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healthcare data

Healthcare Payments

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.

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

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
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