The story of Mum-Zi offers a glimpse into historical practices and raises important contemporary issues in Nigeria. This article delves into the origin of Mum-Zi's story, its cultural context, and the broader implications for maternal and child health in Nigeria.
The Historical Context of Mum-Zi
Mum-Zi was a member of her chief’s harem on the island of Awka Akpa, now known as Calabar in Nigeria.
The international organizations for certifying and ratifying widely used digital health standards are the ISO/TC (International Organization for Standards’ Health Informatics Technical Committee) 215 and CEN/TC (European Committee for Standards’ Health ICT Technical committee) 251. For instance, ISO 21090:2011 is a ratified HL7 version 3 data type for information interchange. Similarly, ISO 13606-1:2019 is a ratified description of archetype reference models.
The Problem of Child Marriage in Nigeria Today
Sadly, Mum’s story is not just a historical one; today, Nigeria is one of the worst countries in the world in terms of child-brides, with as many as one third or more girls married before age eighteen.
Impact of child marriage on girls' education
Part of the issue stems from the fact that the age of consent in Nigeria is eleven, and the constitution states any woman or girl who is married, regardless of age, is considered an adult.
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Map of Nigeria
Health Care Interoperability Standards
HL7 is a leading health care standard development organization that has facilitated many standards, including the HL7 version 2 messaging standard, HL7 version 3 Clinical Document Architecture document exchange standard, and the HL7 FHIR. FHIR was popularized because it supports REpresentational State Transfer (REST)-based web-based (real-time) transactions and its extension for services. FHIR is now emerging as the de facto global standard for health care data interchange.
The FHIR community includes Microsoft, Google, Apple, and many electronic medical record and EHR vendors [-]. In addition, the WHO has recently published a digital adaptation kit to support countries deploying standards for antenatal care [].
FHIR Profiles and Maternal and Child Health Referral Use Cases
The objective of this study is to design FHIR profiles and present methodology and the profiled FHIR resource for Maternal and Child Health referral use cases in Ebonyi state, Nigeria-a typical low- and middle-income country (LMIC) setting.
Practicing doctors, midwives, and nurses were purposefully sampled and surveyed. Different referral forms were reviewed. The union of data sets from surveys and forms was aggregated and mapped to base patient FHIR resource elements, and extensions were created for data sets not in the core FHIR specification.
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This study also introduced FHIR and its relation to the World Health Organization’s (WHO’s) International Classification of Diseases.
We found many different data elements from the referral forms and survey responses even in urban settings. The resulting FHIR standard profile is published on GitHub for adaptation or adoption as necessary to aid alignment with WHO recommendations.
Understanding data sets used in health care and clinical practice for information sharing is crucial in properly standardizing information sharing, particularly during the management of COVID-19 and other infectious diseases. Development organizations and governments can use this methodology and profile to fast-track FHIR standards adoption for paper and electronic information sharing at PHC systems in LMICs.
Health Care Interoperability
Globally, health care interoperability has been identified as vital to seamless care coordination among the different stakeholders.
According to the World Health Organization (WHO) Europe’s 2016 e-Health in practice report, Estonia is the first country to implement electronic health records (EHRs) []. The famous X-Road facilitates Estonia's exchange network, an interoperability layer launched in 2001, with several different services added over the years. Estonia achieved success with over 99% of electronic medical subscriptions in 2018.
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Estonia's X-Road interoperability layer connects over 2700 services across 700 institutions and enterprises across several sectors, including health care.
Interoperability in LMICs
Some LMIC health systems services are still paper-dependent for recording and transmitting health information. Paper records are limited because only one person can access them at a time. Systematic digitization of health systems has driven the development and implementation of national digital health strategies in Nigeria and other LMICs [,].
From our literature search [] and to our knowledge, LMICs still struggle with patient-level interoperability project implementations, which has limited recording successes. Nigeria is a typical LMIC because it has one of the highest global burdens of maternal mortality []. Furthermore, there are more primary health care (PHC) facilities (approximately 10 times) than hospitals in Nigeria; hence, here we focus on PHCs.
PHCs have the highest potential for impact in the Nigerian health system because most health services are delivered at the PHC level. In a typical PHC network, the possible use cases for health information interchange may include the following:
- Interdepartmental care communication
- Inter-PHC or PHC to secondary hospital referral
- Reporting of decentralized laboratory results
- Triangulation of immunization and surveillance information
- Payment settlement
- Diagnostic information exchange
Nigeria used the DHIS2 for routine reporting of the delivery of health information system services. Routine health information systems (RHISs) continue to collect data on a wide range of diseases and conditions []. These RHIS data are analyzed to assess community-level initiatives such as policies to boost community engagement and strengthen referrals from traditional birth attendants to increase demand for maternal and child care [-]. The COVID-19 pandemic has further exposed the weakness in health systems worldwide and the value of linkages.
The project's main objective is to use a referral use case to profile, validate, and present data elements relevant to exchanging health information at the PHC level of care. Profiling is the strategy for defining FHIR models by domesticating the international core standard through specific use cases by structured authoring and publishing. Global best practices facilitate digital health information exchange for better care by using standardized data. Digital tools can only communicate using data in certain formats (eg, XML or JSON), organized in an agreed structure [].
We reviewed paper referral forms and surveyed frontline health workers, drawing inspiration from similar work conducted by Odisho et al []. We checked how consistent the referral data sets were. Aggregated referral data sets were then mapped to and FHIR extensions profiled. We also modeled data types and cardinalities, including references to other profiles, resources, and terminology binding to ICD-10.
We established the research focus by addressing the data flow in the maternal and child health information flow value chain in Ebonyi State, Nigeria. Nigeria has between 28,000 and 36,000 health facilities overall. Ebonyi state is one of the 36 subregional governments in Nigeria with 171 “functional” PHC centers and 13 general hospitals []. Although from the National Health workforce Registry, there are up to 830 health facilities in the state []. Based on our use case, a strategic point of data exchange among multiple PHC centers or PHC centers and hospitals is the referral chain for pregnant women.
We used the purposeful snowball sampling technique to identify health care providers in Ebonyi State and share the survey questionnaire.
We sent out questionnaires and a request for a copy of “referral forms” was used for 24 health workers in their respective health facilities in Ebonyi State. Between June 10 and 17, 2019, all 24 health workers completed and returned the questionnaires, and only 3 provided referral paper forms. Respondents were a mix of medical doctors, midwives, and nurses, as shown in .
We acknowledge the possibility of selection bias, and, for instance, these providers were mostly from health facilities in Abakaliki, the state capital and the main metropolitan city. We consider this bias insignificant as we measured consistency or variation in referral data sets among providers, which was significant.
We started by creating a default patient profile with no extension by using the Forge tool and uploading it on the simplifier.net web interface under the BlockMom project for validation []. This first step was to confirm that the example of the base patient resource instance is FHIR-conformant. From the stakeholder interviews, we aggregated information data sets. We then mapped them to the standard patient resource to create a referral resource with extensions that capture all the identified data points. We further created the bare XML schema for easy file-based resource instance validation.
We modeled the FHIR referral use case profile of information flow regarding pregnant women from one PHC center-for example, PHC center 1 to PHC center 2-or general hospital. Afterward, these resource mapping outputs were then synthesized into JSON and XML machine-readable data formats on the basis of FHIR resources for antenatal referral.
Steps to profiling and publishing the Fast Healthcare Interoperability Resource
Asuma, a pregnant woman described in the use case, was referred to the clinic from the community by a roaming community health extension worker. See page 25 of Sierra Leone’s national digital health strategy for more information on this use case. In ICD-10, the “Personal History of malaria” code is Z8613. The code allows for unique identification in any information system using the same coding system, thus distinguishing this from, say, B500, which represents “Plasmodium falciparum malaria with cerebral complication,” which is a case of complicated malaria with intermittent coma.
Code O98.6 represents “Protozoal disease complicating pregnancy, childbirth, and the puerperium.” This is synonymous with “Malaria in pregnancy” or “Maternal malaria during pregnancy,” both not explicitly coded in ICD-10 [].
Key Maternal and Child Health chapters of the International Classification of Diseases, Tenth Revision coding system
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