Innovative Models for Integrative Prenatal Care
DOI:
https://doi.org/10.18662/brain/15.1/532Keywords:
healthcare technology, prenatal care, innovation, artificial intelligence, blockchain, virtual reality, telemedicineAbstract
In the rapidly evolving healthcare landscape, integrative care delivery stands at the forefront of pioneering change, particularly in prenatal care. This comprehensive narrative review delves into the development of innovative models for integrative prenatal care, such as telemedicine-integrated home monitoring systems, mental health apps, virtual reality, artificial intelligence-powered predictive analytics and blockchain for secure health data management, proposing a paradigm shift from traditional methodologies to a more holistic, technology-empowered approach. We explore the interplay between cutting-edge technological advancements and interdisciplinary collaboration in crafting a care model that is patient-centric and adaptable to diverse healthcare settings. Moreover, key areas where integration can be significantly enhanced such as telemedicine, patient education, and continuous monitoring were identified, emphasizing the importance of synergy between medical expertise, patient engagement, and technology, aiming to improve outcomes for both mother and child and argue that the future of prenatal care lies in embracing innovation, flexibility, and inclusivity, setting a new standard in healthcare delivery. This work offers practical insights for healthcare professionals and policymakers aspiring to transform prenatal care into a more effective, accessible, and patient-friendly experience.
References
Abuelezz, I., Hassan, A., Jaber, B. A., Sharique, M., Abd-Alrazaq, A., Househ, M., Alam, T., & Shah, Z. (2022). Contribution of Artificial Intelligence in pregnancy: A scoping review. In J. Mantas, A. Hasman, M. S. Househ, P. Gallos, E. Zoulias, & J. Liaskos (Eds.), Studies in Health Technology and Informatics. IOS Press. https://doi.org/10.3233/SHTI210927
Alexander, K., Short, V., Gannon, M., Goyal, N., Naegle, M., & Abatemarco, D. J. (2021). Identified gaps and opportunities in perinatal healthcare delivery for women in treatment for opioid use disorder. Substance Abuse, 42(4), 552–558. https://doi.org/10.1080/08897077.2020.1803178
Atkinson, J., Hastie, R., Walker, S., Lindquist, A., & Tong, S. (2023). Telehealth in antenatal care: Recent insights and advances. BMC Medicine, 21(1), 332. https://doi.org/10.1186/s12916-023-03042-y
Baradwan, S., Khadawardi, K., Badghish, E., Alkhamis, W. H., Dahi, A. A., Abdallah, K. M., Kamel, M., Sayd, Z. S., Mohamed, M. A., Ali, H. M., Elhalim, A. E. M. A., Mahmoud, M., Mohamed, A. A., Mohamed, D. F., Shama, A. A. A., Hagras, A. M., Ali, H. A. A., Abdelhakim, A. M., Saleh, M., … Bakry, M. S. (2022). The impact of virtual reality on pain management during normal labor: A systematic review and meta-analysis of randomized controlled trials. Sexual & Reproductive Healthcare, 32, 100720. https://doi.org/10.1016/j.srhc.2022.100720
Bertini, A., Salas, R., Chabert, S., Sobrevia, L., & Pardo, F. (2022). Using machine learning to predict complications in pregnancy: A systematic review. Frontiers in Bioengineering and Biotechnology, 9, 780389. https://doi.org/10.3389/fbioe.2021.780389
Bingham, D., & Jones, R. (2012). Maternal death from obstetric hemorrhage. Journal of Obstetric, Gynecologic & Neonatal Nursing, 41(4), 531–539. https://doi.org/10.1111/j.1552-6909.2012.01372.x
Brown, H. L., & DeNicola, N. (2020). Telehealth in maternity care. Obstetrics and Gynecology Clinics of North America, 47(3), 497–502. https://doi.org/10.1016/j.ogc.2020.05.003
Cantor, A. G., Jungbauer, R. M., Totten, A. M., Tilden, E. L., Holmes, R., Ahmed, A., Wagner, J., Hermesch, A. C., & McDonagh, M. S. (2022). Telehealth strategies for the delivery of maternal health care: A rapid review. Annals of Internal Medicine, 175(9), 1285–1297. https://doi.org/10.7326/M22-0737
Carus, E. G., Albayrak, N., Bildirici, H. M., & Ozmen, S. G. (2022). Immersive virtual reality on childbirth experience for women: A randomized controlled trial. BMC Pregnancy and Childbirth, 22(1), 354. https://doi.org/10.1186/s12884-022-04598-y
Chauhan, A., & Potdar, J. (2022). Maternal mental health during pregnancy: A critical review. Cureus. https://doi.org/10.7759/cureus.30656
Chu, R., Chen, W., Song, G., Yao, S., Xie, L., Song, L., Zhang, Y., Chen, L., Zhang, X., Ma, Y., Luo, X., Liu, Y., Sun, P., Zhang, S., Fang, Y., Dong, T., Zhang, Q., Peng, J., Zhang, L., … Kong, B. (2020). Predicting the risk of adverse events in pregnant women with congenital heart disease. Journal of the American Heart Association, 9(14), e016371. https://doi.org/10.1161/JAHA.120.016371
Davidson, L., & Boland, M. R. (2021). Towards deep phenotyping pregnancy: A systematic review on artificial intelligence and machine learning methods to improve pregnancy outcomes. Briefings in Bioinformatics, 22(5), bbaa369. https://doi.org/10.1093/bib/bbaa369
Dimitrov, D. V. (2019). Blockchain applications for healthcare data management. Healthcare Informatics Research, 25(1), 51. https://doi.org/10.4258/hir.2019.25.1.51
El Zowalaty, M. E., Young, S. G., & Järhult, J. D. (2020). Environmental impact of the COVID-19 pandemic – a lesson for the future. Infection Ecology & Epidemiology, 10(1), 1768023. https://doi.org/10.1080/20008686.2020.1768023
Elangovan, D., Long, C. S., Bakrin, F. S., Tan, C. S., Goh, K. W., Yeoh, S. F., Loy, M. J., Hussain, Z., Lee, K. S., Idris, A. C., & Ming, L. C. (2022). The use of blockchain technology in the health care sector: Systematic review. JMIR Medical Informatics, 10(1), e17278. https://doi.org/10.2196/17278
Estrella‐Juarez, F., Requena‐Mullor, M., Garcia‐Gonzalez, J., Lopez‐Villen, A., & Alarcon‐Rodriguez, R. (2023). Effect of virtual reality and music therapy on the physiologic parameters of pregnant women and fetuses and on anxiety levels: A randomized controlled trial. Journal of Midwifery & Women’s Health, 68(1), 35–43. https://doi.org/10.1111/jmwh.13413
Evans, K., Donelan, J., Rennick-Egglestone, S., Cox, S., & Kuipers, Y. (2022). Review of mobile apps for women with anxiety in pregnancy: Maternity care professionals’ guide to locating and assessing anxiety apps. Journal of Medical Internet Research, 24(3), e31831. https://doi.org/10.2196/31831
Farrell, R., Collart, C., Craighead, C., Pierce, M., Chien, E., Frankel, R., Tucker Edmonds, B., Perni, U., Coleridge, M., Ranzini, A. C., & Rose, S. (2022). The successes and challenges of implementing telehealth for diverse patient populations requiring prenatal care during COVID-19: Qualitative study. JMIR Formative Research, 6(3), e32791. https://doi.org/10.2196/32791
Fazal, N., Webb, A., Bangoura, J., & El Nasharty, M. (2020). Telehealth: Improving maternity services by modern technology. BMJ Open Quality, 9(4), e000895. https://doi.org/10.1136/bmjoq-2019-000895
Feduniw, S., Golik, D., Kajdy, A., Pruc, M., Modzelewski, J., Sys, D., Kwiatkowski, S., Makomaska-Szaroszyk, E., & Rabijewski, M. (2022). Application of Artificial Intelligence in screening for adverse perinatal outcomes—a systematic review. Healthcare, 10(11), 2164. https://doi.org/10.3390/healthcare10112164
Fikadu, K., Meskel, F., Getahun, F., Chufamo, N., & Misiker, D. (2021). Determinants of pre‐eclampsia among pregnant women attending perinatal care in hospitals of the Omo district, Southern Ethiopia. The Journal of Clinical Hypertension, 23(1), 153–162. https://doi.org/10.1111/jch.14073
Ghahremani, T., Magann, E. F., Phillips, A., Ray-Griffith, S. L., Coker, J. L., & Stowe, Z. N. (2022). Women’s mental health services and pregnancy: A review. Obstetrical & Gynecological Survey, 77(2), 122–129. https://doi.org/10.1097/OGX.0000000000000994
Ghimire, S., Martinez, S., Hartvigsen, G., & Gerdes, M. (2023). Virtual prenatal care: A systematic review of pregnant women’s and healthcare professionals’ experiences, needs, and preferences for quality care. International Journal of Medical Informatics, 170, 104964. https://doi.org/10.1016/j.ijmedinf.2022.104964
Ghulmiyyah, L., & Sibai, B. (2012). Maternal mortality from Preeclampsia/Eclampsia. Seminars in Perinatology, 36(1), 56–59. https://doi.org/10.1053/j.semperi.2011.09.011
Giouleka, S., Tsakiridis, I., Kalogiannidis, I., Mamopoulos, A., Tentas, I., Athanasiadis, A., & Dagklis, T. (2022). Postpartum hemorrhage: A comprehensive review of guidelines. Obstetrical & Gynecological Survey, 77(11), 665–682. https://doi.org/10.1097/OGX.0000000000001061
Goffman, D., Nathan, L., & Chazotte, C. (2016). Obstetric hemorrhage: A global review. Seminars in Perinatology, 40(2), 96–98. https://doi.org/10.1053/j.semperi.2015.11.014
Griffen, A., McIntyre, L., Belsito, J. Z., Burkhard, J., Davis, W., Kimmel, M., Stuebe, A., Clark, C., & Meltzer-Brody, S. (2021). Perinatal mental health care in the United States: An overview of policies and programs: Study examines perinatal mental health care policies and programs in the United States. Health Affairs, 40(10), 1543–1550. https://doi.org/10.1377/hlthaff.2021.00796
Hackelöer, M., Schmidt, L., & Verlohren, S. (2022). New advances in prediction and surveillance of preeclampsia: Role of machine learning approaches and remote monitoring. Archives of Gynecology and Obstetrics, 308(6), 1663–1677. https://doi.org/10.1007/s00404-022-06864-y
Hajesmaeel-Gohari, S., Sarpourian, F., & Shafiei, E. (2021). Virtual reality applications to assist pregnant women: A scoping review. BMC Pregnancy and Childbirth, 21(1), 249. https://doi.org/10.1186/s12884-021-03725-5
Hofmann, G., Hampanda, K., Harrison, M. S., Fasano, M., Nacht, A., & Yeoman, M. (2022). Virtual prenatal and postpartum care acceptability among maternity care providers. Maternal and Child Health Journal, 26(7), 1401–1408. https://doi.org/10.1007/s10995-022-03412-7
Hug, L., Alexander, M., You, D., & Alkema, L. (2019). National, regional, and global levels and trends in neonatal mortality between 1990 and 2017, with scenario-based projections to 2030: A systematic analysis. The Lancet Global Health, 7(6), e710–e720. https://doi.org/10.1016/S2214-109X(19)30163-9
Hussain-Shamsy, N., Shah, A., Vigod, S. N., Zaheer, J., & Seto, E. (2020). Mobile health for perinatal depression and anxiety: Scoping review. Journal of Medical Internet Research, 22(4), e17011. https://doi.org/10.2196/17011
Jabeen, R., Mubarak Jabeen Salman, & Ibtisaam Qazi. (2022). Evidence of mobile health integration into primary health care systems for better maternal mental health in LMIC during COVID-19 pandemic—Review. Journal of the Pakistan Medical Association, 73(1), 125–128. https://doi.org/10.47391/JPMA.5155
Jamee, A. R., Kumar Sen, K., & Bari, W. (2022). Skilled maternal healthcare and good essential newborn care practice in rural Bangladesh: A cross‐sectional study. Health Science Reports, 5(5), e791. https://doi.org/10.1002/hsr2.791
Johnson, K. B., Wei, W., Weeraratne, D., Frisse, M. E., Misulis, K., Rhee, K., Zhao, J., & Snowdon, J. L. (2021). Precision medicine, AI, and the future of personalized health care. Clinical and Translational Science, 14(1), 86–93. https://doi.org/10.1111/cts.12884
Kallas, K.-A., Marr, K., Moirangthem, S., Heude, B., Koehl, M., Van Der Waerden, J., & Downes, N. (2023). Maternal mental health care matters: The impact of prenatal depressive and anxious symptoms on child emotional and behavioural trajectories in the French EDEN cohort. Journal of Clinical Medicine, 12(3), 1120. https://doi.org/10.3390/jcm12031120
Kumar, R., Arjunaditya, Singh, D., Srinivasan, K., & Hu, Y.-C. (2022). AI-Powered blockchain technology for public health: A contemporary review, open challenges, and future research directions. Healthcare, 11(1), 81. https://doi.org/10.3390/healthcare11010081
Lasater, M. E., Murray, S. M., Keita, M., Souko, F., Surkan, P. J., Warren, N. E., Winch, P. J., Ba, A., Doumbia, S., & Bass, J. K. (2021). Integrating Mental Health into Maternal Health Care in Rural Mali: A Qualitative Study. Journal of Midwifery & Women’s Health, 66(2), 233–239. https://doi.org/10.1111/jmwh.13184
Lassi, Z. S., Salam, R. A., Das, J. K., & Bhutta, Z. A. (2014). Essential interventions for maternal, newborn and child health: Background and methodology. Reproductive Health, 11(S1), S1. https://doi.org/10.1186/1742-4755-11-S1-S1
Lee, S. J., Garcia, G.-G. P., Stanhope, K. K., Platner, M. H., & Boulet, S. L. (2023). Interpretable machine learning to predict adverse perinatal outcomes: Examining marginal predictive value of risk factors during pregnancy. American Journal of Obstetrics & Gynecology MFM, 5(10), 101096. https://doi.org/10.1016/j.ajogmf.2023.101096
Lin, H.-H., Chang, Y.-C., Chou, H.-H., Chang, C.-P., Huang, M.-Y., Liu, S.-J., Tsai, C.-H., Lei, W.-T., & Yeh, T.-L. (2019). Effect of music interventions on anxiety during labor: A systematic review and meta-analysis of randomized controlled trials. PeerJ, 7, e6945. https://doi.org/10.7717/peerj.6945
Liu, C. H., Goyal, D., Mittal, L., & Erdei, C. (2021). Patient satisfaction with virtual-based prenatal care: Implications after the COVID-19 pandemic. Maternal and Child Health Journal, 25(11), 1735–1743. https://doi.org/10.1007/s10995-021-03211-6
Lomonaco-Haycraft, K. C., Hyer, J., Tibbits, B., Grote, J., Stainback-Tracy, K., Ulrickson, C., Lieberman, A., Van Bekkum, L., & Hoffman, M. C. (2019). Integrated perinatal mental health care: A national model of perinatal primary care in vulnerable populations. Primary Health Care Research & Development, 20, e77. https://doi.org/10.1017/S1463423618000348
Marko, K. I., Krapf, J. M., Meltzer, A. C., Oh, J., Ganju, N., Martinez, A. G., Sheth, S. G., & Gaba, N. D. (2016). Testing the feasibility of remote patient monitoring in prenatal care using a mobile app and connected devices: A prospective observational trial. JMIR Research Protocols, 5(4), e200. https://doi.org/10.2196/resprot.6167
Mehralizade, A., Schor, S., Coleman, C. M., Oppenheim, C. E., Denckla, C. A., Borba, C. P., Henderson, D. C., Wolff, J., Crane, S., Nettles-Gomez, P., Pal, A., & Milanovic, S. (2017). Mobile health apps in OB-GYN-embedded psychiatric care: Commentary. JMIR mHealth and uHealth, 5(10), e152. https://doi.org/10.2196/mhealth.7988
Mehta, S., Grant, K., & Ackery, A. (2020). Future of blockchain in healthcare: Potential to improve the accessibility, security and interoperability of electronic health records. BMJ Health & Care Informatics, 27(3), e100217. https://doi.org/10.1136/bmjhci-2020-100217
Morgan, A., Goodman, D., Vinagolu-Baur, J., & Cass, I. (2022). Prenatal telemedicine during COVID-19: Patterns of use and barriers to access. JAMIA Open, 5(1), ooab116. https://doi.org/10.1093/jamiaopen/ooab116
Pflugeisen, B. M., McCarren, C., Poore, S., Carlile, M., & Schroeder, R. (2016). Virtual visits: Managing prenatal care with modern technology. MCN: The American Journal of Maternal/Child Nursing, 41(1), 24–30. https://doi.org/10.1097/NMC.0000000000000199
Ramakrishnan, R., Rao, S., & He, J.-R. (2021). Perinatal health predictors using artificial intelligence: A review. Women’s Health, 17, 174550652110461. https://doi.org/10.1177/17455065211046132
Saeed, H., Malik, H., Bashir, U., Ahmad, A., Riaz, S., Ilyas, M., Bukhari, W. A., & Khan, M. I. A. (2022). Blockchain technology in healthcare: A systematic review. PLOS ONE, 17(4), e0266462. https://doi.org/10.1371/journal.pone.0266462
Singh, K., Brodish, P., & Suchindran, C. (2014). A regional multilevel analysis: Can skilled birth attendants uniformly decrease neonatal mortality? Maternal and Child Health Journal, 18(1), 242–249. https://doi.org/10.1007/s10995-013-1260-7
UNICEF. (2023). Maternal mortality. https://data.unicef.org/topic/maternal-health/maternal-mortality/
Van Den Heuvel, J. F. M., Kariman, S. S., Van Solinge, W. W., Franx, A., Lely, A. T., & Bekker, M. N. (2019). SAFE@HOME – Feasibility study of a telemonitoring platform combining blood pressure and preeclampsia symptoms in pregnancy care. European Journal of Obstetrics & Gynecology and Reproductive Biology, 240, 226–231. https://doi.org/10.1016/j.ejogrb.2019.07.012
Van Den Heuvel, J. F. M., Teunis, C. J., Franx, A., Crombag, N. M. T. H., & Bekker, M. N. (2020). Home-based telemonitoring versus hospital admission in high risk pregnancies: A qualitative study on women’s experiences. BMC Pregnancy and Childbirth, 20(1), 77. https://doi.org/10.1186/s12884-020-2779-4
Wong, M. S., Spiegel, B. M. R., & Gregory, K. D. (2021). Virtual reality reduces pain in laboring women: a randomized controlled trial. American Journal of Perinatology, 38(S 01), e167–e172. https://doi.org/10.1055/s-0040-1708851
World Health Organization. (2023). Maternal mortality. https://www.who.int/news-room/fact-sheets/detail/maternal-mortality
Wu, K. K., Lopez, C., & Nichols, M. (2022). Virtual visits in prenatal care: An integrative review. Journal of Midwifery & Women’s Health, 67(1), 39–52. https://doi.org/10.1111/jmwh.13284
Xu, N., Chen, S., Liu, Y., Jing, Y., & Gu, P. (2022). The effects of virtual reality in maternal delivery: Systematic review and meta-analysis. JMIR Serious Games, 10(4), e36695. https://doi.org/10.2196/36695
Zizzo, A. R., Hvidman, L., Salvig, J. D., Holst, L., Kyng, M., & Petersen, O. B. (2022). Home management by remote self‐monitoring in intermediate‐ and high‐risk pregnancies: A retrospective study of 400 consecutive women. Acta Obstetricia et Gynecologica Scandinavica, 101(1), 135–144. https://doi.org/10.1111/aogs.14294
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 The Authors & LUMEN Publishing House
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant this journal right of first publication, with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work, with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g. post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g. in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as an earlier and greater citation of published work (See The Effect of Open Access).
BRAIN. Broad Research in Artificial Intelligence and Neuroscience Journal has an Attribution-NonCommercial-NoDerivs
CC BY-NC-ND
