Predictive Analytics – An Overview of Analytics in Claims Management

Predictive Analytics An Overview of Analytics in Claims Management

Claims management experts today, if one has to draw up comparison can be associated to a warrior on mission. The only difference here is that he/she are not there to assassin people, but to make sure they get their payouts, dues, co-pays and reimbursement on time.

While we talk about rolling a crystal ball in Healthcare Claims Management process, there are very few organizations mainly small and medium scale payers who are aligning to use the predictive modeling to forecast their business future. If healthcare insurance providers could some way or another figure the future, what would be a more secure, more beneficial, and more productive future it may want to be? Loss prevention departments could extraordinarily diminish both claims severity and frequency, while development could be engaged to alleviate catastrophic risk.

Utilizing analytical tools to enhance results

Spotting high-risk claims at a very early stage in their life-cycle stands vital to alleviating costs and enhancing results for all people involved. With the expanding accessibility of big data analytics, it has been easier to connect predictive modeling with managing the claims. This alters the procedure from depending on the experience of a solitary agent to drawing on the claims information at the organization level, which may mean countless cases or even to the business level, which may include a large number of claims.

The test of accurately recognizing possibly severe claims at an early stage can put even the most experienced agents in dilemma. Information supplied by a strong prescient model can quicken experiential learning and give a security net to adjusters/agents by giving information driven pointers to flag claims that spiral out of control. Matching severity identification with business procedure can yield much more noteworthy results.

Why predictive modeling?

With daily flow of structured and unstructured data, it has never been less demanding to profit from establishing a predictive analysis model for your claims management process. Consolidating a hearty model can yield substantial statistical basis for decision making, which firmly expands the basic leadership capacity of claims adjusters. Whenever deciding to zero in on an analytical platform, it’s a wise business decision to go for a third party approach.

Basic and advanced benefits predictive analysis provides in the claims management processing:

 

  • It improves three key claims management objectives, which is to provide a superior customer experience, to achieve operational excellence and to manage risk.
  • Facilitates the ‘predict and act’ approach that is the basis for the highest quality of business decision-making
  • Delivers measurable and proven ROI
  • Is applicable to a range of decisions making process during the claims processing stage
  • Applies analytics at the point of decision-making
  • Leverages the data you already have
  • Makes your existing systems smarter
  • Uses a pragmatic approach

 

Outsourcing is the name of the game, so why not to use such business set-up for your advantage.  This way you can be rest assured that someone is working in tandem with you to chalk out ways to reduce the ‘FWA’, which means fraud, waste and abuse claims. Also, do keep in mind that your organizations return on investment also improves drastically, as the payers can then focus on more important function at the front end of the business. Claims severity keeps on rising, driven by medical and reimbursement costs, for which the National Council on Compensation Insurance reports a total increment of 226.7% and 135.2% over the past 20 years, individually.

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