Latest Interaction of AI and Automation in the Healthcare Sector
Published by : Industrial Automation
The interaction between AI and automation could transform the way we work. In healthcare, these tools offer much more than commercial value. Here, there’s a potential to improve the cost, accessibility, and efficacy of patient care and have a positive impact on people’s quality of life.
Of course, automation is not a new concept in the medical field. Robots have been laboring away in labs and operating theaters for quite some time now. However, the improved accessibility of AI and automation in healthcare in 2020 has brought these technologies into mainstream consciousness. Essentially, these systems are becoming more affordable and easier to work with.
When Accenture surveyed healthcare executives in 2019, 94% reported that the pace of innovation in their organizations had “accelerated over the past three years due to emerging technologies”.
The interaction between AI and automation
There’s plenty of potential for AI and automation in healthcare in 2020. This is especially true when these capabilities are combined. This approach, often called intelligent automation, creates an environment where AI and automation work together to amplify their value—and that of every resource within the organization, including data and human expertise.
In 2020, AI can extend automation beyond simple, repetitive tasks into more complex areas that have previously relied heavily on human input. Moreover, healthcare professionals’ skills can be redirected to more valuable work; and they can access AI-driven insights and intelligent support to stretch their capabilities even further.
Intelligent Automation Ingredients
Key components of the intelligent automation environment include:
Process Automation Solutions: All-in-one features, technologies like robotic process automation (RPA) and low-code application development systems can be used to transform a series of slow, manual tasks into fast, error-free digital workflows.
Ai-Driven Technologies: Capabilities such as machine learning, deep learning, optical character recognition, and natural language processing push the boundaries of automation. While the applications are broad, you could, for example, use AI-enabled tools to decipher and organize unstructured data, identify patterns from previous experiences, and use advanced or predictive analytics to support smarter decision making.
Integration: When AI and automation technologies are connected to each other and integrated with the existing systems used in your organization, you can innovate and manage multiple processes on one platform. You can also ensure that all these tools are used to support common goals.