Article Text
PDF
Statistics from Altmetric.com
SUMMARY BOX
Data is critical to effective governance of a mixed health system, where a mix of public and private entities deliver health-related goods and services.
Innovative technologies and data sources demonstrate the potential of a data-driven governance approach to deliver more client-centred, effective, responsive and accountable health systems.
There is a significant problem with a lack of widely available and high-quality data from the private health sector which has long been acknowledged. In many low-income and middle-income countries, some progress has been made to close the gap, but numerous challenges remain.
We believe that data-driven governance can shift the needle on the capacity of governments to use the WHO governance behaviours to drive performance improvements in health systems.
Introduction
Data are used in many different ways as part of the practice of health systems governance. These include informing policy design, resource allocations, impact evaluation, community engagement, trend analyses, maintaining public data sets and records, ensuring regulatory compliance and preventing fraud and corruption.1
Most countries have mixed health systems, where a mix of public and private entities deliver health-related information, goods and services. This may include a large variety of private health sector entities, from informal drug shops to not-for-profit providers, such as faith-based organisations, medium for-profit health facilities and large multinational private for-profit companies.2 Effective governance of mixed health systems involves identifying and solving health system policy problems, such as high out-of-pocket expenditures, service coverage gaps or poor service quality, which can lead to financial hardship—by forcing people to choose between health expenses and other necessities—and to health access inequity as well as poor health outcomes, hindering equitable, efficient and sustainable healthcare.
Having accurate and up-to-date data is therefore crucial for effective decision-making and problem-solving. It enables governments to identify and address issues promptly, allocate resources effectively, reduce costs and enhance overall healthcare system efficiency. Without accurate and up-to-date data and information, problems such as lack of access to quality services can go unnoticed. Diagnosing problems leading to poor health outcomes becomes difficult, and generating solutions may rely on assumptions and resort to guesswork.
The promise of data-driven health systems governance
Many low-income and middle-income countries (LMICs) have attempted to address health system information requirements through the collection of better and myriad types of data on the private sector; increasingly, this is collected in a routine manner, through national health information systems.3 Alongside these efforts, other sources of information may exist, such as programmatic, financial, geospatial, survey and other structured or unstructured data sets. Innovations in data collection and interoperability between information sources, as well as advanced analytics using machine learning and artificial intelligence techniques, are also increasingly available and being tested in LMICs as part of a broader toolkit of technological solutions. Despite these advances, data may not be used to govern the private sector, inform resource allocation to harness the reach of the private sector in expanding services to underserved communities or build understanding of the sectors’ resources, expertise and innovation within a county.
Government stewardship involves build understanding of the whole health system—both public and private—through information gathering and sharing. This encompasses all elements of public and private service provision and should ensure that the information is used effectively in policy design, strategy development, planning and resource allocation. For many governments, this will involve the collection of better and diverse types of data on the private sector and the development or improvement of systems to capture this in a routine manner. It means strengthening the government’s capacity to collect data from both the private and the public sector and to analyse it, to ensure these insights can be used to inform policies and plans at the national and subnational levels. It also means routinely sharing information with all health system actors through multiple channels.
Data-driven health systems governance leverages data, analytics and information technology to improve decision-making, access to and quality of care and governance processes.4 It involves collecting, analysing and using data and information to enhance the effectiveness, efficiency and transparency of policymaking, resource allocation, service delivery and governance tools and mechanisms. This approach also includes monitoring, evaluating and using predictive analytics for health system performance improvement. The ultimate goal of data-driven governance is to make governments and health systems more effective, responsive and accountable to the health needs of their populations.5
The promise of data-driven health systems governance is significant. Governments can bridge longstanding data and information gaps historically hindering effective governance of the entire health sector, both public and private.6 Through targeted interventions, governments can ensure the efficient allocation of resources that swiftly adapt to evolving circumstances and geographies. This allows governments to address policy problems, or issues with poor utilisation or quality of services based on evidence instead of opinions or anecdotal evidence. Consequently, governments can develop targeted solutions that tackle the root causes of health system problems, thereby optimising resource utilisation and leveraging private sector resources and capabilities.
Furthermore, data-driven health systems governance facilitates continuous monitoring and evaluation of health service interventions and their impact. Beyond operational benefits, it also promotes transparency and accountability. When decisions are grounded in data and analysis, they are easier to justify and communicate. Transparency allows a country’s population to understand the evidence behind policy choices and hold their governments accountable for their decisions. Through a more participatory and inclusive governance process, this can foster trust among all health system actors and stakeholders in both sectors.
The focus on the private sector
A lack of information of the operations of the private sector in healthcare represents one of the biggest constraints for many LMICs governments. Accessible and up-to-date data on the whole health system is critical for interoperability between the public and private systems, and for governance work, and yet is often lacking at the country level.
The scarcity of data is especially acute regarding the role of the private sector in health service delivery.7 Significant data gaps exist about the private sector and its operations for several reasons. Existing studies and surveys provide some explanations. They highlight challenges such as low compliance rates with regulatory requirements for reporting due to inadequate incentives, practical difficulties associated with both paper-based records systems and interoperability with reporting into national health information systems; privacy concerns about data collection, analysis and use; concerns from the private sector about the government using the collection of data for taxation and revenue collection rather than health purposes; as well as practical problems with collecting, integrating and aligning data from the large numbers of individual private providers who operate outside of the formal government reporting systems.
Given this, issues with private sector activities often go undetected, and opportunities to harness the private sector’s capacity, innovation and expertise for health service delivery are untapped. Without a comprehensive understanding of private sector service provision, governments cannot effectively govern their health systems because they are in the dark when deciding on appropriate policy approaches to incorporate the private sector in health. Innovations in data capture, improvements in interoperability among information sources and the adoption of advanced analytics, including machine learning and artificial intelligence techniques, are becoming increasingly available and are being tested in LMICs as part of a broader toolkit of technological solutions, all of which have the potential to overcome capacity challenges in moving the needle on the deficit of data and power new data-driven governance paradigms.
To address the problem of the under governance of the private sector, the WHO2 launched in 2020 a strategy report on ‘Engaging the private health service delivery sector through governance in mixed health systems’.8 The strategy report conceptualises six governance behaviours (ie, build understanding, deliver strategy, foster relations, align structures, nurture trust and enable stakeholders) to promote effective public–private engagement as part of more resilient and responsive health systems. These governance behaviours are discussed at length in this special edition.
The use of data is critical for the performance of these governance behaviours, especially when it comes to ‘building understanding’ about the private sector. Figure 1 provides a simple logic model illustrating how we conceive data and data-driven governance empowering the performance of health systems governance and the governance behaviours.
Mapping data for governance to the governance behaviours.
Before delving into potential solutions, we argue that we need better quality and more robust data about the role of the private sector related to the delivery of health service delivery in order to strengthen health outcomes in the whole health system. This involves understanding what information is being collected and used, what barriers exist and formulating strategies to overcome them.
Next steps
It is important to understand what data and information are available on private healthcare providers and what data and information are needed for governments to steward both the public and the private sector in the health system and contribute to moving their public health priorities forward. Key questions that need to be addressed are as follows:
Performance: How do national health entities use information to govern the health system? What sources are used? Do these capture the private sector in health?
Structural: How do private sector entities report into national health systems? Are there concerns with the quality of reporting? Has this changed over time?
Procedural: What are the incentives and disincentives for private sector reporting in national health systems? Have these changed over time?
Innovations: How do technology solutions address fragmentation across sectors, sources and systems?
Technical: What are the minimum information requirements to generate/demonstrate situational awareness and perform specific governance behaviours and functions as pertains to the private sector in health?9
A data-driven governance approach should be front and centre in answering this question and in developing policy recommendations.
Conclusions
We believe that data-driven governance is critical to unlocking the WHO governance behaviours’ potential and promoting an approach that effectively empowers governments to steward the whole health system, not just the portion they directly control.
We note that there are untapped data sources concerning the private sector in health, both within the health system and beyond. Coupled with advances in data capture, interoperability between information sources and analytics, this makes a shift to data-driven governance feasible and compelling.
There is a need for a deeper understanding about the transformative potential of data-driven governance to empower governments to formulate and implement an inclusive public policy for the whole health system. Future works should aim to increase understanding of the data and information that governments collect, identify the reason for existing problems and provide advice on feasible solutions. This with the aim to lead to a new focus on data-driven governance, ultimately resulting in more client-centred, effective, responsive and accountable health systems.
Data availability statement
There are no data in this work.
Ethics statements
Patient consent for publication
Ethics approval
Not applicable.
Footnotes
Contributors DC conceived the development of the commentary. DC and AC wrote the first draft, and all authors reviewed the commentary and made substantial contributions to revisions. All authors reviewed and approved the final version.
Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Disclaimer The author is a staff member of the World Health Organization. The author alone is responsible for the views expressed in this publication and they do not necessarily represent the views, decisions or policies of the World Health Organization.
The contents of this manuscript represent the views and opinions of the authors and do not necessarily reflect the views and opinions of the U.S. Agency for International Development (USAID) or the United States Government or the WHO.
Competing interests None declared.
Provenance and peer review Not commissioned; internally peer reviewed.