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Justin Nicols

Patient Payments

How a 120-Day Machine Learning Experiment Led to a 6.5% Increase in Patient Collections

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.

Justin Nicols
June 19, 2019
Healthcare Payments

Healthcare Providers Have Become Lending Organizations

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.

Justin Nicols
June 19, 2019
Healthcare Payments

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.

Justin Nicols
June 19, 2019
Revenue Cycle Analytics

Filling The Gap In Revenue Cycle Reporting

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.

Justin Nicols
June 19, 2019
Patient Payments

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.

Justin Nicols
June 19, 2019
Healthcare

Thank You For Prioritizing Care

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.

Justin Nicols
June 19, 2019
Claim Denials

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.

Justin Nicols
June 19, 2019
Innovation

The Impact Of The Chief Innovation Officer

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.

Justin Nicols
June 19, 2019
Revenue Cycle Analytics

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.

Justin Nicols
June 19, 2019
Healthcare Payments

Want To Control Your Cost To Collect & Utilization? You Need A Holistic View Of Your Healthcare Payments.

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.

Justin Nicols
June 19, 2019
Patient Payments

Why You Should Be Thinking About Call Cadence

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....

Justin Nicols
June 19, 2019
Healthcare Payments

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.

Justin Nicols
June 19, 2019
Data Science

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.

Justin Nicols
June 19, 2019
Claim Denials

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.

Justin Nicols
June 19, 2019
Healthcare Payments

Rural & Community Hospitals Need AI The Most

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.

Justin Nicols
June 19, 2019
Healthcare Payments

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.

Justin Nicols
June 19, 2019
Healthcare Regulation

Surprise Medical Bills Are Bad For Healthcare Providers

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.

Justin Nicols
June 19, 2019
Patient Payments

Stop Using Credit Scores For Propensity To Pay

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.

Justin Nicols
June 19, 2019
Healthcare Regulation

Is Your Patient Payment Targeting Legal?

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.

Justin Nicols
June 19, 2019
Data Science

Your Data Is Too Messy For Data Science

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.

Justin Nicols
June 19, 2019