All.Can welcomes the European Health Data Space (EHDS) initiative which seeks to ensure access and optimal use of health data as well as digital health products and services. 

Efficient healthcare systems that deliver the best possible outcomes for patients are evidence-based learning systems that incorporate meaningful input from healthcare users, make continuous assessments of products and processes, and report the results of healthcare interventions transparently. Robust data collection is a key driver to ensure that inefficiencies are systematically identified and remedied and health outcomes are continuously improved for the benefit of patients and their families.

In cancer care, systematic and holistic reporting of robust data is vital to create a cycle of continuous improvement and drive accountability across the entire care pathway. For a comprehensive EHDS that can help realise this goal and importantly, has patients’ needs at its core, All.Can calls attention to the following challenges and proposed recommendations to address them.

  • Data quality: Low quality and unreliable data often limits the ability to impact decision-making across cancer care and damage the trust of stakeholders.
    • Applying various tools and techniques to improve data quality and strengthening quality control mechanisms can help generate and maintain high-quality datasets.
  • Data representativeness: Data not representative of entire populations result in inequities, hampering access to timely diagnosis and high-quality treatment.
    • Promoting collection of equitable and representative data is key to ensuring that all patient populations benefit equally from healthcare improvement efforts.
  • Data relevance: Data collected in our healthcare systems are often not patient-centred or aligned with patient values.
    • Patient-reported outcome measures (PROMs) and patient-reported experience measures (PREMs) data should be systematically collected and analysed to guide quality improvement efforts.
  • Data silos and interoperability: Data silos and poor interoperability of datasets hinder the measuring of healthcare services performance, and linking of health data across the care pathway for secondary uses.
    • Creating national and international data standards fosters database linking, through improved data standardisation and interoperability of key national health datasets.
  • Healthcare professional buy-in: Cooperation from healthcare professionals is key to continuous collection, use and sharing of clinical data.
    • A positive culture of data sharing should be promoted among healthcare professionals through incentives, including minimising the burden of data collection by streamlining, integrating and standardising data collection systems.
  • Building trust: Lack of trust and transparency coupled with burdensome data collection systems disincentivise sharing of data.
    • Initiatives to build public trust in data and data sharing should be fostered. The burden of data collection on individuals should be mitigated to boost participation in data collection efforts.
  • Data governance: Countries remain slow in adapting approaches to harness big data in health, due to governance-related barriers such as gaps in funding, leadership, technical expertise and competing priorities within the health systems.
    • Strong data governance frameworks that can facilitate effective data collection, use and sharing should be developed, with attention to privacy confidentiality, privacy and security for system users.
  • Data analytics: There remains an unmet need in providing better linkages between health information systems and big data analytics that can transform healthcare by helping to extract insights from vast amounts of data.
    • To facilitate primary and secondary uses of data and tap into the potential of wide-ranging applications of AI and machine learning, countries should invest in data analytics and their integration in care pathways.