Over the past two decades, the use of software systems has lead to a paradigm shift in healthcare, particularly in the United States. Medical records went digital at a rapid rate, driven in large part by federal mandates (e.g. the American Reinvestment & Recovery Act pushing “meaningful use” of electronic health records). As the industry responded to the operational and legislative incentives to digitize medical records, a number of EMR (Electronic Medical Record) systems emerged to meet demands. Overall, this push towards a digital age in medical record keeping has been a success; over 95% of all hospitals in the United States have certified Health IT according to The Office of the National Coordinator for Health Information Technology.
While this shift to digital had significant benefits, it was not without its drawbacks. One of the side effects of the switch to digital was that, as a whole staff now often spends more time at a PC than with patients. This is due in large part to one of the main problems in healthcare: the lack of interoperability between systems. Lack of interoperability means that the various systems in a healthcare environment are often unable to communicate with one another in an efficient and scalable manner This leads to significant friction and manual action in business processes.
EMR systems were designed to be secure and reliable means of storing and recording sensitive patient data. Interoperability wasn’t a primary requirement when these systems were designed, but with the benefit of hindsight, we can now see the shift to digital record keeping and lack of EMR interoperability has created a groundswell of administrative workloads across the organization. An organization full of siloed systems leads to an environment where data transfers between systems can become tedious, time-consuming, and costly. In this piece, we will review this problem in more detail and dive into one of the most promising solutions: Artificial Intelligence (AI).
Understanding the impact of poor interoperability
In modern healthcare facilities, it is a given that many employees with clinical skills like nurses and technicians will spend a non-trivial amount of time moving data from one format or system to another. Humans now effectively act as data routers and processes between discrete systems, moving from one interface to another to type and retype data. Not only does this keep them away from patients, it significantly contributes to increased administrative costs.
To help quantify the astounding administrative costs impacting healthcare in the United States, check out the statistics cited in this New York Times article. The article cites research that puts the administrative cost of healthcare in the U.S. higher than anywhere else in the world and data that indicates that in the U.S. administrative costs account for over 25% of healthcare spending while our neighbor to the North, Canada, spends about 12% of their healthcare dollars on administration.
This isn’t to say that the shift in how data is input into EMR systems will solve all of the nation’s healthcare spending woes, or even to suggest that the makers of EMR software are at fault (after all, they built solutions based on market demands and requirements). The point here is that today this is an area where healthcare organizations have significant bottlenecks and inefficiencies in business processes. Viewed differently, given the right solution, this is an opportunity for healthcare businesses to reduce cost and drive down overhead.
As we will see, automated intelligence is an ideal way to address many of these bottlenecks and organizations that adopt AI to help optimize their work processes can take advantage of this opportunity. In so doing, they will be able to save a significant amount of time and money, while also freeing up staff to do the more important and creative work humans excel at. The takeaway here is this is one of the health care issues we can solve pragmatically without the need for legislators to take action (which can always be a roadblock when dealing with problems within healthcare).
Understanding how AI can solve interoperability issues with healthcare today
The problem is clear, but the solution is still up for debate. Some have suggested that an overhaul of systems is required. Creating an “Internet for Healthcare” that enables secure, reliable, and fast data exchanges is the ideal for many. However, there is some concern that such a “scorched Earth” approach goes too far and creates more of an administrative hassle than it is worth. Agreeing to standards and implementing entirely new systems at scale, while also meeting the stringent requirements healthcare organizations must adhere to (e.g. HIPAA) while taking a significant amount of time, effort, and coordination.
A solution that is able to work with current systems would enable healthcare organizations to continue to leverage many of the trusted and secure EMRs they are comfortable with today, while still resolving the interoperability problem. Given that, AI is uniquely capable of resolving these challenges and helping to address one of the biggest issues in healthcare. One of the ideal use cases for intelligent automation technologies like AI and RPA (Robotic Process Automation) is one where humans are tasked with high-volume work that is done in s similar way every time. Offloading these tasks to software enables human workers to focus more time on the complex and creative work they should be focused on (e.g. caring for patients), while also increasing speed and reducing exposure to human error.
The counterargument some make to leveraging AI in healthcare is that APIs (Application Programming Interfaces) or HL7 (Health Level Seven) data streams aren’t always readily available or require complex development work to feed into an AI software. However, when AI is built with the idea in mind of being able to pass the Turing Test for AI (as mentioned in our Artificial Intelligence 101 article), these APIs and HL7 feeds aren’t required. AI is capable of using the same user interfaces (UIs) a human would use to complete the task. This leads to a new paradigm where AI is treated as an employee. For example, in the “onboarding” of our AI Olive, often Olive can be assigned user accounts and email addresses much in the same way a new employee would.
This creates a scenario where healthcare organizations are able to continue to leverage existing systems, while still freeing up human capital to focus on core healthcare functions like patient care. This helps to drive down costs, increase efficiency & speed in administrative processes, and improve patient satisfaction. In short, while there is a myriad of current problems in healthcare, you can resolve many of your interoperability problems with AI.
Conceptualizing the benefits of AI to healthcare administration
To help conceptualize the power of AI to healthcare administration, let’s walk through a real-world example, eligibility checks, and compare the manual process to Olive. Manual eligibility checks are often time-consuming and prone to human error, with technical errors causing 61% of initial medical billing denials for eligibility. This is an excellent microcosm for how many small errors can scale to create bigger issues affecting healthcare providers. There are multiple disjointed systems involved in completing a single eligibility check and if you are having a human go through these processes repeatedly, you can expect a typo or oversight fairly regularly.
Looking at the AI approach with Olive, she will:
- Automatically pull patient information from the existing HER
- Use the information to check the same eligibility portals a human would
- Report the information back for review
- Make recommendations
This means that the mundane, repetitive tasks associated with eligibility checks are now quickly completed by a software that is significantly less typo-prone than a human, and processing of eligibility is sped up not only due to less technical errors, but also because AI can work 24/7. Using this example, you can now see how AI can be leveraged to elegantly resolve many of the problems in healthcare today.
Conclusion: AI helps optimize healthcare administration
While asking anyone in the industry “what are some health care issues?” will often lead to a laundry list of healthcare problems, not all of those problems have readily apparent, pragmatic solutions. Fortunately, the EHR interoperability challenges faced by healthcare organizations do have a solution that can be implemented today in the form of AI that is built specifically for the healthcare industry. By leveraging AI, healthcare organizations can improve work processes, minimize human error, decrease turnaround times, lower expenses, and free up human resources to focus on more valuable work like patient care.
Here at Olive, we are dedicated to building world-class automated intelligence solutions specifically designed to solve the unique challenges facing the healthcare industry. If you have questions about how AI can help drive your healthcare business forward, please contact us today to work with our team of automation experts.