Artificial intelligence is paving the way in healthcare innovation by reinventing the way care is delivered, and potentially saving billions for the US healthcare economy over the next 10 years.
Gone are the days when artificial intelligence (AI) was solely reserved for Hollywood blockbusters. As a digital-centric society that eats, sleeps and breathes the benefits of Internet of Things (IoT), AI is being called upon – not for the sake of some diabolic Westworld reenactment – but rather as a welcomed solution in today’s evolving healthcare agenda.
With the emergence of mobile apps, enterprise cloud software, predictive analytics and wearable technology, the healthcare industry’s big data deluge commands nothing less than an arsenal of supercomputers just to keep pace. Cue the introduction of artificial intelligence: employed to help unearth patterns and predictions that can better enable healthcare delivery and improved patient outcomes.
AI is comprised of a broad set of advanced and emerging technologies that harness machine learning, natural language processing, and advanced analytics to unlock tremendous areas of improvement across a wide spectrum of clinical care that include:
Helping to diagnose illnesses/conditions;
Providing a medley of patient-centric treatment options;
Reconciling and preventing human-generated errors; and
Taking on repetitive, clerical tasks that bog down healthcare processes.
Addressing the gap between clinician supply and clinician demand.
With these measures, clinical efficacy goes up, wasted expenditures go down, and the patient experience improves.
Based on its extraordinary potential to redesign today’s healthcare system, investments in artificial intelligence are on the up-and-up. In fact, according to a recent Accenture report, clinical health AI applications as a key component of Health Information Technology can create $150 billion in annual savings for the US healthcare economy by 2026, alone.
$150 billion in annual savings leveraging the use of health information technology
AI in Action
Today, there are meaningful advances that combine machine learning, NLP and human expertise. One such advance can be found in the area of sepsis, which relies on advanced algorithms and NLP to identify infection sources from real-time EHR data feeds. Inclusion of clinical documentation ranging from progress, consult, and admit notes (which are often times unstructured), as well as nursing assessments, pathology and radiology reports, can help bridge data silos that hinder clinical decision-making and patient outcomes. In turn, this can accelerate the speed and accuracy of alerts, along with the actionable information generated.