How Four Healthcare Complexities Are Driving Up Claim Denials

How Four Healthcare Complexities Are Driving Up Claim Denials


Today, 90% of insurance claim denials are avoidable1, yet they’re still occurring on a massive scale at healthcare organizations across the country – many times for simple reasons like missing patient identifier information or spelling errors. And given the ever-changing complexities around claims management and processing, it comes as no surprise that approximately 9% of insurance claims submitted are denied, costing health systems as much as 3.3% of net patient revenue.²  And that’s not considering the expense of rework –  this added effort also drives up hospitals’ total cost to collect.

Although a large portion of these denials are preventable, only about two-thirds are recoverable – that means gaining real traction in reducing claim denials rests heavily on denial prevention. According to research from the American Medical Association, the industry could save $15.5 billion each year if companies processed claims correctly the first time.³ Despite that, prevention has proven extremely difficult, resulting in increasing denial write-offs from 2011 to 2017.4

If we know claim denials are draining the industry of resources, why haven’t we solved the problem?

Today, the claims management process is far too complicated for any one solution to solve alone – continual changes in payer policies create an added level of complexity, claims management processes are too decentralized, human error and process inefficiencies are plaguing the industry… so on and so on.  

Because of this complexity – and despite being largely preventable – denials are a growing problem that still costs hospitals $262 billion annually5 – making it one of the most talked about challenges facing healthcare today. Hospitals and other health systems simply don’t have the people, the process or the time to solve for the complexity afflicting our claims management processes – from workflow inefficiencies and lacking resources, to overly complicated claims management policies among payers.

1. Resources Are Stretched Thin And Processes Are Wrought With Error

Although accuracy and thoroughness are critical when managing claims processes, the resources required to manage them are often stretched too thin to handle the large volume of work. That’s because healthcare employees are often overworked with multiple competing priorities, and many hospitals lack sufficient resources — such as staff, time or budget — needed to touch each and every patient account. 

That’s partially because today, providers can choose from an almost never-ending list of technologies and tools to help manage their business operations, yet 31 percent are still using manual claims denial management processes.6 Let’s look at eligibility and authorization processes, for example: 

Manual data entry contributes to the staggering fact that 23.9% of claims are denied due to eligibility and registration issues7 and another 12.4% are caused by incomplete or missing authorizations.8Aside from being time-consuming and burdensome to healthcare employees, providers and clinicians, manual claims processes are incredibly susceptible to human error – errors that cost healthcare organizations time and money. 

However, understanding the problem and being able to solve it are two different things.  Unfortunately, most organizations don’t have additional labor to dedicate to more frequent checking – whether that be the status of the authorization, the status of the claim or the most up to date eligibility information.  To help quantify the problem, it takes a staff member around 10 minutes on average to manually check one patient’s eligibility9 and 14 minutes to check a claim status10 – at that rate, even entire teams of healthcare employees dedicated to these tasks don’t have the capacity to keep up with the influx of work, leaving money on the table for services rendered by healthcare organizations across the country. 

Because of these time and capacity constraints, many organizations focus their time and resources primarily on the high-dollar accounts. That means low-dollar accounts are often outsourced to a third-party vendor – reducing a hospital’s visibility to the causes behind their denials – or they slip through the cracks altogether. 

Unfortunately, all these missed payments from low-dollar accounts add up – and accounts that are worked are often plagued with errors due to the unmanageable, complicated claim process. Healthcare employees just aren’t humanly capable of handling the high volume of manual data entry necessary to submit clean claims 100% of the time. The result? Burned-out employees and downstream denials costing healthcare organizations millions of dollars in lost revenue that could have been avoided.

2. Denials Management And Financial Clearance Processes Are Too Decentralized

Organizations can get ahead of many denials with a proactive approach to claims management and a dedicated team focused on denials prevention. Unfortunately, the process today is often too decentralized for many hospitals to manage efficiently and thoroughly. One best practice healthcare organizations are using is to centralize these processes, helping to build an infrastructure of dedicated teams and accountability among those handling their claims.

Building a centralized denials team helps create accountability and allows organizations to focus not only on claims management, but claims prevention. This is important because finding and preventing the root cause of denials has a much larger financial impact on their bottom line than working to overturn denials. Unfortunately, staff  today don’t always have the bandwidth. 

When it comes to financial clearance, for instance, one of the biggest issues is that the process is often handled by employees wearing multiple hats with many competing priorities. For example, the employees handling insurance eligibility are not only checking for eligibility, but also gathering patient demographic information, providing pre-service instructions, answering questions, handling financial counseling, answering phone calls, and more on any given day. The result? There’s often not enough time to handle the volume and collect the necessary information for reimbursement. That leads to an increased amount of patient balances to be collected at a later point in the revenue cycle – and we know that’s not always an easy task. Today, 85% of healthcare organizations say that collecting payment from patients after they’ve already left is incredibly difficult.10

Lack of transparency and sharing of data between the various teams handling denials is another result of this decentralization. Today, many hospitals still have separate reporting by entity and don’t always share critical data. Despite that, most healthcare executives agree that a proactive denial prevention process should be grounded in analytics, using data to determine and solve the reason for recurring denials. Transparent reporting and sharing of data helps organizations uncover these insights and trends to prioritize the denials work. Increasing transparency internally and externally helps hospitals share important data to find the root cause behind their denials, allowing them to see the bigger picture.

3. EDI Transactions Are Complex And Imperfect

EDI transactions are used by many healthcare organizations to handle large volumes of information and expedite reimbursement. For instance, electronic eligibility checking is the most widely adopted electronic transaction today. So, why are 23.9% of claims still denied due to eligibility and registration issues?7 That’s partly because of a broken billing process across the revenue cycle and disadvantages that come with the current electronic data interchange options.

Let’s look at the current options around eligibility and authorization for this example, 270/271 and 276/277. Prior to a patient’s visit, the 270/271 inquires and identifies the health care benefits and eligibility associated with a patient, but often times the responses have a lack of complete information, causing an additional touch for staff to fix the missing information from the front end. There are limitations with EDI transactions after a patient has received care, too. The 276/277 options are also limited by the information they return – organizations may know a claim was rejected because of missing information, but won’t necessarily know what information is missing. We can tell the industry is still struggling with this issue, because many inquiries about status are still done outside of the EDI transactions. And manual checking of each claim requires exponentially more staff than most organizations can hire, not to mention added room for human error and the added cost to collect.

Reprocessing claims drives up labor costs as billing staff are forced to devote more time to unpaid claims. We see the capacity constraints show up in the numbers, too – only around ½ of denials are appealed11 and it’s estimated that up to 90% of those should have been paid.1 Another 4% don’t get paid just because they’re too late.12

These denied claims trigger effects that spill over to disrupt other aspects of the hospital’s finances, too. For example, after a payer denies a claim, administrators are forced to chase the denial, which often requires multiple calls to the ordering physician and working closely with the payer. We all know this is time-consuming and may not always lead to reimbursement. The claim then sits in accounts receivable until it is adjudicated, which can take weeks – even months. Not knowing where claims stand in the adjudication, and for what reason, has a negative impact on days in A/R and contributes to writing off accounts as uncollectible. To have a significant reduction in these uncollectible claims and improve an organization’s overall cash position, organizations need to know more information, sooner.

4. Payer Complexity Continues To Increase

Payer complexity is nothing new – it’s a moving target that organizations need to have a proactive process in place to stay on top of their denials. Unfortunately, there’s not always someone assigned to the task of reviewing and keeping up with payer policies. And as commercial and public payers are now denying about 1 in every 10 claims submitted today,13 it’s clear why denials are on the rise. The Doctor Patient Rights Project even found that insurance companies are increasingly leaning on utilization management techniques like prior authorization to avoid payment,14 and even after patients receive authorization, their insurers may still refuse to cover treatment costs right away. All of these additional touches to patient accounts are increasing the overall cost to collect for hospitals across the country.

Submitting clean claims – or claims that were accurately processed and reimbursed the first time it was submitted to the payer – significantly reduces denial rates, but is increasingly challenging due to complex and changing payer reimbursement policies and procedures. 

That’s partly because authorizations vary by payer, so hospitals are constantly battling denials as payers require authorization for more types of procedures, or adjust policies without any notification to providers. That’s not including the large volume of claims that end up not requiring authorization at all – today, 30% of services don’t require prior authorization, yet organizations still waste time by checking their necessity.15

Of the services that do require prior authorization, many include high-cost services or procedures, or services that may be considered unnecessary all together, making the authorization window a critical time period for preventing rejections and denials. But with increasing regulatory requirements and the added complexity of value-based care coming into play, claims management has only become more complicated for healthcare providers to manage. And as these value-based care models begin to replace traditional fee-for-service structures, healthcare organizations are anticipating added strain on their already-declining claim reimbursement rates.

Tackling the Problem: Hospitals are optimizing the process with AI and Automation 

Human error, payer complexity, broken processes and strained resources – all of these tangled challenges contribute to the fact that hospitals across the country are losing the war against denials. And as reimbursement dollars shrink and complexity continues to increase, more and more healthcare organizations are considering a different approach to tackling the claims denial challenge. 


Meet Olive, the only AI-powered digital employee designed specifically for healthcare.

Olive was designed to interact with EMRs, insurance portals, and other healthcare applications the same way a human would – only faster, smarter, and more securely. 

The best part? Olive automates common claims management processes 24/7, never fatigues, and is less error-prone than a human. 

For instance, at one midwestern hospital, Olive was trained to access the EMR/EHR work queues and automate claim status checks, emulating all of the manual steps associates had once done – only smarter, faster and more accurately – checking the status of their claims 7x faster than their human employees. That means one Olive does the work of about 9 full-time employees per week – if they could work non-stop for 40 hours like Olive.

In addition to giving you significantly more capacity, leveraging Olive in your claims management processes helps centralize your efforts and uncover valuable data and insights into your denials problem. That’s because Olive provides better and faster information that will reduce your denials – and the reason for denials – by identifying trends and issues with claims that need to be solved before submission. 

Olive was built to handle payer transactions with speed and ease. For instance, if Olive doesn’t find eligibility information on her first check, she deploys a search for benefits among other health plans commonly serviced by the health system to uncover the necessary information to prevent a denial. 

And by checking eligibility, authorization and claim status early, frequently, and more thoroughly than your human employees have the bandwidth to, Olive accelerates cash flow and increases successful appeal efforts, impacting organization’s revenue recognition and resolving recurring denials.

Want to implement AI and automation to transform denial and rejection management at your healthcare organization? Contact us today to speak with an AI and automation expert to learn more.


      7. Change Healthcare Healthy Hospital revenue cycle index
      9. 2018 Council for Affordable Quality Healthcare Index 

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, an online platform that connects providers to their future business partners. 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.

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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.

Ethics in Artificial Intelligence

Ethics in Artificial Intelligence

As artificial intelligence and automation continue to advance industries from healthcare to financial services and beyond, there is a critical conversation happening around the ethical implementation of these technologies. At Olive, ethical considerations are at the center of everything we do.

Artificial intelligence already offers great promise to humanity – the possibilities range from improving operational efficiencies to predicting environmental threats to combating poverty. And it’s already proven to be a revolutionary tool in terms of optimizing burdensome, robotic processes currently done by humans on the administrative side of healthcare, proving AI can reduce the cost of care at a massive scale.

“That’s why we created Olive – to carve a trillion dollars out of the cost of healthcare, and increase the quantity and quality of human life. We think the best way to achieve that ethically is by scaling humans with AI. We’re able to accomplish so much more as a humankind with the assistance of AI in terms of providing better, more affordable healthcare to all,” – Sean Lane, CEO of Olive.

At Olive, we’ve already seen the workforce landscape fundamentally changing, because AI and automation are being used to supplement the work that our human employees don’t have time to get to. We call it “Shiftwork” because it’s the trajectory through which our jobs as human employees will evolve over time.

For the healthcare companies we’ve partnered with to deploy AI, we’ve already seen the landscape really start to change and human jobs begin shifting, as well. As their digital workforce starts to take over all of the robotic administrative work, it’s giving their humans the chance to handle tasks that require more of a human touch – like higher value claims and Quality Assurance, to name a few.

An ongoing conversation about the ethical implementation of AI stems from the fear and trepidation about whether or not AI will take human jobs. But that’s not what AI should be doing at all – AI is automating the most robotic tasks bogging down our most inundated industries, so humans can focus on more meaningful, human-centric initiatives such as patient care. For example, in Healthcare today, workers spend more time in front of screens than they do in front of patients, a real problem that AI can effectively solve.

“In the future, entire companies will be created with the intention of being staffed by a digital workforce. It will allow lots of new technology to emerge because the creation of new services and technology companies will be more accessible to more people, increasing entrepreneurship and expediting innovation. And Shiftwork means more than just new job opportunities and new tech companies, but possibly entirely new industries themselves.” – Lane

Another hot topic surrounding the ethics of AI is centered around governance. When healthcare organizations begin implementing AI, there are many things they must consider from how it will be used to how it will be managed. Which processes are the best candidates for automation? What security measures will be in place to maintain patient confidentiality?

How will you train your employees to refocus their time after AI frees them up for more meaningful work?

At Olive, we believe in the transformative power of AI and want everyone to experience its benefits, so we work hard to ensure it’s used responsibly. That’s why the questions above drive everything we do and shape the world we’re dedicated to building with artificial intelligence.  It’s also why we made Olive specifically for healthcare. Unlike other AI solutions on the market, Olive uses her healthcare-specific skills to address common bottlenecks – most importantly, she does it with unrivaled security measures built for healthcare, working seamlessly with common industry processes and technology.   

We also ensure our AI technology is only available to customers after understanding it’s potential use case. We also integrate our product development, sales and customer success teams in our considerations, allowing them to help drive the ethical use of artificial intelligence.  As the implementation of AI reaches critical mass, ethics will remain central to the conversation of how we “shift human work” from robot-friendly tasks to ones that require a human touch and human mind to improve the overall quality of human life.