RIN82579)
,
Michael John Fassett
2   Obstetrics & Gynecology, Kaiser Permanente West Los Angeles Medical Center, West Los Angeles, United States (Ringgold ID: RIN554661)
3   Clinical Science, Kaiser Permanente Bernard J Tyson School of Medicine, Pasadena, United States (Ringgold ID: RIN547934)
,
Theresa M Im
1   Research & Evaluation, Kaiser Permanente Southern California, Pasadena, United States (Ringgold ID: RIN82579)
,
Daniella Park
1   Research & Evaluation, Kaiser Permanente Southern California, Pasadena, United States (Ringgold ID: RIN82579)
,
Vicki Y. Chiu
1   Research & Evaluation, Kaiser Permanente Southern California, Pasadena, United States (Ringgold ID: RIN82579)
,
Darios Getahun 
1   Research & Evaluation, Kaiser Permanente Southern California, Pasadena, United States (Ringgold ID: RIN82579)
4   Health Systems Science, Kaiser Permanente Bernard J Tyson School of Medicine, Pasadena, United States (Ringgold ID: RIN547934)
› Author AffiliationsSupported by: Partially funded by Kaiser Permanente Direct Community Benefit Funds
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Objective: Distinguishing between medically indicated induction of labor (iIOL) and elective induction of labor (eIOL) is a daunting process for researchers. We aimed to develop a Natural Language Processing (NLP) algorithm to identify eIOLs from electronic health records (EHR) within a large integrated healthcare system. Study Design: We used structured and unstructured data from Kaiser Permanente Southern California’s EHR of patients who were <35 years old and had singleton deliveries between 37 and 40 gestational weeks. Induction of labor (IOL) pregnancies were identified if there was evidence of an IOL diagnosis code, procedure code, or documentation in a delivery flowsheet or progress note. A comprehensive NLP algorithm was developed and refined through an iterative process of chart reviews and adjudications, where IOL-associated reasons (medically indicated versus elective induction) were reviewed. The final algorithm was applied to discern the indications of IOLs performed during the study period. Results: A total of 332,163 eligible pregnancies were identified between 01/01/2008–12/31/2022. Of these eligible pregnancies, 68,541 (20.6%) were IOL, of which 6,824 (10.0%) were eIOL. Validation of the NLP process against 300 randomly selected pregnancies (100 eIOL, iIOL, and non-IOL cases each) yielded a positive predictive value of 83.0% and 88.0% for eIOL and iIOL, respectively. The rates of eIOL among the maternal age groups ranged between 9.6% -10.3%, except for the < 20 years group (12.2%). Non-Hispanic White individuals had the highest rate of eIOL (13.2%), while non-Hispanic Asian/Pacific Islanders had the lowest rate of eIOL (7.8%). The rate of eIOL increased from 1.0% in the 37 weeks gestational age group to 20.6% the 40 weeks gestational age group. Conclusion: Findings suggest that the developed NLP algorithm effectively identifies eIOL. It can be utilized to support eIOL-related pharmaco-epidemiological studies, filling in knowledge gaps and providing content more relevant to researchers.



Publication History

Received: 24 May 2024

Accepted after revision: 25 August 2024

Accepted Manuscript online:
29 August 2024

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