Estimates suggest that billing and insurance-related costs make up the largest share of the total administrative costs in healthcare. The good news is that the adoption of technological advancements such as data analytics can help healthcare organizations considerably reduce costs.
Insurance claims processing follows a complex workflow that includes several checkpoints such as the patient’s insurance plan, the payer’s guidelines for claim submission, and the provider’s contract with the payer. A customized, well-built insurance claims analytics software solution can provide better accuracy, improve cost-efficiency and help healthcare providers process claims faster.
The common bottlenecks which might slow down your business growth
The overall process of an insurance claims involves back and forth between the customer and insurance companies and is quite cumbersome.
Paper-based processes and a general lack of technology innovation have hampered the process of insurance claims.
There are volumes of data available in the insurance claims industry, but the lack of structure makes this data unusable.
Outdated and time consuming processes are pervasive in the industry, thereby causing considerable delays in the outcome.
The traditional process is highly dependent on specific employee execution and results in low levels of satisfaction.
The current system of acquiring information from customers for insurance claims requires a substantial amount of time.
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Reduced processing time and increased quality of service through big data analytics in insurance claims
Manual checking of claims by multiple employees in healthcare organizations severely affects the quality of the process. Through claims analytics in insurance, the claims process is considerably automated, which reduces claims cycle times and also enhances customer experiences.
Touchless claims, with no human intervention, are hugely beneficial during disasters and outbreaks as it lessens the dependence on loss-adjusting resources. Claims analytics systems automate various processes like capturing, auditing, communication, notifications, and reporting according to a client’s needs. Customized insurance claims data analytics solutions simplify claims processing, especially in the case of straight-forward claims.
All industries are using chatbots to improve efficiency across the process workflow. Chatbots leverage popular messaging apps on customers’ phones to share information with people, just like a person. Natural language processing and sentiment analysis allow for automated and effective communication.
Claim analytics in health insurance allows for customized interactions with customers. Bots can assist customers to save time by answering questions, resolving claims, selling products, addressing leads, and identifying coverage issues. A bot eliminates the need for analyzing data from multiple dashboards and various applications by providing relevant information within a single report. Through these intelligent and actionable insights on key metrics, management can make data-driven decisions with ease and efficiency to increase productivity
Research shows that personalized marketing strategies can have a positive impact on customers and may even influence their purchasing decisions. Claim analytics in health insurance can help organizations personalize sales tactics to gain a competitive edge over other health insurance providers.
AI-powered claim analytics solutions allow decision-makers to interpret large amounts of customer data and customize profiles with relevant insurance products. Claim data analytics solutions help create hyper-personalized experiences by ethically examining an individual’s digital footprint and recommending the most receptive forms of engagement.
Risk Assessment is a crucial procedure in the insurance industry wherein insurance providers review all significant details to ascertain high and low insurance risk prospects.
The traditional underwriting process is tedious and somewhat ineffective. Insurance claims data analytics helps carriers improve the risk management process by identifying unhappy customers or potential prospects. Existing customers likely to cancel or lower coverage, and people with other insurers looking to change their carrier fall into this category.
With the help of high-level key insights, insurers can reach out to such customers and provide personalized attention to address their concerns in time.
Customized insurance claim data analytics with telematics provide specific and valuable insights about customers and assets.
Telematics includes wireless communications, GPS, and other onboard diagnostics that assist in real-time tracking of traffic conditions, risk factors, customer behavior, and lifestyle patterns.
With analytical information from claim data analytics systems, insurance companies can improve customer satisfaction by offering rewards, discounts, and lowering premium amounts for qualified customers. The company also benefits through improved operational efficiency and reduction of costs, which translates to an increase in revenue.
Research states that though companies have access to a lot of information, they barely use 10-15% of this information. Advanced data science techniques like Machine Learning can help insurers interpret structured, semi-structured and unstructured data effectively for better decision making.
Through machine learning, automation of many routine claims processes, including claims registration and claims settlement is possible. Carriers can also understand claims costs better and save a lot of money through dynamic management, quick processing of claim settlements, focused investigations, and better case management.
Claim data analytics solutions offer customized insurance advice, reduce input time, minimize the risk of human errors, leading to faster and hassle-free claims processing, and improved customer experiences.
The current system has insurers adopting speedy processes to settle claims quickly. However, this causes many instances of overpayment, due to the eagerness to settle claims. Our deep data analysis assists in the optimization of payouts to curtail the time involved in claim cycles and reduce insurer costs through limited instant payouts. Additionally, the insurer is able to save more revenue loss.How we do it
Insurers often adopt speedy processes to reduce costs and settle claims quickly. However, sometimes this results in tremendous losses for carriers due to overpayment on insurance settlement claims.
OSP’s advanced claim data analytics solution assists in the optimization of payouts to curtail the time involved in claim cycles and reduce insurer costs through limited instant payouts and save revenue.
Claim Management is a painstakingly complicated process that takes up a lot of time, and any errors in claim submissions result in loss of revenue through claim rejections and denials.
OSP helped a California-based healthcare claim management company automate its complete claim process from submission with CMS codes to reimbursements. The claim analytics solution also manages claim denials with electronic remittance advice, EOB, and appeal letters that helped to reduce the unpaid bill ratio.
Research shows that one of the prime reasons for revenue leakage in healthcare is the denial of claims. But now, thanks to AI, it is possible to reduce the health claims denial rate and increase a provider’s ROI.
OSP built a customized AI-driven claims value prediction system that could accurately predict claim approvals, the estimated date of settlement, and the final amount receivable. The client uploads the claims data on the digital trading platform. The predictive analytics solution then displays all the relevant A/R information, so A/R traders can make informed decisions when buying or selling A/Rs.
Insurance companies are always looking for solutions that can simplify the claims adjudication process. Available claim analytics solutions can spot routine flags in the claims adjudication process but are ineffective when it comes to complex catastrophic claims.
A US-based client was looking for an intuitive claims data analytics system to process the claims data from several clearinghouses using a set library of rules.
OSP developed a robust health insurance claims analytics solution with comprehensive dashboards that allows the client to predict, evaluate, and manage high dollar healthcare claims. The highly efficient system segregates large amounts of data quickly and accurately predicts if an insurance claim is payable or not.
At OSP Labs, our insurance claims analytics work to automate the entire lifecycle of insurance claims, from prioritization, forecasting, to improvisation of speed and accuracy of claims. Our process-driven solutions leverage big data analytics to improve risk management toward underwriting, resource utilization to settle claims, prediction of disputed claims, and data mining toward automation of claims processes.
We engage with your organization to create systems that are geared to perform intelligent predictive analysis of data that is able to highlight the likelihood of claims that are bound to be frauds, cause wastage or even abused. This is executed through data model and patterns that are developed through broad exposure and investigative data mining abilities that hunt customer data to minimize the chances of frauds, waste and abuse.
Compensation insurance is one that requires keen monitoring through automated features due to the high volume of claims in this arena. Our solutions are empowered with predictive analytics to gauge the risk factors with compensation insurance, specific to customers, through the analysis of their data. This preemptive approach avoids expenses through identification and risk analysis of inaccurate payments, and suggestions for early intervention.
We follow every government's regulatory mandate and create solutions that adhere to strict protocols.
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