As an integral part of every major healthcare facility, the process of Revenue Cycle Management generates a huge amount of data, constantly used by stakeholders within the health care industry.

This data is carefully managed, stored, and interpreted by every health care facility to improve the quality of patient care whilst simultaneously reducing claim denial cases to increase revenue collection.

The ever-growing need for establishing a better grip on data analytics also directed the health care industry to look deeper into Predictive Analytics. As a concept adopted and appreciated by health care industry stalwarts, Predictive Analytics, is touted to use data in ways never imagined before.

Black Book Research conducted a survey where financial juggernauts and revenue cycle leaders of approximately 1500 hospitals were questioned regarding the relevance of Predictive Analytics. 76 percent of these individuals shared concrete plans to invest 10 percent (or more) of their respective facilities’ IT budget towards Predictive Analytics. Here’s why-

Reckoned as a game-changer in health care, facilities use Predictive Analytics to forecast revenue collection and rectify errors that can affect the flow of revenue. Besides, more than 50 percent of health care leaders believe that predictive analytics could help reduce overall costs by 15 percent or more.

Predicting contingencies in the revenue cycle process

For health care facilities, it is pertinent to conduct eligibility checks during patient registration.
These checks need to be rechecked at the time of claim submission. If both these steps are not completed with coherence, providers are at risk of experiencing a claim denial as the patient might outrun coverage from a specific insurance payer.
This contingency hampers cash flow and increases administrative expense. But predictive analytics can recognize such breakdowns and bring them to your attention at the earliest.

Payers’ remittance pattern

Predictive Analytics enables providers to determine when a specific payer will complete claim submission. The approach is so accurate that facilities can even predict the date and time of a specific remittance. Innovative machine learning techniques are put to use to achieve a foresight of such sophistication.
This information empowers health care providers to manage their revenue cycle operations with ease and efficiency.

Predicting Claim Denials before they take place

On average, a hospital carries a risk of losing $5 million annually towards claim denials. Even though 63 percent of such denials can be rectified, the entire process causes a surge in administrative overheads which eventually eats into the revenue of the facility.
Predictive Analytics helps the facility in identifying claims (before submission) which have a higher probability of denial. This creates a window for employees to correct these claims driving an increase in the number of clean claims.

Foresee changes in payer rules

Payer-specific rules for claims adjudication are constantly changing and if the facility’s revenue cycle doesn’t improvise such changes, there is a good chance of a claim being denied or delayed. In both scenarios, the facility stands to lose a considerable chunk of revenue whilst incurring additional costs.
With the help of predictive analytics and machine learning, operators can make necessary changes in advance to adjust accordingly and improve the function of the revenue cycle.