Healthcare Organizations (HCOs), whether Accountable Care Organizations (ACOs) or Managed Care Organizations (MCOs), all share similar problems in Revenue Cycle Management (RCM). How can they solve the toughest RCM issues quickly and easily?
RCM is the critical function of all providers in getting paid quickly. Effective RCM ensures continued access to services for members, Healthcare Materials Management (HMM) billing, collections, and so on.
The provider’s Director of Revenue Cycle Management role is to make sure the company is paid as quickly and accurately as possible. That means minimizing denials and delays in the reconciliation process.
The Challenge
Simply put, the challenge is resolving issues quickly through the vast complexities of processes and associated data. It takes the claims management director too long to figure out what needs to change in the process to get payments processed faster. Finding the patterns and relationships amongst factors requires a deep dive into the data. That is not easily feasible with existing approaches to analysis and reporting. The analytics team does not possess the domain knowledge about procedures, claims, diagnosis to be able to deliver useful analytics for quick resolution.
The Solution
What is required is an easy interface to the vast, complex landscape of data. With the advent of the power of ChatGPT and its GPT-4 API, a chat interface to the data solves the problem. A secure, IT-governed chat interface can enable the RCM team to perform fast ad-hoc, iterative analysis quickly and easily. That accelerates time to uncovering the required insights to help process claims faster.
Examples Scenarios
The following are common scenarios for RCM managers to overcome:
- Claim Denials
Take for example a member is a snowbird and temporarily relocates from New York to Florida every winter. The health insurance company issues a member card which shares information between its growing number of entities, including the ones in Florida. Sometimes the card works in Florida, sometimes it does not work. For example, the insurance company’s newest entity in Florida does not yet have the functionality in place for such processing. If the RCM manager can quickly perform iterative analysis to determine the issue, such as the information sharing is not yet in place with the new entity in Florida, the insurance company could take steps to eliminate that issue for late payment.
- Re-admission Claims
Take an example for a re-admission claim. If there is re-admission for the same diagnosis for a procedure, such as an inpatient hip replacement, within X days, the provider will not get paid by the insurance company. The insurance company may consider that as part of initial admission. In this context, the hospital is in danger of bearing the cost yet not be paid.
An example ad hoc analysis could be:
- Find out which doctors are having the most readmissions when performing services for a given implant
- Or perform another analysis to see if a particular part such as a particular screw is a part that is the reason for getting denied 90% of the time
Today RCM managers cannot find that common denominator, because it is so deep in the data and processes.
- Payment Delays
A considerably basic problem is to determine why payments are delayed while the insurance company acknowledges the claim, but not paying promptly.
A first step would be to understand what is considered overdue payment for this insurance company for a given clean claim versus a non-clean claim. Thus, the analysis question could be “What is the average payment return time on a clean claim vs non-clean claim?
The answer is determined, e.g., “This payer on average pays clean claims within 15 days.” The next step is to determine what the issue is about the non-clean claims that is preventing payment.
Several iterative questions could be asked, such as, ”What is the most common type of claim for delayed payments greater than 15 days?”
Once commonalities are discovered, providing additional information during the claim process could eliminate or at least minimize overdue payments for that scenario.
How DvSum Helps
DvSum Agile Data Catalog semantically links data in the RCM database to the business entities in question. That alone saves countless man hours of mapping technical data to business items. More importantly, DvSum keeps all data in the entities ecosystem vs. ChatGPT queries opens the query to the internet. Thereafter, DvSum CADDI can interface with the data catalog to retrieve the data corresponding to the questions posed.
Conclusion
DvSum securely combines OpenAI’s GPT-4 with a powerful underlying data infrastructure so that RCM managers can query their data quickly, easily, safely, and iteratively. This empowers them to achieve fast, iterative analysis for overly complex data sets and thus determine obstacles and courses of action to accelerate payments. Contact us today to see how DvSum CADDI can help you.
