Collection of Unpaid Invoices


Case III:

Analytics helps leading provider identify likely incremental collections from 30% of unpaid invoices

Escalating healthcare costs have forced employees with employer-provided insurance to bear a higher proportion of self-payment costs such as copay, coinsurance, deductible and out-of-pocket expenses. Hospitals are challenged to collect the patients’ portion of medical expenses, which according to American Hospital Association comprises 6.1% of all services.

Multispecialty practices collect only 56.6% of their accounts receivables in the first 30 days. Many hospitals, especially a growing number of nonprofit companies are particularly vulnerable and are seeing their access to capital weakened and their capital ratings downgraded due to bad debts.

While maximizing point-of-service collections is a key goal, improving account receivable collections after discharge is extremely important for hospitals. Successful scoring approaches for self-pay accounts require segmenting them by understanding patients’ ability as well as their willingness to pay.

To design a well-rounded collections strategy, it is critical to: a) know which patients require assistance with payment plans, b) where to apply discounts, and c) define what if any additional collection resources are needed.

Leading multi-specialty multi-location healthcare provider
Improve collectability of the self-pay portion of patients’ medical expense

Improved collectability by $20 million by identifying right strategies by patient segments

We applied over 50 hypotheses to identify over 100 different data elements for our analysis, including patient characteristics, policy benefits, credit worthiness, and medical and procedures data to define the key drivers of patient payment behaviour.

A suite of advanced predictive models was developed using machine learning techniques to gain insights into a patients’ propensity to pay, likely amount a patient will pay, and the timeframe a patient is likely to pay. The solution helped the provider to:

  • Target the right accounts
  • Speed collection receivables
  • Collect more unpaid fees

Segment Target Segment Size Strategy and Tactics
Full (< 90 days) Patient likely to pay full amount in less than 90 days 40% Standard billing
For low scores and low outstanding amount, send the bill as-is with no follow ups
For patients with high outstanding amounts, have multiple touch points (call, email, text).
Full (> 90 days) Patient likely to pay full amount in more than 90 days 10% Collect faster
Offer payment plans in return for 30% down payment
For patients with high outstanding amounts, ensure follow ups by patient accounting office.
Partial Patient likely to pay partial amount and don't qualify for charity care 10% Targeted collections with low discounts
Offer 20% discounts with immediate 20% payment and a 12-month payment plan
Prioritize high credit score, and patients with high outstanding amounts.
Zero Pay Patient likely to pay no amount and don't qualify for charity care 20% Targeted collections with high discounts
Offer 30%-40% discounts with immediate 10% payment and a 24-month payment plan.
Prioritize high credit score, and patients with high outstanding amounts.
Charity Care Patient qualifies for charity care 20% Targeted collections for charity
Offer patients payment plans, with 10% down payment.
Offer charity care - prioritizing patients with high outstanding amount and low credit score Model.

We identified five key segments with distinctive payment behaviours and recommended the following treatments:  Continue to bill 40% patients with little or no additional follow-ups  Offer payment plans and low discounts to 20% patients to collect sooner  Leverage collectors to target remaining segments that are highly unlikely to pay with higher discounts The recommendations helped Provider identify segments to collect $20M in payments from unpaid health service invoices.

Providers can recover significantly higher incremental dollars by selectively offering payment plans, discounts and collection resources

More than $20 million could be collected from outstanding invoices for unpaid health services by targeting 30% of outstanding accounts

Delivered best-in-class solution of ensemble machine learning models that predict amount, time to pay, and propensity to pay