A Hybrid Approach to Prior Authorization Data Mining from Fax PDFs Using Altova MapForce and Natural Language Processing

Another major challenge requiring solutions by healthcare organizations is how to process prior authorization requests that are sent over fax and presented in PDF files, which constitute unstructured or semi-structured data. The continuous use of manual processing incurs the creation of bottlenecks in operations, the escalation of administrative expenses, and delays in the access of patients to the needed medical care. The technologies of Natural Language Processing have developed quite a way, but standalone NLP systems are not capable of identifying essential information with high reliability in the faxed documents, which are usually full of handwritten notes, low-quality images, uneven structure, and complicated medical terms. This article introduces an amalgamation of NLP functionality and Altova MapForce data conversion tools, which can be used to deal with the complexities of automated prior authorization processing. The suggested architecture uses NLP, such as named entity recognition, document classification, and contextual inference, to understand unstructured text in scanned fax PDFs, and MapForce to process extracted data into structured formats that can be processed by the downstream healthcare information systems. The combination of these complementary technologies in the hybrid solution saves a significant volume of manual processing, shortens authorization turnaround time, ensures the quality and accuracy of the data, and provides scalability of the process to address the varying volumes of requests. Implementation issues cover training requirements of NLP models, MapForce configuration plans, quality assurance, and integration issues. The resultant structure provides quantifiable operational efficiencies without compromising the accuracy requirements needed by healthcare applications, and eventually aids in the delivery of patient care more promptly and decreases administrative overhead throughout the healthcare ecosystem.