Healthy Data, Happy Patients
I saved $4.6M for a healthcare revenue company by helping over 168k people by improving overpayment recovery strategies.
*Information under NDA*
Between insurance premiums, healthcare coverages, and pre-existing conditions many people in the US face health related financial issues.
With 98.3 Million COVID-19 cases, and an estimated 30 million people in the US who have Diabetes, healthcare is a necessary yet costly service for many people in the US.
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Despite over 90% of the US population having some form of health insurance, medical debt remains a persistent problem.
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A recent Census Bureau analysis on US medical debt at the household level found 17% of households owed medical debt as of 2019.
Roles
- Data Team Lead
- AR Credit Analyst
Responsibilities
- Data Dissection and Discovery
- Process Mapping & Time Studies
Time
- 32 Weeks / 1 Year
Industry
- Healthcare
- Finance/ Revenue Cycle Management
Partners
- AR Staff
- Insurance Representatives
The Process
We began our iterative process improvement project with the following steps:
The Task:
A healthcare clearinghouse company tasked my team with looking into their Accounts - Receivable (AR) process to responsibly address their hand in the healthcare based debt problem within the US. This process would involve active overpayment returns and prevention of future overpayments for future users of their insurance partners/services.
Our Shared Goals:
ELIMINATE OPEN BALANCES
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Why do the balances take so long to resolve?
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What is the best way to resolve the balances?
UNCOVER ROOT CAUSES OF TOP 10 PATIENT CREDIT BALANCES
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Why do the credit balances continue to come in?
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What is primarily responsible?
UNDERSTAND THE INFLUENCES OF CONTRACT JARGON
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How can we both serve the hospital and the insurance companies who work for them?
Conducted interviews with insurance representatives to gain insights on key contracts of AR Delivery.
Our data analysis looked into the classifications of information where we prioritized legal constrictions, urgency, and AR value.
The valuable insurance developments were completed by synthesizing contract jargon, data, and process inefficiencies.
Eliminating Open Credit Balances
We began by conduct data investigations into the insurance companies which held the highest volume and value of credit in their accounts.
Of the four categories two would be eligible to be returned back to the patients while the others would belong to the insurance companies at hand.
Our initial data dealt with time and cash, we wanted to know which insurance types held the highest volume of open accounts as well as those which had the highest retained overpayment value for patients. Once we knew which accounts were the largest we began to categorize them by their ease of resolution and quick wins for the company.
Uncovering Root Causes:
We established ways to immediately begin to influence change through the company, but we also wanted to ensure that over collections and other payment issues would decrease over time.
To do so we needed to understand the deep contract jargon from many insurance companies and how they correlated to the data findings, thus truly making a long lasting change at the root of the problem.
We utilized a questionnaire each time we called a different insurance company to gather a baseline of information that we would refer to as our contract matrix.
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Contract analysis and dissection was completed within three parts, in depth AR studies, contacting providers, and contract investigations. Actionable insurance insights were revealed with revealing questions over key contract details and the AR delivery system process. Many developments were completed by fully integrating all contract language, data findings, and revealing process inefficiencies.
Persona Development:
The best representation of this research strategy would be its culmination, the tale of Jeff, the AR persona developed by our team.
Jeff struggled to learn about contracts, insurance details, and processing guidelines as a new company employee.
Motivations
- Wants workload to feel less overwhelming for the AR team and department
- Wants to be a top performer and get a promotion
Goals
- Wants to be an innovative performer in his team
- Establish a simpler and more efficient process
Jeff Cho B.S.
Age: 28
Role: Accounts Receivable Representative
Jeff is the newest addition to the AR Team and is one of the rising stars. He is known by his whole department due to his work ethic and friendliness.
Daily Routines
- Invoice Creation
- Contacting Customers
- Invoice Validation
Impediments
- Eliminate over/under collection of charges
- Repetitive daily tasks
- Mass backlog data
Jeff would go on to complete time studies with the team of AR Representatives who look into the 168K cases within the clearinghouse. His studies totaled more than 8 hours of observation within the team observing processing niches where some balances would be returned to insurance, patients, and given the timeframe others would be left to the clearinghouse.
An example of this understanding is Jeff's Journey Map which incorporates his data insights, emotions, and AR process management and optimization strategies.
Results:
Our research and design thinking methods allowed us to help the company obtain these monetary and cash savings.
168,000 Accounts
With an average 8 min. account task
22,400 Total Hours
Saved on total task completions
2,800 Work Days
Freed to solve overpayments