The Role of Data in Healthcare
As digital technology continues to evolve with devices like health trackers, home medical equipment, and digital health records, the amount of data gathered in the healthcare sector has greatly increased. In fact, as of 2022, nearly one-third of all the data in the world is healthcare data, and this is expected to grow to more than a third by 2025.
Using the Data We Collect
In healthcare, we often find ourselves juggling between direct patient care (primary documentation) and the collection of data for research, quality improvement, or administration (secondary documentation). Primary patient care must always take precedence, but the secondary data we collect gives us a crucial wider perspective.
However, the real value of data comes from its effective utilization. A data-driven healthcare system transforms raw data into actionable insights, offering us a broader understanding of patient care patterns, treatment outcomes, and health trends. Yet, this bigger picture can sometimes be obscured when data doesn’t flow smoothly or standardly across various healthcare entities. For instance, a general practitioner might not have access to a patient’s hospital data, or patients might struggle to access their own data.
Addressing these challenges requires integrated smart Electronic Health Record (EHR) systems and a focus on the quality rather than the quantity of data. By doing so, we can make data collection during patient interactions more efficient. Additionally, AI technologies can support by easing data collection and helping to identify important trends.
Effective data management can, therefore, integrate documentation into patient care, making it a tool for seeing the bigger picture rather than just an additional task for healthcare professionals.
European Health Data Space
As we aim to streamline the use and exchange of health data, initiatives like the European Health Data Space (EHDS) has the potential to play a significant role. The EHDS is the first common EU data space and is aimed at unlocking the full potential of health data. This framework has been designed to make data exchanges safe, secure, and seamless between member states, making health data more accessible and easier to reuse. By providing a safe, secure, and seamless framework for data exchanges between member states, EHDS paves the way for enhanced accessibility and reuse of health data.
Most importantly, the alignment with such frameworks allows for improved patient outcomes. With comprehensive and integrated data, healthcare professionals can gain more profound insights, enabling them to make more informed decisions, predict health trends, and personalize patient care. Thus, initiatives like EHDS has the potential to not only promote better data management but also contributes to a more effective and efficient healthcare system.
Data protection and information security
When using digital platforms, it is crucial to ensure they comply with the country and organization’s data protection, such as the European Union’s General Data Protection Regulation (GDPR) guidelines and other relevant regulation such as Telehealth regulations.
An example is the rise of video consultations; healthcare professionals can now work from home. However, this convenience brings with it new privacy and data protection considerations. For instance, private health information could be at risk if a computer is left unattended, if the user fails to log out, or if consultations are conducted in a non-private setting where others could overhear.
To mitigate these risks, clear guidelines should be provided. These could cover the importance of conducting consultations in a private setting, using secure networks, employing encryption and two-factor authentication, and making sure to log out when not in use.
Artificial intelligence – harnessing the data
The use of artificial intelligence (AI) in healthcare is considered very promising and has the potential to disrupt the future healthcare system. A strength of AI is the ability to process huge amounts of data and based on different defined parameters develop algorithms that can discover patterns in data. Algorithms in the healthcare system are not limited to clinical use, but can also be used to predict administrative processes like patient turnout. At the forefront of the emerging AI-technology are radiology. Algorithms have been developed so they can process x-ray scans to detect, for example cancer tumors and bone fractures. However, the implementation of AI in a clinical settings is not without its barriers and demands a multi-disciplinary approach. For inspiration, check out MAS-AI, which is a health technology assessment-based framework to support the introduction of AI technologies into healthcare in medical imaging.
Questions for reflection
- What kinds of health data do you use in your workplace and how is it integrated into your practice for patient care and research or quality improvement? Reflect on a situation where this balance between primary and secondary documentation was particularly challenging.
- How would you describe your experience of using health data? Consider this both from your own perspective and from the perspective of your patients. What impact does the flow of data between different healthcare entities have on this experience?
- Given the vast amount of data being generated in the healthcare industry, how can we ensure this data is effectively used rather than just collected?
- Reflect on the steps you take in your workplace to ensure data protection and information security. How do these measures adapt to changing circumstances, such as the rise of video consultations and remote healthcare services?
