Heathcare is Ripe for Disruption

Heathcare is Ripe for Disruption

In the past decades, technology has changed how consumers – and companies – think about almost every aspect of our lives. From AirBnB to Uber, Amazon to WiFi thermostats, consumer behaviors and expectations have changed drastically, and companies have been born or evolved to meet them.

However, due to conflating factors like ever-changing compliance and regulatory requirements, complex infrastructures, and the lack of interoperability to name a few, healthcare has digitally lagged behind. But perhaps nowhere else is disruption needed more than the healthcare industry.

We’re all well aware of the problems we’re facing in healthcare today: it’s too expensive, complicated, and time-consuming for patients and providers alike. It’s why consumers, employees, the government, and employers are all looking for solutions to solve the industry’s most costly and burdensome challenges.

As industry disruption becomes top of mind for healthcare leaders, the question now is where will they have the biggest impact?

To answer that question, we must first look at one of the most pressing challenges today: trying to reduce costs while improving the patient experience.

Process inefficiencies are one of the leading reasons healthcare has become unaffordable for many Americans, even with insurance – consumers, payers, and the government are all putting pressure on health systems to cut costs. And it’s why healthcare systems across the country are leveraging technology and complex software purchases to increase efficiency and revenue recognition.

Additionally, there are the added pressures of value-based care and community health initiatives pushing healthcare organizations to figure out how to provide better care and improve outcomes in their communities. Big data and analytics have promised to help in these areas, but with cost constraints increasing, physicians and nurses are being squeezed to do more and see more patients during their work hours, meaning less time with each individual. So, how are doctors, PAs, and nurses supposed to improve patient outcomes and experience if they don’t have adequate time to spend with them? And which disruptive technologies are positioned to create the biggest economic impact on healthcare organizations bottom lines?

The Tech Giants: Google, Amazon, Apple Are Looking to Make In-Roads

At Becker’s 10th Annual Hospital Review in April, we heard how Google, Amazon, and Apple all hope to be the next big innovator in healthcare. each has a different approach for how they could revolutionize the industry and what challenges they want to tackle. But are these disruptors uniquely positioned to transform industry inefficiencies, or will they only make a marginal impact on improving care?

Let’s take a look at each:

In Google’s case, it and its parent company, Alphabet, have invested in various health companies as well as its own platform developments, across the spectrum of care. But at its core, what it really wants is data. Storing data is the first step, and Google believes that whoever has all of the data will be able to have the biggest impact. That will be their play.  

Amazon is leveraging its unique capabilities in a different way: size and supply chain. Taking over the hospital and pharmacy supply chain is an obvious potential disruptor, but Amazon also recently formed an independent healthcare company with Berkshire Hathaway Inc and JPMorgan Chase & Co for their combined 1.2 million employees. They’re currently developing their own primary care clinics, which while originally for their own employees, could signal a move into the larger primary care market.  With supplies being a large part of the healthcare spend, it is an interesting way to think about care delivery and controlling cost.

Apple has yet another approach, based on its unique consumer presence. Their Health app comes preinstalled on iPhones, which means 140 million Americans already have access to the app. By creating, and imbedding this app, into their ubiquitous solution, Apple has the opportunity to more seamlessly connect consumers with their medical information – potentially increasing interoperability and placing the patient in the center of their care.

These industry giants could fundamentally change the manner in which care is delivered.  However, they fail to address the back-office challenges that sources estimate could result in $1 trillion dollars wasted  on process inefficiencies.

So, what will?

RPA & Artificial Intelligence: The Disruptive Technologies That Will Change The Game

One of the most impactful technologies for the administrative side of operations is artificial Intelligence and robotic process automation, which are estimated to save healthcare $18B by 2026.¹ The technologies are widely used in other industries, as well – robotic accounting, for instance, is an increasingly popular solution used in finance and accounting operations to streamline operational efficiency, reducing data transcribing tasks by 80% in accounts payable, financial close, tax accounting and more.²

We are starting to see more and more people in healthcare familiar with the concept of RPA. And with the addition of other technologies like Computer Vision and Machine Learning, the potential impact to organizational efficiency is huge. This is game-changing in healthcare, because although other industries are faced with process inefficiencies, it is hard to argue that few are as crippled with the  deluge of difficult-to-use yet business critical software programs like healthcare.

With healthcare’s frustrating lack of interoperability, employees have taken on the job of the router – or data processor – shifting the hours spent by humans from being in front of patients to being in front of computer screens, logged in to disparate EMRs and EHRs, shepherding patient data into the right fields – and the consequences come in the form of burnt-out employees, skyrocketing administrative costs, and less human-to-human experiences decreasing the quality of care.

Operational AI provides improve the speed, cost, capacity, quality, and consistency of care today. It works alongside human employees to handle the large amounts of data and repetitive tasks that are bogging down the healthcare system, reducing errors, speeding up processing time, and increasing operational efficiency. This not only reduces costs, but also allows employees to get back to higher-level, more meaningful work. Work that drove many to choose a career in healthcare in the first place.

And that’s really what it comes down to: bringing humanity back to healthcare. An industry where workers spend more time in front of screens than they do in front of patients.

Here at Olive, we believe that a digital workforce is the disrupter that healthcare has been waiting for. Olive uses her healthcare-specific skills to address common bottlenecks when it comes to time-consuming, error-prone workflows that result in process inefficiencies and costly denials. And she does it with unrivaled security measures built specifically for healthcare, working seamlessly with your existing processes, technology, and current systems you already have in place. The best part? Olive works 24/7, doesn’t get fatigued, never resigns and is less error-prone than a human, so healthcare organizations can refocus their employees to more meaningful work.

Today, Olive is focusing on improving business operations inside and outside of the healthcare revenue cycle, but her capabilities go far beyond that. Using Machine Learning, Olive uses algorithms to find patterns in data without instruction, giving her the ability to learn and improve from each task, uncovering insights and new opportunities to optimize workflows and hospital processes. Through continued adoption, AI will continue to innovate and improve how healthcare does business across the entire continuum of care.

If you want to learn more about how Olive can help your organization, contact us today. Our automation experts can help you understand how Olive is addressing healthcare’s biggest challenges and can help drive your healthcare system forward.

Industry Insights from a Revenue Cycle Leader: Healthcare Tech Today and Tomorrow

Industry Insights from a Revenue Cycle Leader: Healthcare Tech Today and Tomorrow

As part of a new interview series with Healthcare Leaders across the country, the Olive team had the chance to interview healthcare consultant, Six Sigma Black Belt and Revenue Cycle expert April Langford about the biggest challenges facing healthcare today. Previous to starting AML Consulting, her own revenue cycle consulting firm, April was the VP of Finance at UPMC, a leading integrated healthcare delivery system. In addition to AML Consulting, she currently is co-founder and CEO of revcyclematch.com, an online platform that connects providers to their future business partners. revcyclematch.com reimagined Revenue Cycle management partnerships with a new platform that makes tedious marketing and client acquisition practices obsolete.

What are the biggest challenges you see leaders in Revenue Cycle facing?

Today, there are so many new regulations Revenue Cycle leaders have to react to, increasing federal, state and payer requirements, and the shift to value-based care has also come into play. The movement to value based care is more closely aligning the clinical and financial world in healthcare.

This expansion of Revenue Cycle into quality of care is interesting – Revenue Cycle leaders now find themselves responsible for health information management and care management, expanding their scope of management.

What role do you see technology playing in solving these challenges now, and five years from now?

As health systems grow and continue to accumulate multiple EMR, EHR and other disparate software, technology has to play a growing role in interfacing and connecting those systems so that organizations can operate as one integrated health system. Interoperability is important for the institution and the quality of care for consumers. At each point along the Revenue Cycle today, there are new technologies emerging to solve any given issue. Defining, selecting and implementing new systems is paramount to a smooth-running revenue cycle.

In 5 years, I foresee technology playing an even bigger role across the continuum of care. Being able to tie patient data together so that physicians and care-givers have a holistic view of a patient will increase the quality of care. What will the implications to billing be? Contracting and billing for value-based care will bring about increased complexities and new technologies to be able to manage billing and collections to ensure proper payment.

How does the shift to value-based care impact revenue cycle?

This has been an ongoing conversation, and as the shift to value-based care continues, it’s becoming more imperative for healthcare systems to master the evolving value-based payment and delivery models. It’s an evolution and it’s still rather new so organizations are just starting to model how to get there. Working to improve reimbursements, focusing on patient care and looking outside of the industry for innovative strategies to implement are a few areas where I see people focusing as the industry shifts to value-based care. Navigating that shift is one of the biggest challenges facing healthcare organizations today.

At Olive, we talk a lot about the concept of ShiftWork (shifting the mundane work currently done by employees to technology, so people can focus on work that requires a human touch) and how it will impact the current staff at health organizations. What higher-value activities do you see employees taking on as burdensome tasks are handed off to technology?

Honestly, it always seems like there is more work to do in the healthcare industry – often times even more work then there are people to complete the tasks. At many hospitals, there are a significant amount of open positions to fill at any given time.

I think the best approach to shifting work successfully is to talk to the employee first, because I’ve learned that if you understand what a person likes to do, it’s generally what they’re good at, as well. And when you have the focus to re-allocate employees time to more meaningful work, it gives them opportunities to grow and learn, ultimately helping them be more fulfilled.

Tell us about a person who mentored, inspired or impacted you during your career.

I have so many! I would say early on at UPMC, Don Riefner hired me not once, but twice, across different areas of the healthcare industry, so he spent many years influencing the trajectory of my career. And he was a great boss, extremely smart and thoughtful, never micro-managed and was always a calm voice of reason.

Can you share a piece of advice that someone gave you over the course of your career?

I’ve gotten a lot of great advice over the years, but the previous CFO and COO at UPMC was a great mentor and taught me something that always served me well. He taught me that I needed to be able to answer the who, what, when, where, and how before he accepted a recommendation. I needed to be 8 levels deep to achieve expertise. That’s always stayed with me as a truly valuable piece of career advice.

Subscribe to OliveReads here to read more about healthcare trends and the future of the industry.

How to Effectively Implement Artificial Intelligence in Healthcare

How to Effectively Implement Artificial Intelligence in Healthcare

In a time when nearly every technology vendor is touting AI-enabled products, it can be difficult to determine where to begin your AI journey. So, what are the key considerations that will help you take an impact-driven approach to AI implementation, providing both immediate and long-term value to your healthcare organization?



1) A focus on healthcare

Most AI technology is not designed for the unique challenges of healthcare. Look for a vendor that understands healthcare and provides the expertise to customize your AI and automation solution to thrive within your existing hospital processes, not add to them.

2) Put healthcare data security first

When you begin your AI journey, industry-specific regulations like HIPAA privacy rules and SOC2 compliance should be the least of your team’s worries. Begin your vendor security evaluation early to ensure your AI solutions can be built with the security complexities of your organization in mind.

3) A partner and trusted advisor

Your vendor should help your team develop a long-term AI and automation strategy focused on achieving the goals of your organization. This means finding a vendor that will educate your team about the tools and technologies available, and help guide your team through evaluation of candidate processes for automation that will provide the largest economic impact on your organization.

4) Driven to provide excellent support

Many AI and automation vendors do not offer support services that cover changes to the applications or updates to the processes you have automated. Ensure you’re covered for growth from the beginning by choosing a vendor that has a long-term strategy for technology integration – one that significantly lessens the burden on your employees and resources is minimized.


As you go into the implementation process, work with your vendor’s team to ensure all stakeholders understand the technology and level of engagement required for successful custom development of your AI technology. As you go through the development and implementation of AI, consider the following :


1) Start with the lowest hanging fruit

There are many processes that can benefit from AI and automation. As you begin, start with workflows that can leverage less complex technologies such as Robotic Process Automation and Computer Vision to provide value and return on investment quickly. These technologies are perfect for many revenue cycle processes such as checking the status of claims, eligibility and benefit verification, and more. Simpler workflows that provide high value returns lay a solid foundation to provide the business case for a larger investment in your long-term AI strategy.


2) Consider when the 80/20 rule applies

It’s possible to automate 100% of a process, however, you may see a diminishing return on investment if you automate the low-volume portions of a larger workflow. For example, checking the status of a claim often requires automating the action of logging into a variety of insurance portals. Instead of trying to automate 100% of the portals immediately, you may find more value in focusing on the 20% of the portals that account for 80% of the total volume as the first priority. You can always revisit workflows in the future if it becomes necessary or valuable to automate the remaining 20% of the volume.


3) Be engaged

Communicate frequently with your vendor team and be sure to ask for demos and updates to allow for feedback during the development process. Remember, you are purchasing AI to execute best practice processes. While the vendor should be delivering updates often, and have the technology and healthcare expertise to deliver an optimized AI solution, frequent evaluation and feedback will help ensure your AI is delivered quickly and successfully.


Going live with your first AI or automation project is a big step towards your long-term AI strategy. You can expect improvements from immediate efficiency gains to reduced errors, to more time for your staff to focus on complex and important tasks. After implementation, it is important to understand the impact AI is having on your organization and use a data-driven approach to evaluate success towards your AI strategy goals. Collaborate with your vendor after implementation to:


1) Establish implementation impact

Work with your vendor to measure the impact of implementation on your business metrics. By comparing the data before and after the deployment of AI, you can evaluate its performance and use the data as a benchmark for what you can expect from other processes as you expand to new workflows. Focus on areas where you are able to evaluate hard data that can be compared directly (efficiency, error reduction, reduction in denials, etc.)

2) Think beyond the initial KPIs

Hard data only tells part of the story. Analyze the overall impact AI has made on the organization by looking at the downstream effects. Ask questions such as: Has AI met my expectations? Are there opportunities to expand workflows to add more value? What does the reduction in errors, decreased days in A/R, or decreased denials mean for the organization? Are we able to reallocate full-time employees to focus on other high priority initiatives? If we could replicate the same efficiency gains from process A to process B, C, D, E, etc. what would the impact be? Ask your vendor for support in helping you prove the value of your AI investment. The right partner will be there for you as a consultant and advisor, helping you to meet your AI goals.

Learn how to build the business case for AI and automation at your healthcare organization here.

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:


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. 


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.


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.


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.