Utilisation of routine eRegister data for maternal and neonatal health during peri-natal period in Africa: a scoping review

Abstract Background of the study: Electronic health register (eRegister) data from millions of patients are now routinely collected across diverse healthcare institutions. They consist of heterogeneous data elements, including patient demographic information, diagnoses, laboratory test results, medication prescriptions, clinical notes, and medical images. eRegister adoption rates are high in countries such as America, United Kingdom, Australia, and New Zealand (at, or above 95%), perhaps due to the use of incentives. However, adoption rates vary greatly between countries and is much lower in Africa. Aim: The aim of the review is to map and synthesize literature on utilization of routine eRegister data for maternal and neonatal health during perinatal period in African countries Methods: Joanna Briggs Institute’s methodological approach will be applied to guide this scoping review. The researchers will search PubMed, Scopus, Ebscohost, ScienceDirect, and Google Scholar for articles published between 2010 and the present. Selected articles' reference lists will also be examined. Disagreements will be settled through conversation after two reviewers independently carry out the data extraction and article screening. Descriptive data analyses will be used to guide the data analysis process. Ethics and dissemination: The study does not require ethical approval. To promote awareness on the use of eRegister data findings of this scoping review will be published and shared during academic conferences and relevant events. Keywords: Africa; routine eRegister data; maternal; neonatal; perinatal; scoping review Background to the study Electronic health register (eRegister) data from millions of patients are now routinely collected across diverse healthcare institutions [1]. They consist of heterogeneous data elements, including patient demographic information, diagnoses, laboratory test results, medication prescriptions, clinical notes, and medical images [1]. eRegister adoption rates are high in countries such as America, United Kingdom, Australia, and New Zealand (at, or above 95%), perhaps due to the use of incentives [2]. However, adoption rates vary greatly between countries and is much lower in Africa. Malawi and Ghana have attempted to put in place a national eRegister system. However, such initiatives failed due to lack of government support, poor infrastructure, an intermittent supply of electricity, as well as resistance from healthcare professionals. To date many African countries such as South Africa and Lesotho are still using the paper based records. eRegisters offer numerous advantages over traditional paper records, including enhanced patient safety, reduced errors, more efficient workflows, and improved provider-to-provider communication. However, some of the documented barriers to the use of its use is variability of eRegister data. Kohane et al., (2021) report that different styles of practice and use of eRegisters, have a very large impact on the nature of eRegister data as each data type requires its own specific quality control and transformations to standard terminologies for example, use of different local codes may not be known regionally and internationally [3]. Therefore, the authors cation that a multinational use of eRegister data should at least acknowledge such differences as a limitation. Robustness of eRegister data is another area of concern. some countries have far comprehensive eRegister data whereas others do not. For instance, in the America, the coding of different ethnicities or multiracial identification is not standardized. Thus pose a problem in cases there is an association between patient race or ethnicity and their risk for acquisition of a disease [3]. Karen, Kirchgässler and Levine (2019) caution that effective us of eRegister data requires a multidisciplinary approach. For eRegister data sets to be analyzed effectively, a team of clinicians and scientists with knowledge of the diseases under study and the specific practices of the health care systems involved should work together [4]. Additionally, experts with experience in biomedical record repositories, data harmonization experts, machine learning experts, and at least one expert in regulatory and ethical standards are all necessary collaborators. In developed nations eRegister data is mostly utilized particularly to improve respiratory conditions. For instance, Lee et al., (2021)’s study used electronic health register data to identify patients with chronic obstructive pulmonary diseases [5]. Stransky et al., and Karen, et al., also used electronic register data with the goal of improving patient care outcomes in patients with chronic obstructive pulmonary disease [6,4]. Furthermore, electronic health records have been used to revolutionize clinical practice and research in oncology [7]. Increasing access to as electronic health registers, has the potential to offer unique and important insights into perinatal and paediatric epidemiology. The authors indicate that studies using big data can often be completed more quickly, at a lower cost, and with fewer ethical concerns than primary data collection. In developed nation such as America, eRegister data has been used to improve child health outcomes [8]. Furthermore, Nguyen and Benjamin-Chung (2023)‘s study focused on the challenges and opportunities for rigour and reproducibility in the context of big data perinatal and paediatric epidemiology [9]. Raglan, et al., note that eRegister data use has historically been lower among obstetrician-gynecologists than many physician groups [10]. Notably, contemporary midwifery practice is based on a philosophy of evidenced based thus the eRegister are a good source of such evidence. In Africa, use of eRegister data on several conditions including respiratory conditions, HIV and AIDS, diabetes mellitus, hypertension, mental health as well as maternal and childhealth has received little attention. Action towards targets to end the more than 5 million infants, stillbirth, and maternal fatalities that occur each year in low- and middle-income country settings is hindered by data shortages to follow care around the time of birth [11]. Even though over 80% of deliveries worldwide take place in facilities, regular records are not often used as a source of data for the care of mothers and newborns [11]. Notably, most United Nations member states and more than 80 development partners have agreed to the Every Newborn Action Plan which includes an ambitious roadmap for measurement improvement with a pressing focus on improving measurement around the time of birth, particularly routine Health Management Information System data Sustainable Development Goal No. 17 One of the explicit goals of "Revitalize the Global Partnership for Sustainable Development" is to make more high-quality, timely, and trustworthy data available. In LMICs, population-based surveys continue to be a primary source of information on mother and child health. These household surveys, like the Multiple Indicator Cluster Surveys and the Demographic and Health Surveys Program, gather data on births during the two to five years prior to the survey; as a result, they are not intended to track progress month-to-month or year-to-year [11]. Although the adoption of eRegisters has grown significantly in recent years, eRegister data usage that is specific to maternal and neonatal health is minimal. Little is known about using electronic medical records in maternal and neonatal care. This review seeks to evaluate utilisation of routine eRegister data for maternal and neonatal data health in Africa with the view of promoting eRegister data use by bridging the research gap hence improving maternal and neonatal health. Definitions Perinatal period There are several definitions of ‘perinatal’ that are in use hence perinatal period is defined in diverse ways. Definition I Perinatal period refers to the period of time when you become pregnant and up to a year after giving birth [12]. Definition II Perinatal period, as defined by the WHO (2016), starts at 22 completed weeks of gestation and ends 7 completed days after birth [13]. Definition III Perinatal period starts at the 20th to 28th week of gestation and ends 1 to 4 weeks after birth [14]. For the purpose of this scoping review, the first definition is used as it encompasses the other two definitions. Maternal health Maternal health refers to the health of women during pregnancy, childbirth and the postnatal period (six to eight week after childbirth) [13,15]. Neonatal health WHO (2016), defines neonatal period as the first 28 days of life when newborns adapting to life outside the womb [13]. Merriam-Webster (2024), state that the neonatal period is the refers to the human infant’s life in the first month after birth [16]. The later definition is used in this study as it incorporates the WHO definition. Aim of the review The aim of the review is to map and synthesize literature on utilization of routine eRegister data for maternal and neonatal health during perinatal period in African countries. The following specific review questions will be answered: • What are the publication characteristics of evidence on eRegister data use for maternal and neonatal health? • What is the extend of eRegister data use for maternal and neonatal health in Africa? • Which maternal and neonatal care areas is eRegister data being utilized in Africa? • What are the benefits and barriers to utilisation of eRegister data use for maternal and neonatal health in Africa? Methods The study will employ a scoping review process. This approach is appropriate as it's a kind of review method that maps out the evidence that's already available and maps out and clarifies important ideas inside a study field. The Joanna Briggs Institute's (JBI) methodological approach to scoping reviews will serve as the basis for this review [17-18]. Table 1 PCC elements used in the study PCC element Inclusion criteria Population Pregnant women Women in labour Women who gave birth in the last six to eight weeks Children in their first year of life Concept Electronic health register data, electronic health records Maternal health Neonatal health perinatal perio, Context Northern Africa sub-Saharan Africa Inclusion criteria The study will use the population, concept, and context (PCC) framework (Table 1) to establish if the research question was eligible for the scoping review and frame the search for relevant evidence. According to the framework, the study's population will consist of women and older, and neonates, the study's concept is eRegister data and its context is Africa. The following parameters will also apply: • Reviews of all types with or without meta-analysis will be included which either adopted qualitative, quantitative or mixed will be included. • Studies published in English will be included as the reviewers are proficient in English • Studies published in the period of January 2010 to date will be included. Search strategy, sources of evidence and Study Selection process A three-step search technique recommended by JBI was employed in this study. A preliminary search in Ebscohost and google scholar, followed by analysing keywords and index terms used in the initial findings was conducted to refine the search strategy provide guidance on databases. A list of references of the selected sources were reviewed to provide guidance on additional relevant studies. Databases that will be searched are PubMed/Medline, Scopus, Ebscohost, ScienceDirect and Google scholar. The following search strategy will be used and Boolean operators modified accordingly for the databases as reflected in Table 2: Electronic Health Records OR electronic Medical Records, OR digital health record OR digital medical record OR computerized health record OR computerized medical record AND data OR information AND uptake OR adoption OR usability OR utility OR utilization OR use OR evaluate OR evaluation OR implementation AND Africa. Table 2 Search strategy used in databases Construct Search terms Population- related terms Maternal OR Obstetric OR Midwifery OR Labour OR Perinatal OR Pregnancy OR Postnatal Or Neonatal Or Childbirth Concept related terms Electronic Health Records OR electronic Medical Records, OR digital health record OR digital medical record OR computerized health record OR computerized medical record Context related terms Northern Africa OR sub-Sahara OR Algeria OR Egypt OR Libya OR Mauritania OR Morocco OR Tunisia OR Sahrawi Arab Democratic Republic OR Angola OR Benin OR Botswana OR Burkina Faso OR Burundi OR Cameroon OR Cape Verde OR Central African Republic OR Chad OR Comoros OR Congo OR Deogratic Republic of Congo OR Côte d'Ivoire OR Djibouti OR Eritrea OR Eswatini OR Ethiopia OR Gabon OR Gambia OR Ghana OR Guinea OR Guinea Equatorial OR Guinea-Bissau OR Kenya OR Lesotho OR Liberia OR Madagascar OR Malawi OR Mali OR Mauritania OR Mauritius OR Mayotte OR Mozambique OR Namibia OR Niger OR Nigeria OR Rwanda OR São Tomé and Príncipe OR Senegal OR Seychelles OR Sierra Leone OR Somalia OR South Africa OR South Sudan OR Sudan OR Tanzania OR Togo OR Uganda OR Zambia Zimbabwe Impact related terms Uptake OR adoption OR usability OR utility OR utilization OR use OR evaluate OR evaluation OR implementation Each reviewer will independently complete the first screening of all retrieved articles, and any disagreements will be settled through conversation. The inclusion criteria mentioned above will be used to filter the title and abstract to decide which articles are eligible, and the reasons for rejecting papers at this point will be recorded. After that, the reviewers will separately locate full-text publications, and they will exclude any that do not have full-text access. Each reviewer will study the full-text articles on their own, and any decisions regarding their inclusion or exclusion will be recorded. Both reviewers will reread excluded articles to make sure they are still excluded. We will use PRISMA flow diagram, to provide a concise overview of the search and screening procedure. Data extraction A data extraction tool based on the JBI data extraction template will be used (Table 3). The fields for this instrument will include aspects of the inclusion criteria and the following information: author(s), title, year of publication, language of publication, concept, study design, context of study, sample, data collection instrument as well as the summary of findings [19]. Table 3 Data charting table Source of evidence/citation Setting/country Research design, Population/sample size Use of eRegister (Yes/No) Care areas Benefits of eRegisters Barriers to utilization Collaboratively, the reviewers will test the tool on five full-text papers. The reviewers will then carry out the data extraction procedure on their own. All of the reviewers' extracted data will be combined into a single summary, and disagreements will be settled by having a group reread the relevant article and talk about the extraction in order to come to an agreement. The results summary will also contain any additional data that is judged relevant for achieving the study's goals and supporting the findings description. Data analysis and presentation The purpose of the scoping review was to catalogue and examine the extent to which eRegister data is used to improve maternal and neonatal health in Africa. Descriptive analysis such as frequency count, percentages, arithmetic averages and cross-tabulation, if necessary will be used. In order to report the findings, the extracted data will be compiled and explained in a narrative summary. Findings of the scoping review will be displayed in tables and or visual maps as per PRISMA-ScR flow diagram. Implications The results of this scoping review will shed light on the gaps in using eRegister data for improving maternal and neonatal health. Researchers and other decision-makers could utilize this knowledge to create pro-active preparations that will enhance the wellbeing of women and their neonates. The results could also be used to inform curriculum design and ongoing professional development initiatives for midwifery students and midwives. Strengths and limitations This study is important for shedding light on the use of eRegister data for improving maternal ad neonatal health. However, it has limitations in that it will only consider articles published in 2010. In addition, only articles published in English will be reviewed. Author contributors Funding: The authors have not received a grant for this research. Declaration of interests: The authors declare no conflict of interest. References 1. Xiao, C., Choi, E., & Sun, J. (2018). Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review. Journal of the American Medical Informatics Association, 25(10), 1419-1428. 2. Jimenez, G., Spinazze, P., Matchar, D., Huat, G. K. C., van der Kleij, R. M., Chavannes, N. H., & Car, J. (2020). Digital health competencies for primary healthcare professionals: a scoping review. International journal of medical informatics, 143, 104260. 3. Kohane, I. S., Aronow, B. J., Avillach, P., Beaulieu-Jones, B. K., Bellazzi, R., Bradford, R. L., ... & Cai, T. (2021). What every reader should know about studies using electronic health record data but may be afraid to ask. Journal of medical Internet research, 23(3), e22219. 4. Karen, G. F. L. A. D., Kirchgässler, B. C. J. K. U., & Levine, A. (2019). Epidemiology of Pulmonary Fibrosis: A Cohort Study Using Healthcare Data in Sweden. 5. Lee, S., Doktorchik, C., Martin, E. A., D'Souza, A. G., Eastwood, C., Shaheen, A. A., ... & Quan, H. (2021). Electronic medical record–based case phenotyping for the charlson conditions: scoping review. JMIR medical informatics, 9(2), e23934 6. Stransky, M. L., Bremer-Kamens, M., Kistin, C. J., Sheldrick, R. C., & Cohen, R. T. (2024). Using electronic health records to identify asthma-related acute care encounters. Academic Pediatrics. 7. Birnbaum, B., Nussbaum, N., Seidl-Rathkopf, K., Agrawal, M., Estevez, M., Estola, E., ... & Richardson, P. (2020). Model-assisted cohort selection with bias analysis for generating large-scale cohorts from the EHR for oncology research. arXiv preprint arXiv:2001.09765 8. Vesoulis, Z. A., Husain, A. N., & Cole, F. S. (2023). Improving child health through Big Data and data science. Pediatric research, 93(2), 342-349. 9. Nguyen, A., & Benjamin-Chung, J. (2023). Rigour and reproducibility in perinatal and paediatric epidemiologic research using big data. Paediatric and perinatal epidemiology, 37(4), 322. 10. Raglan, G. B., Margolis, B., Paulus, R. A., & Schulkin, J. (2014). Electronic health record adoption among obstetrician/gynecologists in the United States: physician practices and satisfaction. Journal for Healthcare Quality. 11. Shamba, D., Day, L. T., Zaman, S. B., Sunny, A. K., Tarimo, M. N., Peven, K., ... & Lawn, J. E. (2021). Barriers and enablers to routine register data collection for newborns and mothers: EN-BIRTH multi-country validation study. BMC pregnancy and childbirth, 21, 1-14. 12. Nelson-Piercy, C. (2020). Handbook of obstetric medicine. CRC press. 13. World Health Organization. (2016). WHO Recommendations on Antenatal Care for a Positive Pregnancy Experience. Philippines: World Health Organization. 14. Casanova, R., Goepfert, A. R., Hueppchen, N., Weiss, P. M., & Connolly, A. M. (2023). Beckmann and Ling's obstetrics and gynecology. Lippincott Williams & Wilkins. 15. Moyer, C. A., Lawrence, E. R., Beyuo, T. K., Tuuli, M. G., & Oppong, S. A. (2023). Stalled progress in reducing maternal mortality globally: what next?. The Lancet, 401(10382), 1060-1062. 16. Merriam-Webster. (n.d.). Neonatal. In Merriam-Webster.com dictionary. Retrieved August 4, 2024, from merriam-webster.com 17. Peters, M. D., Godfrey, C., McInerney, P., Munn, Z., Tricco, A. C., & Khalil, H. (2020). Scoping reviews. JBI manual for evidence synthesis, 10.Shamba, D., Day, L. T., Zaman, S. B., Sunny, A. K., Tarimo, M. N., Peven, K., ... & Lawn, J. E. (2021). Barriers and enablers to routine register data collection for newborns and mothers: EN-BIRTH multi-country validation study. BMC pregnancy and childbirth, 21, 1-14. 18. Hadie, S. N. H. (2024). ABC of a Scoping Review: A Simplified JBI Scoping Review Guideline. Education in Medicine Journal, 16(2), 185-197.