Case Study: Self-Pay Reimbursement

Health system increases recovery rate by 15%.



  • Top 10 Ohio health system
  • 1,951 hospital beds


  • Growing size of patient balances
  • Recovery rate from individual patients is lower than insurance carriers


  • Premier Health decided to partner with MedAssist to outsource self pay collections
  • Premier Health’s Epic system would be the system of record, while MedAssist’s account processing system would score a guarantor’s propensity to pay and define the appropriate work events based on the score

Bigger, Faster Results

  • Over 18 Months, self pay after insurance collections improved 4%.
  • After four months of scoring and segmentation, Premier Health’s cash increased $584,340 which equates to an annual cash increase of $1.75 M.
  • Accelerated account resolution process
  • Maintained high level of patient satisfaction
  • Fewer patients reaching collection agencies
  • 4 percentage point increase on collections
  • Reduced bad debt reserve for self-pay

Premier Health

Premier Health is an acute care health system serving Southwest Ohio. Premier generates over $2 billion annually from 1,951 beds in five acute care hospitals. As exemplified by their multiple Press Gainey Beacon of Excellence awards, Premier Health delivers clinical excellence with a focus on patient satisfaction.

The Challenge

Recent trends indicate that the cost of healthcare continues to shift from employers and insurance companies to patients themselves. Because the financial recovery rate from individual patients is lower than from insurance carriers, growing patient balances are causing financial pressures for many hospitals. Determined to effectively collect patient balances, Premier Health began a search for a high-performing revenue cycle partner that could both optimize the account resolution process and enhance patient satisfaction.

The Partnership

Shari Bailey, Director of Patient Financial Services, sought a partnership for Premier with a company that is committed to improving revenue cycle performance and improving the patient experience. MedAssist Solutions was selected from 15 candidates to be Premier Health’s self-pay account resolution partner.

MedAssist has been a remarkable partner for us. Their propensity to pay scoring and best practices have made a substantial improvement to our self pay performance.
— Shari Bailey, Director, Patient Financial Services, Premier Health Partners

The Process

In an effort to tightly coordinate and automate the balance collection process, the IT departments from MedAssist and Premier collaborated to build a highly efficient infrastructure. Employees from both organizations would use a dedicated VPN tunnel and TLS encrypted access in order to assign accounts to work queues using custom Epic billing indicators. Premier Health’s Epic system would interact daily with MedAssist’s account processing system which utilizes a proprietary scoring and segmentation model; this system would assign a ‘propensity to pay’ score to each account and indicate the appropriate work events accordingly. Work events, such as statements, letters, attended calls, unattended calls, etc., are the best practices necessary to resolve each type of account and are derived from analyses of years of production and operation experience.

MedAssist’s model evaluates a number of data elements based on their relative significance in payment prediction and produces a ‘propensity to pay’ score, ranging from 1 (low propensity) to 100 (high propensity). The model produces a high correlation between the guarantors propensity to pay and actual patient payments. The data elements evaluated are:

Enriched Account Data. This data provided by the client at the time of referral evaluates individual patient data such as age, gender, marital status, employment, type of account (inpatient, outpatient, ED), account balance, etc.

Socioeconomic Data. This demographic data compares the income or assets of the patient to the average income and median home value in the patients’ zip code. If the patient has ever made a payment to MedAssist, this payment data is added.

The model evaluates each data component. Accounts are scored at the time of placement and continue to be scored bi-monthly until the account is resolved or returned to the client. Accounts with similar scores are grouped together and assigned to a specific Account Flow Manager (AFM). This algorithm then specifies which collection-based work events are necessary for each group of accounts.

In 2014, Premier Health initiated The Premier Patient Experience Program, which trained employees on patient satisfaction practices using the “Acknowledge, Introduce, Determine, Explain and Thank” (AIDET) methodology. As an example of true partnership and dedication to the Premier patient experience, all MedAssist team members must complete this training and continue to participate in the quarterly booster training. MedAssist has even adopted the AIDET model as required training for all patient account representatives.

Premier Health and MedAssist have also incorporated service level agreements into their partnership. This includes evaluation of hold times, goals for one call resolution, abandon rate, conversion goals for promises to pay, audits of recorded calls, audits of payment posting, and an internal survey of Premier employees who work with MedAssist. Results of the audits, account resolution performance, and service level agreements are reviewed quarterly.

The Results

After just four months of propensity to pay scoring and segmentation, the new process improved the account resolution process and collected $584,340 in incremental cash. There was also a reduction in bad debt write-off as well as a reduction to the number of patients reaching bad debt. Over the course of 18 months the Premier Health and MedAssist partnership improved cash collections by a combined total of $2.63 million, accelerated the account resolution process, and maintained the high level of patient satisfaction Premier patients have come to expect.

Balance After Insurance Regression Analysis

Long Term Impact

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