The importance of data security in AI-driven healthcare
The COVID-19 pandemic is said to have accelerated the healthcare space into the phase dubbed ‘Health 4.0’ – a data-driven healthcare reality empowered by artificial intelligence (AI), machine learning (ML) and robotic technologies. Alongside increased potential to reimagine patient outcomes and clinician capabilities, this revolution has made way for considerable concerns surrounding data use, security and privacy.
It’s a reservation extending from a rapid healthcare transformation that has occurred in recent years. Even well before the virus, health records were almost exclusively digitised, virtual consultations were being normalised and the potential of data-driven technologies was slowly becoming realised. It’s simply become clearer that quality and accessible healthcare data is the integral currency enabling it all.
But at what risk? As described by Cameron F. Kennedy in a Brookings Institution report discussing the concerns of next-gen technologies: “[they] use personal information in ways that can intrude on privacy interests by raising analysis of personal information to new levels of power and speed”. He states that to avoid widespread data breaches, this information must be ethically governed and secured.
Read on to explore the importance of data security and the difficult balance between data sharing and protection in the AI-led world of Health 4.0. Then, discover platforms such as Datarwe’s Clinical Data Nexus (CDN) and initiatives like IntelliHQ’s National AI in Healthcare Datathon that are paving the way for ethical, data-led healthcare.
A healthcare revolution reliant on shared data
Shared healthcare data undoubtedly fuels the tech-led reality of Health 4.0. Whether it’s in the form of electronic health records, clinical results, patient demographics, patient-generated data or research findings, health datasets provide the real-world groundwork from which medical advances blossom.
For example, electronic health records are constantly being shared between information depots, health agencies and the patients in everyday healthcare settings to allow efficient and personalised care. These records can disclose health data covering single patients to entire populations. Beyond the doctor’s office, this data is also steadily being utilised in the training of AI and ML technologies, allowing their capabilities to be expanded by authentic health snapshots of society.
To share this information ethically and securely, data repositories require a complex level of data protection that ensures sensitive patient data is not inappropriately used, disclosed, accessed, altered or deleted.
Without steadfast data security measures, databanks hosting sensitive patient data are at significant risk of security breaches and mishandling. They’re a prime target for cyberattackers monetising data, organising scams or holding ransoms. In fact, according to data collected from the U.S. Department of Health and Human Services in 2021, the number of individuals affected by healthcare cybersecurity breaches has more than tripled since pre-pandemic levels.
This exposes a great need for the healthcare industry to rethink its data handling in line with the rapidly increasing reliance on patient data. This ensures advances in patient care and medical research does not come at the sacrifice of data privacy and security.
