When discussing AI in healthcare, the reaction among most U.S. consumers leans towards optimism and excitement. A recent survey reveals that 81% of consumers believe AI will significantly enhance patient care by enabling quicker, more accurate diagnoses, reducing paperwork, and shortening waiting times.
However, not everyone is entirely at ease with AI in healthcare. About 25% of those surveyed expressed concerns about patient privacy risks associated with AI. Their concerns are not unfounded; AI's reliance on data, including sensitive patient information, does pose a risk, making it a potential target for cyber threats.
This situation underscores the need for healthcare providers to adopt AI with a patient-first data governance strategy. Effective data governance, or the exercise of authority and control over data asset management, is crucial in ensuring not only the highest standard of care but also the utmost privacy of patient information.
AI can help human professionals overcome two challenges that currently hinder the healthcare industry.
Underutilized Human Resources
The World Health Organization (WHO) forecasts a global shortfall of over 1.5 million healthcare workers by 2030. A significant factor contributing to this shortage is the underutilization of specialists, who spend valuable time on administrative tasks like paperwork. AI can handle these tasks and free specialists to focus on patient care.
Disorganized Data
The healthcare sector saw a staggering increase in data in 2020, with a global volume rise of 2,314 exabytes. Unfortunately, much of this potentially life-saving data remains untapped, sitting idle on servers. Human limitations in processing such vast amounts of data mean that crucial insights and potential medical breakthroughs remain undiscovered. AI, with its ability to process data exponentially faster than humans, can accelerate progress.
The COVID-19 pandemic highlighted the urgent need for AI in healthcare. Initially, there was a failure to effectively analyze healthcare data, leading to devastating consequences. However, as the pandemic progressed, AI began to play a pivotal role, being used to identify COVID-19 clusters, predict outbreaks, and assist in early diagnoses.
Planning for Future Pandemics
Learning from COVID-19 and leveraging AI and modern technologies will be critical in developing strategies to mitigate future pandemics. Effective data governance, including legal and ethical frameworks, is essential to resolve privacy concerns, facilitate data sharing, and enable AI to function optimally.
Overcoming the 'Black Box' mentality in AI, which stems from its perceived mystery and lack of transparency, is crucial for effective data governance in healthcare. To achieve this, healthcare providers need to focus on several key areas. Firstly, data-collection tools must be user-friendly, making them more accessible and less intimidating for both patients and healthcare professionals. Transparent communication about how patient data is used and safeguarded is essential for building trust. Clarifying who has access to patient data and for what purpose helps demystify the data usage process. Additionally, addressing and actively working to reduce algorithmic bias is vital for maintaining data integrity and fairness. Collectively, these measures not only enhance the quality of data but also bolster trust and confidentiality, which are foundational to robust data governance in the healthcare sector.
Synaptiq is an AI and data science consultancy based in Portland, Oregon. We collaborate with our clients to develop human-centered products and solutions. We uphold a strong commitment to ethics and innovation.
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