The Problem with Healthcare

The Problem with Healthcare

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.  
3 Benefits of Intelligent Process Automation for Your Business

3 Benefits of Intelligent Process Automation for Your Business

As a healthcare business professional, what are the biggest benefits of intelligent process automation to your business? If you have never considered that question, then you may be missing out on a myriad of potential benefits. The use cases for it in healthcare businesses are seemingly endless. Given that these technologies are ideal for processes that are high-volume and similar every time, the healthcare industry, with its wide variety of administrative tasks taking up valuable human time, is a prime candidate to leverage the power of automation.

In this article, we will dive into the details of some of the more common intelligent process automation technologies and 3 specific benefits intelligent process automation can bring to the healthcare businesses.

Explaining Process Automation & RPA

Before we dive into the specific benefits intelligent process automation can bring to your healthcare business, let’s dive into some of the nuts and bolts of process automation and RPA (Robotic Process Automation).

What is Process Automation?

Process automation is as simple as the title suggests—a process done automatically, not by a human being but by computer software that does not get tired, frustrated, and doesn’t need to sleep, drink or eat. Processes that become automated require less human involvement and certainly less human time to execute to its utmost efficiency.

Robotic Process Automation

One of the most apt examples of process automation and its benefit to a healthcare business is Olive’s Robotic Process Automation. Olive utilizes RPA to automate cyclical and time-consuming tasks that are rule-based and trigger-driven, freeing your staff from enduring countless hours of productivity that could have been better applied elsewhere in your business’ developments.

Now we’re sure when you hear the word “robotic”, you might immediately think about Rosie from the Jetsons, who performed menial house tasks as a robot made for a futuristic cartoon family from the Hannah Barbera cartoons, or, if you would like a more modern example, the Transformers who can transform into vehicles or other machinery assist human beings in various ways. Those aren’t the types of robots we’re talking about here.

Robots exist in other forms in technology, as detailed by Olive’s blog that also touches upon Robotic Process Automation. These technologies can consist of soft-bots, a computer program that acts on behalf of another user or program, or sensor networks, a group of spatially separated and dedicated sensors that monitor and record an environment’s physical conditions and organize the data collected at a vital location.

Oftentimes, RPA is considered the simplest form of Artificial Intelligence and is therefore used in business practices that require little skill. RPA specifically reaps benefits by giving skilled and specialized workers the opportunity to focus all of their attention on jobs that demand full human cognition and subjective decision making.

RPA vs Cognitive Automation

To put it simply, RPA takes a given set of inputs and produces a predictable, repeatable set of outputs. Not unlike a grunt or, aptly, a robot designed solely to follow instructions without freedom to think independent of its design, while other more advanced forms of intelligent automation, like cognitive automation autonomously improve in performance over time using machine learning. Machine learning is similar to humans gaining experience and figuring out more efficient ways to do things, but it is computers doing the iterating and learning instead of people. Both cognitive automation and RPA are beneficial tools for a myriad of  work processes ranging from simple rule-based processes (RPA) to more complex judgement-based processes (cognitive automation).

Benefit 1: Minimize errors

In order for your organization to fire on all cylinders with maximum profitability and productivity, the main things you have to invest on are: saving time and decreasing or outright eliminating the risk of errors. Why? As they say, time is money, and errors are setbacks that can be avoided if you leverage the benefits of process automation. Software like Olive can assist healthcare organizations, hospitals, and their staff to remedy a human-made mistake or miscommunication.

To help conceptualize and quantify the benefits, let’s consider a common healthcare business process: eligibility checks. Often times, eligibility checks require a human to manually transfer data from one system to another system, and then make a decision (or have one provided to them) about eligibility. This mundane, but important process is prone to typos and human error given the same data being entered multiple times into different forms and User Interfaces (UIs). It is no surprise then that technical errors cause 61% of initial medical billing denials for eligibility. By offloading this business process to  Olive, healthcare organizations can benefit from a high level of automation and repeatability in executing these tasks that minimizes susceptibility to human error and typos while still enabling businesses to use existing EHRs.

Benefit 2: Enhance problem-solving capacity

Automating processes in within your organization business doesn’t simply stop at the ‘cyclical and time-consuming tasks’. Enter, intelligent automation. Intelligent Automation is what is says on the tin: software that actually thinks for you, thus is the wonder of artificial intelligence and its role in intelligent automation. It isn’t simply mind-numbing repeatable tasks with minimum human monitoring, but actual problem-solving software that can actually think independent of human guidance and assist problem-solving on every level imaginable.

As best described in Olive’s article, 3 Trends to Consider Before AI Deployment, by 2026, intelligent automation might save the US healthcare economy a total of $150 billion annually according to a recent analysis by Accenture. It’s no wonder healthcare organizations are investing in intelligent automation, powered by AI software like Olive.

It doesn’t stop at healthcare, however, we’re seeing intelligent automation overtake the workplace and our daily lives all around us, from automated tellers at banks replacing human tellers, to booking hotel rooms online without needing to speak to a live person, to letting Google Maps navigate your next drive, just to name a few.

That being said, the most astounding example of intelligent automation, may indeed lie in healthcare. AI-assisted robots, as the article further explains, are aiding surgeons with medical decision support, image analysis and diagnostics, reducing and eliminating the potential of human error by joining human and machine in order to achieve the best results possible.

Benefit 3: Free clinical staff to work on clinical tasks

Another aspect of intelligent automation is cognitive automation software, which brings intelligence to information-intensive procedures. Cognitive automation is effectively the combination of Artificial Intelligence and Cognitive Computing. What sets cognitive automation apart is its performance of jobs that only human beings used to be able to do.

Often times, healthcare employees are bogged down with tedious administrative tasks that, while important to business, are inherently time-consuming and repetitive (e.g. insurance verification and data recording). These responsibilities can easily be outsourced to an RPA to execute in order to free up said staff so they can concentrate on tasks that humans excel at which require uniquely human skills like empathy and creativity (e.g. corresponding with patients, resolving more complex issues, etc.).

Part of cognitive automation is machine learning in order to have computing technology imitate human operations to complete tasks. While RPA is required to operate on a rule-base that limits its decision making, Cognitive automation expresses its artificial intelligence as a resource that learns as any human would in order to adapt and execute a job to its utmost efficiency, while becoming fatigued as a human being would, mind you.

Conclusion

In conclusion, in the field of healthcare alone, studies have found the increase in automation processing and data recording has decreased the in-hospital mortality rate by 15% and administrations that have adopted RPA have noticed a 200% return of investment in the first year of use according to this Olive white paper. Given the power of the technology and the myriad of high-volume tasks ripe for outsourcing to an intelligent automation solution in healthcare but it’s no wonder that intelligent process automation is a problem solver and driver of profitability-growth in the industry.

Robotic Process Automation Vs Machine Learning: What’s the Difference?

Robotic Process Automation Vs Machine Learning: What’s the Difference?

The rapid advancements in automation are revolutionizing business operations for organizations in practically every industry.  As automation technology continues to evolve and uncover new opportunities to showcase its effectiveness, healthcare companies are one of the industries rapidly discovering the benefits of its methodologies.  While hospitals are projected to invest over $50 billion dollars towards artificial intelligence and robotic process automation solutions by 2020, some in the industry are only beginning to look into the potential of these solutions and their game-changing advantages.  After spending time with over 300 revenue cycle and IT executives at Becker’s 4th Annual Health & IT Revenue Cycle conference, our teams at Olive were able to garner some details behind executives’ findings and concerns.  The top 5 takeaways we found include a sense of hesitation regarding the ability to prove ROI, but also reveal that the most agreed upon application of AI will prove its worth most in repetitive high volume tasks like eligibility checks, authorizations, and claims.

Robotic process automation and machine learning are often the two technologies discussed the most when broaching this topic, but what is the difference between the two? Further, which of these two work the best for a given use case?  We’ll discuss the details of both methods and help you answer both of those questions in this piece.

What is robotic process automation?

It’s quite common for robotic process automation (RPA) to be thought of as actual robotic devices performing operations on an assembly line or robot constructs like The Iron Giant and Transformers.  However, robots exist in other forms as part of other technologies like soft-bots, AI, sensor networks, and data analytics. Fundamentally, the simplest way to describe RPA is that it’s a process by which a repeatable rule-based task is executed through an automation solution.

Operating within predefined rules and procedures, RPA solutions are able to complete an action through a machine that would normally require human interaction.  Whether the task is in a factory environment or office space, RPA can help with the construction of a component for a finished product or even help office productivity by brewing coffee through Wi-Fi enabled coffee makers.  Because RPA solutions require a thoroughly practiced, documented, and familiar procedure to fulfill its automation benefits, some believe it will eliminate the need for humans in some areas, however, that isn’t really the case.  RPA is designed to handle the tedious repetitive tasks humans currently must do, enabling enhanced human productivity by allowing humans to focus on the more complex and creative tasks they excel at.

Sometimes considered to be the most basic form of AI, robotic process automation is best utilized in business practices that require little skill and are performed under set parameters including how often a task needs to be executed and within specified timeframes.  In healthcare applications, RPA reaps loads of benefits by allowing skilled and/or specialized staff to focus their attention towards tasks that require human cognition and subjective decision making. There are often instances within hospitals where employees with clinical skills, such as nurses and aides, are tasked with additional tasks of insurance verification and data recording.  While these responsibilities are expected within their roles along with other staff members, these duties are ideal for an RPA solution to tackle. Having these non-clinical jobs being addressed through automation allows for staff to concentrate their attention on their principal tasks better suited for their skills of patient care and advocacy.

Studies have already shown that the increase of automation in processing medical records and documentation has led to a 15% decrease in the odds of in-hospital deaths and administrations that have adopted RPA have seen a 200% ROI within the first year of use (Olive AI white paper). As the U.S. nears a projected shortage of 250,000 nurses by 2025, identifying and implementing automation solutions within healthcare infrastructures has become a much more pressing need thus allowing clinical staff to dedicate their abilities towards tasks exhibiting their skillsets.

What is machine learning?

Similar to robotic process automation, the primary objective of machine learning (ML) is to also have computing technology mimic human operations.  However, where RPA is required to operate within a rule and process-based environment that limits decision making under unfamiliar situations, ML truly expresses its artificial intelligence as a learning resource exhibiting what most feel is the biggest characteristic of AI; adaptation.  Simply put, RPA acts more like a straightforward resource that executes actions based on its configuration, which places it in more of a grunt perspective with little freedom to “think” outside the box or exhibit any learning abilities. Machine learning, on the other hand, autonomously improves its performance over time, like humans, as the system is provided with observational data and real-world interaction.  Some have even made the comparison between the two as brains over brawn with ML being the former.

In the healthcare industry, ML also adds exponential benefits to administrations acting as the router between systems and data by automating repetitive high traffic tasks.  Serving as its own employee within an organization, an ML solution utilizes its own credentials to access system databases to record and report patient information or EHR (electronic health record).  By following the local credential structure, this allows for seamless integration into existing systems with little change to accommodate its inclusion and no additional workflows. For example, our Olive AI can be used to perform patient insurance eligibility checks.  After reviewing the patient record and history from their respective EHR, Olive can assist with checking against insurance eligibility portals. With a baseline of information gathered, the system can then proceed to offer approved solutions, compare previously approved authorizations, schedule future appointments and post-visit follow-ups, and payments.  Having this level of automation 24/7 365 days of the year empowers hospital and clinic staff to center attention towards their most critical role of patient care.

An article published in Healthcare IT News reported a prediction from IDC (International Data Corporation) that global investment towards AI solutions will jump 60% this year totaling $12.5 billion and then up to $46 billion by 2020.  As automation continues its seemingly endless upward trend and creates countless prospective breakthroughs in practically every industry, machine learning continues to be a key proponent towards technological advancement.

 

So which one is better?

To answer this question, decision-makers and executives must first determine their most critical business needs that can be best be improved through automation.  Overall, robotic process automation and machine learning are both invaluable solutions that are sure to drastically enhance business performance for any organization.  Some businesses may opt to incorporate an RPA option in order to automate their easier low skill functions as this will require little effort to integrate and in the smallest amount of time.  Other organizations have decided to use RPA as a starting point in their AI implementation with machine learning as their end goal for automation. Nonetheless, having discussed the capabilities of both RPA and ML, it seems the only one who can determine which is better for a business is the business itself based on their requirements and ultimately the option that will provide the highest ROI over time.

At Olive, we strive to build revolutionary artificial intelligence and robotic process automation solutions for the healthcare industry that layer in ML for a more robust robotic process automation solution. Our focus is on improving business productivity through automation of the error-prone and mundane tasks of healthcare administration so that staff can focus on patient care.  Our efficient cost-reducing options continue to deliver immediate positive results with Olive AI overseeing repetitious high traffic processes and workflows. These specialized tools empower our customers with the freedom to let their teams express the creativity and empathy that only a person is able to provide.  Please contact us to schedule a demo of our Olive AI and let us begin developing a solution that can address your automation demands and be your first step towards an AI environment.