Secure and Ethical Use of Regional Clinical Data for Predictive Care

Utilisation of real-world data (RWD) is transforming the field of healthcare research and moving towards the ability to conduct large-scale research on how medical procedures work, how diseases develop and how resources are utilized in real clinic environments. In comparison to the randomized controlled trials (RCT), where strict inclusion criteria are used and varied patient groups are usually left out, RWD gives a more comprehensive and representative picture of clinical outcomes. Based on data contained in electronic health records (EHRs), hospital admissions, pharmacy records, laboratory tests, and disease registries, among others, researchers are able to build upon that to develop real-world evidence (RWE) and use this to make clinical decisions, shape regulatory policy, and fuel medical innovation.

Among the advantages of RWD, it is possible to note the ability to complement conventional trials and accelerate the research, improving patient outcomes. It is especially useful where RCTs cannot be done well, e.g., in rare conditions with few patients, or where there are urgent situations, as in a public health emergency, when quick evidence is needed. Post-marketing surveillance is also informed by RWD, allowing medicines to be evaluated in wider groups of people. RWD can also be used in retrospective studies that may facilitate preventive care since potential signs of disease or wellness phases might be recognized.
RWD, though, has potential issues that are of concern regarding matters of privacy, security and regulatory compliance. Privacy of critical patient records and facilitation of valuable research are at the top of the agenda of contemporary healthcare. EHDS is aimed at establishing the legal framework of ethical and safe utilization of health data at the European level, complying with the General Data Protection Regulation (GDPR).

In this context, it is worth noting that the Andalusian Health Population Database (BPS) is one of the largest repositories of health data in Europe and includes EHRs of more than 15 million patients since the year 2001. PAGEM is a Secure Processing Environment (SPE) integrated into the Andalusian Public Health System in which it represents a platform to conduct medical evidence generation, backed by the Andalusian Platform of Medical Evidence Generation (Andalusian PAGEM). Using PAGEM, researchers may conduct an experiment on the effectiveness of treatment, survival, and modelling predictions without compromising the level of data security and patient confidentiality.

The discussed paper presents PAGEM as an example of ethical and secure utilization of RWD, with the help of which healthcare can shift towards a predictive dimension instead of the reactive one. Through data protection strength, compliance with regulations, and access through research paths, it shows the use of clinical big data present in the field and how it can be used by public health systems in pushing innovation, better outcomes, and implementing a pattern, in general, in both regions and continents in line with the emergent EHDS.

A secure and ethical evidence-generation region-wide collaborative environment

An ethical, GDPR- and EHDS-compliant use of medical data in a regional system is based on THREE components: the Andalusian Platform for Medical Evidence Generation (PAGEM), a Secure Processing Environment (SPE) to analyze confidential data, the Health Population Database ( BPS ), a huge repository that contains EHRs and other clinical data of more than 15 million patients, and a legal frame that avoids privacy abuses but allows responsible research.

Researchers deal with the healthcare network and perform their work within the one, which gives no opportunity fordata loss. The configuration is able to facilitate advanced analytics, such as using AI in studies, survival analysis, and drug safety.

Safeguard of data in PAGEM

It adheres to GDPR principles of lawfulness, fairness, transparency, purpose limitation, data minimisation and integrity/confidentiality.

Within the Andalusian Public Health System (SSPA) corporate network and managed by its staff, PAGEM makes sure that data do not leave the comfortable environment. Such an arrangement will be overseen by reliable members of the health system and reduce risk and meet the Data Protection Impact Assessment (DPIA) obligation.

The data life cycle as defined in the DPIA entails the acquisition, storage, processing, transferring to a third party and final deletion of the data with the possible risks that may arise and how these risks can be mitigated, and finally, the safeguards that have been put in place.

Data treatment & processing occurs as follows:

1. The committee approves the study by the data access committee.
2. The employee of the PAGEM contacts the BPS to ask to provide the pertinent information.
3. The BPS staff derives and anonymizes the information.
4. The data in pseudonymized form is securely transferred to PAGEM.
5. This data gets analyzed in accordance with the research protocol by the PAGEM team.
6. After the research has been concluded, the data are erased in the PAGEM system.

This is the secure and closed circle mechanism that protects delicate information about the patient and facilitates healthy and ethical research in a very strong manner.

Secure and ethical data access Regional Regulation

In Andalusia the access to medical data to conduct research was approved by the Joint Resolution 1/2021, on 4 December 2021. This regulation is in line with the European Health Data Space (EHDS) framework in that data can be used either in primary use (direct patient care) or secondary uses (research, planning and policy of public health). Ethical review and rigorous privacy protections will apply to PAGEM, which is deployed to secondary use.

The regulation shall set up a Data Access Committee (DAC) to evaluate requests on scientific, ethical and legal grounds. Resources have to include a research protocol, ethics committee approval (CCEIBA), a Data Protection Impact Assessment (DPIA) and a signed undertaking of the principle investigator with respect to non-redistribution, no re-identification and deletion of datasets with the study.

PAGEM also particularly highlights in situ analysis and federated research and is thus particularly suitable to EU efforts against data transfer, such as DARWIN EU and EHDEN, where only aggregate results can be shared.

Governance and Collaboration:

PAGEM facilitates the alignment of its research with strategic priorities of the Andalusian Health System via comprehensive evaluations of scientific and ethics committees of proposals. Partnerships with pharmaceutical firms, CROs, and other stakeholders in health research assist in maintaining the infrastructure and developing research without interfering with individual-level patient information. They only release privatized, aggregate outcomes, giving privacy on one hand and scientific understanding on the other. The management system will dictate transparency, ethical and data security. However, PAGEM also is engaged in such federated networks as country or even international networks in order to facilitate large-scale Real-World Evidence (RWE) research, facilitate predictive models, and improve population health outcomes by safe and responsible utilization of healthcare data.

Precision Preventive Medicine of the Future

Precision preventive medicine is concerned with the creation of very early predictors that predict poor health conditions prior to the manifestation of the same. These models allow precision interventions to be taken based on machine learning utilization of routinely collected health data as demonstrated in cardiovascular and cancer disease and cancer and ovarian cancer risk prediction of BPS data. The large datasets, computing resources and expertise needed to scale predictors can turn healthcare from reactive to preventive, with better outcomes and lesser costs.

Future Sustainability and Prevention of Health System

Transferring healthcare to less costly and more effective systems of predicting and preventing failures can be used to increase effectiveness and optimize resources. Real-world feedback and predictive analytics make evidence generation less time- and resource-consuming, especially in post-marketing surveillance and comparative effectiveness research. The PAGEM-BPS framework combines multi-information security, the legal framework, and massive data analysis efforts making it possible to use patient data in an ethical way in the research process, with benefits flowing back to society. The relationship between the population and the corporations could assist in maintaining the infrastructure and in widening the availability of the research regarding public health. Future directions encompass wider analytical capabilities, federated analysis capability, and responses to emerging EHDS needs, providing a scalable model of sustainable curation of data-driven health systems.

Conclusion:

Healthcare can be revolutionized by adopting a collaborative and secure process to develop a regionwide clinical data source capable of creating powerful evidence and accurate predictive care. When combined with good governance, advanced analytics will help stakeholders improve patient outcomes, innovation, and trust, which makes them sustainable improvements in terms of both clinical decision-making and long-standing health system performance.