Introduction
The effectiveness of health data-driven actions and decision-making depends on many factors, including data quality, analytical tools, the organization of the data analysis process itself, and the manner in which the observations and conclusions derived from it are used. However, a key issue influencing all of these elements is public acceptance and trust in these actions, and this is where we will focus our attention. The use of health data poses numerous challenges, such as the protection of individual rights, data security, and the requirement for transparency and public participation. To analyze these issues, we will use Moore’s strategic triangle, which allows for a comprehensive view of the problem from the perspective of public value, its political justification, and the state’s enforcement capacity.
Recall that Moore’s strategic triangle is a concept of public management that serves to evaluate and improve the actions of public organizations. The triangle consists of three vertices: public value, political justification, and operational capacity. Public value refers to the benefits that actions bring to society. Political justification encompasses financial, organizational, and legal support, as well as the political legitimacy necessary to implement actions. Operational capabilities refer to the ability of government institutions and stakeholders to effectively carry out assigned tasks. In the context of health data, Moore’s strategic triangle will help us understand how these three dimensions influence the outcomes and effectiveness of decisions and actions.
Collective acceptance of public value
Public acceptance and trust in the purposes for which health data are collected are crucial to recognizing their public value. The conflict between the public interest and the individual’s right to privacy poses one of the greatest challenges in this area. The public interest encompasses the effective management of public health, the development of research, and new medical technologies, while the individual’s interest focuses on protecting the privacy and autonomy of personal data. The fact that public health data also serves the individual’s well-being by providing the best possible care is overlooked from the patient’s perspective. For this reason, the General Data Protection Regulation (GDPR) prioritizes the public interest over the individual’s interests, raising the question of whether this approach is fair and just.
For example, health data is exempt from the right to erasure (the right to be forgotten) under the GDPR. This means that even if a patient requests the erasure of their data, their request will be denied, and their health data will continue to be stored and processed in the public interest. Data processing for public purposes is widely permitted, as it allows healthcare and research institutions to use this data under certain conditions without requiring additional consent from patients. This approach can be justified by arguments for the greater good that arises from analyzing large sets of health data. This allows for a faster response to epidemics, better planning of medical resources, and conducting research that can lead to breakthrough medical discoveries. For example, during the COVID-19 pandemic, access to large sets of health data has enabled scientists and policymakers to quickly make decisions and implement protective measures. It could even be argued that these opportunities have not been fully utilized, and as a result, the number of avoidable deaths caused by the pandemic has not been sufficiently reduced.
However, this approach also raises controversy and privacy concerns. In some countries, such as the United Kingdom, the approach to health data protection differs, offering patients greater rights to control their data. An example is the right to be forgotten for cancer patients, which allows them to have their data deleted from most registers if they deem it necessary. This approach places greater emphasis on individual autonomy and the right to privacy. The decision to delete data should rest with the patient, and information campaigns highlighting the value of data sharing can help increase public acceptance. Critics may fear that if too many people opt out of their data, the effectiveness of public action will be severely limited. However, British experience shows that the percentage of people who opt out is negligible. The motivations for such decisions are most often related to a desire to forget the trauma of illness, not a lack of trust in the healthcare system. Data erasure also carries personal consequences, as doctors, lacking a complete picture of the patient’s health, may struggle to make optimal clinical decisions. However, this can be prevented by making the patient the custodian of their own medical data. However, under the General Data Protection Regulation (GDPR), this concept sounds revolutionary.
The fundamental question of whether prioritizing the public interest over the individual’s interests is justified depends on the context and ethical perspective. On the one hand, the benefits of broad access to health data are undeniable and can contribute to significant improvements in public health. On the other hand, violations of individual privacy can lead to a loss of trust in the health system and fears of misuse, or even surveillance and medical espionage.
To balance these two interests, it is essential to ensure transparency in data collection and processing processes. Citizens must be aware of why their data is being collected, how it will be used, and what benefits it will bring to society. It is also crucial that data protection systems are flexible enough to accommodate the diverse needs and concerns of individuals while enabling the achievement of public goals.
To this end, public involvement in the decision-making process regarding the collection and use of health data is crucial and should not be a government-sponsored initiative. Public participation, implemented through consultations, citizen panels, and collaboration with non-governmental organizations, allows for the consideration of diverse perspectives and the development of a shared understanding. This operating model not only increases acceptance but also legitimizes decision-making processes among the public, which is essential for the long-term success of healthcare policies.
However, just as important as social dialogue is the subjective treatment of patients and their effective involvement in the process of controlling their data. Patients should be involved in the data processing process, and their participation should not merely consist of signing blanket consent forms before their first visit. Mechanisms such as blocking personal data from the PESEL register, used to prevent fraud and unauthorized use of data, could be successfully adapted to health data as well. Furthermore, introducing a system that allows patients to track and control access to their health data could significantly increase trust and social acceptance. Solutions from the credit information sector could serve as a model here.
In addition to patient empowerment, data security is the foundation of trust in system operations. Health data should remain personalized only to the extent strictly necessary for the purpose of processing, and all data processing procedures should comply with personal data protection regulations. Patients must be assured that their data is adequately protected against unauthorized access and breaches. Examples of misuse of medical data, particularly by politicians, or large-scale data leaks, undermine patient trust.
A second means of building trust is transparency in communicating the results of health data analyses. Regularly sharing results in a way that is understandable to the public provides evidence of how data is being used to improve public health. Examples of concrete successes where health data has contributed to improved health or saved lives can significantly increase public acceptance.
Social acceptance and trust in the purposes of health data collection are essential for recognizing the public value of these activities. Clear definition of goals, public participation, protection of individual rights, fairness in data processing, security, and transparency of activities are the foundations of this trust. Taking into account the rights and concerns of individuals while pursuing public goals allows for a harmonious balance between individual interests and the public interest. Only when individuals have confidence that their data is used responsibly and with respect for their rights will it be possible to achieve the full public value of health data.
Political legitimization and systemic support
Political legitimacy and systemic support are the second pillar of effective public policy implementation, including those using health data. In a rapidly changing geopolitical and economic environment, political priorities often focus on immediate and urgent challenges, such as armed conflicts, migration, or economic crises. For decisions based on health data to gain political attention, it is necessary to demonstrate their value in the context of current priorities and understand how these investments can support broader strategic goals. In other words, arguing that health is paramount and, therefore, that tools using health data should be developed to guide optimal public interventions will most often be ineffective.
Healthcare communities, which prioritize patient well-being, will find this situation difficult to understand. However, politicians will be more likely to support public policies based on health data if they see clear and broad benefits that can generate voter support. The problem is that lofty public goals too easily fade in the shadow of all sorts of particularisms. Political parties’ priorities are determined not only by what is just and fair, but also by what will allow them to seize or maintain power. It is true that data-driven actions can yield quick and tangible results. The implementation of e-prescriptions is a prime example of this in recent years. However, recognizing future benefits in the face of current challenges remains a formidable challenge. For this purpose, one need only examine the decade since the adoption of the Public Health Act.
So how can we convince political circles to fully utilize health data for public health purposes, on the one hand, and optimally treat individual patients, on the other? Public value accounting, a method for analyzing public policy goals developed by Mark Moore, provides the answer.
Moore’s public value accounting is a proven tool for assessing public policy priorities. Public value analysis allows for a comprehensive understanding of the costs and benefits associated with investing in health data, taking into account financial, social, and individual aspects. The development of applications serving millions of citizens, investments in technological infrastructure, database maintenance, and information protection require significant resources that could be allocated to more direct interventions. To justify such expenditure and commitment, it is necessary to provide evidence that the benefits of such investments outweigh the costs.
When analyzing the costs, financial costs include both capital expenditures on technological infrastructure and application development, as well as ongoing costs related to database maintenance, information security, management, and general administrative activities. From a social perspective, these investments can generate costs related to potential social resistance to new technologies and the need to educate the public on the benefits of processing health data. This is precisely what we observed in the case of e-prescriptions.
However, understanding the full costs and benefits requires an analysis of both expected and unexpected consequences. Among the latter, negative consequences can be identified, such as increased bureaucracy and digital exclusion, which can hinder access to health services for certain social groups. From an individual perspective, it is crucial to minimize concerns about the misuse of health data by implementing effective privacy protection mechanisms and the impossibility of withdrawing consent to data processing.
On the benefits side, the financial benefits of investing in health data are now clear. Effective health data management leads to savings through better resource planning, reduced hospitalizations, and support for research that can lead to the discovery of new therapies and drugs, which in the long term reduces medical costs. Societal benefits include improved population health through more effective prevention programs, faster responses to health threats, and greater social cohesion through transparency and participation.
From an individual perspective, the benefits of collecting health data include access to more personalized care and improved health outcomes through faster diagnosis and more effective treatments. Patients can benefit from a more integrated healthcare system where their medical history is easily accessible to various providers, improving continuity and quality of care. However, this must first be made clear to them.
Moore’s public value calculus is an indispensable tool in assessing public policy priorities, particularly in the context of data-driven public health investments. By taking into account financial, social, and individual costs and benefits, it allows for a comprehensive analysis and presentation of the value of such investments. In the face of competing political priorities, this calculus demonstrates that public health and medical data can be treated as political goals on a par with combating economic or geopolitical crises. Its most important feature is providing policymakers with arguments that help justify long-term investments by demonstrating that they deliver tangible benefits that outweigh the initial costs.
These premises inspired the author to submit to the Patient Rights Ombudsman a proposal during the first months of the COVID-19 pandemic for applying public value calculus to the discussion and selection of optimal public interventions in the face of the pandemic. This proposal received a positive opinion from the Patient Rights Ombudsman’s Expert Team. However, it was not accepted by the Prime Minister, who regulated the decision-making process for state interventions in the face of the epidemic threat in a different way. Even from today’s perspective, the lack of a transparent system for making critical decisions that impact the lives of millions of citizens, based on public value, remains, in the author’s opinion, a mistake.
Executive abilities
Executive capabilities constitute the third key cornerstone of Moore’s strategic triangle, essential for effective operations and decision-making based on health data. While a well-developed technological infrastructure, staff competencies, and effective data management processes are the foundations of these capabilities, their actual translation into operational action depends on the understanding of the purpose by those involved in their implementation. Effective implementation of these capabilities requires the commitment and awareness of employees, who must see the meaning and value of their work to avoid routine checkboxes and potential errors resulting from misunderstanding the purpose of activities.
Effectively transforming executive capabilities into concrete operational actions begins with ensuring that everyone involved in the process has a clear understanding of the goals and benefits of collecting and analyzing health data. Employees must be aware that their work directly impacts public health and the quality of healthcare. Therefore, investing in education and training that not only teaches technical skills but also demonstrates how these skills translate into tangible benefits for patients and society is crucial.
One of the key challenges is ensuring that employees understand and respect both the collective and individual interests. For example, in data analysis, there is a risk that focusing solely on population-based results may lead to the disregard of the specific needs and rights of individuals. To avoid this, effective implementation of ethical and legal principles that protect patient privacy and ensure that data is used in accordance with the highest ethical standards is essential.
Enforcement capabilities must also ensure that new obligations do not impose an excessive administrative burden on all parties involved, including stakeholders. It is not successful to impose a new public obligation without providing implementation tools that allow it to be fulfilled almost imperceptibly or without providing the necessary organizational, technological, or financial support.
An organizational culture that promotes openness, transparency, and collaboration is also crucial. In healthcare organizations, where data is a key resource, this culture must support information exchange and collaboration between various departments and specialists. Interoperability of IT systems and data exchange standards are essential for the effective integration of data from various sources, which in turn enables a better understanding of population health and more informed decision-making.
Implementation capabilities also encompass data quality management. Data must be accurate, complete, and up-to-date to be effectively used for decision-making. Data quality management is a continuous process that requires the involvement of all employees, from data collection standards and collection to analysis and reporting. Employees must be aware of the importance of data quality, have the appropriate tools, and be accountable for maintaining it.
Implementation capabilities are crucial for transforming theory into practice in health data-driven operations. Success depends on ensuring an appropriate data collection and processing environment, and on the involvement and awareness of employees, who must understand the goals of their activities and the value their work brings to the healthcare system.
Summary
Moore’s strategic triangle is a key tool for shaping public interventions, which ideally encompass the development of data-driven public health and health systems. It encompasses three main perspectives: public value, political legitimacy, and implementation capacity.
From a public value perspective, understanding policy objectives by all stakeholders and effectively communicating the benefits are crucial. Political legitimacy requires addressing the concerns of all stakeholders, sustained government support, and effective communication. Politicians will be more likely to support data-driven health policies if they see clear benefits that can generate voter support. Implementation capacity requires adequate resources, an effective organizational structure, and an organizational culture.
In summary, Moore’s strategic triangle allows public health, health systems, and health data to be treated as equal priorities in strategic policy priorities, alongside other key challenges such as economic or geopolitical crises.