MODALIDADES Y CRITERIOS DE ATRIBUCIÓN DE LA CUSTODIA COMPARTIDA
3. INCONVENIENTES Y VENTAJAS DEL RÉGIMEN DE CUSTODIA COMPARTIDA
7.3.1. Data linkage and interoperability
The ability of data systems to be able to communicate and share information is critical to increase efficiency and reduce duplication. In recent years the importance of data linkage or interoperability has been highlighted. Data interoperability is defined as “the ability of two or more systems or components to exchange information and to use the information that has been exchanged”.393 Going forward the interoperability of data between CRVS and HMIS will be
important to improve coverage, accuracy and detail of the data.
Data linkage between different data sources is increasingly feasible, especially where electronic data systems have been designed with in-built data interoperability such as through the use of a common individual identifier (ID). This has the potential to improve the quality of birth outcome data, however, capturing the full range of these outcomes requires careful planning. The ability to link the mother’s and child’s unique ID can improve both the availability of birth outcome data, but also enables future inter-generational studies. Ideally assignment of a child’s ID could be done through antenatal clinic, thus allowing the tracking of all pregnancy outcomes including stillbirth.394 Where this is not possible, the child IDs should be assigned immediately at
birth as part of the birth notification process. The child ID could be assigned for both live and stillbirths, allowing comparable information to be collect as part of vital statistics.
It is important to acknowledge that data linkage adds another level of complexity to the data. Both a clear understanding of the data and guidance at each step of the data linkage are required to ensure that the data generated are reproducible, accurate and valid.395 Although the practice
of data linkage is common in Nordic countries, it is currently under-utilised, even in settings with high coverage of CRVS and electronic health information systems.396 However, recently several
Latin American countries have fully integrated their HMIS into CRVS with benefits in terms of enumeration of the population, but also to support care provision, health monitoring, identify service delivery gaps and inequities, and improve accountability. In Peru this has been achieved through the development of an on-line free system that registers newborns in the labour ward, providing them with a unique identifier which can be used in both the health and CRVS systems.397
New initiatives, such as OpenHIE which aims to improve health outcomes, especially in LMICs, through supporting pragmatic implementation of health data sharing architectures could play an important role in facilitating data availability for the user.398 However, impact will only be
seen if data users at a local level value and are able to access the data that they require in a timely manner.
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eRegistries can also play a role in improving data utility. eRegistries are “systems using information and communication technologies for the systematic longitudinal collection, storage, retrieval, analysis and dissemination, of uniform information on health determinants and outcomes of individual persons, to serve healthcare services, health surveillance, health education, knowledge, and research”.394 The potential for eRegistries to act as a backbone to
health information systems, increasing the interoperability within the system has been proposed. eRegistries can be used to identify and follow up all women accessing antenatal care without a birth outcome recorded in the system. However, to maximise this potential all stakeholders should be involved in this process including the community, women and families; healthcare providers: facility and community-based, Traditional Birth Attendants, private sector; and other systems collecting data on vital events: including village administration units and community volunteers.
In all cases data interoperability will be critical to ensure capture of every birth event and reduce duplication. Figure 7-3 shows the three main platforms, CRVS, HMIS and household surveys where outcome data to inform stillbirth, preterm and low birthweight estimates are collected. The orange arrows show the potential routes of communication between the three data platforms – through direct interoperability between HMIS and CRVS or via handheld health records for communication between the health system and household surveys, and the health system and CRVS.
Going forward it will be important to build interoperability into data systems. DHIS-2 tracker is one example where interoperability between registers is used to create an individual patient level ‘pregnancy e-registry’ where data are entered once and ‘tracked’ through the system at each visit from antenatal care, through delivery and postnatal care to child health services and immunisations. Interoperability between health data systems for example between HMIS and Logistics Management Information Systems, MPDSR and data from the private sector could increase the coverage and quality of data. In some cases, additional benefits can be achieved by building interoperability with external non health data systems, for example with CRVS systems.
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Figure 7-3 Data linkage and interoperability in birth outcome data
*registration and certification are two separate steps in most CRVS systems but are included here together to simplify as the focus here is on registration.
7.3.2. Data quality processes
The importance of data quality in ensuring social robustness of data and to improve use of data for action has been highlighted above. Data quality is an important function of any data collection system. The importance of routine data quality assurance systems for birth outcome data has been discussed above (Section 6.5). Such systems should be tailored to birth outcome data and developed alongside, and integrated into all data systems using the principles expanded in Section 6.5.399 Attention is required to prevent sub-optimal data quality during the
set up and organisation of the data collection system, alongside data quality assurance checks throughout data collection and actions to identified problems to facilitate data quality improvements.
Clear guidance should be developed on data quality checks and actions to be taken to address potential issues. These should include measures internal to the data system, such as the percentage of births with missing or non-valid entries, examining the data distributions/outlier analysis, and comparison to previous trends. Where feasible, data can be benchmarked against
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an external source. Implementation of data quality processes will require clear guidance to be drawn up, and these processes included in both pre-service and refresher trainings for all data platforms. The development of a short set of birth outcome specific data quality indicators could be a useful tool to facilitate data improvement. These indicators should include both coverage of data and measures internal to the data system such as the proportion of births with missing or non-valid entries, data distributions and comparison to previous trends, and where feasible benchmarking against an external source (Section 6.5). These indicators could be included in a data quality report which could be communicated to data systems to facilitate action to address quality concerns, and also to data users to increase the social robustness of the data e.g. withhold specific data from final reports where data concerns are present.
Implementation of data quality processes will vary across data platforms. For HMIS mortality data investment in building local analytical capacity, regular national audits of perinatal mortality data, development of improved pre- and in-service perinatal data training and strengthening Maternal and Perinatal Death Surveillance and Response, where possible linked to pregnancy registries, could be important first steps to improved data quality.296
A good understanding of data flow through a data system is required to identify potential bottlenecks and develop tailored data quality guidance.400 Frameworks have been a useful tool
to improve this understanding. For example, within the health system, the Performance of Routine Information System Management (PRISM) framework developed by MEASURE evaluation seeks to promote continuous evaluation and data improvement through the development of performance targets, tracking progress, and knowledge management.379 These
frameworks could be refined to specifically address the challenges of perinatal data.
The increasingly widespread use of electronic data systems has the potential to simplify the running of routine data quality checks, as these can be easily integrated into the system. They can be programmed to allow validation of the data entry for each data element e.g. that the entry is in the correct format, and within a plausible pre-defined range. Data validation rules can be used to ensure internal consistency of data elements in an individual record e.g. an individual entry cannot be both a stillbirth and a neonatal death. Data checks on aggregated data detailed above such as missing values, examining the data distributions and benchmarking/ triangulating against external data sources can be undertaken in a more time-sensitive manner, to enable timely investigation and clinical action or correction of data where required. DHIS2’s quality tool is an example of such an inbuilt system, with easily generated dashboards to facilitate the communication of the information to the user,401,402 and there is some evidence that mhealth
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