Healthcare Companies See Many Benefits from AI

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In the race to AI, enterprises in the healthcare vertical are among the top adopters. They are showing some of the most focused and efficient applications, as well as some of the longest lived. According to Futuriom Cloud Tracker Pro's Enterprise AI Index, multiple hospital chains have been using a variety of AI tools since at least 2020.

New York’s Mount Sinai Health System, for instance, deployed AI in 2020 to help remotely identify patients infected with COVID-19. The application, which combined imaging with patient data, allowed patients to isolate and prevent further spreading of the virus. Since then, Mount Sinai has expanded its AI use to include diagnostic image analysis, predictive care, and streamlining administrative operations.

Mount Sinai wasn’t alone in getting an early start on AI. Since before 2022, the Mayo Clinic in Minnesota has used AI for a range of applications, including patient scheduling and other administrative tasks, as well as in diagnostic imaging and matching patients with clinical trials.

Since 2022, Chelsea and Westminster Hospital in the UK has deployed an AI-enabled tool called DERM from Skin Analytics to capture images from an enhanced iPhone for diagnosing skin cancers in real time.

Why AI Is Great for Healthcare

AI benefits hospitals, other healthcare facilities, and medical research institutes in a variety of ways. According to Futuriom’s data, collected in over 100 records in our Enterprise AI Index, the top factor is patient outcomes, closely followed by operational efficiency. Any AI that can reduce the number of hours spent in tasks such as filling out forms or documenting patient visits results in better use of time by medical staff—and more time available to address patient issues to achieve better results.

Improved efficiency also makes room for better scientific and/or medical advancements, including discoveries about the nature of illnesses and their treatment. This in turn leads to the capability to hone patient treatment plans, our data shows.

UCLA Health, for instance, has devised predictive algorithms that work with electronic health records to identify patients with rare diseases that typically take over 10 years to diagnose. UCLA Health also uses AI to detect and treat cancers.

Notably, operational efficiency is also cited as a leading benefit of AI for the other top vertical markets (financial, retail, and manufacturing) among the many we're tracking:

Time Savings Lead to Better Patient Outcomes

India’s Apollo Hospitals network demonstrates how the leading AI benefits reported by healthcare enterprises work together. The hospital system has over 10,000 beds in an environment where medical staff time is limited while patient populations are increasing. Apollo has leveraged a variety of third-party and proprietary AI tools to streamline medical procedures and glean information for better patient outcomes.

AI applications at Apollo include a Clinical Intelligence Engine (CIE) and AskApollo, which are generative AI apps built on Google’s Vertex AI and Med‑PaLM 2. Both apps are trained on decades of Apollo’s internal clinical data, aiding doctors in diagnosis, treatment planning, and patient Q&A.

"Our aim is to free up two to three hours of time daily for doctors and nurses with AI interventions," Apollo's Joint Managing Director Sangita Reddy said in an interview with Reuters earlier this year. That time savings is vital, since the hospital is seeing a 25% attrition rate among nursing staff, which it expects will increase to 30% by the end of this year.

Apollo also used Microsoft's Azure and AI Network for Healthcare products to generate an India-specific cardiac risk application trained on over 400,000 patient records. Separately, a proprietary ProHealth Predictive Engine is the core of an application that gauges patients' potential for future health risks. Apollo also uses speech recognition and natural language products from Augnito to automate clinical documentation. And the network also uses an operating room scheduling tool based on AI.

Nursing a Focus for AI Assistance

Like Apollo Hospitals, Mount Sinai is focused on time savings for nurses. In one of its applications, electronic medical record (EMR) data from nursing notes has been combined with machine learning algorithms to predict which patients admitted to the hospital are likely to fall or become delirious. Another application focuses on preventing bed sores based on client data.

“In many cases the nursing documentation can really power AI,” said Robbie Freeman, DNP, who serves as the Chief Digital Transformation Officer for Mount Sinai Health System. “Much of nursing documentation data reflects their expert observations and has predictive power. So, using things like natural language processing algorithms, the nursing observations and assessments are really helpful in the development of AI tools that have broader use and impact.”

As noted, the information above was gleaned from Futuriom’s Enterprise AI Index, for which data will be released soon to Cloud Tracker Pro subscribers. Stay tuned for updates.