The hype surrounding artificial intelligence often gets caught up in attention-grabbing aspects of the technology, like using robots to perform surgery. But one of the most prominent benefits of AI that often goes unnoticed is facilitating automation in healthcare. Using AI to automate basic tasks and administration leads to a better patient experience, improved quality of service, improved project implementation and decreased overall costs.

Technological developments in AI and machine learning are the main driving factors for adopting the technology in everyday healthcare scenarios. As practitioners, businesses and IT leaders collaborate to improve clinical outcomes, patient experience and medical operations, having a clear understanding of the opportunities these technologies can create is essential.

Using AI and machine learning, computers are programmed to identify patterns in unstructured data and turn it into structured data in a manner that facilitates automation. In the next few years, we can expect innovations in electronic health records, revenue cycle and operation to feature AI and machine learning prominently throughout the healthcare system. This includes integrating AI into the clinical workflow in existing tools to empower medical professionals with real-time data at the point of care.

Ways Automation Can Make an Impact on the Healthcare Industry

As these tools are being integrated more and more into medical processes, medical institutions will need to have the correct infrastructure to support high-performance computing. Something that is fast, dependable and able to handle massive volumes of data.

Let’s look at a few aspects of automation using AI that can have a big impact in several operational administrative areas:

  1. Faster data to enrich Electronic Health Records (EHRs) Vendors are striving to modernize the capabilities of EHRs to create and extract data in real-time using new application programming interfaces and using new and innovative methods to collect data. It will help reduce physician frustration as well as enhancing patient care as medical professionals have access to the information they need at the right time and place.
  2. Improved ordering One time-consuming process for physicians is the ordering process within EHR. to put this into perspective, ten years ago, physicians would simply scrawl and prescription in seconds and now it takes multiple clicks to complete an order. Using AI and predictive technologies can create a significant impact and drive efficiency.
  3. Smarter billing Using machine learning decision-making aspects like pre-authorizations can be automated. Ai based tools can be used in revenue cycle applications to enable organizations to generate bills quicker, ultimately allowing bills to be presented to patients before they leave the hospital or medical practice. This is crucial for high deductible health plans with higher patient payments.
  4. Adaptive staffing Health systems are starting to implement machine learning to manage staff to support fluctuating emergency department patient volumes and decrease the waiting time for ambulatory services. By incorporating historical data from multiple sources, organizations can assess when to increase staff to handle an influx of patients during a flu season or increase other support staff during warm weather to ensure a better patient experience in the emergency room.