Health data as an infrastructure

Health data can be considered a crucial component of healthcare infrastructure, as it forms the basis for decision-making, research, policy development, and patient care within the healthcare system. This concept was shared by Andrew Morris, Director, HDR UK at Health Data Research UK (HDR UK) at the Norway Life Science 2024 in Oslo.

Data serves as infrastructure at different levels of healthcare.

  • At the macro level, population-level data is utilized to plan programs and interventions across various services and track health outcomes.
  • At the meso level, data is used for population segmentation and risk stratification to target different population segments effectively.
  • At the micro level, data aids in coordinating services through integrated health records and delivering interventions to individuals to improve health outcomes.

Health data serves as the backbone of modern healthcare systems, driving decision-making processes. It could contribute to personal care optimization, population health management, augmentation of clinical trials, research, and development. However, the volume and complexity of health data generated daily present significant challenges in harnessing its full potential. To effectively utilize health data, structuring is paramount.

Structured health data refers to information organized in a standardized format, enabling interoperability, accessibility, and analysis across various platforms and healthcare settings. Structuring health data involves categorizing information into discrete elements, such as patient demographics, medical history, diagnostic tests, treatment plans, and outcomes. This structured approach enhances data integrity, consistency, and reliability, facilitating seamless exchange and integration across healthcare systems, providers, and stakeholders.

Structured health data fosters comprehensive insights into population health trends, disease patterns, treatment efficacy, and healthcare disparities. Moreover, it enables real-time monitoring, early detection of outbreaks, and evidence-based interventions, bolstering public health initiatives and crisis management efforts.

Furthermore, structured health data underpins the development and implementation of health informatics tools, such as electronic health records (EHRs), clinical decision support systems, and predictive analytics algorithms. These tools streamline clinical workflows, enhance care coordination, and empower healthcare professionals with actionable insights to deliver patient-centered care efficiently.

Sqilline continues to advance its analytical platform, ‘Danny Platform’, integrating and structuring extensive datasets from real world clinical practice. By employing machine learning algorithms, ‘Danny Platform’ extracts structured and unstructured data from free text, normalizing and preparing it for practical use.

Sqilline’s ‘Danny Platform’ processes millions of patient records from nearly 60 leading hospitals in Central and Eastern Europe, covering various therapeutic areas such as oncology, cardiology, ophthalmology, rare diseases, etc. That’s the reason it was officially announced as a Data Source for Real-World Data and Studies by the European Medicines Agency (EMA).

These structured medical data can be directly utilized by medical professionals and researchers or accessed in an anonymized and aggregated form by other interested parties, forming the foundation for decision making.

In essence, health data structuring is integral to optimizing healthcare delivery, improving patient outcomes, and advancing public health initiatives. By transforming raw data into actionable knowledge, structured health data forms the cornerstone of a robust and responsive healthcare infrastructure in the digital age.

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