D I S S E C T I N G B U S I N E S S conclusions on the efficacy of the test itself . The study data will be stored and used in a highly impactful way to conduct additional studies to advance scientific research and public health – projects that may involve bringing together coded information from the NHS-Galleri Trial with information from other studies , electronic health records or biobanks .
It ’ s not just the treatment of major global diseases such as cancer where data has a pivotal role to play . A recent study assessing the impact of rare diseases ( RD ) on patients and healthcare systems concluded that ‘ Machine Learning strategies applied to healthcare system databases and medical records using sentinel disease and patient characteristics may hold promise for faster and more accurate diagnosis for many RD patients and should be explored to help address the high unmet medical needs of RD patients ’. This proves that although a greater data volume is likely to deliver more reliable insights , smaller sets of relevant data can be equally useful when brought together under the right conditions .
Crafting the keys to unlock data ’ s potential
While these are excellent use cases for connected patient data , we must also evaluate the obstacles to its effective utilisation . Foremost among these is the siloed , disconnected nature of the majority of existing social , demographic and health data . Information is ‘ locked ’ in internal computer systems , coded in a multitude of ways , disjointed and dispersed . In the NHS , for example , disjointed and dispersed patient data often makes it incredibly difficult for clinicians to ‘ join the dots ’ regarding a patient ’ s treatment journey across different NHS services . For research and planning , regulatory requirements such as the GDPR and UK DPA include having a valid legal basis for secondary data use , such as specific individual consent .
As a rule , accessing and uniting multiple types of source data is therefore complex , slow , expensive and inaccurate . This is hampered further by the mistakenly perceived inability to integrate anonymised data , which preserves privacy , negates the need for consent , and maximises data volume and utility for all stakeholders ’ and patients ’ ultimate benefit .
PATIENT DATA HAS A POWERFUL ROLE TO PLAY IN OVERCOMING THE BIGGEST HEALTH CHALLENGES OF OUR GENERATION .
It follows that the first step towards reaping the benefits of connected data is to deploy technologies that enable a clear , unified and holistic view of patient information . We need to transition to systems designed for interoperability and complete health profiles , so we can put an end to the timeconsuming , frustrating work of patching together poor quality information to gain poor-quality insights .
Security as first priority
Finally , it ’ s worth noting that the most stringent safeguards and access controls must always remain in place to protect patients when unlocking the latent value of their data . This can be best achieved by minimising the movement of data through the use of single point-of-access cloud storage platforms and by employing ongoing ‘ fire drill ’ security tests on regularly updated systems .
In addition , while pseudonymisation or tokenisation has its uses , wherever possible the anonymisation at the source of all types of patient health data ought to be non-negotiable as Article 89 ( 1 ) of the GDPR requires , meaning that Personal Identifying Information ( PII ) cannot be linked to health data .
In summary , population-level patient data has a powerful role to play in overcoming the biggest health challenges of our generation . To realise this potential , we must deploy proven systems to unlock and unites health and activity data and to deliver safer , faster and better profiles of record-level health . From these firm foundations of evidence , informed decisions can be made to accelerate global disease eradication . �
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