Becker’s 2019 Trend Recap

Becker’s 2019 Trend Recap

We’re back at Olive HQ after an amazing few days at Becker’s 10th Annual Hospital Meeting, and we have to say, this was the most informative year yet. We attended over 30 sessions and spent time with over 250 healthcare leaders in Chicago discussing the biggest challenges facing the industry today, and one thing is clear – top hospital executives agree that healthcare is ready for meaningful change. So, what did these leaders have to say about the industry, artificial intelligence and the biggest challenges facing healthcare today?

Here are the key insights we identified from our conversations:

INSIGHT #1: HEALTHCARE IS RIPE FOR INDUSTRY DISRUPTION

The challenges healthcare organizations face are unique – like complex software integrations, overburdened staff, shrinking margins and increasingly strict security and compliance requirements. And as these challenges grow more complex, the industry is ripe for disruption. But where will innovative technologies have the biggest impact? Healthcare leaders believe the digitization of healthcare and the introduction of companies like Amazon and Google to healthcare will help reduce the burden of an extremely inefficient, bogged down industry.

As Amazon forms an independent healthcare company for its employees and Apple updates their App capabilities to display patient medical records, these advancements can only help streamline an industry that’s fraught with inefficiencies. Even Uber has entered the space, launching a ride-sharing program called Uber Health with sights on the $3 billion non-medical emergency transportation market. Although many of these emerging technologies are still in their early stages’, healthcare leaders predict that their prominence will only continue to grow in 2019. AI is also a driving force feeding healthcare industry innovation, allowing organizations to automate the most repetitive, time-consuming tasks. And although AI is already improving business operations inside and outside of the healthcare revenue cycle, the possibilities go far beyond that. With advanced computer vision, RPA and machine learning skills, AI will continue to transform burdensome healthcare processes and create opportunities to improve efficiency across the continuum of care. We’ll keep you posted about the most meaningful technologies as they continue to advance, and in the meantime, learn more about how AI can impact your organization. 

INSIGHT #2: COST CONTAINMENT IS A TOP PRIORITY FOR HEALTHCARE EXECUTIVES

Human capital is the highest cost driver in healthcare today. As annual expense growth outpaces the annual revenue gains, cost containment continues to be a top priority for  healthcare executives. That’s partly due to the fact that 1 of the 3 trillion dollars spent in healthcare each year comes from operational inefficiencies alone. A great example being the bottlenecks in registration and eligibility processes. Flaws in these processes are the primary cause of denials, leading a typical health system to risk $4.9 million annually.

So what did the leading experts at Becker’s predict would help solve these growing issues and help healthcare organizations do more with less? AI was certainly one of the big buzzwords flying around the conference floor, and with new technologies continuing to emerge to automate healthcare’s most robotic tasks, healthcare employees can finally begin to focus on what matters most. Experts expect AI for Healthcare IT application market to surpass $1.7 billion by the end of 2019, and through this automation, healthcare systems have already begun to optimize revenue and eliminate entire backlogs of work created by time-consuming, repetitive tasks that make up much of the administrative side of the business.  In turn, they’ve been able to reduce costly errors and take an impact-driven approach to AI implementation, providing both immediate and long-term value to their organizations.

INSIGHT #3: THE SHIFT CONTINUES TO OUT OF HOME CARE AND SURGICAL CENTERS

One of the big trends we heard discussed at Becker’s was the shift to out-of-home care and the rise of surgical centers. Jll stats claims that surgery centers have grown 82% since 2000 and predicts the trend will continue into 2019. And with telehealth technology moving far beyond traditional care systems, leading experts predict that this space will continue to grow by 30% and surpass $25 billion dollars by the end of 2019.

The increasing cost of care and aging populations facing chronic health issues are both leading drivers behind innovative digital health solutions like RPM devices, telehealth platforms and more. Through favorable reimbursement policies, digital health applications will continue to expand care delivery models beyond traditional hospital systems, innovating areas like behavioral health, digital wellness therapies, dentistry, nutrition and prescription management, empowering individuals to better manage their own health. Because home health clinicians are on the front lines with patients, gathering key information about their conditions and recovery status, they’re uniquely positioned to promote interoperability and ultimately the growing shift to out-of-home care in the industry.

INSIGHT #4: HEALTHCARE WORKERS ARE EXPERIENCING “DEATH BY A THOUSAND CLICKS”

A recent study of 1,750 healthcare leaders found that almost three-quarters of them feel some degree of burnout. While alarming, it’s not actually surprising, given most hospitals today are toggling back and forth between 10 or more various EHRs or EMRs, creating a “button olympics”  for their overworked employees – not to mention the resulting backlog of work and wasted resources. So, how are healthcare executives approaching the subject of interoperability and employee burnout while also optimizing revenue?

Today, studies show that 42% of respondents seeking new employment believe their job does not make good use of their skills and abilities. That’s why many innovative health systems across the country have already implemented artificial intelligence to take on the most robotic processes in healthcare and reduce employee burnout. AI has allowed these organizations to optimize their revenue recognition and take burdensome tasks off their employees’ “to-do” lists, reallocating their time to more human-like initiatives, not the repetitive tasks that make up much of the administrative side of healthcare. This is something the team behind Olive is particularly committed to. By creating the industry’s first true “digital employee,” we’ve already been able to help shift employees time from robotic tasks to improving patient care.  To learn more about how AI can impact your organization, subscribe to OliveReads.

Challenges of HC Integrations: Why is Healthcare So Complex?

Challenges of HC Integrations: Why is Healthcare So Complex?

Automation is one of the biggest buzzwords in business and IT today, and for good reason. As we move into the era of Industry 4.0, big data, Artificial Intelligence (AI), and the Internet of Things (IoT) are enabling advancements that were unimaginable in years past. However, while manufacturing and technology progress at breakneck speeds, many healthcare processes that are seemingly prime candidates for automation continue to be done manually.

This isn’t because healthcare decision makers are averse to change or unaware of the possibilities, but rather because healthcare is a unique industry with a unique set of challenges. There are regulations and requirements healthcare organizations must navigate that other industries never have to think about. Many of the inner workings of IT systems in a healthcare organization are inherently different than “standard” IT infrastructures. All this comes together to add layers of complexity and make the integrations that could enable automation in healthcare difficult to achieve, despite the fact healthcare organizations are full of mundane, repetitive, data-entry intensive work processes that are prime automation candidates.

In this piece, we’ll review the main drivers of complexity limiting healthcare integrations, explain how Olive is unique in that it was built specifically to help automate healthcare work processes, and review some of the benefits of implementing intelligent automation in healthcare.

Drivers of Complexity

As anyone in the industry will tell you, healthcare is complicated. The healthcare industry is different from other industries for a number of reasons. At a high level, two of the biggest drivers of complexity of healthcare system software: data integration challenges and unique security and compliance requirements. Here we will discuss those in more detail and dive into why this is the case.

Data integration challenges

One of the main problems with healthcare is the lack of standardization and consistency between EMR systems. For example, across different systems there can be different ways to do something as simple as identify a patient. This is because EMR systems were built with the intent to be secure and reliable, but interoperability was an afterthought.

This has lead to a scenario where a significant amount of human time and effort is spent manually moving data from one system to another. Where automation is possible, it is often based on APIs (Application Programming Interfaces) or HL7 (Health Level 7) streams that are difficult to integrate and often lack all the information needed to complete a given work process. In addition to HL7, some of the other standards, formats, and databases those working with healthcare data “in the wild” may encounter include:

  • FHIR (Fast Healthcare Interoperability Resources)
  • NCPDP (National Council for Prescription Drug Programs)  SCRIPT
  • X12
  • JSON
  • ICD (International Classification of Diseases)-9&10
  • LONIC (Logical Observation Identifiers Names and Codes)
  • NPI (National Provider Identifier)
  • SNOMED CT (Systematized Nomenclature of Medicine — Clinical Terms)

..and more. As you can imagine, this makes getting data from point A to point B problematic without compromising the integrity of the data a daunting task. For this reason, processes like eligibility checks, claims processing, and other data-entry heavy tasks that would seem prime candidates for automation in other verticals, are labor intensive tasks in the healthcare sector. While each of these formats serves a purpose and has some upside on its own, often being created to improve standardization, solve problems with older standards, or meet new requirements; taken as a whole they aren’t always conducive to interoperability. The end result of a myriad of well-intentioned standards is a number of different systems within a healthcare facility using different standards. This leads to difficulties  tying everything together costs a significant amount of time and resources.

Just how bad is the problem is the data integration problem? To try and quantify the scope, consider that HealthData Management reported that data integration issues cost health and human services agencies $342 billion.

Drilling down further, let’s consider what seems to be a very preventable problem that has cost the industry billions: denials. Denials cost hospitals and health systems over $262 billion annually, and over 60% of these denials are related to missing information. This statistic is at least in part symptomatic of the consequences of tasking people with transferring data across multiple, discrete systems. Human error and oversights are bound to occur. There are too many potential points of failure in today’s EMR systems and data entry work processes.

 

Security & Compliance Concerns

While information security is vital in all industries, the healthcare market is unique and this contributes to the complexity of healthcare organizations. In most cases, working in the healthcare sector in the United States inherently requires working with PHI (Protected Health Information) and being subject to regulations like HIPAA (Health Insurance Portability and Accountability Act). This means data handled on networks within hospitals and clinics become subject to much more stringent security and handling requirements. As anyone who has ever worked in IT can tell you, adding security also comes with added complexity.

In the world of healthcare IT, administrators must ensure that their handling of electronic PHI is complaint. This means partnering only with compliant vendors, accounting for encryption of data at rest and in transit, using only improve encryption methods, and much more. Given the extremely high costs of falling out of compliance, healthcare organizations must prioritize security and staying within regulatory guidelines. Often this means what may help streamline a process in another industry is a non-starter in the world of healthcare. This further exacerbates the challenges associated with healthcare integrations and often puts true automation out of reach.

How artificial intelligence can address healthcare complexity

Consistent with the same concepts that are driving the popularity of Industry 4.0, AI and automation in healthcare administration can lead to industry-changing improvements. However, in order to be able to achieve the benefits, healthcare organizations must first identify tools that can meet the unique demands of the sector.

The importance of a solution purpose-built for the healthcare sector

In simple terms: a standard intelligent automation solution can’t meet all the challenges of the healthcare market without significant modifications, and significant modifications mean complexity, which is what we’re trying to minimize in the first place. Further, even when modified, using a standard automation solution in the healthcare sector is simply using the wrong tool for the job.

Olive was built to fill this market need and designed specifically for healthcare. For example, Olive is able to “check all the boxes” when it comes to healthcare related security and compliance in the U.S., supporting features and functionalities such as:

  • AES256 encryption
  • Amazon AWS HIPAA complaint services
  • Up to date ciphers
  • NIST 800-53
  • Encryption of data at rest and in transit
  • Multifactor Authentication
  • Shamir’s Secret Sharing
  • Record Level Access Logs

What is most impressive about how Olive addresses the complexity challenges of healthcare integrations is how she abstracts away complexity. As opposed to forcing dependence on incomplete or non-existent APIs and HL7 streams, Olive works in a manner similar to a human employee, leveraging User Interfaces (UIs) to capture data and streamline workflows. This opens up a world of possibilities for integrating multiple disjointed EHR systems throughout a healthcare facility. With a purpose-built automation solution, what was once prohibitively complex in healthcare becomes easily achievable.

 

The benefits of artificial intelligence and automation to healthcare

Now that we know automation in healthcare is possible using a purpose-built solution like Olive, the obvious question is: is it beneficial? The answer is a resounding yes. Qualitatively this is because, as mentioned previously, the healthcare sector is full of work processes that are repetitive and heavy on data-entry; prime candidates for automation. Shifting these workloads away from humans and to software enables organizations to optimize healthcare administration and improve the bottom line.

To give just a few real-world examples of the benefits of automation in healthcare, consider the case studies of Heart of Ohio Family Health Centers (PDF) and Hancock Regional Hospital (PDF). By leveraging the power of OliveAI, the former was able to save automate eligibility checks for an average of 90% of daily and save over 200% of the original cost of a workflow offloaded to Olive. The latter was able to eliminate denials for no-coverage from Anthem, Medicaid, and Medicare as well as reduce their days in accounts receivable by 34%. For more micro and macro level statistics related to the power of automation in healthcare, check out this infographic.

 

Conclusion: Olive can help resolve the challenges of HC integrations & abstract away complexity

As we have seen, the healthcare integrations are uniquely complex and come with a set of challenges other industries don’t have to worry about. What this means is that, while healthcare organizations can reap the benefits of intelligent automation, they must be careful to only use solutions designed to meet the challenges of healthcare. Olive is a holistic process automation solution built from the ground up for healthcare. This means that by making Olive their next “employee”, healthcare businesses can rest assured that they are using the right tool for the job. By using paradigm-shifting technologies like machine learning, AI, computer vision, and RPA, Olive can abstract away the complexities of healthcare and make operations faster and more economical.

If you’re interested in learning more, we’re here to help! At Olive, we are dedicated to building world-class automated intelligence solutions specifically designed to solve the challenges facing the healthcare industry. If you have questions about how A.I. can help drive your healthcare business forward, please contact us today to work with our team of automation experts.

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.  
Dispelling 10 Myths about AI in Healthcare

Dispelling 10 Myths about AI in Healthcare

Depending on whom you ask, artificial intelligence has the potential to transform our economy, take our jobs, overrun the human race, or maybe even a combination of the three. While AI holds a lot of promise, it has also turned into a buzzword that is frequently misunderstood and misapplied to both our lives and our businesses. This is especially true in healthcare, where the promise of better patient outcomes is often overshadowed by the threat of compromised jobs or regulatory red tape. To help set the record straight on the current state of AI in healthcare, we’ve put together a list of the top 10 myths we hear most frequently.

1. There is just one type of AI.

In fact, AI is a blanket term that comprises multiple types of technology, including optical character recognition, natural language processing, and machine intelligence. At its core, artificial intelligence refers to technology that mimics sophisticated human processes in a way that makes it indistinguishable from a human. In this way, our definition of AI comprises not just computer vision and machine intelligence, but also Robotic Process Automation, which automates repetitive, rule-based tasks.

2. Automation will take away more jobs than it creates.

In fact, industry experts speculate that the opposite will happen. The reason why is because AI adoption won’t happen overnight––and when it does happen, it will primarily replace repetitive, lower-skilled jobs that don’t require human traits such as creativity or empathy. This won’t invalidate the need for a human workforce; on the contrary, it will provide an opportunity for jobs to become less menial and more thoughtful.

3. AI is first and foremost a way to cut costs.

Some organizations may turn to AI as a way to cut costs, but that also can be a side-effect of so many other benefits that automation can bring. AI can help organizations improve their efficiency and KPIs, reduce risk, improve employee satisfaction and retention, and more…while also cutting the costs associated with them.

4. AI’s ROI is difficult to calculate.

Because AI is still a new business tool and ROI may not be as cut-and-dry as it is for other, more conventional business tools, many mistakenly think that AI’s ROI is difficult to ascertain and they choose to avoid the perceived risk. However, the key to assessing ROI is to be diligent in measuring your current-day spend––not only in terms of salary and benefits, but also in terms of risk, of extra days in A/R, and any other subsidiary metrics that might be positively influenced by AI. If you choose to work with an automation vendor, they will be able to help you think through the metrics to consider when evaluating AI’s ROI potential. Then, they will help you understand how AI will impact those metrics so that you can generate buy-in from your team––while also feeling confident yourself.

5. AI is a magic fix for your business.

Many organizations think that AI is the “secret sauce” that will help them improve efficiency, reduce costs, and make their employees happier. And while all of those things can and do happen when AI is executed properly, those effects don’t come quite as easily as some may believe. Many AI companies sell insights, not action, to their clients, which means that your organization still needs to do the work in order for AI to have a tangible impact on your business.*

6. AI requires large amounts of data.

It’s true that more data is always better when it comes to artificial intelligence––after all, the more historical data you have at your disposal, the more opportunities you have to “train” an algorithm to act a certain way based on similar data in the future. However, depending on the task that you intend to automate, certain AI frameworks are flexible enough to work with limited subsets of data.

7. Only large companies with in-house IT teams can benefit from AI.

Even as recently as a few years ago, organizations needed to employ a sophisticated internal IT team in order to build, customize, and implement an AI model for their organization. Not so today. At Olive, we’ve seen AI successfully implemented everywhere from the nation’s largest health systems down to 17-bed rural clinics. If your organization is on the smaller size but is still interested in implementing AI, you might consider contracting with a third party to build and manage your automations rather than bringing on extra in-house support.

8. You can’t build AI in-house.

With the advent of cloud technology, it’s easier than ever to create data-intensive automations and harness the power of AI for your organization. This, coupled with robotic process automation and AI platforms, provides organizations with more of a “do-it-yourself” option so that they can harness the benefits of AI without having to rely on external consultants. If you’re trying to determine whether building in-house or hiring an external consultant would be more beneficial for your organization, ask yourself whether AI is a part of your organization’s core competency (meaning, it directly aligns with the value proposition with which you face the market). If AI is a key component to your organization’s value statement, you might want to consider if you have the skills in-house to execute on your workflows. If that isn’t the case for your organization (which it likely is not for a provider), outsourcing your workflow to an automation vendor will help you harness the same impact without straining your internal resources.

9. AI inherently possesses the ability to learn from itself.

AI “learns” by analyzing test data and determining which inputs translated to a given output. It will make its best educated guess based on the data it has at its disposal, but just like humans, it needs additional support to help it differentiate “right” from “wrong.” Often, humans will re-train the algorithm to help it refine its predictions.

10. AI is still too risky to apply to healthcare.

There are too many applications of AI in healthcare––from diagnostics to imaging to revenue cycle management––for reservations to pervade the entire healthcare industry. It’s true that in a clinical setting, AI can pose a greater risk because the stakes of being “right” are much higher. However, administrative AI is a burgeoning subset of healthcare AI that focuses on improving operational efficiency by optimizing data transfers between healthcare tools and systems. In our experience, the impact potential is undeniable: one of our customers was able to reduce their Days in Accounts Receivable by 34% in the first 180 days of using AI to manage their organization’s insurance eligibility checks.

Even though AI in healthcare still is a new concept and will continue to be defined over time, early signs indicate that the sky is the limit in terms of its potential to benefit an organization’s operational efficiency. If your organization is looking for ways to harness the power of automation and AI, reach out to us today to learn more.

*That said, Olive isn’t one of those “Brain in a Jar” companies (as we like to call them). Our AI solutions automate your repetitive and high-volume tasks so that you can reap the rewards of AI without having to sacrifice your team’s time and bandwidth.

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.

“Will a Robot Take My Job?”: How to talk with your team about Artificial Intelligence

Artificial intelligence is one of the hottest trends in the healthcare industry (and, let’s face it, just about every other industry right now). People have touted it as the cornerstone of the Fourth Industrial Revolution, which might seem exciting to some of us––but to individuals working in repetitive, task-driven roles, this can take on more of an ominous tone. After all, the past Industrial Revolution completely reshaped the workforce and how humans approached their jobs and livelihoods. Can (and will) automation do the same thing, particularly in the healthcare industry?

In our last webinar with HFMA about optimizing the Revenue Cycle using Artificial Intelligence, several attendees asked us how artificial intelligence will impact their teams and if they should plan to downsize if they intend to introduce automation into their organizations. This is a common concern, and one that we hear time and time again at Olive. In order to help you better weather the storm and start a healthy dialogue about automation with your team, here are a few pointers to get you started. 

1. Frame automation as a solution, not a threat. When discussing the potential for automation within your organization, you can take a similar approach with your fellow leadership and with your own team: rather than taking a doomsday approach, start a brainstorm about how automation can free up your team’s bandwidth, and where those individuals can be leveraged in a way that’s more meaningful to the organization as a whole (and to them!). After spending so long stuck in the status quo, this can be a challenge. Be sure to give all stakeholders plenty of context in advance of your conversation; that way, everyone can come prepared and open-minded to engage on the future of the organization.

2. Make your human team feel….well, human. It’s scary and vulnerable to think of technology invalidating your job, so approach the topic with empathy and optimism when talking with your team. Genuinely listen and respond to your team’s apprehensions in a way that makes them feel supported and appreciated. If you treat your team with respect and openness during these initial conversations, they will be less likely to see automation as a threat to their livelihoods, and more as a tool to help them do their jobs even better than before.

3. Keep them involved. No one likes having a major change dropped on them at the last minute, let alone without their input. Once you start talking with automation vendors about potential workflow solutions, keep your team closely involved––after all, they’re your in-house experts! They are closest to the problem and, if involved in the process from the beginning, they can help your workflow automations truly shine. Make sure that they have a direct line to your workflow automation vendors and that they feel a sense of ownership over the automation project.

    1. Artificial intelligence and automation can have an exponential impact on healthcare organizations’ operational efficiency and care delivery. But the first step to achieving that benefit is to gain buy-in from other stakeholders and especially from your own team. By speaking openly, early, and often about the impact it will have––on your entire organization––you can foster a sense of collective ownership and excitement for, not fear of, the future.

    2. 4. Clarify your intentions and expectations for how artificial intelligence will impact your organization. Some leaders do turn to automation in order to downsize their teams–-and in some cases, it’s the ugly reality of what has to happen for their organization to stay in business. But other leaders look to automation as a way to scale and empower their existing workforce to achieve more than ever before. Having a clear stance on this––and understanding why, as a leader, you need to do this for your organization–will make subsequent conversations easier both for you and your team.

    If you’re starting to explore automating part of your healthcare organization, our team is always happy to help you structure these early-level conversations with your team or with other stakeholders. Reach out to us today to learn more.  Start here with us today.