Everything you should know about Predictive Analytics in Claims Management

Home, car, property, business, and travel – can you think of a word that can bind all these together? Insurance. Investing in insurance is good, but ask anyone who has claimed insurance, and they will tell you it’s far from easy.

The healthcare industry also faces the same challenge. Healthcare providers are always looking out for smart healthcare payment solutions to simplify the claims process, offer better protection against fraud and disburse payments faster.

Many industries, including healthcare, are currently using AI predictive analytics. Though many healthcare providers are looking at predictive analytics to provide better treatment to patients, not many are aware that its benefits can improve the claims management process as well.

Introduction to Predictive Analytics

Predictive analytics helps organizations evaluate current and historical data and predict future results. This technology uses a combination of different techniques such as data mining, predictive modeling, machine learning, and artificial intelligence to make these predictions. 

Technology has helped healthcare in many ways

Predictive analytics helps healthcare providers to create personalized treatment plans through accurate diagnosis on the care front as well as to increase operational efficiency on the administrative front.

Chatbots, real-time reporting, AI and ML algorithms, and other such predictive analytic tools can help to make the healthcare claims experience stress-free. 

Applications of Predictive Analytics in Claims Management

Presently, claims management is a challenge for claimants and healthcare providers alike majorly because it involves loads of paperwork and traumatic phone calls. Using the different techniques of predictive motion analytics, it is now possible to simplify the claims process.

Reduce claims-processing times

AI-driven chatbots can record all health claims calls automatically and help healthcare facilities to analyze patterns in these calls faster. Predictive analytics solutions can then identify healthcare claims that can be handled by bots and only route complicated cases to the staff for review. These solutions help to improve productivity, care quality, and efficacy as healthcare providers can focus on claimants that need more emotional support. 

Faster diagnosis of claim outcomes

Claim handlers working on complex claims are often confused with the manual reports and are not sure about the right solution for the claimant. Customized dashboards generated using predictive analytics help in the faster diagnosis of claim outcomes and present the best solution for the claimants. As the accuracy of claim outcomes increases, claim costs will reduce.

Share real-time updates with claimants

Predictive analytics systems will help healthcare providers foresee the claimant’s needs and address them proactively. These personalized solutions can provide detailed notifications when the claims are incomplete so the provider can immediately alert the claimant. The video and data-sharing features built into these systems provide real-time information and regular updates. You can improve the customer experience by sending these updates through the claimant’s preferred method – email, text message, or multimedia message.

Prevent claims

Is it possible to alert people about risk before it becomes a loss? With technology, it is. Telematics and electronic devices such as wearable trackers, sensors, and mobile phones can help healthcare providers and claims organizations focus on claim prevention rather than claim handling. These technologies can notify customers and would-be claimants about potential risks before losses occur. 

Some ways in which predictive analytics can help in claimprevention are:

OSP’s Predictive Analytics Solutions

OSP has helped healthcare providers by building predictive analytics solutions that predict the possibility of claim approval as well as the estimated date of settlement and final amount receivable. 

Our customized AI-based claims prediction system assesses claims before sending it to the payer companies and predicts the possibility of claim approval. The system tests historical claims data from diverse insurance provider companies and then leverages the random forest algorithm to predict the value of possible claim approval against the actual value claimed. The solution provides highly accurate statistics of the generated claims that help traders in decision making. Furthermore, the Machine Learning model learns from the current predictions and continually enhances prediction accuracy.

OSP’s highly advanced predictive motion analytics systems with comprehensive dashboards display all the relevant A/R information that helps A/R traders make informed decisions when buying or selling A/Rs. A boost company can boost its productivity, thereby increasing its revenue by employing highly accurate and efficient claims prediction software solutions.

Conclusion

Predictive analytics though still in its nascent stages, isalready revolutionizing the healthcare industry. While some people believe thatpredictive analytics is all about statistically derived probabilities and isnot entirely transparent and accountable, there is no denying the fact that itsbenefits outweigh its potential issues. 

A robust, well-designed, and executed predictive analytics solution in the healthcare claims sector will benefit both healthcare providers and claimants and make claim management a hassle-free process.

References:

https://www2.deloitte.com/us/en/insights/topics/analytics/predictive-analytics-health-care-value-risks.html

https://www.mckinsey.com/industries/financial-services/our-insights/claims-2030-dream-or-reality

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