Growing Investment in AI: Here's What Healthcare Providers Need to Know

Healthcare AI

AI offers significant benefits for the healthcare industry, yet organizations have been lagging when it comes to implementation. It’s time for AI’s potential to be activated.

Artificial intelligence is rapidly expanding its reach into industries from transportation to healthcare. While medical practices have initially been slow to implement this technology, healthcare leaders are now acknowledging that adopting AI is an important step toward greater efficiency and reduced costs.

In recent years, trust in AI has been growing. The healthcare industry has seen the successful implementation of AI not only in simplifying administrative tasks, but also in aiding with diagnosis and drug discovery. With the continued expansion of AI, healthcare providers will likely see an increased acceptance of the benefits it can provide. 

The Importance of AI in Healthcare 

One of the most obvious benefits of AI includes taking over administrative tasks. The technology can help with managing electronic health records (EHRs) and detecting waste and fraud in reimbursement. It can also be used to synthesize large amounts of patient data, which in turn improves disease prevention, diagnosis, and treatment. 

As healthcare leaders continue to invest in AI, its benefits will become even more extensive. For example, Mount Sinai’s 2021 launch of a new AI research center is intended to not only improve the quality of care that patients receive, but also more accurately predict patient outcomes and develop new therapies. Other AI initiatives aim to assist emergency departments in treating triage patients, help radiologists prioritize cases that involve collapsed lungs, and boost the efficiency and speed of coders and clinicians. 

With AI, the door is open to more innovative research and the development of better treatment options for patients. These new treatments not only have the potential to improve patients’ clinical outcomes, but they are also likely to be less expensive than drugs that are produced on the standard timeline. 

The Challenge of Implementing AI

While AI presents incredible possibilities, the healthcare industry remains hesitant to fully embrace it. One of the major sticking points for many organizations is the large upfront cost that can be involved in adopting AI. While it’s true that AI can be costly in the short term, it is expected to help organizations save money in the long term. 

Another major hurdle is the struggle to train doctors and other care staff in new technologies. In a recent Optum survey, nearly 90 percent of healthcare leaders reported that AI training is not occurring quickly enough. As a result, many of them said that hiring people with experience in AI was a priority. The reality is that most healthcare organizations believe they are not prepared to successfully implement the latest AI tools.

Yet the biggest challenge with implementing AI is that while most healthcare leaders appreciate the benefits of the technology, many are hesitant about its applications in a clinical environment — the very area in which it holds the most potential. 

Promising Developments in Healthcare AI

In recent years, there has been a lot of noteworthy progress in using AI to manage patient records as well as develop new drugs and therapies. There are also great strides being made in using AI to identify potential patients for clinical trials, which saves time for both medical professionals and participants. 

The progress in AI implementation fits into the general trend of greater patient centricity within the healthcare industry. In particular, AI can make care more accessible to patients and help medical professionals create more specialized and individualized treatment plans. 

Most importantly, AI has the potential to find previously unidentifiable treatments for serious genetic and chronic conditions. It may also be able to provide those treatments at a significantly lower cost. With AI shortening drug development timelines and enhancing the ability to predict drug efficacy, the cost of developing new drugs could be reduced by tens of millions of dollars, which ultimately translates into lower costs for consumers.

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