Estimates of current use vary, but at least one study puts the percentage of healthcare organizations using AI in some capacity at 79%. Another study measures general ML usage at around 86%.¹ ML has been around longer, and the two technologies are now intertwined; machine learning collects and analyzes data, and artificial intelligence makes “smart” decisions based on that data. Together, they represent a significant sea change in how healthcare organizations do business.

Areas of hospital operation such as diagnostics, developing treatment plans, preventative care, clinical trials and even general administration can improve results by using this rapidly developing technology, and many healthcare organizations are already seeing worthwhile results.

The AI/ML advantage in hospitals

AI, coupled with ML, could be a huge boon in a variety of areas. In radiology, for example, AI algorithms can more quickly and accurately analyze patterns in medical imaging, leading to reduced workloads on radiologists and better patient outcomes. As of late last year, nearly 700 AI algorithms have been approved by the FDA for healthcare use, and most are for diagnostic radiology.²

The thorough collection and rapid analysis of data offered by AI/ML can also significantly impact patient treatment plans and preventative care. The powerful predictive capabilities of AI, driven by patient data, can dial in a more effective treatment plan for patients and identify the need for proactive intervention.

Any discussion about AI and ML must include the potential for making administrative operations more efficient. Implementing automated processes has proven to be a time and money saver in a wide variety of industries, including hospital administration. AI is built to support optimizing staff scheduling, reducing patient wait times and streamlining or automating routine tasks.

AI/ML in action: Transforming hospital departments

Let’s take a closer look at how AI and ML can impact specific departments in a hospital facility.

Radiology was previously mentioned as being one of the big winners for adopting AI algorithms to help analyze medical imaging. In many hospitals, AI is leveraging a large data bank and expansive computing power to identify minute abnormalities and subtle patterns that might elude an overworked radiologist. It can also help prioritize treatment based on the results of the analysis.

AI tools can help hospital oncology departments to diagnose cancer more quickly and to develop more effective treatment plans. One trial found that AI and ML were adept at predicting optimal radiation doses for patients, increasing patient outcomes and decreasing side effects.³ AI’s ability to detect patterns can augment clinicians’ efforts to identify cancers through biopsies and tissue analysis.

Emergency rooms (ERs) are busy, chaotic environments where AI can assist clinicians in assessing and triaging patients to elevate experiences for both patients and staff. Streamlining check-ins, calculating peak inflow times to ensure adequate staffing and predicting the likelihood of critical conditions like sepsis are other ER-specific functions that AI and ML can streamline.

The fiber internet and cloud imperative

All these potential improvements depend on one critical element: massive, comprehensive data sets that can be accessed quickly. A network with high enough bandwidth to handle large data sets and low latency to reduce the response time for requests is imperative for AI and ML applications.

This network also needs to be fast and reliable to handle this volume of data and take advantage of high bandwidth and low latency. AI and ML need to access constant streams of information from various sources over the internet. The network must be sturdy as well; speed and bandwidth are no help if the connection is constantly going down. Creating seamless data transfer that enables real-time analytics is one of the main reasons to invest in AI.

Flexibility is important to the AI support structure as well. Existing resources that establish bandwidth, speed and low latency must be able to adjust to the demands of your ML and AI systems. A high capability for scaling means that, if an AI application needs to draw more resources than usual to complete a task, that request can be handled without overburdening your network.

Data security is paramount in healthcare, particularly since the industry is a high-value target for cybercriminals. In addition, regulatory compliance with HIPAA and other policies requires that protected health information (PHI) is shielded along every step of its journey through the healthcare ecosystem, which makes security a vital consideration when evaluating AI’s role in business. The information exchanged across the network must be safeguarded, whether in flight between AI and the cloud or at rest in your data store.

Finally, efficiency is a major goal for any technological advancement, including AI coupled with ML. Making a smart choice with your network structure can not only ensure better outcomes, but also help you make the best use of your resources when engaging with AI technology.

Fiber internet coupled with cloud computing can help your organization reach your speed, capability, effectiveness and safety goals when utilizing AI and ML. Fiber internet is the proven approach to establishing the connectivity needed for AI in many industries. Cloud computing has been around long enough to establish a well-earned reputation as a solid approach to increasing the accessibility and flexibility needed for AI while maintaining the highest standards of data security.

Harnessing AI and ML for enhanced hospital efficiency

Whether you are focused on improving your emergency room wait times, the diagnostic accuracy of your medical imaging or the efficacy of your drug research programs, AI coupled with ML offers many benefits. It can reduce your patient outcomes and staff burden while optimizing processes and saving your hospital money. To leverage these exciting new approaches successfully, you’ll need a fast, reliable fiber network internet connection teamed up with a solid cloud computing solution. Hospitals that have implemented these elements are already seeing healthy gains, and with the rapid advancement of AI and ML, as well as the technologies available to support these systems, their gains will likely grow exponentially.

Learn how Cox Business can help you with our cloud and fiber solutions.

References

1. Alanazi, A. 2022. Using machine learning for healthcare challenges and opportunities. Informatics in Medicine Unlocked 30. March 23. https://doi.org/10.1016/j.imu.2022.100924.
2. Fornell, D. December 13, 2023. FDA has now cleared 700 AI healthcare algorithms, more than 76% in radiology. Health Imaging. https://healthimaging.com/topics/artificial-intelligence/fda-has-now-cleared-700-ai-healthcare-algorithms-more-76-radiology.
3. Wei, L., Niraula, D., Gates, E.D.H., et al. 2023. Artificial intelligence (AI) and machine learning (ML) in precision oncology: a review on enhancing discoverability through multiomics integration. British Journal of Radiology 96(1150). October 1. https://doi.org/10.1259/bjr.20230211.