The experts agree; we are in the midst of a healthcare revolution. Change is happening at an unprecedented pace, driven largely by remarkable advances in artificial intelligence and machine learning.
Just how big an impact are these technologies having on health industries? The numbers speak for themselves:
- According to CB Insights, there are currently more than 100 AI startups focused on healthcare, a dramatic increase from fewer than two dozen only a few years ago
- Industry analysts IDC predict that 30 percent of healthcare providers will use cognitive analytics with patient data by 2018
- Chatbots could save healthcare providers $8 billion annually worldwide by 2022, up from $20 million in 2017 says Juniper Research
- A report from McKinsey estimates big data could save medicine and pharma up to $100 billion per year, thanks to more efficient clinical trials and research, improved insights into diagnoses, predictive analytics to help determine the most effective treatment plans and new technologies that will help insurers, regulators, administrators, healthcare providers, patients and caretakes make better decisions
The digital transformation will touch every facet of the healthcare industry, from management and administration to patient treatment and follow up care. We’ve compiled a list of the top five areas in which AI is the catalyst for healthcare revolution:
1. Access to care
Long wait times, prohibitive costs, and living in remote locations are common barriers many patients face when trying to access care. Virtual healthcare providers are relieving many of those burdens through AI. Using chatbots and / or speech recognition, a virtual doctor can check reported symptoms and personal medical history against a database of known diseases and conditions, to provide a diagnosis and recommend a treatment plan. This technology looks especially promising in the field of mental health care, where video chat and teleconferencing has long been an established way of delivering counselling and talk therapy. But it seems pschyotherapy delivered by a virtual caregiver and driven by AI may be just as effective in many cases, enabling many more patients to receive the care they need.
When it comes to using AI to diagnose illness, the future is now. A number of apps and tools already exist which analyze the symptoms inputted by a user to generate a list of likely medical condition culprits. But the technology is capable of much more advanced diagnostic applications. Technologies currently being tested include those that can analyze blood test results and screen radiology images more quickly and in much more detail than the human eye, flagging potential areas of concern and resulting in earlier, more effective treatment.
The benefits of AI as tool to help physicians develop treatment plans are considerable. IBM’s famous Watson, for example, is now able to recommend detailed and personalized evidence-based treatment plans to oncologists for cancer patients by searching, analyzing and understanding millions of data points including the patient’s medical records, clinical notes, and data from external research, trials and studies. In a double-blind study, the doctors at one hospital group found that Watson’s treatment recommendations were concordant with their tumor board’s recommendations in 90 percent of breast cancer cases.
4. Patient monitoring and follow up care
With a rapidly aging population and a steady increase in chronic conditions which require ongoing management, monitoring and providing adequate follow up care to patients is an expensive and time-consuming challenge for most healthcare providers. Artificial intelligence can improve efficiencies and reduce costs, while maintaining or even improving patient compliance and satisfaction rates. Biosensors can analyze data and send alerts to healthcare providers, allowing patients to recover at home rather than in a hospital. An AI healthcare provider can act like a personalized assistant (think Siri for healthcare). It records daily activities, tracks pain levels and other symptom, and provides medication reminders and appointment alerts to monitor a patient’s recovery and boost compliance with the recommended treatment plan.
5. Drug development and testing
The initial development and testing phase for new drugs is expensive and sometimes frustratingly slow. A number of health tech startups are aiming to change that using AI. Machine learning algorithms remove much of the guesswork that comes with the early stages of drug development and employ predictive analytics to determine what impact various modifications in formulations and dosages will have on a drug, and which changes should to be made to the drugs to make them safer and more effective. The net results is more effective treatments brought to market more quickly, and at a lower cost.