Health insurance document automation solutions take advantage of artificial intelligence (AI) to improve productivity in numerous processes involving unstructured content, including the following.
Health insurance claims automation
Processing an individual claim can be a painstaking process. Say a hospital files a claim for services it renders, such as an MRI. Before paying the claim, the health insurer must go through the patient’s electronic health record (EHR) file, which may contain hundreds of pages, to find the doctor’s order for the MRI. The claim may also involve the actual MRI image, which would be far out of reach for an RPA tool to handle, but no problem for the Indico Unstructured Data Platform.
Long-term care invoices
Dealing with invoices for patients in long-term care can be especially demanding for health insurance companies because the stakes are high and there are numerous values to extract. Beyond the basics of name and dates of care, those details include hours, rates, and line items for each required service. Traditionally, agents must read the invoices and manually input relevant values into a downstream processing system, such as K2. Healthcare document processing models, combined with an RPA tool for data entry, can automate 85% or more of the process. In some instances, an IDP model can enable straight-through processing, such as for invoices pre-processed by a third-party provider like AssureCare.
Patient record processing
An IDP platform can also help health insurers deal with patient records. IDP models can be trained to ingest paper documents, images and digital records from hospital EHR systems. They can automate the process of sorting, identifying and matching paper documents, including faxed documents, to a patient’s EHR.
Claim denial documentation
When a health insurer denies a claim, it’s likely the matter will come up again from either the provider, patient, or both. When it does, it behooves the insurer to quickly assemble its supporting evidence for why the claim was denied. Intelligent document processing models can help in that effort, by finding relevant documents, extracting supporting evidence on why the claim was denied, and assembling it to expedite a response to the provider or patient.
Ingesting claims documents
Claims documents tend to arrive in large bundles containing numerous discreet documents that pertain to different claims. Employees spend hours going through these bundles and determining where each individual document belongs. A sound healthcare document processing model would be able to automate the process of sorting and categorizing the individual documents, and keeping associated documents together for further processing.
Automate document workflow
Intelligent document processing can help health insurance companies classify all sorts of documents, including patient and provider correspondence, clinical documentation and various kinds of reports. Healthcare document automation models can be trained to extract key data from each type of document, such as a patient date of birth, data of service, address, provider name, patient name, member ID numbers and more.
Patient correspondence
Processing thousands of pieces of correspondence from patients is another pain point for health insurers. IDP can help them read and classify this correspondence, whether it’s paper or email. Health insurance automation models can extract pertinent data such as the patient ID number and date of birth, and match it with other recent patient activity to help streamline a response.
Applying analytics to health insurance
Health insurers have a treasure trove of historical data at their disposal that can help drive better business decision-making. The problem is most of it is in trapped in historical claims records and other documents, the vast majority in an unstructured format. Intelligent healthcare automation models can help unlock that unstructured data and turn it into a structured format. Companies can then apply analytics tools to the now-structured data. That would enable them to identify trends in pricing ranges, for example, and adjust pricing accordingly – among numerous other potential uses.
