Clinics in the Dark?
The founders of Sift Healthcare are using predictive analytics to solve the mystery of unpaid medical bills and assist healthcare facilities in managing budgets more effectively.
Georgiana Medical Center in Alabama shut its doors this month, becoming the 13th hospital in the state to do so in the previous eight years. Seven of the 13 hospitals that were closed served rural areas.
Donald E. Williamson, president of the Alabama Hospital Association, characterised the closing of the Georgian Medical Center as another another sign of the unsustainable situation in which healthcare costs are rising faster than reimbursements.
Williamson stated that hospitals "just cannot continue to provide uncompensated care to thousands of uninsured Alabamans." This is made worse by the fact that our hospitals don't get paid enough to pay for the services they give.
Sadly, Alabama's tale is not exceptional. According to a report by the Medicare Payment Advisory Commission, 67 rural hospitals nationwide have shut their doors since 2013.
Although hospital closures are a complicated and multifaceted problem, businessman Justin Nicols identified a chance inside one contributing issue: falling reimbursements. As the founder and CEO of Sift Healthcare, the Milwaukee native is reimagining what health system administrators refer to as Revenue Cycle Management with the help of his team of predictive analytics and e-commerce software professionals (RCM). Sift Healthcare's data platform offers the chance to better understand which patients are most likely to pay their bill and which would frequently chuck their payment into the trash for health systems losing money due to unpaid invoices. Leaders of the sales cycle can choose a profitable path with the use of these insights.
This is how it goes. To allow high-power rejections management and propensity-to-pay modelling, the company's technology integrates predictive analytics and machine learning to all reimbursement and patient pay data streams. In other words, they are delving into the depths of billing data, where no RCM analysis has ever ventured before, in order to uncover previously unreachable granular payment insights.
Virgil Bistriceanu, the company's CTO, sums up what they perform as "root cause investigation of denials payer contract discrepancies." Fortunately, Bistriceanu is also a professor at the Illinois Institute of Technology in addition to his vast expertise in startups and public corporations [he was the first CTO at Centro Media ($300mm+ valuation) and a director at United Airlines]. He can enrol us in classes on this.
According to Bistriceanu, "while other systems are looking at 10, 20, or 100 data points, we are looking at thousands." "Simply because we are ready to work with a lot more data, we are better at building highly rich models than others. Others don't see things as we do.
The various service lines that make up a claim typically represent the stages of a patient's healthcare journey through a procedure or disease. One service line may be paid for by the insurer, but not another. Most RCM studies merely examine these claims' top-level financial information. Healthcare Sift excavates. According to Bistriceanu, it is essential to know if a claim is being denied in its entirety or just a few service lines, as well as how much money is included in each line, in order to effectively forecast a person's ability to pay or tendency to pay.MedsIT Nexus billing and coding experts are highly proficient in processing claims for your dental practice. Moreover, our dental billing services are certified by HIPAA providing you an utmost experience in maintaining the privacy of the communication between you and your patients
Sift has been able to determine that just 4% of a provider's patient group was responsible for almost half of its write-offs using data from this trial and the company's analytics technology.
Managing the billing process accurately is not easy as providers might face hurdles in revenue cycle management. Moreover, Net Collection Rate below 95% shows that your practice is facing troubles in the billing process. To eliminate all these hurdles and maintain your NCR up to 96%, MedsIT Nexus medical billing and coding services are around the corner for you so that your practice does not have to face a loss.
Then there are the phone calls for customer service.
You'd be astonished at how many new insights our data analysis may gain from speaking with patient clients, says Bistriceanu. "A well-run call centre has specialised codes for practically every form of client interaction."
There is a code for when a patient answers a billing collection call but is unable to pay because their payment is not due until the following Friday. There is a code for this, so the following caller will know to present a specific bill amount to the person if the client was more receptive to an attempt to collect a $75 debt as opposed to a $150 debt.
According to Nicols, "We're going far beyond notifying you which patients can't pay their bill." Based on the patient population, we created highly segmented suggestions for physicians. We can tell you not only which people won't pay their bills, but also the ideal contact strategy and frequency for those high-risk patients, as well as what kind of payment plan or buyout offer to place in front of those individuals.
Currently, Sift Healthcare's first ambulatory services pilot is being conducted with the leading RCM vendor in the nation (2,000 physicians). Sift has been able to determine that just 4% of a provider's patient group was responsible for almost half of its write-offs using data from this trial and the company's analytics technology.
Hospitals are attempting to find a way to connect their various systems, according to Nicols. "The ability to operationalize data and arrange it into one common data platform is crucial for providers and billing organisations. We see ourselves as the platform that organises and normalises data before using predictive modelling to provide the operation with better, more insightful insight and recommendations.
Identifying the RCM Issue: It's Not Just the Money
One must first analyse the significant, ironic problem in order to fully appreciate Sift Healthcare's answer.
On the one hand, despite the implementation of key elements of the Affordable Care Act, both the number of uninsured patients and those with balances after insurance (PBAI) have steadily increased in recent years. According to a December 2018 TransUnion Healthcare report, the percentage of uninsured patients reached above 12% in 2017, while the PBAI rate increased from 8% in 2012 to 12.2% in 2017. Less people are able to pay their medical bills due to escalating healthcare expenses.
Ironically, the uninsured rate is rising even as providers encounter more obstacles to receiving just compensation. As a result of payers' reduced reimbursement rates and tightened claim submission and payment standards, it is now more challenging for providers to collect payments from all patient populations, whether they are in need of assistance or not.
All of this results in scenarios like Alabama's rising number of shuttered hospitals when compared to the traditional revenue model for hospitals, which relies on 30% of self-pay accounts made up mostly of uninsured patients to generate 80% of self-pay revenue for hospitals. Furthermore, in a stressed-out healthcare system, the areas that most require access to care, such as rural areas in the US, are frequently the ones who suffer first and worst from these clumsy administrative procedures.
Hospitals and leaders in the healthcare industry's revenue cycle are aware of the irony of this enormous challenge. They're looking for aid.
Hospitals are growing more dependent on vendors, according to Nicols, to assist them manage their increasingly difficult revenue cycle. However, many providers employ antiquated technology that offers a scanty glimpse of previous claim data. A chance to use machine learning and predictive analytics to make genuine, proactive, long-term enhancements to satisfy the demands of the revenue cycle today has been lost.