Today is an exciting milestone for Sift as we’re proud to announce the completion of Sift Healthcare’s $9 million Series A fundraising round, led by Allos Ventures and First Trust Capital Partners. Funding supports the accelerating sales of our denials management and patient payment products and the launch of our mid-cycle denials prevention solution.
Automation (RPA) makes the revenue cycle more efficient — saving time and decreasing errors. While this is an improvement for health systems and hospitals RPA efficiencies are one-dimensional. Fully optimizing the revenue cycle and getting the most out of AI (including RPA) requires a clear and holistic view of payments, an understanding of the full lifecycle flow of claims.
So you want to add AI to your revenue cycle? You have to start by establishing a solid foundation of data intelligence. This comes from normalizing and organizing payments data in a way that provides actionable insights. This works will establish the where/why/how of your AI goals.
The impact of insurance claim denials for healthcare providers in 2021, and how providers' own historical payments data and machine learning provide solutions for both denials prevention and appeals prioritization.
Machine Learning makes RPA more effective in the revenue cycle. Being able to truly leverage payments data to drive decision-making makes automation efforts meaningful. ML enables RPA efforts to move from automating repetitive human processes to attacking the root causes of inefficiencies and implementing data-driven strategies around revenue cycle work efforts, vendor outsourcing, patient financial engagement and payer reimbursement.
RPA Follows Rules. Machine Learning Generates Intelligence. Your revenue cycle will benefit from both. Learn about the limitations of RPA and how machine learning provides can have a more meaningful impact on the healthcare revenue cycle.
Micro-segmentation of patient accounts and machine learning equip healthcare providers to shift to an ROI-focused approach to patient collections -- while maintaining empathy improving patient engagement.
In late 2019, Sift Healthcare and State Collection joined forces to test AI in the revenue cycle. Together, we conducted a rigorous 120-day live, scientific test (with a control group) for Wake Forest Baptist Health, to determine if machine learning impacted patient financial engagement outcomes. The results were impressive — a 6.5% increase in patient collections.
Every day healthcare providers are extending credit in the form of care, and they have little idea whether, how much or when they will be paid. It’s time for healthcare providers to start deploying well-established data science and analytics tools to forecast payments and optimize outcomes.
AI, machine learning and predictive models are abstract terms in the revenue cycle. How do you actually move past the buzzwords and get value out of your healthcare payments data? Here are six ways healthcare providers and RCMs can truly operationalize healthcare payments data to improve patient collections and revenue cycle operations.
Sift Healthcare's Rev/Track provides revenue cycle leaders with instant access to detailed intelligence that enables them to extract meaningful insights from their healthcare payments data -- information that drives better-informed decisions around revenue cycle operations.
While triage around COVID-19 continues, patient bills are going to pile up, cash on hand is going to dwindle and a more significant number of patients will struggle to pay their medical bills. Now is the time to be strategic and truly commit to being a flexible payment partner for patients. The starting point? Data analytics. Which easily dovetails into machine learning, both providing a powerful advantage for patient collections.
At Sift Healthcare we have seen time and time again that healthcare leaders are clear on care. When we talk to healthcare leaders, from CFOs and VPs of Revenue Cycle to CTOs and Chief Innovation Officers, they always start and end with care. Even the revenue cycle has to advance the mission around patients and care.
Unless your hospital or health system's denial rate is 0% you're missing earned revenue. Denials management is more than clean claim rate and prevention. How you prioritize insurance denials has a powerful impact on cash flow and revenue. Predictive analytics can tell you which denials your teams should work, in what order, to get the best payment outcomes.
Hats off to the Chief Innovation Officers. 97% of health systems that have Chief Innovation Officers also had positive operating margins. For healthcare providers, a commitment to innovation and strategic change improves the quality of care, patient experience and financial performance.
93% of healthcare administrators say that data analytics are “crucial” to future healthcare operations. At the same time, 84% say the usage of advanced analytics at their organization is “negligible”. In healthcare payments, there are three key roadblocks to the utilization of advanced analytics to improve the revenue cycle.
Every healthcare CFO and revenue cycle leader should be looking at their insurance payer payments in relation to patient payments -- identifying how they relate and influence one another. This is essential intelligence in an ever-complex revenue cycle.
For the patient revenue cycle, when, how often, and the method of outreach have a direct impact on dollars collected. But nobody is talking about contact cadence....
We're at a tipping point where healthcare providers will *have* to implement automation and AI into the revenue cycle. Not only to recover more dollars but also to keep up with the growing (& massive) administrative burden that is persistent in healthcare payments.
Improving the revenue cycle means minimizing costs and increasing collections, from both patients and payers. Even on a small scale, artificial intelligence has a meaningful impact on healthcare payments and operations.
Healthcare providers face an uphill battle when it comes to claim denials. Their denials management strategy should be to prioritize the denials that are most likely to be overturned (paid). These denials can be identified using data science -- advanced modeling on denials history, data pulled from 835’s and 837’s.
Unpaid patient medical bills are a growing problem for community hospitals and a downright crippling problem for rural hospitals. These healthcare providers can’t afford not to optimize their revenue cycle, improve their understanding of patient payment behavior and implement collection strategies based on their unique patient populations.
The healthcare payments market is in desperate need of approaches and innovations that reduce manual intensive processes — and save money. Even in today's day and age of big data, artificial intelligence and automation, many healthcare payments processes are painfully manual. Manual typically means cumbersome, prone to error, complicated and expensive.
Patient payments are often difficult for healthcare providers. Even more so when the expense is unexpected, as in surprise billing. Surprise bills (or balance billing) increase the odds of underpayment or no payment for provider services. Surprise bills are a problem for the patients who unexpectedly get hit with new expenses as well as the providers who are trying to collect earned revenue.
Consumer debt on credit reports is not an indicator of a patient's (or rather, consumer’s) ability or willingness to pay their healthcare bills. Most propensity to pay models rely on credit scores. Healthcare providers need to know how their payments perform — taking into account the uniqueness of their facilities, providers, specialties and regions.
As the volume of patient payments increases, many healthcare providers unknowingly violate regulation around patient targeting (like ECOA). Developing a collections strategy that maximizes patient payments and maintains compliance is critical for healthcare providers of all sizes.
In healthcare payments, where data flows from multiple systems and standards are a moving target, data can be pretty filthy. This means mismatched formats, errors and inconsistencies. Having clean data is often the biggest roadblock to being able to reap the benefits of data science.