The ongoing pandemic has thrust renewed focus on the capabilities of the insurance sector to provide adequate, timely coverage to patients. The coverage may not necessarily be COVID-specific — the global uncertainty and general air of anxiety has prompted many to seek universal coverage, especially among the millennials.
This has given rise to new market entrants with products that help reach out to claimants more swiftly, reducing reimbursement cycle times and ensuring timely payments. The new thinking among industry leaders in this sector is to make medical claims processing leaner, more agile, and less expensive.
The incessant lockdown measures have forced companies to revisit traditional working models with the aim of ensuring business continuity even with limited on-site personnel and capital. And the insurance sector is no exception to this new found way of working. With ‘learning to live with the pandemic’ being the general notion among policy makers, it has become a priority for many companies to transform entire sectors in an effort to get on a path of ‘continued operations’, no matter what the economic, social, or political scenario.
In other words, businesses are prepping up to become pandemic-proof and in the process, also develop long-term business models that will keep them recession-proof.
Technology has played a vital part in business transformation, especially in these uncertain times.
Claims processing is error-prone, simply because it entails a lot of manual effort. Robotic Process Automation (RPA) has, to an extent, reduced the burden on manpower by automating monotonous tasks — the bots are pre-trained to perform specific tasks that are repetitive and do not change. But the technology still falls short in the case of new tasks for which the robotic process automation bots have no pre-trained knowledge.
Take the case of data entry from invoices from a certain vendor. The RPA bots can be trained to pick up data from specific parts of the invoice template and populate it in the destination file. This is a repetitive task that can be programmed because the field placement on the invoice template from which data needs to be extracted is fixed, so the RPA bot knows exactly where on the invoice to look for relevant data and extract those. RPA becomes very useful in this case if the invoice template from the related vendor does not change.
Come to another scenario now, where you are faced with collecting data from a new vendor with a completely different invoice format. The robotic process automation bot does not have pre-trained knowledge on where exactly on the template to look for data and extract the same — the RPA bot may look to pick up data from the right-bottom corner of the template, which traditionally holds the field placement for TOTAL; but in the case of the new invoice, that region may in fact be holding the SIGNATURE field. The RPA bot would, in this case, be populating the wrong data into destination files, rendering the role of RPA completely useless.
What RPA requires is intelligence to perform new tasks for which it is not trained. The need for intelligent robotic process automation is felt in any document-based process like accounts payable, order processing, etc.
We’ll look at why one document-dependent process in the insurance sector, which is medical claims processing, a critical yet often highly error-prone function, is in need of intelligent RPA or Intelligent Process Automation:
Paper Introduces Latency & Inefficiency: traditional medical claims processing is paper-based and requires manual effort to process. Data entry errors, missing files, duplicate claims, etc. all come with handling manual paperwork. Robotic Process Automation helps mitigate the burden of claims processing to an extent, but is insufficient when processing new claims documents with a vast array of claim templates and processing requirements.
Raw Data: data in claims forms is unstructured or semi-structured. Manually parsing and sorting data from claims documents can take forever. Even RPA does not eliminate the fundamental difficulty of sorting and extracting the right information from claims forms that are consistent with the requirements for every new form.
Lack of Process Visibility: getting in touch with claimants, providers, and claims managers to process a single claim takes time and manual effort. Couple that with the fact that you need to make sure you are not processing the same claim twice or forwarding false claims, so as not to over-reimburse or reimburse the wrong claimant. Manual paper claims processing does not provide the visibility needed to know you are on the right trajectory as far as claims processing and approvals are concerned. What is needed is a medical claims processing software, which is a single platform solution that onboards payers, providers, and claimants on a common portal to manage, authenticate, and disburse claims payments.
Intelligent Process Automation combines robotic process automation and cognitive technologies like AI and Machine Learning to digitize and enable straight-through processing of medical claims forms. Machine Learning helps the IPA bot learn from user actions and apply that learning to process new forms automatically, helping reduce user intervention with every new claim form.
Apart from the vast library of pre-configured workflows that intelligent process automation offers to accommodate and process a large number of claims forms, the solution also adds to the library any new learnings from user actions, and retrieves that learning for processing new forms.
Intelligent process automation can scale operations exponentially without adding to staff, as this insurance firm found out, having gone from processing 100-120 forms/day to now 300 forms/day.
Low operating costs, reduced claims processing cycle times, faster reimbursements — intelligent process automation facilities lean medical claims processing so insurers can guarantee security even in these uncertain times. Want to reach out to your claimants more effectively? Talk to Artsyl about medical claims processing software.