Predicting the likelihood of claims getting invoiced
Client
An RCM Company
Region
Texas, US
Industry
Healthcare
Completed
26 Aug 2024
Executive Summary
Project overview and client background
The client is a revenue cycle management (RCM) company that provides technology-enabled services for hospitals and healthcare providers in the US. They offer a range of services including eligibility & enrollment, self-pay/early out, complex claims, and accounts receivable services.
They wanted to improve the overall efficiency of their claims management process for seamless patient experience and maximized revenue recovery for healthcare providers. To do this, they focused on being able to assess the likelihood of claims getting invoiced, understand drivers of claims denial and deploy appropriate mitigation strategies.
Finarb's solution enabled the client to pinpoint various relevant features contributing to claims denial. Segregating the in-patient and out-patient cases helped us in understanding the nuances in the two cases. For instance, inpatient cases often involve more complex procedures and longer stays than outpatient cases.
Challenge
Key problems and industry context
Recent findings from a 2023 survey conducted among healthcare providers and RCM stakeholders in the US revealed that an alarming 77% of healthcare providers experienced delays exceeding one month in receiving payments.
The client works with healthcare providers in getting their claims invoiced. For any claims that are denied, they also work with patients and providers to ensure fast claim resolution.
The client wanted to identify the actionable factors behind claims denial to make prompt interventions basis the likelihood that a claim might get denied per a patient's profile. The goal of the model is to provide a data-driven, predictive solution to aid healthcare facilities in managing their billing process efficiently and effectively that will lead to reduced administrative efforts, swift processing of claims, better turnaround times, and improved customer satisfaction.
The Result
Outcomes and achievements
Finarb's machine learning models were able to differentiate between claims that were likely to get invoiced and those that were likely to get rejected for both the in-patient and out-patient settings. In addition, the top factors driving claims denial in both cases were identified so that the client can mitigate them.
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