By
January 2021, millions of those who suffered job losses in the wake of COVID-19
will see their unemployment insurance end. Medicaid and subsidized coverage
under the Affordable Care Act (ACA) will be a safety net for many, but nearly 2 million Americans could find themselves stuck in the ‘coverage gap’, where
their household income exceeds the eligibility threshold for Medicaid, yet
falls below the lower limit required to receive ACA marketplace subsidies.
Without
large group or government coverage, these consumers will be left uninsured or forced
to purchase individual plans with high deductibles. Considering this will
likely contribute to larger patient balances and more struggles with patient collections, many are bracing for a hit to their bottom line.
To help minimize accounts receivable
and avoid bad debt write-offs, choosing the right data model should be a top
priority. Here, we look at one piece that’s often missing from the patient
collections puzzle: credit data.
Don’t
overlook credit data in your self-pay collections strategy
Many providers already use demographic and behavioral data to power patient collections, but there can be gaps in what’s known about a consumer’s ability to pay. Credit data can help fill in the blanks. Here are three ways this can be used to optimize your collections strategy:
1. Get a complete view of your patients’ financial situation for faster decision-making
Credit data can reveal how a patient is managing other
financial obligations, giving you insights about how to handle their healthcare
account for a greater chance of payment. Have they just maxed out a credit
card? Have they missed a student loan payment or fallen behind on their
mortgage? If so, they’re probably going to find it difficult to pay off their
medical bill. Knowing this, you can move quickly to help them find alternative
coverage or offer a more manageable payment plan.
Conversely, if they’ve just bought a new car or paid off a personal loan, there’s a high chance they’re in a good position to pay their medical bills too, so contacting them with a straightforward and easy payment plan means they can clear their balance promptly.
2. Segment patient accounts and allocate them to the right payment pathway
The sooner
you can get patients onto the right payment pathway, the more robust your
cashflow will be. Credit data can help you segment accounts quickly and
accurately. Experian Health data shows that when patients are segmented
according to propensity to pay, collections increase by around 2% when credit
data is included, compared to segmentation without credit data.
Martin Health System used Collections Optimization Manager to segment patients and check for available charity support or Medicaid eligibility. By getting patients on the right pathway and making sure agencies were focusing on the right accounts, they increased recovery rates by more than $3.1 million and identified an extra $975,000 in Medicaid coverage in just seven months.
3. Create a more compassionate patient financial experience
Using
credit data also helps create a more compassionate patient financial
experience. Instead of adding to a patient’s financial worries by chasing
payments they’ll never be able to cover, you can run charity checks to see if
there’s any missed coverage and quickly connect them to the right financial assistance program.
A tool such as Collections Optimization Manager lets you
segment patients based on their individual circumstances, for a more
patient-friendly approach to collections. You can then personalize their communications and payment
options so they can manage their expenses with less anxiety and more
confidence.
Discover why 60% of US hospitals are already using Experian Health’s advanced collections software and unrivaled datasets
to optimize patient collections, and find out how we can help you build a
resilient revenue cycle as self-pay accounts continue to rise.
The post 3 types of data providers should leverage to improve self-pay collections strategies appeared first on Healthcare Blog.
