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It's Time To Move Past These 3 Common Revenue Cycle Management Mistakes
It's time for health systems and hospitals to move past these three all-too-common business tactics that have remained constant, despite the ever-changing payer landscape and the mounting pressure for providers to lower costs and provide a more seamless payments experience.
3 Ways Data Can Fix Denials
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 In Action - 5 Revenue Cycle Examples
Integrating Machine Learned adds a decision engine to the revenue cycle, enabling you to determine the next best action that should be taken with a claim, denial, patient account, payer contract or vendor. Can't envision it? Here are five examples of what ML in the revenue cycle looks like, in action...
5 Ways Machine Learning and RPA Work Together In The Revenue Cycle
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.
3 Reasons Why The Future Of Healthcare Payments Is Not Automation.
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.
6 Ways To Use Your Healthcare Payments Data To Improve Collections
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.
Rev/Track Leverages AI To Improve Revenue Cycle Operations
Rev/Track leverages Sift Healthcare's AI and machine learning to help healthcare providers and RCMs to optimize revenue cycle operations. Rev/Track delivers detailed intelligence around payments behavior, insurance denials, collection trends, patient segments and revenue cycle work efforts.
Introducing The Sift Quality Score
Introducing The Sift Quality Score. For revenue cycle managers, understanding account quality and its impact on patient collections is essential for forecasting revenue, optimizing rcm workflows and ensuring the best strategy is in place for managing resources. Sift’s Quality Score is derived from Sift’s predictive model scores — it tracks account quality over the early-out period, enabling RCMs to more intelligently forecast revenue throughout each billing cycle.
How Do You Optimize Patient Collections During COVID-19?
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.
What Dirt Do You Move First?
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.
Hospital CFO’s & The “Negligible” Use Of Data Analytics
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.
Automation Does Not Mean Data Science
The term “automation” can refer to any number of automatic processes within the revenue cycle workflows. But, it doesn’t necessarily refer to the use of data science. Just because a process is “automated” doesn’t mean predictive analytics or any data science is applied. However, when you do truly use data science to automate your workflow (which you absolutely should do), you pick up a host of efficiencies and improvements. Our breakdown on automation vs rule-based segmentation vs true predictive analytics.
There Aren't Enough Bodies To Save Healthcare
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.
Every Revenue Cycle Improvement Matters
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.
4 Innovative Strategies For Patient Payments
When healthcare providers leverage their patient payments data to drive their collections strategies, they create new and powerful opportunities to increase the revenue they collect, building patient relationships and using payments data to inform business operations. Here are four new and intelligent ways that healthcare providers are using their patient payments data to be more strategic and to drive better payment outcomes. Here are four innovative ways that healthcare providers are leveraging their patient payments data
High Dollar Denials Are Often The Least Likely To Be Paid.
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.
3 Healthcare Payments Processes That Are Still Manual
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.
