To improve the accuracy of the claim submission process with clinical evidences/medical documents, the client wanted to build a product that could redefine the RCM process with digital automation. This includes automating the transcription of doctor-patient encounters and generating clinical summaries that can be used to simplify and accelerate the claim submission process using AI and Natural Language Understanding (NLU)-powered speech engines. Improving operational efficiency, First Pass Rate and reducing the claim denial rate are the key business drivers.

The client is a technology service provider of healthcare platforms for hospital systems and RCM companies that partner with hospitals, specialized clinics, health systems, health information exchanges, and integrated delivery networks to offer high-caliber, expert-level services and solutions across the globe.

• Healthcare organizations face numerous challenges, including the requirement to take accurate clinical notes during patient consultations.
• Clinical note creation impacts the efficiency of healthcare providers and led to errors such as missing critical patient information.
• The need to identify healthcare-specific terms in conversations and extract relevant metadata information, such as timestamps, the number of visits, medication lists, and vitals is critical, while protecting patient privacy and data security.
• Employing traditional methods to recognize the voices of doctors/providers, patients, and nurses; identify the conversation’s context; and summarize it into easily accessible text delays the process.
• Furthermore, extracting relevant data from the doctor-patient transcript for processing claims without errors to submit to payers took a long time for the client.