Curing pain points: the role of AI in healthcare

AI is a compelling fit for healthcare, especially when there’s promise to advance diagnosis, assist surgery and enhance drug discovery. However, the narrative of AI in healthcare can be slightly skewed. Breakthrough developments such as early cancer detection understandably make headlines; but they’re often some way from leaving research labs and making it to real-world datasets. As an industry, healthcare also comes with certain challenges to achieving an AI-centered approach – among them questions around access to healthcare data, standardization of regulations and AI performance critical situations. Nonetheless, AI has a lot to offer and is already gaining notable traction in the healthcare industry, both behind the scenes and directly with patients.

Gartner believes AI is on the cusp of becoming one of the most important transformational forces in healthcare. By 2026, AI has the potential to save the US healthcare industry $150 billion annually and truly augment the capabilities of human health workers. As health institutions cope with an aging population, increasing patient demands and a steep shortage of physicians, investing in AI will be crucial to helping tackle some of these pain points.

To get a clearer picture of AI in healthcare, let’s look at the applications providing the most value in the industry today:

Reducing admin time

Completing routine duties such as chart notes, treatment plans, prescriptions and billing is critical for any health institution. However, excessive administrative tasks keep medical professionals from giving more time to patient care. With the shortage of physicians expected to reach 120,000 by 2030 and the number of people aged 65 and over set to double within the next 40 years, solutions are needed to avoid worrying delays as time becomes inevitably scarcer.

Subsets of AI, natural language processing (NLP) and speech recognition are readily used across health institutions to handle repetitive, time-consuming tasks. From transcribing text to extracting and structuring information, these technologies streamline clinical documentation. For example, speech recognition is allowing medical professionals to transcribe voice into text, while NLP is being used to structure and analyze large amounts of medical research. As well as saving time, these applications ensure a higher level of accuracy, fewer errors and better consistency across patients’ history and recommended treatment plans.

Improving care with virtual nursing

Preventing unnecessary hospital visits not only lessens the load for medical professionals, it also leads to a happier patient. So, when looking to advance care outside facilities, AI is a frontrunner. Powered by NLP and using voice or text, virtual nursing assistants are now supporting patients around the clock by quickly responding to concerns and monitoring recovery progress. As a result, they’re also helping prevent readmissions through frequent communication, extracting critical insights and alerting a medical professional on-duty if any action is required. Harvard estimates that AI-powered virtual nurse assistants could save $20 billion annually and 20% of nurses time.

Enhancing surgery with AI-assisted robotics

Leading the value potential for AI in healthcare, AI-assisted robotic surgery is becoming increasingly popular with the potential to save the industry $40 billion annually. Using machine learning to combine pre-op data with real-time metrics, the technology physically guides surgeons during procedures. Bringing several talents to the table, AI-assisted robotics enhances instrument precision, reduces variations across patients and improves future surgery through data collection. Considered minimally invasive, patients’ hospital stay post-surgery is cut by 21% due to significantly faster healing times. A win-win for both the patient and medical staff.

Preventing (cyber) viruses

The healthcare industry is relying on internet-connected devices more than ever, playing a vital role in hosting medical records, billing and patient care. The downside? Every new device opens a new door to cyberattacks, so protecting sensitive patient data is a top priority. While attacks are on the rise across all industries, healthcare is really feeling the brunt of it, the worst-case scenario being the loss of patient records and history, posing a dangerous threat in emergency situations. Beyond the conventional cybersecurity systems, AI and machine learning can help identify and prevent cyber viruses and attacks – detecting suspicious files, websites and domains and isolating them before they can do harm. Predictive analysis can highlight future suspicious behavior and NLP can support prevention by selecting useful information from scanned data on previous attacks, enabling better risk analysis and management.  

Looking to the future

So what barriers are impeding the safe and ethical progress of the more transformative, breakthrough AI initiatives in healthcare? Firstly, access to large healthcare data is limited and high-quality data – a necessity for efficient applications – is extremely difficult to come by outside the medical research profession. Secondly, regulations and guidelines are currently ambiguous and lack stringent management, meaning stronger restrain towards AI growth. And finally, the legal risk of algorithms undertaking critical tasks and their failure to perform. However, the future is bright and the life-saving potential of those initiatives is most certainly worth the wait.

Meanwhile, as long-term innovations continue to progress behind the scenes, AI capabilities have started to make a marked difference across many healthcare touchpoints. For now, the focus for providers should be towards areas where AI is mature and can deliver value today. Looking forward, as barriers are addressed and developments gain momentum, we can expect an even fuller realization of AI’s potential to truly revolutionize the healthcare industry.