For many executives in the healthcare industry, artificial intelligence brings to mind robot-assisted surgeries, AI-developed drugs, or automated image diagnoses. And while these clinical applications may be exciting, operational AI applications represent a significant opportunity for optimization across health system administration.
Healthcare’s complex behind-the-scenes administrative and operational workflows are causing inefficiencies, physician frustration and burnout, increasing costs, and a diminished patient experience. The solution to these problems may just be artificial intelligence and the growing promise of an AI-powered workforce.
AI applications in operations management can quickly provide positive ROI and free up valuable employee time to work on higher-value projects.
Healthcare technology advances such as machine learning, computer vision, and machine learning have increased the potential for applications across operational AI – today, artificial intelligence is already automating many of healthcare’s most costly, high-volume routine processes. Healthcare AI has proven to improve workflows, cut waste, reduce errors, and shift human employees focus from manual data entry to more complex tasks that require a human touch. Here are eleven ways that healthcare is already using AI in operations management to create efficiencies, reduce costs, and improve the patient experience.
Applications of artificial intelligence and automation in healthcare operations:
1. Eligibility Checks and Prior Authorizations
By combining robotic process automation (RPA) with computer vision (CV) and machine learning (ML), artificial intelligence can automate much of the eligibility check and prior authorization processes that bog down healthcare administration today. For instance, an AI workforce can securely log in to EMRs, check insurance information, and verify benefits with increased speed and accuracy. Healthcare AI can also determine when a prior authorization is needed, automate submissions, check statuses, and increase the speed of approvals and reimbursement. One hospital saw a 30% reduction in days in A/R after hiring Olive to automate eligibility checks, and it would take 127 full time employees to accomplish Olive’s authorization workload at this 3,000 bed health system.
2. Denials Management
The economic magnitude of claim denials is huge, costing the average hospital $4.9 million1. Using AI for automated eligibility checks, prior authorizations, and claims management significantly reduces denials before they happen, but AI can also manage those that still occur. Artificial intelligence can identify the problems with a claim submission, resubmit claims, and monitor the resubmission process. Advanced solutions with machine learning capabilities can even identify trends and make recommendations to resolve recurring denials that cost health systems billions of dollars each year.
3. Claims Management
Claims and denial management is a growing challenge in healthcare. An AI workforce can log in to disparate systems to complete and submit claims faster and more accurately than humans using RPA – Olive checks claims status 7x faster than a human, to be exact. Once a claim is submitted, claim statuses are checked and updated daily, alerting staff to those claims that need attention and human intervention. In one health system, automating the daily claims status checks with Olive freed up 12 FTEs per day, giving the revenue cycle staff time to focus on the claims that needed follow-up.
4. Supply Chain Management
The average annual supply expense reduction opportunity for individual hospitals is now $12.1 million2. AI can analyze the millions of data points about current inventory, past utilization trends, and expected future utilization to provide insights and recommendations to reduce supply costs. These solutions can optimize inventory levels, increase standardization, improve workflow efficiency and supply throughput – AI can even alert staff to expired and recalled supplies.
While cybersecurity may not fall strictly under operations, it is still one of the Top 10 AI Applications in healthcare according to the most recent Accenture report3. AI-enabled solutions can help health systems reduce risk by recognizing behaviors that are unlikely to be taken by humans, such as a user trying to access patient logs from a remote location. They run continuously in the background to monitor activity and can even automatically add additional layers of security when a potential threat is recognized, helping hospitals avoid data leaks and other security threats.
6. Invoice Processing
The largest suppliers typically invoice through a hospital’s own EDI system, but finance teams still process and pay many invoices manually. Artificial intelligence solutions – like an AI workforce – can automate that process, extracting the necessary data, routing for internal approval, and payment. With advances in healthcare technology including computer vision and machine learning, AI can process limitless invoices or receipts, no matter the format. This helps hospitals reduce late payment penalties and fees, eliminate mistakes and lost invoices, get greater visibility into finances, and improve compliance.
7. Patient Flow and Staffing
Healthcare AI can combine hospital historical data, patient data, current status of internal resources, external factors such as weather or epidemics, and even behavioral science to optimize patient flow and staffing resources. And with an AI workforce, these AI-powered, more accurate models of patient demand and bed utilization can reduce the average length of stay, more efficiently dispatch ambulances, prevent discharge delays, and shorten patient wait times.
Using data on the number and type of medical appointments, available physicians, and anticipated patient demand, artificial intelligence is significantly improving appointment scheduling for patients and physicians. With artificial intelligence, patients can easily book, confirm, cancel, and reschedule appointments. Waitlists can be created that auto-fill cancellations and open spots. And automatic reminders help reduce no-shows and optimize clinician time.
9. Vendor Contract Management
Finding the best products for the best price is critical to de-risking net assets and delivering on value-based care. That means, the larger the health system, the more ongoing contracts to manage. Healthcare artificial intelligence solutions can easily scan contracts and automatically extract the relevant information to compare to other contracts. An AI workforce helps manage a robust and well-maintained item file, helping health systems assess risk, look for savings opportunities, manage supplier relations, and increase compliance.
10. Data Migrations
Health systems today still struggle with legacy systems, custom applications, and data in disparate programs across the enterprise. Healthcare AI, especially RPA, is the easiest and more accurate solution for aggregating and migrating this data into the appropriate system. Healthcare AI and automation not only reduces the time needed to migrate data, but also does so without any errors, missed records, or typos, increasing the accuracy.
11. Medical Billing and Coding
Using robotic process automation, computer vision and natural language processing (NLP), healthcare AI applications are able to recognize and extract data from medical documents, patient records, and clinical notes to prepopulate ICD codes. This is how an AI workforce can speed up the billing process, improve documentation, and reduce billing errors that lead to costly rework.
Operational artificial intelligence is transforming the back office of healthcare.
These are only some of the current applications of healthcare artificial intelligence – more are being implemented every day, and some healthcare AI solutions, like Olive, are getting continually more intelligent. With healthcare artificial intelligence innovation, we can continue to tackle the waste and inefficiency in healthcare, reduce physician and nurse burnout, and improve the patient experience.
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