Healthcare organizations receive a vast amount of data from various channels like new patient forms, handwritten notes, faxes, medical claim forms, X-rays and prescription notes in a variety of formats. All of them exemplify the challenges of managing unstructured data and making the data useful in adjacent systems. There is no magic “AI wand” that will correct the ingestion of data. Hence the modern capture software is important that can correct and perfect data as it comes into the organization, enable document classification and automate data extraction. Healthcare organizations can climb five levels to achieving AI with capture combined with RPA:
- The orchestration of basic repetitive tasks: For example, the data is automatically populated into the system instead of an administrator manually inputting data from the patient information form.
- The extraction of metadata from forms: This means ensuring the information provided by patients in their onboarding process as a new patient is captured, validated, and entered in the electronic medical records system. Also, they should be accurately processed in claims processing.
- The inclusion of unstructured content: By unstructured content, we mean correspondence, explanation of benefits, previously inputted worker’s compensation, X-ray and prescription information included within appropriate forms for processing.
- The learning of human interaction with RPA robots and the eventual recommendation and automation of action: Automatic treatment plans and referrals to specialists based on a diagnosis, triggering a warning for prescription allergies and exceptions in claims processing.
- Better understanding of the impact automation has on a patient and within an organization: Based on patient-generated data, healthcare organizations can customize services and offer social community support through partnerships to coordinate care. They can also obtain process intelligence to know the real-time status of patient tracking, claims processing and operational efficiency.