Artificial intelligence (AI) has been used in healthcare as early as the 1960’s. These early applications focused on medical diagnostics, clinical decision support, and medical imaging. Most of these initial systems were not widely adopted but paved the way for current advancements.
Understanding the long history of AI in healthcare is important. It reveals how far the technology has evolved and gives us an opportunity to understand its potential and to set expectations.
In the 1980s, machine learning (ML), a branch of AI, was being applied to basic pattern recognition systems in medical imaging. Fast-forward 45 years, and this capability has transformed radiology by detecting cancers, fractures, and other abnormalities with higher accuracy, often surpassing human diagnostics, leading to faster and more precise treatment decisions.
We are in an era where improvements in computing technology have expanded the capabilities of ML. It’s not just about identifying patterns in radiology anymore. Now, AI can address complex issues related to hospital operations and public health policy decisions. These advancements enhance the management of resources, better coordinate patient care, and assist in crucial decision-making processes for both administrative and clinical staff.
To enhance healthcare outcomes and efficiencies, we must have the necessary data to leverage these technologies. One of the industry’s biggest challenges is effective data management. Fragmented systems, inconsistent data formats, and privacy concerns have made it difficult to access and integrate information. This has hampered decision-making, slowed down care delivery, and created costly inefficiency. By focusing on managing healthcare data and the systems that generate and house them, we can improve patient outcomes and reduce operational costs. These savings can then be reinvested into patient care.
Data is the lifeblood of AI. The cleaner the data, the better the results. To this end, we’re helping our clients overcome their data challenges and reap the immense benefits of AI.
One client wanted to reduce chronic disease rates, improve vaccination coverage, and effectively manage pandemic responses. To achieve these goals, data from multiple health services were used to identify at-risk groups, monitor disease outbreaks, and distribute resources for equitable healthcare access. This required seamless, secure health data sharing among providers, citizens, and organizations across all care levels.
KPMG tackled their data management complexities by creating new processes, adjusting policies, adding security protocols, and engineering a data fabric to integrate information across multiple systems and applications. The project also included training on how to get the most out of the technology, which helped address broader challenges like workforce adoption and burnout.
This new era of AI has presented a once-in-a-century chance to significantly improve the health and betterment of people. It will require continued collaboration, investment in modern data practices, and a belief that we can do better, which we absolutely can.
To learn more about how KPMG can help, visit our website.
