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Healthcare Intelligent Automation: Use Cases & Examples [2024]

The use of artificial intelligence (AI) and automation technologies such as RPA is transforming the healthcare industry by reducing the reliance on repetitive and manual tasks that prevent healthcare workers from focusing on higher-value activities. According to McKinsey, 33% of healthcare providers’ tasks are likely to be automated, which can reduce costs and improve healthcare affordability.

Intelligent automation, also called cognitive automation or hyperautomation, which is the combined use of automation technologies with AI methods such as:

to enable end-to-end process automation through intelligent bots with decision-making capabilities. In this research, we’ll explore several intelligent automation use cases in the healthcare industry.

Use cases

Customer service and scheduling

AI-powered chatbots integrated with RPA bots can handle repetitive customer service tasks and enable self-service patient scheduling by:

  • Interacting with patients about their health problems, 
  • Extracting information from other systems about suitable physicians and time slots,
  • Sending reminders,
  • Allowing rescheduling and canceling appointments.

This can allow healthcare providers to:

  • Improve employee productivity by freeing them from mundane customer service tasks and assisting them with complex tasks.
  • Reduce no-shows through reminders and follow-ups.
  • Improve patient experience with timely and 7/24 customer service.

Regulatory compliance

Healthcare organizations must comply with numerous regulations, including HIPAA, which protects patients’ medical information. Failure to comply with HIPAA results in penalties ranging from $100 to $100,000 per violation.

Smart bots can enable healthcare providers to automatically log every action, track and document the activity log, increase transparency, and ensure compliance. In addition, intelligent bots can predict potential fraud and prevent intended or unintended data breaches.

Health insurance processing

AI-enabled RPA bots can automate tasks such as:

  • Prior authorization (preauthorization): This is a time-consuming process in which healthcare providers obtain approval from health insurers to determine whether a healthcare service or a medication is necessary and cost-effective for the patient. Intelligent bots can check whether prior authorization is required and make the necessary updates to the electronic health record (EHR).
  • Claims processing: Smart bots can significantly improve processing health insurance claims by:
    • automating labor-intensive tasks
    • reducing human errors
    • extracting necessary data with NLP models
    • detecting healthcare fraud more accurately with ML models
    • improving customer satisfaction with chatbots

Feel free to check our article on how AI changes health insurance.

Healthcare analytics

AI-enabled bots can run analytical models for the processes they are deployed on. This can help healthcare providers conduct health and operational analytics to improve various processes such as:

  • Fraud prevention
  • Data security
  • Treatment planning
  • Predicting drug outcomes

You can check our article on healthcare analytics for more examples.

Case studies

Feel free to read our article on intelligent automation case studies. Some example case studies in healthcare include:

ApprioHealth

Problem: ApprioHealth provides technology solutions for healthcare providers that facilitate health insurance claims processing to expedite and maximize reimbursements for their customers. The company began using RPA ten years ago to automate Medicaid applications and insurance reimbursement processes. However, diverse and ever-changing interfaces of different systems that are part of today’s healthcare infrastructure still require a great deal of human effort.

Solution: The company implemented an intelligent automation solution that leverages RPA and computer vision. This computer vision-based solution helped ApprioHealth create an automation system that can capture and process data from different systems regardless of their interfaces.

Results: ApprioHealth uses four software robots that can process seven times the number of claims previously handled by four employees.1

AMN Healthcare

Problem: AMN Healthcare provides healthcare staffing and recruitment process outsourcing solutions to healthcare providers in the US by placing freelance medical staff for open healthcare positions. The company receives timecards for hours completed, pays the medical staff for their labor, and bills the hospital. Processing timecards was prone to errors and around 200 timecards went lost each year, posing a legal risk to the company.

Solution: The company created a mobile app and adopted an intelligent automation solution that allows nurses to photograph their timecards and upload them to the company’s system. The intelligent automation tool processes the timecard photos, determines what type of work is being billed, and routes it to the appropriate team for processing.

Results: The company reduced the time spent on processing timecards by up to 68% and reduced number of hours requiring human intervention during processing from 8000 to 2600.2

For more on intelligent automation

If you want to explore intelligent automation use cases in your business, feel free to check our comprehensive article on intelligent automation use cases. We also have a data-driven list of intelligent automation solutions. If you need an expert opinion, we can help:

Find the Right Vendors

Sources

1, 2

Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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