Enhancing Maternal Health Surveillance in the United States Through Natural Language Processing

Abstract

Maternal health outcomes are essential indicators of overall health care quality and societal well-being. However, in the United States, the maternal health surveillance is often inaccurate, restricting the clinical utility of the data gathered. The limits imposed by these inaccuracies restrict timely policy responses and hinder effective innovations, despite the increasing availability of electronic health records. This paper explores the potential use of natural language processing in improving maternal health surveillance. By combining rule-based linguistic processing with machine learning, natural language processing can transform narrative text into structured, analyzable data, allowing it to be used for predictive purposes, as well as the development of real-time public health surveillance systems.

Key Points

• Maternal health surveillance is often inaccurate, restricting the clinical utility of the data.

• Natural language processing can extract key insights from unstructured clinical notes.

• Artificial intelligence-driven surveillance in obstetrics may improve data accuracy and timeliness.

• Ethical use of natural language processing needs to ensure privacy, bias control, and validation.

Keywords

natural language processing - artificial intelligence - obstetrics - surveillance

Publication History

Received: 07 September 2025

Accepted: 03 December 2025

Accepted Manuscript online:
05 December 2025

Article published online:
19 December 2025

• References

• 1 MacDorman MF, Declercq E. The failure of United States maternal mortality reporting and its impact on women's lives. Birth 2018; 45 (02) 105-108
• 2 Chakhtoura N, Chinn JJ, Grantz KL. et al. Importance of research in reducing maternal morbidity and mortality rates. Am J Obstet Gynecol 2019; 221 (03) 179-182
• 3 Beck D, Hall S, Costa DK, Admon L. Leveraging population health datasets to advance maternal health research. Matern Child Health J 2023; 27 (10) 1683-1688
• 4 Kilpatrick SK, Ecker JL. American College of Obstetricians and Gynecologists and the Society for Maternal–Fetal Medicine. Severe maternal morbidity: screening and review. Am J Obstet Gynecol 2016; 215 (03) B17-B22
• 5 Layton AT. Artificial intelligence and machine learning in preeclampsia. Arterioscler Thromb Vasc Biol 2025; 45 (02) 165-171
• 6 Venkatesh KK, Strauss RA, Grotegut CA. et al. Machine learning and statistical models to predict postpartum hemorrhage. Obstet Gynecol 2020; 135 (04) 935-944
• 7 Wang Y, Wang L, Rastegar-Mojarad M. et al. Clinical information extraction applications: a literature review. J Biomed Inform 2018; 77: 34-49
• 8 Meystre SM, Savova GK, Kipper-Schuler KC, Hurdle JF. Extracting information from textual documents in the electronic health record: a review of recent research. Yearb Med Inform 2008; 17 (01) 128-144
• 9 Uzuner Ö, South BR, Shen S, DuVall SL. 2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text. J Am Med Inform Assoc 2011; 18 (05) 552-556
• 10 Sohn S, Wu S, Chute CG. Dependency parser-based negation detection in clinical narratives. AMIA Jt Summits Transl Sci Proc 2012; 2012: 1-8
• 11 Kreimeyer K, Foster M, Pandey A. et al. Natural language processing systems for capturing and standardizing unstructured clinical information: a systematic review. J Biomed Inform 2017; 73: 14-29
• 12 Deneux-Tharaux C, Berg C, Bouvier-Colle MH. et al. Underreporting of pregnancy-related mortality in the United States and Europe. Obstet Gynecol 2005; 106 (04) 684-692
• 13 Kuklina EV, Meikle SF, Jamieson DJ. et al. Severe obstetric morbidity in the United States: 1998-2005. Obstet Gynecol 2009; 113 (2 Pt 1): 293-299
• 14 Ford ND, Cox S, Ko JY. et al. Hypertensive disorders in pregnancy and mortality at delivery hospitalization - United States, 2017-2019. MMWR Morb Mortal Wkly Rep 2022; 71 (17) 585-591
• 15 Blencowe H, Hug L, Moller AB, You D, Moran AC. Definitions, terminology and standards for reporting of births and deaths in the perinatal period: International Classification of Diseases (ICD-11). Int J Gynaecol Obstet 2025; 168 (01) 1-9
• 16 Labgold K, Stanhope KK, Joseph NT, Platner M, Jamieson DJ, Boulet SL. Validation of hypertensive disorders during pregnancy: ICD-10 codes in a high-burden Southeastern United States hospital. Epidemiology 2021; 32 (04) 591-597
• 17 Stout MJ, Macones GA, Tuuli MG. Accuracy of birth certificate data for classifying preterm birth. Paediatr Perinat Epidemiol 2017; 31 (03) 245-249
• 18 Roberts CL, Bell JC, Ford JB, Hadfield RM, Algert CS, Morris JM. The accuracy of reporting of the hypertensive disorders of pregnancy in population health data. Hypertens Pregnancy 2008; 27 (03) 285-297
• 19 Yasmeen S, Romano PS, Schembri ME, Keyzer JM, Gilbert WM. Accuracy of obstetric diagnoses and procedures in hospital discharge data. Am J Obstet Gynecol 2006; 194 (04) 992-1001
• 20 Geller SE, Ahmed S, Brown ML, Cox SM, Rosenberg D, Kilpatrick SJ. International Classification of Diseases-9th revision coding for preeclampsia: how accurate is it?. Am J Obstet Gynecol 2004; 190 (06) 1629-1633 , discussion 1633–1634
• 21 Agency for Healthcare Research and Quality. Overview of the HCUP nationwide inpatient sample (NIS) release. 1997.
• 22 Wu P, Haththotuwa R, Kwok CS. et al. Preeclampsia and future cardiovascular health: a systematic review and meta-analysis. Circ Cardiovasc Qual Outcomes 2017; 10 (02) e003497
• 23 Creanga AA. Maternal mortality in the United States: a review of contemporary data and their limitations. Clin Obstet Gynecol 2018; 61 (02) 296-306
• 24 American College of Obstetrics and Gynecology. Addressing social and structural determinants of health in the delivery of reproductive health care. Committee Statement 2024
• 25 DiTosto JD, Holder K, Soyemi E, Beestrum M, Yee LM. Housing instability and adverse perinatal outcomes: a systematic review. Am J Obstet Gynecol MFM 2021; 3 (06) 100477
• 26 Girardi G, Longo M, Bremer AA. Social determinants of health in pregnant individuals from underrepresented, understudied, and underreported populations in the United States. Int J Equity Health 2023; 22 (01) 186
• 27 Thomason J. Data, digital worlds, and the avatarization of health care. Glob Health J 2024; 8 (01) 1-3
• 28 Singhal K, Azizi S, Tu T. et al. Large language models encode clinical knowledge. Nature 2023; 620 (7972): 172-180
• 29 Miotto R, Wang F, Wang S, Jiang X, Dudley JT. Deep learning for healthcare: review, opportunities and challenges. Brief Bioinform 2018; 19 (06) 1236-1246
• 30 Meddeb A, Ebert P, Bressem KK. et al. Evaluating local open-source large language models for data extraction from unstructured reports on mechanical thrombectomy in patients with ischemic stroke. J Neurointerv Surg 2025; 17 (09) 986-991
• 31 Li R, Wang X, Yu H. Two directions for clinical data generation with large language models: data-to-label and label-to-data. Proc Conf Empir Methods Nat Lang Process. 2023;2023:7129–7143.
• 32 Clapp MA, Kim E, James KE, Perlis RH, Kaimal AJ, McCoy Jr TH. Natural language processing of admission notes to predict severe maternal morbidity during the delivery encounter. Am J Obstet Gynecol 2022; 227 (03) 511.e1-511.e8
• 33 Clapp MA, Kim E, James KE. et al. Comparison of natural language processing of clinical notes with a validated risk-stratification tool to predict severe maternal morbidity. JAMA Netw Open 2022; 5 (10) e2234924
• 34 López-Úbeda P, Martín-Noguerol T, Juluru K, Luna A. Natural language processing in radiology: update on clinical applications. J Am Coll Radiol 2022; 19 (11) 1271-1285
• 35 McCarthy JF, Cooper SA, Dent KR. et al. Evaluation of the recovery engagement and coordination for health-veterans enhanced treatment suicide risk modeling clinical program in the Veterans Health Administration. JAMA Netw Open 2021; 4 (10) e2129900
• 36 Levis M, Levy J, Dent KR. et al. Leveraging natural language processing to improve electronic health record suicide risk prediction for Veterans Health Administration users. J Clin Psychiatry 2023; 84 (04) 22
• 37 Walsh CG, Wilimitis D, Chen Q. et al. Scalable incident detection via natural language processing and probabilistic language models. Sci Rep 2024; 14 (01) 23429
• 38 Waltzman M, Ozonoff A, Fournier KA. et al. Surveillance of health care-associated violence using natural language processing. Pediatrics 2024; 154 (02) e2023063059
• 39 Shi J, Liu S, Pruitt LCC. et al. Using natural language processing to improve EHR structured data-based surgical site infection surveillance. AMIA Annu Symp Proc 2020; 2019: 794-803
• 40 Shi J, Hurdle JF, Johnson SA. et al. Natural language processing for the surveillance of postoperative venous thromboembolism. Surgery 2021; 170 (04) 1175-1182
• 41 Verma AA, Masoom H, Pou-Prom C. et al. Developing and validating natural language processing algorithms for radiology reports compared to ICD-10 codes for identifying venous thromboembolism in hospitalized medical patients. Thromb Res 2022; 209: 51-58
• 42 Karhade AV, Bongers MER, Groot OQ. et al. Natural language processing for automated detection of incidental durotomy. Spine J 2020; 20 (05) 695-700
• 43 Harrigian K, Tran D, Tang T. et al. Improving the identification of diabetic retinopathy and related conditions in the electronic health record using natural language processing methods. Ophthalmol Sci 2024; 4 (06) 10057
• 44 Shao Y, Zhang S, Raman VK. et al. Artificial intelligence approaches for phenotyping heart failure in U.S. Veterans Health Administration electronic health record. ESC Heart Fail 2024; 11 (05) 3155-3166
• 45 Pan J, Lee S, Cheligeer C. et al. Integrating large language models with human expertise for disease detection in electronic health records. Comput Biol Med 2025; 191: 110161
• 46 Baclic O, Tunis M, Young K, Doan C, Swerdfeger H, Schonfeld J. Challenges and opportunities for public health made possible by advances in natural language processing. Can Commun Dis Rep 2020; 46 (06) 161-168
• 47 United States Food and Drug Administration. Clinical language engineering workbench (CLEW) user guidance document; 2019. Accessed at: https://aspe.hhs.gov/sites/default/files/private/pdf/259016/NLP-CLEW-UserGuidanceDocument-508.pdf
• 48 Scroggins JK, Hulchafo II, Harkins S. et al. Identifying stigmatizing and positive/preferred language in obstetric clinical notes using natural language processing. J Am Med Inform Assoc 2025; 32 (02) 308-317
• 49 Barcelona V, Scharp D, Idnay BR. et al. A qualitative analysis of stigmatizing language in birth admission clinical notes. Nurs Inq 2023; 30 (03) e12557
• 50 Barcelona V, Scharp D, Moen H. et al. Using natural language processing to identify stigmatizing language in labor and birth clinical notes. Matern Child Health J 2024; 28 (03) 578-586
• 51 Ayre K, Bittar A, Kam J, Verma S, Howard LM, Dutta R. Developing a natural language processing tool to identify perinatal self-harm in electronic healthcare records. PLoS One 2021; 16 (08) e0253809
• 52 Ralevski A, Taiyab N, Nossal M, Mico L, Piekos S, Hadlock J. Using large language models to abstract complex social determinants of health from original and deidentified medical notes: development and validation study. J Med Internet Res 2024; 26: e63445