February 24, 2020
Current automation bots have moved way beyond simple robotic process automation (RPA). With advances in artificial intelligence technology, automation bots can now exhibit decision-making capabilities, enabling them to handle far more complex processes than ever before.
Artificial intelligence is a broad category encompassing many different technologies. And as these specific areas advance, the decision-making abilities of automation bots continue to improve. Let’s look at six of these technologies to understand how they help automation bots make decisions:
Machine learning (ML) refers to a system that can learn by itself and autonomously improve its performance. Based on the data inputs that it continuously gathers and reads, it adjusts its models in real-time for optimal outcomes. Unlike RPA that requires explicit coding for each iteration of a task, machine learning makes decisions about its models based on the knowledge it gathers over time.
Some of this machine learning is supervised – a human provides a training dataset with labeled inputs and the correct outputs for the bot to learn from and apply to future scenarios. Machine learning can also be unsupervised, where the bot takes unlabeled input data and finds naturally occurring patterns, groupings, and trends.
Computer vision (CV) is the technology that enables a bot to “see” an image as a human does. It can identify scenes and find objects, and then make decisions about the images, such as classification, identification, verification, detection, segmentation, and recognition. Computer vision also encompasses optical character recognition (OCR) that recognizes words in images, such as a scanned invoice, and converts it to text.
One of the biggest benefits of computer vision is a bot’s ability to understand the visual makeup of a user interface, enabling the bot to interact with existing programs and systems the same way as humans. And when the interface changes due to an update, the bot can still find the information it needs.
Natural language processing (NLP) is the technology that enables a bot to “read” text the same way a human would. Natural language processing can read through essentially unlimited unstructured data, from contracts to social media posts, and extract the relevant data.
NLP has existed for a long time, but it has continued to improve its performance. And as its performance improves, so does the value of the information it extracts. This data can then be used as inputs for other machine learning algorithms.
Predictive analytics may not be new, but when paired with the computational power of AI, its abilities are exponentially greater. Before AI and RPA, predictive analytics was limited by speed and the amount of data. Now, a bot has access to exponentially more data and can run thousands of scenarios quickly to optimize performance. These AI-powered decisions and predictions can then be made based on the optimized model and the corresponding probabilities.
Deep learning is one of the most advanced capabilities of artificial intelligence. It attempts to work more like the human brain – only faster, more accurately and with unmatched capacity – by connecting different networks or layers of information, called artificial neural networks (ANNs).
Drawing on the wealth of data available – through traditional data collection as well as computer vision and natural language processing – deep learning has the potential to learn and accomplish tasks that humans cannot. Deep learning can easily identify patterns and correlations and make predictions that outperform our human capabilities.
The collection of data isn’t a technological breakthrough, per se, but the sheer amount of data that is amassing is worth mentioning. Data is what powers every automation bot to exhibit decision-making capabilities. And the more data that a bot has, the more that is possible for creating models and artificial neural networks to improve that decision making.
Over 2.5 quintillion bytes of data are created every single day. That’s eighteen zeroes. As the processing power and speed of artificial intelligence increase, AI will be able to mine this data to find insights that today we can only dream of.
None of these technologies work in a silo. Together, these different advances are making it possible to do more with artificial intelligence. As the “intelligence” of AI grows, so do its real-world applications, especially in healthcare.
AI is uniquely positioned to disrupt healthcare: from digesting huge amounts of data, interoperability challenges, and a pressing need for change. Healthcare is facing a nursing shortage, physician burnout, overwhelming administrative complexity, and rising costs. Automation bots may be the solution. By combining the brute force power of RPA with the problem-solving and decision-making skills of machine learning and deep learning, AI bots can connect healthcare’s disparate data sets to help with everything from billing to fraud to improving the scope of research.
Automation bots that can make decisions are already improving provider’s administrative and operational workflows – and they’re tackling more complex tasks than ever before, from benefit checks to claim management. And unlike the simple RPA bots of the past, these automation bots continue to add value over time by finding new insights and opportunities for improvement. With healthcare’s constantly changing landscape and complex administrative processes, we need AI to handle more of the workload. AI reduces errors, finds and tackles waste, and frees up human employees to focus on patient care, not paperwork.
Let’s look at two examples of how bots are automating some of healthcare’s processes:
Using OCR and CV, artificial intelligence can completely streamline dependent eligibility verification. A bot can read documents – birth certificates, marriage certificates, 1040 tax documents, utility bills – and then decide what kind of document it is. Once the document type is identified, the bot can automatically pull the necessary information to determine whether the insured’s dependents are eligible for coverage.
Another example is AI’s ability to handle insurance subrogation to determine whether a claim should be billed to a patient’s insurance or the liable party’s insurance. For example, imagine you get into a car accident, go to the hospital, and then pick up both pain medication and allergy medication at the pharmacy. AI can determine that the hospital visit and pain medication are related – and the claim should go to the liable party – but the allergy medication is not.
Individually, these may not seem like much, but there are thousands of more examples of automation bots exhibiting decision-making capabilities to eliminate bottlenecks at our healthcare organizations. And each task that can be automated by AI helps our healthcare system serve its patients better.
At Olive, we believe that an AI workforce is the answer that healthcare has been waiting for. That’s why we’re working every day to improve hospitals’ behind-the-scenes workflows with intelligent automation. Olive is more than RPA – she is an intelligent AI workforce that can learn and make decisions to improve operational processes across the network Olive serves.
When you hire Olive, you get an AI workforce that delivers immediate results through RPA and long-term value through ML insights and improvements. Contact us today to schedule a demo of Olive AI to see how artificial intelligence can help your organization do more with your data.
Healthcare looks quite different than it did just a few short years ago. The COVID-19 crisis has created a distinct “before” and “after” for the world, and especially for healthcare.
Artificial intelligence (AI) is revolutionizing healthcare data analytics and changing the way we predict, learn and act based on insights gained through AI-powered data models.