Developing A Central Analytic Repository To Improve Decision Making By Stakeholders

Developing A Central Analytic Repository To Improve Decision Making By Stakeholders | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Developing A Central Analytic Repository To Improve Decision Making By Stakeholders Ime Asangansi, Emmanuel Meribole, Anthony Adoghe, Chiamaka Ajaka, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1967915/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The rise in data analytics has resulted in the need for data to be pooled into centralized large-scale repositories to support more organized analytics. In the health sector, housing health data in a central analytic repository makes it easier for policymakers to access and make faster, more efficient informed decisions that impact the population, especially in cases of emergencies and disease outbreaks. Our study aimed to develop a centralized health data analytics repository for Nigeria called the Multi-Source Data Analytics and Triangulation (MSDAT) platform to improve decision-making by stakeholders. Methods The MSDAT design and development was a data and user-centred process guided and informed by the perspectives and requirements of analysts and stakeholders from the Federal Ministry of Health, Nigeria. The inclusion of health indicators and data sources on the platform was based on: (1) national relevance (2) global health interest (3) availability of datasets and (4) specific requests from stakeholders. The first version of the platform was developed and iteratively revised based on stakeholder feedback. Results We developed the MSDAT for the purpose of consolidating health-related data from various data sources. It has 4 interactive sections for; (1) indicator comparison across routine and non-routine data sources (2) indicator comparison across states and local government areas (3) geopolitical zonal analysis of indicators (4) multi-indicator comparisons across states. Conclusion The MSDAT is a revolutionary platform essential to the improvement of health data quality. By transparently visualizing data and trends across multiple sources, data quality and use are brought to focus to reduce variations between different data sources over time and improve the overall understanding of key trends and progress within the health sector. Hence, the platform should be fully adopted and utilized at all levels of governance. It should also be expanded to accommodate other data sources and indicators that cut across all health system areas. Figures Figure 1 Background The implementation of effective health policies requires the use of data that has been analyzed, simplified and contextualized. Therefore, creating a central space for health data analytics is an approach that can facilitate quick access to the right information to shape policy decisions (Lavis et al., 2004). A considerable gap exists between health data and the formulation and implementation of health policies particularly in low and middle-income countries (LMICs). Although research already plays its role in the area of policymaking, informed national health policies can be improved through an emphasis on healthcare analytics. Furthermore, the complex nature of decision-making requires inputs from a broad analytic base which transcends basic data analysis and includes analytical knowledge generated from local evidence and good practices. In recent years, there has been an exponential increase in data which has led to a considerable rise in data analytics. This has resulted in calls from experts to ensure that this data is pooled into a centralized large-scale repository to support more organized analytics (Prainsack & Buyx, 2013; Steinsbekk et al. , 2013). A data repository is a huge database infrastructure which gathers, stores and manages varying data sets for analysis, distribution and reporting (Naeem, 2020). Having a central analytic repository has numerous benefits including being able to store, manage, access and manipulate stored data at any point in time. In the health sector, policymakers stand a lot to gain from having access to a central analytic repository as health data/information is made available at their fingertips, thereby allowing them to extract insights and make informed decisions to shape health policies at various administrative levels. The Role of Central Analytic Repositories in analytics and decision making A central analytic repository (CAR) is a collection of stored data from existing databases integrated into one so that they can be easily accessed. A CAR can be likened to a conventional library but in this case, physical space is not required. It is essentially created by integrating the data from all available sources thus making it easy to organize, secure and analyze the data (Ma, 2019). Health data in a CAR makes it easier for policymakers to access the data and make informed decisions that will impact the health sector. Decisions can also be made faster and more efficiently, especially in cases of emergencies and disease outbreaks. Benefits of a centralized analytic repository in the health sector Enhances policy decisions: A CAR can determine the performance of an entire healthcare delivery system, due to the fact that policymakers can have access to comprehensive, accurate, and better-organized data. By making data from various sources available in one location, policymakers can make data-backed decisions. Centralized storage and maintenance of data integrity: Data integration through a central analytic repository allows for users to make changes to the data, and these changes are reflected in real-time throughout the healthcare system. Data integrity is maximised as the whole database is stored at a single location. This means that it is easier to coordinate the data and ensure good data quality in terms of data accuracy and consistency. Increases data quality and accuracy: A central repository offers trustworthy data in order to produce accurate trend analysis due to the data being consistently updated and standardised from a central database. Data triangulation: Data triangulation can provide insights into multi-systemic issues in the health space. A centralised analytic repository facilitates the availability of data for cross-programmatic comparisons and triangulation. Big data analytics and machine learning: The use of central repositories can help generate analysis and insights from big data and create machine learning models to predict outcomes based on already existing data points. Reduces redundancies and saves time: The elimination of obsolete information reduces the time needed to review and make decisions leading to an increase in productivity. This enhanced collaboration throughout the health sector ultimately saves decision-makers time. Maintains a comprehensive data history and security: Since all the data is in one place, there can be stronger security measures around it. So, the centralised database is much more secure. Disadvantages Since all the data is at one location, it takes more time to search and access it. If there is a challenge with internet connectivity, this process takes even more time. There may be a lot of data access traffic for a centralised database. Increased traffic may cause database performance issues. The database may require periodic expansions in its capacity to handle multiple requests. Since all the data is at the same location, if multiple users try to access it simultaneously it creates a problem. This may reduce the efficiency of the system. Without a database recovery measure in place, a system failure will most likely result in the destruction of all data in the database. Decision-making in the Nigerian health sector: The data accessibility problem Although there is a lack of evidence to adequately describe the extent of inaccessibility to comprehensive health data, it remains a limitation for Nigeria’s policy implementation and health sector growth. Poor knowledge of data demand and use for health planning and resource allocation is another limiting factor. Data replaces assumptions and allows researchers, clinicians and policymakers to provide informed decisions based on real case studies. In situations where there are multiple data sources for population health metrics, storing and comparing health data from these different sources provides a comprehensive view of the health status of the populace. Innovations to improve data accessibility: The Centralised Data Repository Few innovations exist to address issues of health data accessibility in Nigeria. Most platforms in existence were designed at a global level to provide information related to clinical programmes and services (Uneke et al., 2019). These kinds of resources include; the Health Systems Evidence repository, the Health Technology Assessment Database, the Evidence to Policy Network (EVIPNet), the Virtual Health Library, and the Physician Data Query (PDQ)-Evidence repository. However, in Nigeria, there had never been a resource built for the purpose of aggregating all health-related data, until the development of the Multi-Source Data Analytic and Triangulation (MSDAT) platform. The MSDAT platform was built by Nigeria’s Federal Ministry of Health (FMoH) as a solution to the challenge of data availability and accessibility among health agencies and stakeholders in the country. The MSDAT platform provides a single transparent view of key health indicators from multiple data sources. Recognizing that data quality, trends and interpretation depend on the data source and methodology, the platform offers comparisons of key metrics across three categories of data sources, namely; routine, surveys, and global estimates. Methodology: Building A Centralised Health Data Analytics Repository For Nigeria Conception The eHealth for Everyone Foundation (E4E) team and the Department of Health Planning, Research and Statistics (DHPRS) had collaboratively highlighted some issues that severely hamper the use of data in the country, they include; Limited time, effort and skill to conduct data analysis Poor accessibility and visibility of health data Insufficient quality information Low trust in routinely collected data No comprehensive analysis of the available health data sets and no comprehensive integrated platform to facilitate such analysis With the presence of these issues, the existence of multiple data sources and little trust in the routinely collected data on the National Health Management Information System (NHMIS), the FMoH recognized the need to have a tool to mitigate these gaps and meet these data analytics needs. To this end, the MSDAT was conceptualised to provide data on key indicators and enable comparisons against various data sources. In the conception phase, several requirements were documented, for example; Comparison of indicators across routine and non-routine data sources. Comparison of indicators across states and local government areas (LGAs) Geopolitical zonal analysis of indicators Multi-indicator comparisons across states Design and Development Based on the stated requirements, several mockups were developed to facilitate reviews and adequately meet stakeholder (DHPRS) needs. The design phase took a very pragmatic approach to ensure that the platform is simple to use and easy to understand. Our approach was guided by the 9 principles of digital development (Principles for digital development, 2017). Principle 1: Designing with the users In designing the mockups, we developed personas of the primary users of the platform, to understand their skills, limitations and preferences. This was achieved through conversations, observations and feedback from the users. Principle 2: Understanding the existing ecosystem This principle helped us to understand the use cases and application of the platform as a national tool for health data analytics. With a full understanding of the key players and users, we were able to navigate the best design and implementation strategies to establish ownership of the platform. Knowledge of the Nigerian health information system, major players in the health sector and the challenges of the health sector as regards data use, guided the design of the platform. Principle 3: Design for scale The system architecture was developed using a framework that allows for scalability. With a flexible system architecture, the platform is able to accommodate more data sources and indicators across different health system areas. Designing flexible data systems also allows for adaptability to new use cases. Principle 4: Built for sustainability The success of any project implementation depends on its adaptability and ownership. With this in mind, we designed the platform to be self-teaching through tutorials and tour guides accessible on the platform. This was in addition to the development of training materials for primary users at the FMoH and partner agencies. Principle 5: Be data-driven The availability of quality data was a driving force behind the design and implementation of the MSDAT platform. The design was optimised to simplify the presentation of analysed data with applicable disaggregations. Principle 6: Open standards, open data, open-source and open innovation The E4E team designed the MSDAT platform using open standards and innovative approaches. The platform encourages the use of data that is not readily available on an open web page, by providing a single transparent view of key health indicators from several data sources. Principle 7: Reuse and improve All feedback from stakeholders and partners was documented and taken into consideration to improve the platform development. To improve on any form of expansion, the MSDAT was developed in several modules that can be easily modified when a need for expansion arises. As new challenges arise in the deployment and design of the MSDAT platform, provisions were made for adjustment and modifications as the case may be. Principle 8: Addressing privacy and security At the time of the initial development, the platform did not make use of personal data and did not breach any security measures. Principle 9: Ensuring collaborative efforts To encourage the use and ownership of the platform, several stakeholder meetings and workshops were organised to demonstrate the use and application of the platform. This further strengthened the transparency of the platform and encouraged partnerships and intersectoral collaboration. The MSDAT platform was designed for both public and private agencies interested in the Nigerian health system to utilise. MSDAT System Architecture The system architecture of the MSDAT platform is made up of 7 major components (diagrammatically represented in the image below), namely: MSDAT Web platform: This component enables users to interact with available datasets through intuitive visualisations and allows them to download and share data. MSDAT Application Programming Interface (API): The MSDAT API component is the link between the platform and the database where all data is stored. It manages the relationships between functionalities on the platform and fetches data for the visualisations. Indicator Database: This component stores and manages the indicator metadata within a relational database and interacts with the MSDAT Data Management Interface. MSDAT Data Management Interface (DMI): The DMI is a relational database management system that manages the data for the platform and other components. With the DMI, authorised users can create, upload, delete, review and extract data. MSDAT mobile application: The MSDAT mobile application enables users to access the dashboard data and visualisations via mobile devices (phones, tablets, etc). Artificial Intelligence (AI) and Natural Language Processing (NLP): With the AI and NLP component, for example, users can make use of search terms and the system would intelligently return results of applicable indicators of interest to the user. External API engine: This component allows the platform to interact with other databases (data sources) via their APIs. This engine also transforms the external data into the data structure that the MSDAT DMI can consume for the dashboard visualisations. Engagement with M&E staff, Stakeholders and other partners To facilitate the development of the platform, design mockups were developed and shared with the DHPRS-M&E team to be reviewed. Based on the mock-up designs and implementations carried out after the review, the first version of the MSDAT dashboard was developed. With the involvement of all stakeholders and partners in the developmental phase, all suggestions were taken on board. This was achieved through constant conversations, observations and a series of stakeholder meetings. One of the major reasons for stakeholder engagement was to understand different perspectives to establish what had been done and what is needed for the MSDAT to be developed and implemented. Beyond the purpose of having key health indicator data in a single resource, the MSDAT platform provides ease in comparing data from various sources. Basically, with the development of the MSDAT, information is provided for all stakeholders in the health system, enabling them to make use of the data in making decisions for the Nigerian health system. The platform ensures transparency of health data. To address the issues of privacy and security, the stakeholders were made aware of how data was sourced, used, stored and shared. Development of selection criteria for data sources and indicators In order to govern the development and secure the credibility of the MSDAT platform, the E4E team developed selection criteria for the inclusion of health indicators and data sources on the MSDAT platform. These criteria were centred around; National relevance: An indicator or data source would be included if it would inform health issues of concern to the nation and/or if it helps to track health priorities from the National Health Strategic Development Plan (NHSDP). Global health interest: An indicator or data source would be considered if it speaks to global health interests, as long as the data is published by authorised entities in the health sector. Availability of datasets: Indicators with comprehensive datasets would be prioritised for inclusion in the platform. Specific requests from the DHPRS-FMoH and other relevant stakeholders. Institutionalization of the platform To establish the use of the MSDAT platform as the convention or norm for health data analytics - especially by the FMoH and partner agencies, we embedded the platform within the DHPRS page of the FMoH website. This increased the credibility, ownership and transparency of the platform. By making the MSDAT tool more accessible to various users, the MSDAT team is able to get feedback from all kinds of stakeholders and users on preferred functionalities and data to improve the quality and relevance of the platform. Another initiative was to provide introductions and training sessions across the health sector - at stakeholder committee meetings (including the Health Data Consultative Committee [HDCC] in December 2018) and technical working group discussions. Stakeholder perception The MSDAT platform was successfully presented on 29th November 2018 at the Health partners coordinating committee (HPCC) meeting with the Honourable Minister of Health in attendance. The presentations of the MSDAT at the HPCC and HDCC received positive reviews and feedback from health partners and stakeholders. A mobile version of the dashboard has been developed to improve access to the dashboard. After further reviews, the final version of the MSDAT platform was made ready for use and populated with relevant health data from credible sources. Lessons learned The implementation of the MSDAT platform is revolutionary. However, establishing the needed buy-in and use of the platform can easily be hindered by several bureaucratic processes and a lack of interest from its primary users. To mitigate such issues, E4E ensured that ownership of the MSDAT platform was established by the DHPRS-FMoH. Also, stakeholder engagement and participation are key, and in most cases, need to be frequent and consistent to establish and normalise the use of any innovation in the public sector. Recommendations Given the need for contextual evidence to be made available to policy and decision-makers, it has become imperative for central analytic repositories to be fully adopted and utilised at all levels of governance. It allows for ease of access to health data as well as serves as a one-stop-shop for information by professionals and stakeholders who need it. It is therefore recommended that the platform be continuously improved on and updated with other existing data sources that may be functioning in isolation. Furthermore, the resource should be expanded to accommodate other equally authoritative data sources and indicators that cut across all health system areas. Conclusion Policymakers in Nigeria require evidence-based health data from which to make informed decisions because a wide gap exists between policy making and health research. One sure way to the development of the health sector is through data sharing by health professionals, researchers, private organisations and government agencies. This will help reduce costs and improve the quality of research and data analysis. Central analytic repositories, such as the MSDAT are essential for the improvement of health data quality. By transparently visualising data and trends across multiple sources, data quality and its use are brought to focus so as to reduce variations that exist between different data sources over time and improve the overall understanding of key trends and progress within the health sector. Abbreviations AI API CAR DHPRS DMI E4E EVIPNet FMoH HDCC HPCC LGAs LMIC MSDAT M&E NHMIS NHSDP NLP PQD Artificial Intelligence Application Programming Interface Central Analytic Repository Department of Health Planning Research and Statistics Data Management Interface eHealth4everyone Evidence to Policy Network Federal Ministry of Health Health Data Consultative Committee Health partners coordinating committee Local Government Areas Lower- and Middle-Income Countries Multi-Source Data Analytics and Triangulation Platform Monitoring and Evaluation National Health Management Information System National Health Strategic Development Plan Natural Language Processing Physician Data Query Declarations Ethics approval and consent to participate: This is not applicable as the study did not involve human participants, human data or human tissue. Consent for publication: This is not applicable as this study does not contain data from any individual person. Availability of data and materials: This manuscript does not contain any data, however the repository the manuscript describes, the MSDAT, can be accessed here: https://msdat.fmohconnect.gov.ng/central_analytics Competing interests: The authors declare that they have no competing interests. Funding: Supported by a grant from the Bill and Melinda Gates Foundation (BMGF). The opinions expressed in this paper are those of the authors and the study sponsor had no role in study design, the writing of the report; and the decision to submit the manuscript for publication. Authors' contributions: All authors reviewed and approved the submitted manuscript. Acknowledgements: The authors thank/we’d like to thank our stakeholders within the health sector for valuable comments, suggestions and insights leading to the development of the platform and by extension, this manuscript. Authors' information (optional) eHealth4everyone, Area 11, 8 Kukawa close, off Gimbiya St, Garki 900247, Abuja, Nigeria Ime Asangansi, Chiamaka Ajaka, Ifeoluwa Noiki, Doosuur Adebusola Shiishi-Gyer, Abdulqudus Sanni Office of the Head of Civil Services of the Federation, Abuja/FCT, Nigeria Emmanuel Meribole Federal Ministry of Health, Abuja/FCT, Nigeria Anthony Adoghe References Lavis JN, Posada FB, Haines A, Osei E. Use of research to inform public policymaking. The Lancet. 2004 Oct;364(9445):1615–21. Broekstra R, Maeckelberghe ELM, Stolk RP. Written informed consent in health research is outdated. The European Journal of Public Health. 2016 Nov 3;ckw198. ‌Steinsbekk KS, Kåre Myskja B, Solberg B. Broad consent versus dynamic consent in biobank research: Is passive participation an ethical problem? European Journal of Human Genetics [Internet]. 2013 Jan 9;21(9):897–902. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3746258/ Manyazewal T. Using the World Health Organization health system building blocks through survey of healthcare professionals to determine the performance of public healthcare facilities. Archives of Public Health. 2017 Aug 31;75(1). Saunders PA, Wilhelm EE, Lee S, Merkhofer E, Shoulson I. Data sharing for public health research: A qualitative study of industry and academia. Communication and Medicine. 2015 Aug 17;11(2):179–87. Prainsack B, Buyx A. A SOLIDARITY-BASED APPROACH TO THE GOVERNANCE OF RESEARCH BIOBANKS. Medical Law Review. 2013 Jan 16;21(1):71–91. Schwalbe N, Wahl B. Artificial intelligence and the future of global health. The Lancet. 2020 May;395(10236):1579–86. Wahl B, Cossy-Gantner A, Germann S, Schwalbe NR. Artificial intelligence (AI) and global health: how can AI contribute to health in resource-poor settings? BMJ Global Health [Internet]. 2018 Aug;3(4):e000798. Available from: https://gh.bmj.com/content/3/4/e000798 World Health Organization. Solidarity Call to Action to Realize Equitable Global Access to COVID-19 Health Technologies through Pooling of Knowledge, Intellectual Property and Data. WHO (2020). The Center for Policy Impact in Global Health. Intensified Multilateral Cooperation on Global Public Goods for Health: Three Opportunities for Collective Action. Durham, NC: Duke University (2018). Walport M, Brest P. Sharing research data to improve public health. The Lancet [Internet]. 2011 Feb [cited 2019 Apr 22];377(9765):537–9. Available from: https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(10)62234-9/fulltext DOI Marketing Brochure, International DOI Foundation. available http://bit.ly/2KU4HsK Ma X. Data Repository. Encyclopedia of Big Data. 2017;1–4. http://dx.doi.org/10.1007/978-3-319-32001-4_59-1 AHRQ. 5. Improving Data Collection across the Health Care System | Agency for Healthcare Research & Quality [Internet]. Ahrq.gov. 2000. Available from: https://www.ahrq.gov/research/findings/final-reports/iomracereport/reldata5.html Tilahun B, Teklu A, Mancuso A, Endehabtu BF, Gashu KD, Mekonnen ZA. Using health data for decision-making at each level of the health system to achieve universal health coverage in Ethiopia: the case of an immunization programme in a low-resource setting. Health Research Policy and Systems. 2021 Aug;19(S2). Matthews K. How Centralized Data Improves the Health Care Industry [Internet]. info.cgcompliance.com. Available from: https://info.cgcompliance.com/blog/how-centralized-data-improves-the-health-care-industry Naeem T. Data Repository: Importance, Challenges, and Best Practices [Internet]. Astera. 2020. Available from: https://www.astera.com/type/blog/data-repository/ 1. Oliva SZ, Felipe JC. Optimizing Public Healthcare Management Through a Data Warehousing Analytical Framework. IFAC-PapersOnLine. 2018;51(27):407–12. 1. Uneke CJ, Langlois EV, Uro-Chukwu HC, Chukwu J, Ghaffar A. Fostering access to and use of contextualised knowledge to support health policy-making: lessons from the Policy Information Platform in Nigeria. Health Research Policy and Systems. 2019 Apr 8;17(1). Principles for digital development, 2017. Available digitalprinciples.org Lavis JN, Wilson MG, Moat KA, Hammill AC, Boyko JA, Grimshaw JM, et al. Developing and refining the methods for a “one-stop shop” for research evidence about health systems. Health Research Policy and Systems. 2015 Feb 25;13(1). Broekstra R, Aris-Meijer J, Maeckelberghe E, Stolk R, Otten S. Trust in Centralized Large-Scale Data Repository: A Qualitative Analysis. Journal of Empirical Research on Human Research Ethics. 2019 Nov 18;155626461988836. Additional Declarations No competing interests reported. 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Therefore, creating a central space for health data analytics is an approach that can facilitate quick access to the right information to shape policy decisions (Lavis \u003cem\u003eet al.,\u003c/em\u003e 2004). A considerable gap exists between health data and the formulation and implementation of health policies particularly in low and middle-income countries (LMICs). Although research already plays its role in the area of policymaking, informed national health policies can be improved through an emphasis on healthcare analytics. Furthermore, the complex nature of decision-making requires inputs from a broad analytic base which transcends basic data analysis and includes analytical knowledge generated from local evidence and good practices.\u003c/p\u003e\n\u003cp\u003eIn recent years, there has been an exponential increase in data which has led to a considerable rise in data analytics. This has resulted in calls from experts to ensure that this data is pooled into a centralized large-scale repository to support more organized analytics (Prainsack \u0026amp; Buyx, 2013; Steinsbekk \u003cem\u003eet al.\u003c/em\u003e, 2013). A data repository is a huge database infrastructure which gathers, stores and manages varying data sets for analysis, distribution and reporting (Naeem, 2020). Having a central analytic repository has numerous benefits including being able to store, manage, access and manipulate stored data at any point in time. In the health sector, policymakers stand a lot to gain from having access to a central analytic repository as health data/information is made available at their fingertips, thereby allowing them to extract insights and make informed decisions to shape health policies at various administrative levels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe Role of Central Analytic Repositories in analytics and decision making\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA central analytic repository (CAR) is a collection of stored data from existing databases integrated into one so that they can be easily accessed. A CAR can be likened to a conventional library but in this case, physical space is not required. It is essentially created by integrating the data from all available sources thus making it easy to organize, secure and analyze the data (Ma, 2019). Health data in a CAR makes it easier for policymakers to access the data and make informed decisions that will impact the health sector. Decisions can also be made faster and more efficiently, especially in cases of emergencies and disease outbreaks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBenefits of a centralized analytic repository in the health sector\u003c/strong\u003e\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eEnhances policy decisions: A CAR can determine the performance of an entire healthcare delivery system, due to the fact that policymakers can have access to comprehensive, accurate, and better-organized data. By making data from various sources available in one location, policymakers can make data-backed decisions.\u003c/li\u003e\n \u003cli\u003eCentralized storage and maintenance of data integrity: Data integration through a central analytic repository allows for users to make changes to the data, and these changes are reflected in real-time throughout the healthcare system. Data integrity is maximised as the whole database is stored at a single location. This means that it is easier to coordinate the data and ensure good data quality in terms of data accuracy and consistency.\u003c/li\u003e\n \u003cli\u003eIncreases data quality and accuracy: A central repository offers trustworthy data in order to produce accurate trend analysis due to the data being consistently updated and standardised from a central database.\u003c/li\u003e\n \u003cli\u003eData triangulation: Data triangulation can provide insights into multi-systemic issues in the health space. A centralised analytic repository facilitates the availability of data for cross-programmatic comparisons and triangulation.\u003c/li\u003e\n \u003cli\u003eBig data analytics and machine learning: The use of central repositories can help generate analysis and insights from big data and create machine learning models to predict outcomes based on already existing data points.\u003c/li\u003e\n \u003cli\u003eReduces redundancies and saves time: The elimination of obsolete information reduces the time needed to review and make decisions leading to an increase in productivity. This enhanced collaboration throughout the health sector ultimately saves decision-makers time.\u003c/li\u003e\n \u003cli\u003eMaintains a comprehensive data history and security: Since all the data is in one place, there can be stronger security measures around it. So, the centralised database is much more secure.\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eDisadvantages\u003c/strong\u003e\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eSince all the data is at one location, it takes more time to search and access it. If there is a challenge with internet connectivity, this process takes even more time.\u003c/li\u003e\n \u003cli\u003eThere may be a lot of data access traffic for a centralised database. Increased traffic may cause database performance issues. The database may require periodic expansions in its capacity to handle multiple requests.\u003c/li\u003e\n \u003cli\u003eSince all the data is at the same location, if multiple users try to access it simultaneously it creates a problem. This may reduce the efficiency of the system.\u003c/li\u003e\n \u003cli\u003eWithout a database recovery measure in place, a system failure will most likely result in the destruction of all data in the database.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eDecision-making in the Nigerian health sector: The data accessibility problem\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlthough there is a lack of evidence to adequately describe the extent of inaccessibility to comprehensive health data, it remains a limitation for Nigeria\u0026rsquo;s policy implementation and health sector growth. Poor knowledge of data demand and use for health planning and resource allocation is another limiting factor. Data replaces assumptions and allows researchers, clinicians and policymakers to provide informed decisions based on real case studies. In situations where there are multiple data sources for population health metrics, storing and comparing health data from these different sources provides a comprehensive view of the health status of the populace.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInnovations to improve data accessibility: The Centralised Data Repository\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFew innovations exist to address issues of health data accessibility in Nigeria. Most platforms in existence were designed at a global level to provide information related to clinical programmes and services (Uneke \u003cem\u003eet al.,\u003c/em\u003e 2019). These kinds of resources include; the Health Systems Evidence repository, the Health Technology Assessment Database, the Evidence to Policy Network\u0026nbsp;(EVIPNet), the Virtual Health Library, and the Physician Data Query (PDQ)-Evidence repository. However, in Nigeria, there had never been a resource built for the purpose of aggregating all health-related data, until the development of the Multi-Source Data Analytic and Triangulation (MSDAT) platform.\u003c/p\u003e\n\u003cp\u003eThe MSDAT platform was built by Nigeria\u0026rsquo;s Federal Ministry of Health (FMoH) as a solution to the challenge of data availability and accessibility among health agencies and stakeholders in the country. The MSDAT platform provides a single transparent view of key health indicators from multiple data sources. Recognizing that data quality, trends and interpretation depend on the data source and methodology, the platform offers comparisons of key metrics across three categories of data sources, namely; routine, surveys, and global estimates.\u003c/p\u003e"},{"header":"Methodology: Building A Centralised Health Data Analytics Repository For Nigeria","content":"\u003ch2\u003eConception\u003c/h2\u003e\n\u003cp\u003eThe eHealth for Everyone Foundation (E4E) team and the Department of Health Planning, Research and Statistics (DHPRS) had collaboratively highlighted some issues that severely hamper the use of data in the country, they include;\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eLimited time, effort and skill to conduct data analysis\u003c/li\u003e\n \u003cli\u003ePoor accessibility and visibility of health data\u003c/li\u003e\n \u003cli\u003eInsufficient quality information\u003c/li\u003e\n \u003cli\u003eLow trust in routinely collected data\u003c/li\u003e\n \u003cli\u003eNo comprehensive analysis of the available health data sets and no comprehensive integrated platform to facilitate such analysis\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eWith the presence of these issues, the existence of multiple data sources and little trust in the routinely collected data on the National Health Management Information System (NHMIS), the FMoH recognized the need to have a tool to mitigate these gaps and meet these data analytics needs.\u003c/p\u003e\n\u003cp\u003eTo this end, the MSDAT was conceptualised to provide data on key indicators and enable comparisons against various data sources. In the conception phase, several requirements were documented, for example;\u0026nbsp;\u003c/p\u003e\n\u003col start=\"1\" type=\"a\"\u003e\n \u003cli\u003eComparison of indicators across routine and non-routine data sources.\u003c/li\u003e\n \u003cli\u003eComparison of indicators across states and local government areas (LGAs)\u003c/li\u003e\n \u003cli\u003eGeopolitical zonal analysis of indicators\u003c/li\u003e\n \u003cli\u003eMulti-indicator comparisons across states\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003eDesign and Development\u003c/h2\u003e\n\u003cp\u003eBased on the stated requirements, several mockups were developed to facilitate reviews and adequately meet stakeholder (DHPRS) needs. The design phase took a very pragmatic approach to ensure that the platform is simple to use and easy to understand. Our approach was guided by the 9 principles of digital development (Principles for digital development, 2017).\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003ePrinciple 1: Designing with the users\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eIn designing the mockups, we developed personas of the primary users of the platform, to understand their skills, limitations and preferences. This was achieved through conversations, observations and feedback from the users.\u003c/p\u003e\n\u003col start=\"2\" type=\"1\"\u003e\n \u003cli\u003ePrinciple 2: Understanding the existing ecosystem\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThis principle helped us to understand the use cases and application of the platform as a national tool for health data analytics. With a full understanding of the key players and users, we were able to navigate the best design and implementation strategies to establish ownership of the platform. Knowledge of the Nigerian health information system, major players in the health sector and the challenges of the health sector as regards data use, guided the design of the platform.\u003c/p\u003e\n\u003col start=\"3\" type=\"1\"\u003e\n \u003cli\u003ePrinciple 3: Design for scale\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe system architecture was developed using a framework that allows for scalability. With a flexible system architecture, the platform is able to accommodate more data sources and indicators across different health system areas. Designing flexible data systems also allows for adaptability to new use cases.\u003c/p\u003e\n\u003col start=\"4\" type=\"1\"\u003e\n \u003cli\u003ePrinciple 4: Built for sustainability\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe success of any project implementation depends on its adaptability and ownership. With this in mind, we designed the platform to be self-teaching through tutorials and tour guides accessible on the platform. This was in addition to the development of training materials for primary users at the FMoH and partner agencies.\u003c/p\u003e\n\u003col start=\"5\" type=\"1\"\u003e\n \u003cli\u003ePrinciple 5: Be data-driven\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe availability of quality data was a driving force behind the design and implementation of the MSDAT platform. The design was optimised to simplify the presentation of analysed data with applicable disaggregations.\u003c/p\u003e\n\u003col start=\"6\" type=\"1\"\u003e\n \u003cli\u003ePrinciple 6: Open standards, open data, open-source and open innovation\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe E4E team designed the MSDAT platform using open standards and innovative approaches. The platform encourages the use of data that is not readily available on an open web page, by providing a single transparent view of key health indicators from several data sources.\u003c/p\u003e\n\u003col start=\"7\" type=\"1\"\u003e\n \u003cli\u003ePrinciple 7: Reuse and improve\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eAll feedback from stakeholders and partners was documented and taken into consideration to improve the platform development. To improve on any form of expansion, the MSDAT was developed in several modules that can be easily modified when a need for expansion arises. As new challenges arise in the deployment and design of the MSDAT platform, provisions were made for adjustment and modifications as the case may be.\u003c/p\u003e\n\u003col start=\"8\" type=\"1\"\u003e\n \u003cli\u003ePrinciple 8: Addressing privacy and security\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eAt the time of the initial development, the platform did not make use of personal data and did not breach any security measures.\u003c/p\u003e\n\u003col start=\"9\" type=\"1\"\u003e\n \u003cli\u003ePrinciple 9: Ensuring collaborative efforts\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eTo encourage the use and ownership of the platform, several stakeholder meetings and workshops were organised to demonstrate the use and application of the platform. This further strengthened the transparency of the platform and encouraged partnerships and intersectoral collaboration. The MSDAT platform was designed for both public and private agencies interested in the Nigerian health system to utilise.\u003c/p\u003e\n\u003ch2\u003eMSDAT System Architecture\u003c/h2\u003e\n\u003cp\u003eThe system architecture of the MSDAT platform is made up of 7 major components (diagrammatically represented in the image below), namely:\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eMSDAT Web platform: This component enables users to interact with available datasets through intuitive visualisations and allows them to download and share data.\u003c/li\u003e\n \u003cli\u003eMSDAT Application Programming Interface (API): The MSDAT API component is the link between the platform and the database where all data is stored. It manages the relationships between functionalities on the platform and fetches data for the visualisations.\u003c/li\u003e\n \u003cli\u003eIndicator Database: This component stores and manages the indicator metadata within a relational database and interacts with the MSDAT Data Management Interface.\u003c/li\u003e\n \u003cli\u003eMSDAT Data Management Interface (DMI): The DMI is a relational database management system that manages the data for the platform and other components. With the DMI, authorised users can create, upload, delete, review and extract data.\u003c/li\u003e\n \u003cli\u003eMSDAT mobile application: The MSDAT mobile application enables users to access the dashboard data and visualisations via mobile devices (phones, tablets, etc).\u003c/li\u003e\n \u003cli\u003eArtificial Intelligence (AI) and Natural Language Processing (NLP): With the AI and NLP component, for example, users can make use of search terms and the system would intelligently return results of applicable indicators of interest to the user.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eExternal API engine: This component allows the platform to interact with other databases (data sources) via their APIs. This engine also transforms the external data into the data structure that the MSDAT DMI can consume for the dashboard visualisations.\u003c/p\u003e\n\u003ch2\u003eEngagement with M\u0026amp;E staff, Stakeholders and other partners\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eTo facilitate the development of the platform, design mockups were developed and shared with the DHPRS-M\u0026amp;E team to be reviewed. Based on the mock-up designs and implementations carried out after the review, the first version of the MSDAT dashboard was developed. With the involvement of all stakeholders and partners in the developmental phase, all suggestions were taken on board. This was achieved through constant conversations, observations and a series of stakeholder meetings. One of the major reasons for stakeholder engagement was to understand different perspectives to establish what had been done and what is needed for the MSDAT to be developed and implemented. Beyond the purpose of having key health indicator data in a single resource, the MSDAT platform provides ease in comparing data from various sources.\u003c/p\u003e\n\u003cp\u003eBasically, with the development of the MSDAT, information is provided for all stakeholders in the health system, enabling them to make use of the data in making decisions for the Nigerian health system. The platform ensures transparency of health data. To address the issues of privacy and security, the stakeholders were made aware of how data was sourced, used, stored and shared.\u003c/p\u003e\n\u003ch2\u003eDevelopment of selection criteria for data sources and indicators\u003c/h2\u003e\n\u003cp\u003eIn order to govern the development and secure the credibility of the MSDAT platform, the E4E team developed selection criteria for the inclusion of health indicators and data sources on the MSDAT platform. These criteria were centred around;\u0026nbsp;\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eNational relevance: An indicator or data source would be included if it would inform health issues of concern to the nation and/or if it helps to track health priorities from the National Health Strategic Development Plan (NHSDP).\u003c/li\u003e\n \u003cli\u003eGlobal health interest: An indicator or data source would be considered if it speaks to global health interests, as long as the data is published by authorised entities in the health sector.\u003c/li\u003e\n \u003cli\u003eAvailability of datasets: Indicators with comprehensive datasets would be prioritised for inclusion in the platform.\u003c/li\u003e\n \u003cli\u003eSpecific requests from the DHPRS-FMoH and other relevant stakeholders.\u003c/li\u003e\n\u003c/ol\u003e\n\u003ch2\u003eInstitutionalization of the platform\u003c/h2\u003e\n\u003cp\u003eTo establish the use of the MSDAT platform as the convention or norm for health data analytics - especially by the FMoH and partner agencies, we embedded the platform within the DHPRS page of the FMoH website. This increased the credibility, ownership and transparency of the platform. By making the MSDAT tool more accessible to various users, the MSDAT team is able to get feedback from all kinds of stakeholders and users on preferred functionalities and data to improve the quality and relevance of the platform. Another initiative was to provide introductions and training sessions across the health sector - at stakeholder committee meetings (including the Health Data Consultative Committee [HDCC] in December 2018) and technical working group discussions.\u003c/p\u003e\n\u003ch2\u003eStakeholder perception\u003c/h2\u003e\n\u003cp\u003eThe MSDAT platform was successfully presented on 29th November 2018 at the Health partners coordinating committee (HPCC) meeting with the Honourable Minister of Health in attendance. The presentations of the MSDAT at the HPCC and HDCC received positive reviews and feedback from health partners and stakeholders. A mobile version of the dashboard has been developed to improve access to the dashboard. After further reviews, the final version of the MSDAT platform was made ready for use and populated with relevant health data from credible sources.\u003c/p\u003e\n\u003ch2\u003eLessons learned\u003c/h2\u003e\n\u003cp\u003eThe implementation of the MSDAT platform is revolutionary. However, establishing the needed buy-in and use of the platform can easily be hindered by several bureaucratic processes and a lack of interest from its primary users. To mitigate such issues, E4E ensured that ownership of the MSDAT platform was established by the DHPRS-FMoH. Also, stakeholder engagement and participation are key, and in most cases, need to be frequent and consistent to establish and normalise the use of any innovation in the public sector.\u003c/p\u003e\n\u003ch2\u003eRecommendations\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eGiven the need for contextual evidence to be made available to policy and decision-makers, it has become imperative for central analytic repositories to be fully adopted and utilised at all levels of governance. It allows for ease of access to health data as well as serves as a one-stop-shop for information by professionals and stakeholders who need it. It is therefore recommended that the platform be continuously improved on and updated with other existing data sources that may be functioning in isolation. Furthermore, the resource should be expanded to accommodate other equally authoritative data sources and indicators that cut across all health system areas.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003ePolicymakers in Nigeria require evidence-based health data from which to make informed decisions because a wide gap exists between policy making and health research. One sure way to the development of the health sector is through data sharing by health professionals, researchers, private organisations and government agencies. This will help reduce costs and improve the quality of research and data analysis.\u003c/p\u003e \u003cp\u003eCentral analytic repositories, such as the MSDAT are essential for the improvement of health data quality. By transparently visualising data and trends across multiple sources, data quality and its use are brought to focus so as to reduce variations that exist between different data sources over time and improve the overall understanding of key trends and progress within the health sector.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"594\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"27.104377104377104%\"\u003e\n \u003cp\u003eAI \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003cp\u003eAPI \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eCAR \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eDHPRS \u0026nbsp;\u003c/p\u003e\n \u003cp\u003eDMI \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003cp\u003eE4E \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003cp\u003eEVIPNet \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eFMoH \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eHDCC \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003cp\u003eHPCC \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003cp\u003eLGAs \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003cp\u003eLMIC \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMSDAT \u0026nbsp;\u003c/p\u003e\n \u003cp\u003eM\u0026amp;E \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNHMIS \u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNHSDP \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNLP \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ePQD \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"72.89562289562289%\"\u003e\n \u003cp\u003eArtificial Intelligence\u003c/p\u003e\n \u003cp\u003eApplication Programming Interface\u003c/p\u003e\n \u003cp\u003eCentral Analytic Repository\u003c/p\u003e\n \u003cp\u003eDepartment of Health Planning Research and Statistics\u003c/p\u003e\n \u003cp\u003eData Management Interface\u003c/p\u003e\n \u003cp\u003eeHealth4everyone\u003c/p\u003e\n \u003cp\u003eEvidence to Policy Network\u003c/p\u003e\n \u003cp\u003eFederal Ministry of Health\u003c/p\u003e\n \u003cp\u003eHealth Data Consultative Committee\u003c/p\u003e\n \u003cp\u003eHealth partners coordinating committee\u003c/p\u003e\n \u003cp\u003eLocal Government Areas\u003c/p\u003e\n \u003cp\u003eLower- and Middle-Income Countries\u003c/p\u003e\n \u003cp\u003eMulti-Source Data Analytics and Triangulation Platform\u003c/p\u003e\n \u003cp\u003eMonitoring and Evaluation\u003c/p\u003e\n \u003cp\u003eNational Health Management Information System\u003c/p\u003e\n \u003cp\u003eNational Health Strategic Development Plan\u003c/p\u003e\n \u003cp\u003eNatural Language Processing\u003c/p\u003e\n \u003cp\u003ePhysician Data Query\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eEthics approval and consent to participate: This is not applicable as the study did not involve human participants, human data or human tissue.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eConsent for publication: This is not applicable as this study does not contain data from any individual person.\u003c/li\u003e\n \u003cli\u003eAvailability of data and materials: This manuscript does not contain any data, however the repository the manuscript describes, the MSDAT, can be accessed here:\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003ca href=\"https://msdat.fmohconnect.gov.ng/central_analytics\"\u003ehttps://msdat.fmohconnect.gov.ng/central_analytics\u003c/a\u003e\u003c/p\u003e\n\u003col start=\"4\" type=\"1\"\u003e\n \u003cli\u003eCompeting interests: The authors declare that they have no competing interests.\u003c/li\u003e\n \u003cli\u003eFunding: Supported by a grant from the Bill and Melinda Gates Foundation (BMGF). The opinions expressed in this paper are those of the authors and the study sponsor had no role in study design, the writing of the report; and the decision to submit the manuscript for publication.\u003c/li\u003e\n \u003cli\u003eAuthors\u0026apos; contributions: All authors reviewed and approved the submitted manuscript.\u003c/li\u003e\n \u003cli\u003eAcknowledgements: The authors thank/we\u0026rsquo;d like to thank our stakeholders within the health sector for valuable comments, suggestions and insights leading to the development of the platform and by extension, this manuscript.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eAuthors\u0026apos; information (optional)\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eeHealth4everyone, Area 11, 8 Kukawa close, off Gimbiya St, Garki 900247, Abuja, Nigeria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIme Asangansi, Chiamaka Ajaka, Ifeoluwa Noiki, Doosuur Adebusola Shiishi-Gyer, Abdulqudus Sanni\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOffice of the Head of Civil Services of the Federation, Abuja/FCT, Nigeria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEmmanuel Meribole\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFederal Ministry of Health, Abuja/FCT, Nigeria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnthony Adoghe\u003c/p\u003e"},{"header":"References","content":"\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eLavis JN, Posada FB, Haines A, Osei E. Use of research to inform public policymaking. The Lancet. 2004 Oct;364(9445):1615\u0026ndash;21.\u003c/li\u003e\n \u003cli\u003eBroekstra R, Maeckelberghe ELM, Stolk RP. Written informed consent in health research is outdated. The European Journal of Public Health. 2016 Nov 3;ckw198.\u003c/li\u003e\n \u003cli\u003e\u0026zwnj;Steinsbekk KS, K\u0026aring;re Myskja B, Solberg B. Broad consent versus dynamic consent in biobank research: Is passive participation an ethical problem? European Journal of Human Genetics [Internet]. 2013 Jan 9;21(9):897\u0026ndash;902. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3746258/\u003c/li\u003e\n \u003cli\u003eManyazewal T. Using the World Health Organization health system building blocks through survey of healthcare professionals to determine the performance of public healthcare facilities. Archives of Public Health. 2017 Aug 31;75(1).\u003c/li\u003e\n \u003cli\u003eSaunders PA, Wilhelm EE, Lee S, Merkhofer E, Shoulson I. Data sharing for public health research: A qualitative study of industry and academia. Communication and Medicine. 2015 Aug 17;11(2):179\u0026ndash;87.\u003c/li\u003e\n \u003cli\u003ePrainsack B, Buyx A. A SOLIDARITY-BASED APPROACH TO THE GOVERNANCE OF RESEARCH BIOBANKS. Medical Law Review. 2013 Jan 16;21(1):71\u0026ndash;91.\u003c/li\u003e\n \u003cli\u003eSchwalbe N, Wahl B. Artificial intelligence and the future of global health. The Lancet. 2020 May;395(10236):1579\u0026ndash;86.\u003c/li\u003e\n \u003cli\u003eWahl B, Cossy-Gantner A, Germann S, Schwalbe NR. Artificial intelligence (AI) and global health: how can AI contribute to health in resource-poor settings? BMJ Global Health [Internet]. 2018 Aug;3(4):e000798. Available from: https://gh.bmj.com/content/3/4/e000798\u003c/li\u003e\n \u003cli\u003eWorld Health Organization. Solidarity Call to Action to Realize Equitable Global Access to COVID-19 Health Technologies through Pooling of Knowledge, Intellectual Property and Data. WHO (2020).\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eThe Center for Policy Impact in Global Health. Intensified Multilateral Cooperation on Global Public Goods for Health: Three Opportunities for Collective Action. Durham, NC: Duke University (2018).\u003c/li\u003e\n \u003cli\u003eWalport M, Brest P. Sharing research data to improve public health. The Lancet [Internet]. 2011 Feb [cited 2019 Apr 22];377(9765):537\u0026ndash;9. Available from:\u0026nbsp;\u003ca href=\"https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(10)62234-9/fulltext\"\u003ehttps://www.thelancet.com/journals/lancet/article/PIIS0140-6736(10)62234-9/fulltext\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003eDOI Marketing Brochure, International DOI Foundation. available\u0026nbsp;\u003ca href=\"http://bit.ly/2KU4HsK\"\u003ehttp://bit.ly/2KU4HsK\u003c/a\u003e\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eMa X. Data Repository. Encyclopedia of Big Data. 2017;1\u0026ndash;4.\u0026nbsp;http://dx.doi.org/10.1007/978-3-319-32001-4_59-1\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e\n\u003col start=\"14\" type=\"1\"\u003e\n \u003cli\u003eAHRQ. 5. Improving Data Collection across the Health Care System | Agency for Healthcare Research \u0026amp; Quality [Internet]. Ahrq.gov. 2000. Available from:\u0026nbsp;\u003ca href=\"https://www.ahrq.gov/research/findings/final-reports/iomracereport/reldata5.html\"\u003ehttps://www.ahrq.gov/research/findings/final-reports/iomracereport/reldata5.html\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003eTilahun B, Teklu A, Mancuso A, Endehabtu BF, Gashu KD, Mekonnen ZA. Using health data for decision-making at each level of the health system to achieve universal health coverage in Ethiopia: the case of an immunization programme in a low-resource setting. Health Research Policy and Systems. 2021 Aug;19(S2).\u003c/li\u003e\n \u003cli\u003eMatthews K. How Centralized Data Improves the Health Care Industry [Internet]. info.cgcompliance.com. Available from:\u0026nbsp;\u003ca href=\"https://info.cgcompliance.com/blog/how-centralized-data-improves-the-health-care-industry\"\u003ehttps://info.cgcompliance.com/blog/how-centralized-data-improves-the-health-care-industry\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eNaeem T. Data Repository: Importance, Challenges, and Best Practices [Internet]. Astera. 2020. Available from:\u0026nbsp;\u003ca href=\"https://www.astera.com/type/blog/data-repository/\"\u003ehttps://www.astera.com/type/blog/data-repository/\u003c/a\u003e\u003c/p\u003e\n\u003col start=\"17\" type=\"1\"\u003e\n \u003cli\u003e1. Oliva SZ, Felipe JC. Optimizing Public Healthcare Management Through a Data Warehousing Analytical Framework. IFAC-PapersOnLine. 2018;51(27):407\u0026ndash;12.\u003c/li\u003e\n \u003cli\u003e1. Uneke CJ, Langlois EV, Uro-Chukwu HC, Chukwu J, Ghaffar A. Fostering access to and use of contextualised knowledge to support health policy-making: lessons from the Policy Information Platform in Nigeria. Health Research Policy and Systems. 2019 Apr 8;17(1).\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;Principles for digital development, 2017. Available digitalprinciples.org\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;Lavis JN, Wilson MG, Moat KA, Hammill AC, Boyko JA, Grimshaw JM, et al. Developing and refining the methods for a \u0026ldquo;one-stop shop\u0026rdquo; for research evidence about health systems. Health Research Policy and Systems. 2015 Feb 25;13(1).\u003c/li\u003e\n \u003cli\u003eBroekstra R, Aris-Meijer J, Maeckelberghe E, Stolk R, Otten S. Trust in Centralized Large-Scale Data Repository: A Qualitative Analysis. Journal of Empirical Research on Human Research Ethics. 2019 Nov 18;155626461988836.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-1967915/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1967915/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground\u003c/p\u003e\u003cp\u003eThe rise in data analytics has resulted in the need for data to be pooled into centralized large-scale repositories to support more organized analytics. In the health sector, housing health data in a central analytic repository makes it easier for policymakers to access and make faster, more efficient informed decisions that impact the population, especially in cases of emergencies and disease outbreaks. Our study aimed to develop a centralized health data analytics repository for Nigeria called the Multi-Source Data Analytics and Triangulation (MSDAT) platform to improve decision-making by stakeholders.\u003c/p\u003e\u003cp\u003eMethods\u003c/p\u003e\u003cp\u003eThe MSDAT design and development was a data and user-centred process guided and informed by the perspectives and requirements of analysts and stakeholders from the Federal Ministry of Health, Nigeria. The inclusion of health indicators and data sources on the platform was based on: (1) national relevance (2) global health interest (3) availability of datasets and (4) specific requests from stakeholders. The first version of the platform was developed and iteratively revised based on stakeholder feedback.\u003c/p\u003e\u003cp\u003eResults\u003c/p\u003e\u003cp\u003eWe developed the MSDAT for the purpose of consolidating health-related data from various data sources. It has 4 interactive sections for; (1) indicator comparison across routine and non-routine data sources (2) indicator comparison across states and local government areas (3) geopolitical zonal analysis of indicators (4) multi-indicator comparisons across states.\u003c/p\u003e\u003cp\u003eConclusion\u003c/p\u003e\u003cp\u003eThe MSDAT is a revolutionary platform essential to the improvement of health data quality. By transparently visualizing data and trends across multiple sources, data quality and use are brought to focus to reduce variations between different data sources over time and improve the overall understanding of key trends and progress within the health sector. Hence, the platform should be fully adopted and utilized at all levels of governance. It should also be expanded to accommodate other data sources and indicators that cut across all health system areas.\u003c/p\u003e","manuscriptTitle":"Developing A Central Analytic Repository To Improve Decision Making By Stakeholders","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-08-24 16:49:07","doi":"10.21203/rs.3.rs-1967915/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"83e20e93-1df9-4e0c-95c2-1bcdfd303831","owner":[],"postedDate":"August 24th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-07T09:09:26+00:00","versionOfRecord":[],"versionCreatedAt":"2022-08-24 16:49:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1967915","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1967915","identity":"rs-1967915","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}