Hyperautomation not only speeds up processes in the healthcare industry, it frees up resources so organizations can focus on providing value-based care for patients.
Artificial intelligence, machine learning, and robotic process automation are making it possible to save time and money for both healthcare organizations and the patients they serve. Hyperautomation is the process of using these tools together to automate as many repetitive tasks within an organization as possible.
“There may be a spectrum of capability in this space, everything from task and workflow automation through robotic process automation to conversational tools like chatbots, to machine learning and predictive analytics and other types of cognitive intelligence through AI,” said Lisa Caplan, Executive Vice President for Technology and Innovation at HIMSS.
Organizations in every corner of the healthcare industry from clinicians, healthcare systems and payers are utilizing hyperautomation to save time and improve accuracy and efficiency.
The State of California Correctional Healthcare Services uses hyperautomation to help serve more than 98,000 patients, according to director of information technology Cheryl Larson.
She explained at a HIMSS21 digital session how hyperautomation can be used for everyday tasks which results in:
One example of how the organization has utilized automation is with their pharmacy.
“We’ve automated five board of pharmacy requirements for drug reconciliation for controlled substances,” Larson said. “The bot gathers the information from our Omni cell machines. It calculates and completes all of the reporting information, and then the pharmacist reviews the data for submission.”
Each of their 35 pharmacies has saved about 10 hours a month with this process.
“It's not only saved time, it's improved the accuracy, and it's helped with our compliance with the board of pharmacy,” Larson said.
They also utilized hyperautomation during the pandemic for mass testing of patients to handle time consuming work,several messages to get results approved and patients notified.
“In early December, we implemented a new automated bulk ordering for the labs. And then we automated the patient notification process,” Larson said. “The process first automated the testing orders once they were received. Then automated lab results would go into our electronic health record. Once it populated the electronic health record, if it was a positive test, it would automatically notify the clinicians, which is really a game-changer when you're trying to identify positive COVID tests.”
If a test was negative, the clinician would approve the results, and a letter would be printed for the patient. According to Larson, this system provided 730,000 notices that would have otherwise been manually done by clinicians.
Another use case for hyperautomation was helping with ADA accommodations.
“This is to assist our visually impaired clinicians to navigate and complete workflows in the electronic health record without an assist,” Larson said.
To begin working with hyperautomation, her organization looked at processes that included repetitive tasks. They recorded keystrokes and documented everything so they could challenge themselves to find new ways of doing things.
“From there, after the development, we pilot. We’ll take an effort that we've developed, and we will pilot it at one institution before rolling it out to all 35 institutions,” Larson said.
She added that there is great value in hyperautomation.
“The good news is that hyperautomation is not hard to sell to the organization. In our use cases, there are some very compelling results using this automation,” Larson said. “We are truly reducing the amount of time our clinical teams spent performing non-clinical tasks and improved accuracy.”
3M Health Information Solutions uses automation as part of their process to improve efficiency in the revenue cycle of healthcare, according to Jared Sorensen, vice president of revenue cycle solutions.
“We do a lot with revenue cycle automation in the space of healthcare, both on the front end with clinician solutions that drive front end speech and conversational AI with a physician while they're documenting patient care through the coding process and billing process on the backend,” he said at a HIMSS21 digital session.
One of the ways that hyperautomation can be used is to help clinicians with documentation, rather than having them dictate notes after a patient visit.
“There’s artificial intelligence that sits behind these tools and can prompt the physician or nudge them for additional information for clarity and documentation,” Sorensen said. “And thus you can get a more complete picture of the patient while they're there with them in a physician office visit. This is a level of creating time to care for the clinician and be able to make their work easier.”
Further downstream, he noted, automated tools can read the physician documentation and patient data, helping to prioritize the work of clinical documentation for quality specialists.
“These AI tools are identifying cases and factors that they need to explore and investigate,” Sorensen said. “Whether it's a documentation gap or a quality issue that needs to be addressed, it can be done in real-time through prioritization findings rather than looked at retrospectively after the patient has left.”
With more complete and accurate documentation, the next step would be to automate coding.
“We have models today that are very interactive with coders or medical coders and human review where it's not them finding all of the codes associated with a patient visit, but they're reviewing a set of autos suggested codes that are derived from artificial intelligence,” Sorensen said. “And in some cases where these are highly repeatable in spaces of radiology, ultrasound, mammography, some of those areas we can actually automate the intelligence there to drive a direct-to-bill revenue cycle model within healthcare.”
He noted hyperautomation should not only help with cost and productivity, but provide value through accuracy and compliance.
“But ultimately, if we can drive a greater level of automation, I think it does help us move closer to more of a value-based care model,” Sorensen said.
He added that there is a lot of time and energy, including staff resourcing, in generating a patient’s bill and sending it to them.
“If we could save on half of that cost and automate some of those processes, how much better could we be at assigning those resources to patient care tasks and quality of patient care in our healthcare system?” Sorensen asked. “I would suggest that when people approach automation, there is a dollars and cents component to it, but ultimately, it's to deliver something better.”
The views and opinions expressed in this content or by commenters are those of the author and do not necessarily reflect the official policy or position of HIMSS or its affiliates.
December 14–15, 2021 | Digital
Machine learning and AI are full of possibilities to address some of healthcare’s biggest challenges. Learn how leading healthcare organizations have leveraged the power of machine learning and AI to improve patient care and where they see real ROI—better care, cost containment, and operational improvements and efficiencies.