Artificial intelligence is transforming every industry including healthcare. With the right data, disruptive technologies, and personnel in place, artificial intelligence is advancing many healthcare activities. While many medical institutions use technology at the point of care, some have moved ahead to signal AI in healthcare for critical decision-making.
The digital wave has introduced AI in healthcare system. But as technology continues to evolve and disrupt more medical services, healthcare companies are moving from artificial intelligence to augmented intelligence. The traditional AI in healthcare was intensely focusing on improving analytical efficiency, while augmented intelligence is all about increasing clinical decision-making capabilities. Even some healthcare companies are using artificial intelligence-based techniques to predict risks for certain diseases or disorders. On the other hand, machine learning models are obtaining patient data to determine their percentage of risk over an illness. In a nutshell, technology has been driving the healthcare sector towards a massive disruption and here we are today with AI in clinical decision-making helping healthcare workers ease their burden. According to a survey conducted by the World Economic Forum, more than 50% of the executives who participated concluded that they expect the first artificial intelligence machine to be on a board of directors of a business by 2025 and the first transplant using a 3D-printing liver is likely by 2024. But despite the acceptance of the technology on many fronts, healthcare professionals still remain quite stubborn in their stance. Attendees at the 2019 Healthcare Analytics Summit (HAS) said that they weren’t ready to hand over medical decision-making to machines.
However, despite some resistance, AI in clinical decision-making is increasingly coming into the mainstream healthcare process. Recently, researchers at MIT have come up with a new machine learning model that could accelerate the use of AI in clinical decision-making. The model was able to identify voicing patterns of people with vocal cord nodules and in turn, use those features to predict the disorders.
Artificial Intelligence in Clinical Decision Support System
Clinical Decision Support System (CDSS) is a disruptive healthcare tool that has the ability to analyze large volumes of data and suggest next steps for treatment, flagging potential problems, and enhancing care team efficiency. In general, it assists the clinical decision-making process. Recently, CDSS has been extending its capabilities, thanks to artificial intelligence and machine learning. With the help of technology, CDSS was able to analyze more data and suggest effective treatments based on patients’ health conditions. The large volume of clinical data available on the web and the dominance of artificial intelligence has accelerated the adoption of CDSS in every healthcare facility. Some of the benefits of using AI-based Clinical Decision Support Systems are listed as follows.
- It is nearly impossible for humans to go through every bit of data. Fortunately, machines can do that in minutes. Therefore, artificial intelligence has increased capability to diagnose the disease and treatment with great accuracy.
- AI-powered CDSS helps healthcare professionals make well-informed decisions in a shorter period of time. Besides, it can even suggest a best practice for post-surgical patient discharge, recommend medications and doses, and recommend periodic follow-up checks and tests to ensure optimal patient care.
- One of the major problems that healthcare professionals are facing today is clinical burnout. However, CDSS can relieve them from repetitive decision-making tasks and ease their burden.
A Study Suggests that AI Makes Better Clinical Decisions
Despite the enormous development, people have trust issues when it comes to artificial intelligence. Many healthcare institutions walk away from tech-based tools over the fear of mishaps. But a recent study has come as a relief to many such medical companies that are in oscillation.
Marc Lanovaz of Université de Montréal and Kieva Hranchuk of St. Lawrence College, in Ontario, have observed the performance of AI in making decisions for behavioral problems. They have compared 1,024 individual’s treatment with the one that AI has generated. As a result, approximately 75% of the conclusion was similar. After obtaining success in the first study, the duo is heading towards even more intense research by integrating their model to facilitate the work of professionals, not actually replace, while also making treatment decisions more consistent and predictable.