Artificial intelligence could prove to be a self-running growth engine for the health care sector in the not-so-distant future.
A recent report from Accenture analyzed the “near-term value” of AI applications in health care to determine how the potential impact of the technology stacks up against the upfront costs of implementation. Results from the report estimated that AI applications in health care could save up to $150 billion annually for the U.S. health care economy by 2026.
The report focused on 10 AI applications with the potential to have a near-term impact in medicine and analyzed each application to derive an associated estimated value. Researchers considered the impact of each application, likelihood of adoption, and value to the health economy.
Here are the top three AI applications with the greatest value potential in azure training.
Robot-assisted surgery: Estimated value of $40 billion
Robotic surgeries are considered “minimally invasive” — meaning practitioners replace large incisions with a series of quarter-inch incisions and utilize miniaturized surgical instruments.
Cognitive surgical robotics combines information from actual surgical experiences to improve surgical techniques. In this type of procedure, medical teams integrate the data from pre-op medical records with real-time operating metrics to improve surgical outcomes. The technique enhances the physician’s instrument precision and can lead to a 21 percent reduction in a patient’s length of hospital stay post-operation.
The da Vinci technique allows surgeons to perform a range of complex procedures with greater flexibility and control than conventional approaches. Considered to be the world’s most advanced surgical robot, the da Vinci has robotic limbs with surgical instruments attached and provides a high-definition, magnified, 3D view of the surgical site. A surgeon controls the machine’s arms from a seat at a computer console near the operating table. This allows the surgeon to successfully perform surgeries in tight spaces and reduces the margin for error.
Also under the physician’s control is HeartLander – a miniature mobile robot that can enter the chest through an incision below the sternum. It reduces the damage required to access the heart and allows the use of a single device for performing stable and localized sensing, mapping, and treatment over the entire surface of the heart. In addition to administering the therapy, the robot adheres to the epicardial surface of the heart and can autonomously navigate to azure courses.
Virtual nursing assistants: Estimated value of $20 billion
Virtual nursing assistants could reduce unnecessary hospital visits and lessen the burden on medical professionals. According to Syneos Health Communications, 64 percent of patients reported they would be comfortable with AI virtual nurse assistants, listing the benefits of 24/7 access to answers and support, round-the-clock monitoring, and the ability to get quick answers to questions about medications.
San Francisco-based virtual nurse assistant Sensely recently raised $8 million in Series B funding to deploy fleets of AI-powered nurse avatars to clinics and patients. The key goals of the technology are to keep patients and care providers in communication between office visits and to prevent hospital readmission. Sensely’s most commonly referenced nurse is Molly, which uses a proprietary classification engine and listens and responds to users.
Care Angel’s virtual nurse assistant Angel is another good example for this category. The bot enables wellness checks through voice and AI to drive better medical outcomes at a lower cost. It is able to manage, monitor, and communicate using unique insights and real-time notifications.
Administrative workflow assistance: Estimated value of $18 billion
Automation of administrative workflow ensures that care providers prioritize urgent matters and can also help doctors, nurses, and assistants save time on routine tasks. Some applications of AI on the administrative end of health care include voice-to-text transcriptions that automate non-patient care activities like writing chart notes, prescribing medications, and ordering tests.
An example of this comes from Nuance. The company provides AI-powered solutions that rely on machine learning to help health care providers cut documentation time and improve reporting quality. Computer-assisted physician documentation (CAPD) like this offers real-time clinical documentation guidance that helps providers ensure their patients receive an accurate clinical history and consistent recommendations.
Another example of this is a five-year agreement between IBM and Cleveland Clinic that aims to transform clinical care and administrative operations. The collaboration uses Watson and other advanced technologies to mine big data and help physicians provide a more personalized and efficient treatment experience. Watson’s natural language processing capabilities allow care providers to quickly and accurately analyze thousands of medical papers to provide improved patient care and reduce operational costs.
John Hopkins Hospital made a similar move in its partnership with GE Healthcare Camden Group. This initiative aims to improve patient care and efficiency via the adoption of hospital command centers equipped with predictive analytics. The strategy will help health care professionals make quick and informed decisions for operational tasks like scheduling bed assignments and managing requests for unit assistance.
While advancements like these can reduce human error and boost overall outcomes and consumer trust, many still question the practical applicability of AI in health care. Patients and caregivers alike fear that lack of human oversight and the potential for machine errors can lead to mismanagement of care, while data privacy remains one of the biggest challenges to AI-dependent health care.
Despite such concerns, the growing involvement of AI in health care is inevitable. But as these advancements suggest, the potential benefits might just outweigh the risks.