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.
It is estimated that missed appointments cost the healthcare industry $150 billion each year. While the blame for this issue is often directed toward patients, inefficiency at the practice and clinic level can also be a cause.
This paper outlines the most common reasons patients miss their appointments, provides benchmarks by speciality, highlights the impact on revenue, and reviews the tactics that practices and physicians can employ as a solution.
Missed appointments by type
To understand the pervasive nature of missed appointments, it is important to know the variance among practices and specialities. For example, no show rates can range from the low end of 2 percent all the way to 50 percent.
Below are several average rates according to the type of practice:
It should be noted that patients with chronic conditions are more likely to not show for a scheduled appointment, as the challenges associated with their condition can make it difficult to maintain a time commitment. Unfortunately, this group of patients are also the ones who stand to gain the most by showing up.
Another group of patients that contribute to higher-than-average no show rates are those with Medicaid insurance. One reason for this can be due to socioeconomic reasons, such as a patient relying on public transportation or living in a rural area far from where their physician’s office is located.
Common reasons for no shows
A patient may miss their appointment for a variety of reasons, however, the most common causes are:
• Lack of reliable transportation to the appointment
• Too much time between the scheduling and the appointment
• Emotional barriers such as a negative perception of seeing the doctor
• Belief that staff do not respect their time or needs
Interestingly, in reviewing the scores of satisfaction surveys, the friendliness of the staff is more important to the patient than the actual outcome of the care that is delivered. This could be due to a lack of clarity the patient has around measuring the quality of care they received. However, it is easy for them to know whether they felt respected or were met with kindness by practice staff.
There is also the belief among patients that they are doing a practice a favor when they cancel an appointment. A patient may think they are giving staff time back in their day or that a new appointment can easily take it’s place, when in reality it creates lost time and resources for practices.
Impact on the bottom line
There are approximately 230,000 physician practices in the U.S. Of those, 47 percent of them are group practices, meaning there is at least more than one physician or doctor at the location. Patient no shows cost this group more than $100 billion dollars each year.
Patient no shows cost group practices more than $100 billion each year.
In a study on one practice, the average rate of appointment no shows was 18 percent, which resulted in a daily loss of $725.42. When employing tactics to reduce the number of no shows, the practice was able to recoup between 3.8% to 10.5% in revenue,
or $166.61 to $463.09.
In another study, a multi-physician clinic had more than 14,000 patient no shows in a single year, resulting in an estimated loss of $1 million dollars in revenue. In single-physician practices, revenue losses can be as as much as $150,000 each year.
On average, a primary care practice earns $143.97 per patient visit, whereas a non-surgical specialty practice earns $78.43 per patient. While these examples outline the revenue a practice stands to lose, they do not take into account other negative impacts, such as increased wait times or patient dissatisfaction. Therefore, the benefit of seeing more patients must be weighed against the risk of increased patient waiting time and staff overtime.
Current solutions and tactics
When considering possible alternatives to decrease the number of patient no shows, practices and clinics have employed several tactics. These include text messages, direct mail, live phone calls, and automated phone calls.
While all these tactics have proven to be successful in reducing the number of no shows, it is important to implement a solution that is cost-effective and complements existing practice efforts. Depending on the goals and objectives, a combination of solutions may be the best option. Below is a baseline introduction to these tactics:
Many software solutions for healthcare practices offer a way to send text messages to patients to remind them of upcoming appointments. These messages also provide an opportunity for a patient to confirm they will keep their appointment, such as replying with a ‘C’ for confirmation. If a patient does not reply or responds with a cancellation answer, this signals the practice staff that there is a need to reach out directly to the patient to either confirm or reschedule their appointment.
Text messages are opened 99–100% of the time.
Depending on the solution, the cost of sending text messages can be free or included as part of a larger software package or service offering. Additionally, text messages have a 99 to 100 percent open rate and reach a patient directly via their mobile device.
Another option practices and clinics use to remind their patients of appointments is direct mail, often in the form of a simple postcard. A printed piece can cut through digital clutter and offers space to include additional information or callouts.
While printing costs can be relatively inexpensive for postcards—averaging $0.15—0.32—they also rely on having accurate addresses for patients. Another drawback is the inability to have a patient immediately confirm they will keep their appointment. A postcard makes the patient aware but a follow up phone call, either by the patient or practice staff, is required for a confirmation.
Live Phone Calls
A call made by practice staff to a patient is a direct and personable way to reduce no shows. The live phone call also allows the patient to reschedule immediately if they are unable to make their appointment.
However, this is a very manual process, requiring a dedicated staff person to devote time and energy to making and completing calls. A patient may not be available or answer when called, requiring a voice message be left or another call be made.
With this in mind, the benefits of speaking directly to a patient versus the resources spent must be weighed against one another.
Automated Phone Calls
An alternative to live phone calls is an automated service that calls patients on a list, using a prerecorded voice. These services can run in the background with minimal maintenance required by staff.
While these calls can be made indefinitely, they can give the impression of being highly impersonal. A patient may not always listen to the length of the call as well, choosing to hang up as soon as they recognize it as an automated call.
Additional measures to take
Missed appointment fees
As an alternative to appointment reminders, some practices have opted to implement a fee when a patient misses their appointment. This can be due to an outright no show or instituted if a patient cancels their appointment too late, such as within forty-eight hours of their scheduled appointment.
While a fee does act as a deterrent, this can also cause a negative perception of the practice as a patient can feel penalized for missing an appointment for a legitimate reason.
Another option to overcome no shows is to overbook an office’s scheduled appointments. When this is done, an additional patient is already present in the event that a patient does not show up for their appointment.
However, the process of overbooking can be highly unreliable as it relies on predicting whether or not a patient will show up. If an unconfirmed patient does show up for an overbooked time slot, this can cause crowding in a waiting room, resulting in longer than normal wait times and a lower quality of service. If a patient’s wait time is severe enough, this can force the practice staff to fall behind for the day and struggle to catch up. Not only can service levels be negatively impacted for patients throughout the day, but this can also force the physician to cut appointments short, sacrificing face time with the patient.
The process of overbooking can be highly unreliable as it relies on predicting whether or not a patient will show up.
With this in mind, overbooking can solve the issue of no show patients and potentially increase revenue, but could create new problems in its place. Therefore, the benefit of seeing more patients must be weighed against the risk of increased patient waiting time and staff overtime.
It is clear that patient no shows represent a significant problem to the healthcare industry, in both the primary care and speciality office space. However, just as the issues with missed appointments impact patients and providers alike, the solution must also be one that accommodates both parties.
For example, one solution may be economically viable for a practice, but not effective or utilized on behalf of the patient. By engaging with patients in the way they prefer, the foundation for an ongoing relationship can be established. Over time, the conversation moves beyond simple transactional communications and becomes more valuable to the patient and practice.
Download as PDF ›
Berg, B., Murr, M. et. al. (2013). Estimating the Cost of No-shows and Evaluating the Effects of Mitigation Strategies. National Center for Biotechnology Information. Found online at http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4153419
Toland, Bill. “No-shows cost health care system billions,” Pittsburg Post-Gazette. Feb 24, 2013. http://www.post-gazette.com/business/businessnews/2013/02/24/No-shows-cost-health-care-system-billions/stories/201302240381
Gold, Jenny, “In cities, the average doctor wait-time is 18.5 days,” The Washington Post. Jan 29, 2014. https://www.washingtonpost.com/news/wonk/wp/2014/01/29/in-cities-the-average-doctor-wait-time-is-18-5-days
Lacy, Naomi. “Why We Don’t Come: Patient Perceptions on No-Shows,” Annals of Family Medicine. vol. 2 no 6. Nov 1, 2004. http://www.annfammed.org/content/2/6/541.full
Evans, Melanie. “When revenue is a no-show,” Modern Healthcare. Nov 3, 2012. http://www.modernhealthcare.com/article/20121103/MAGAZINE/311039954
Mckee, Shawn. “Measuring the Cost of Patient No-Shows.” http://www.poweryourpractice.com/practice-management/measuring-cost-of-patient-no-shows
Molfenter, Todd. Reducing Appointment No-Shows: Going from Theory to Practice. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3962267
The American Journal of Medicine. The Effectiveness of Outpatient Appointment Reminder Systems in Reducing No-Show Rates. http://www.amjmed.com/article/S0002-9343(10)00108-7/pdf
Hasvold PE, Wootton R. Use of telephone and SMS reminders to improve attendance at hospital appointments: a systematic review. Journal of Telemedicine and Telecare. 2011;17(7):358-64.
Guy R, Hocking J, Wand H, Stott S, Ali H, Kaldor J. How Effective Are Short Message Service Reminders at Increasing Clinic Attendance? A Meta Analysis and Systematic Review. Health services research. 2012
Appointment reminder systems are effective but not optimal: results of a systematic review and evidence synthesis employing realist principles http://www.ncbi.nlm.nih.gov/pubmed/27110102
MGMA Cost Survey: 2014 Report Based on 2013 Data. Key Findings Summary Report. http://www.mgma.com/Libraries/Assets/Key-Findings-CostSurvey-FINAL.pdf?source
Ineligible patient insurance coverage is responsible for over 75% of all claim rejections and denials by payers. Though some insurance eligibility checks can now be completed online, many practices still waste hours on the phone with insurance companies, or searching their websites, to verify patient coverage.
This paper outlines the severe financial burden that health care systems face as a result of manual insurance eligibility verification, details the time spent by doctors, nurses, and clerical workers each week on insurance-related tasks, and highlights the immense amount of insurance paperwork that comes along with each patient visit.
The importance of checking patient insurance eligibility
Practices may easily assume that when a patient schedules an appointment, the patient has already checked that their insurance covers that practice and/or the procedure or appointment type. Practices may also assume that if a patient hands them what appears to be a valid insurance card, they will be covered and that the insurance verification process can reasonably begin after the appointment.
This, however, is not always the case—some patients are not aware of verification requirements, or may not check for coverage for their particular appointment type. Additionally, if a patient’s insurance provider changes, they may not realize that they are no longer covered at particular practices or for particular procedures. In some cases, patients may even present fraudulent insurance cards—causing major complications if the error is not caught immediately.
Once presented with a patient’s insurance information, healthcare organizations will then begin the tedious and often time-consuming task of verifying their eligibility. This may be done online, by calling the insurance provider directly, or in some cases, not at all. If a patient passes this check, they can then be passed through the system with a bill eventually being sent to their insurance provider and/or themselves (in the case of copays).
If a patient does not pass this check, however, the organization is typically faced with three options: bill the patient the full amount of the appointment or procedure, write off the appointment or procedure, or deny the patient service (if the check is done prior
to the appointment).
Per physician, practices spend an average of $68,274 each year interacting with insurance providers.
Insurance eligibility checks are a very important and necessary part of the schedule-to-appointment process, as failing to complete them can lead to further complications for the organization. Manual verification can be extremely time-consuming and frustrating, as well
as a costly drain on the organization’s human capital. Integrated verification tools can help,
but still require a good amount of human input in order to function properly.
The cost to practices of insurance eligibility checks
With each patient that schedules an appointment should come a subsequent eligibility check. Typically, such a check requires obtaining the patient’s insurance information, calling the insurance provider, waiting on hold, and finally verifying the patient’s coverage. Insurance verification does not always fall only onto clerical staff—physicians and nurses also spend hours each week interacting with insurance providers, taking away valuable time that could be spent on patient care.
Below are the average times spent per physician in a practice on insurance company interaction.
Aside from causing healthcare employees stress and frustration, time-consuming insurance company interactions cost practices thousands of dollars each year in lost productivity. Per physician, practices spend on average $68,274 each year interacting with insurance providers. Overall, this costs the U.S. $23–$31 billion each year—a significant chunk of the national health care burden of over $3.2 trillion (as last measured in 2015).
Paperwork and human capital associated with insurance eligibility checks
In an increasingly digital world, a surprising amount of health information and data is still processed manually through paper forms. Many insurance companies still require practices to print out forms for patient visits, complete the forms by hand, and fax them back to the insurance provider. Aside from being wasteful of resources, this process is also incredibly
Dependent upon the care setting, time spent on paperwork can even match time of patient care. Below are the ratios of patient care time to paperwork time:
To allow nurses and physicians to focus on patient care, healthcare organizations may hire one or more eligibility specialists, whose entire bandwidths can be dedicated to insurance eligibility verification. These employees, however, can be costly, and are naturally prone to human error. On average, an eligibility specialist earns at least $34,000 each year, not including healthcare benefits and other employer-incurred costs associated with hiring an employee. More experienced eligibility specialists earn more per year, though even they are also prone to human errors such as miskeyed or misheard information. Every human-caused error causes a practice both time and money to resolve.
Current solutions and tactics
Currently, many practices still check patient eligibility manually, either through searching an insurance company’s website or by calling the insurance company directly. 38% review websites while 20% call directly, meaning that over half of all providers still spend a sizeable amount
of employee time verifying patient eligibility. Inaccurate or unupdated websites, as well as frustrating phone prompts, can make these processes last up to an hour or more for a single patient in some cases.
Every human-caused error causes a practice both time and money to resolve.
When considering ways to streamline the insurance eligibility verification process, providers are not left with many options. Current solutions include close to real-time eligibility software that integrates with older systems, or solutions that are tied to EHR vendors. These tools can help staff run through insurance eligibility checks at a much more rapid pace than before, as they automate certain steps in the process.
These tools, however still require human input, and often, human control to carry out. While some of the eligibility process may be automated through integrated systems, employees will typically still need to input the patient’s name, insurance provider, and procedure in order to begin the process. Additionally, providers must spend time and money upfront to train their staff on integration tools.
Patient insurance eligibility checks are a time-consuming, frustrating, and costly drain on health care systems of all sizes that can leave providers and patients equally upset. Future solutions geared towards solving these problems must aim to streamline eligibility checks in a manner that not only speeds up the process as a whole, but also improves first-time accuracy without requiring timely staff training to implement.
Download as PDF ›
Ability Network. (2015) ABILITY network survey shows nearly 60 percent of providers rely on incomplete eligibility verification. [Press Release]. Retrieved from https://abilitynetwork.com/wp-content/uploads/2015/07/ABILITY-Network-survey-shows-dependence-on-incomplete-eligibility-verification1.pdf
Casalino, L. P., Nicholson S., Gans, D. N., Hammons, T., Morra, D., Karrison, T., & Levinson, W. (2009). What does it cost physician practices to interact with health insurance plans? Health Affairs, 28(4), 533-543. Retrieved from http://content.healthaffairs.org/content/28/4/w533.full
Eligibility specialist salary. PayScale. Retrieved from http://www.payscale.com/research/US/Job=Eligibility_Specialist/Hourly_Rate
Morra, D., Nicholson, S., Levinson, W., Gans, D.N., Hammons, T., & Casalino, L.P. (2011). US physician practices versus canadians: spending nearly four times as much money interacting with payers. Health Affairs, 30(8), 1443-1450. Retrieved from http://content.healthaffairs.org/content/30/8/1443.full
NHE fact sheet. Centers for Medicaid and Medicare Services. Retrieved from https://www.cms.gov/research-statistics-data-and-systems/statistics-trends-and-reports/nationalhealthexpenddata/nhe-fact-sheet.html
Pricewaterhouse Coopers. Patients or paperwork? American Hospitals Association. Retrieved from http://www.aha.org/content/00-10/FinalPaperworkReport.pdf
Zamosky, L. (2014). 5 tips to improve your practice’s financial management. From insurance eligibility checks to sound collection strategies, medical practices must build processes for dealing with patients’ financial issues. Medical Economics, 91(12), 34-36.