Following the recently published report “The socio-economic impact of AI in healthcare,” implementing AI in European healthcare systems could save up 380,000 to 403,000 lives annually. The potential financial savings account for €170.9 to 212.4 billion per year.
This study by Deloitte and MedTech Europe covers AI applications that can be used across the entire patient journey. Eight application categories are mapped: wearables, imaging, laboratory applications, physiological monitoring, real world data, virtual health assistance, personalised apps and robotics.
The socioeconomic impact is quantified through impacts on health outcomes, financial resources and time spent by healthcare professionals (HCPs). By estimating the number of saved lives, the cost savings and the hours freed up for HCPs, it is possible to quantify the potential impact of AI on Europe’s healthcare systems. First, annually 380,000 to 403,000 lives can potentially be saved. Wearable AI applications could have the largest impact, saving up to 313,000 lives. This is followed by AI applications in monitoring (42,000 lives) and imaging (41,000 lives).
Second, €170.9 to 212.4 billion could be saved annually, including the opportunity costs of HCP time. Wearable AI applications could have the largest impact with €50.6 billion of potential savings. Add to that AI applications in monitoring (€45.7 billion) and real world data (€38 billion). Finally, AI applications have the potential to free up 1,659 million to 1,944 million hours every year. This impact is led by AI applications in virtual health assistance (VHA) that could save up to 1,154 million hours per year. Other savings through AI applications include robotics (367.5 million hours) and wearables (336.1 million hours). This would allow HCPs to dedicate considerably more time to high-value activities.
AI could have a substantial socioeconomic impact in healthcare by improving patient outcomes and access, and optimising the use of resources. However, to reach its full potential a series of barriers that must be addressed by public and private stakeholders:
- Data challenges include the fragmented data landscape and interoperability, as well as data quality, data privacy and protection and cybersecurity. High-quality data is important to train unbiased, robust and safe AI.
- Legal and regulatory challenges are due to different legal frameworks regulating AI and data in healthcare. Guidance on applying and interpreting existing regulation should describe novel approaches to meet the requirements, promoting innovation and competitiveness.
- Organisational and financial challenges arise where digitalisation and inclusion of AI in European health systems require substantial investments in several areas: infrastructure, digitalisation adoption, technologies, skills and training and shift from care to prevention. Additionally, broader adoption of AI in healthcare will require novel approaches to how technologies are funded, evaluated and reimbursed.
- Social challenges need to be addressed regarding trust and understanding, governance and patient empowerment.
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