The Future of Healthcare: Locked in AI

April 2, 2024

Quinn Cosgrave

2023 was the year that AI became a mainstream sensation. From high school students to Fortune 500 executives, everyone came to integrate ChatGPT and AI assistants into their day-to-day. As with all new technology, our use is frenzied. First comes curiosity, then an excitement to integrate with both our personal and professional environments. Amidst this frenzy can be chaos, as we scramble to adapt and keep pace with the first-movers.

Every industry experiences a few pivotal moments in its existence that almost always lead to a reorganization of the competitive landscape. Troves of brick-and-mortar retailers failed to acknowledge the power of e-commerce through the early 21st century and have since been forgotten. Financial institutions, telecommunications providers, and the myriad of defunct businesses following a technological disruption are endless.

We believe that 2024 is the year that healthcare will be faced with an ultimatum of adopting AI or being left behind. Even so, it is not a binary event to adopt AI or not. The adoption of AI is only as useful as the quality of implementation and data governance. Organizations that integrate AI efficiently will set an industry standard for cost and efficiency. Those who do not will slowly fall victim to the technologies they rejected.

What Can AI Do For Healthcare

In the case of matrixed data systems, we can leverage AI to clean, process, and map variables autonomously. In healthcare, we look at both empirical and qualitative data to shape diagnosis, drive clinical analysis, and perform research. Assistants and generative AI programs can streamline these processes by analyzing vast amounts of data quickly and accurately, leading to more precise diagnoses and personalized treatment plans. Proper integration of these programs leads to a marriage of structured and unstructured datasets spanning administrative paperwork, scheduling, and transcription, freeing up valuable time for direct patient care. Ultimately, the integration of AI technologies holds immense potential to revolutionize healthcare by enhancing efficiency, accuracy, and accessibility while ultimately improving patient outcomes and experiences.

Data Quality

Generative AI and LLMs excel at synthesizing information and generating responses based on the data they've been trained on. Yet, their outputs are only as good as the data fed into them. If the inputs contain inaccuracies, biases, or gaps, the AI's responses may be flawed or skewed. Therefore, ensuring the integrity, relevance, and diversity of input data is paramount. This requires rigorous data curation, cleansing, and validation processes to mitigate biases and errors that could compromise the AI's performance.

Data Governance

Perhaps paramount to the technological challenges integrating AI in healthcare is the standard to which data integrity and logic must be held. Developing an AI strategy requires a meticulous approach to design, characterized by clear intent and discipline in addressing ethical, legal, and

regulatory considerations. By establishing robust governance frameworks, including data privacy protocols, transparency measures, and accountability mechanisms, healthcare organizations can mitigate risks and maximize the benefits of AI technologies while upholding patient welfare as the utmost priority. Moreover, fostering collaboration among stakeholders, including clinicians, data scientists, policymakers, and patients, is essential for crafting inclusive and responsible AI solutions that align with healthcare's ethical principles and societal values.

XponentL has developed a foundation rooted in industry background and driven by world-class engineering. We look toward AI in healthcare as a means to improve today’s efficiency while creating a launchpad for newfound innovation tomorrow. While exuberance toward the technology is easy to understand, we need to approach it with diligence and strategy. Lacking either is as severe as avoiding the technology altogether.