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BUDDI AI Launches First-Ever End-to-End Revenue Cycle Automation Suite Powered by Proprietary Contextual Lake
Practice.AI optimizes 7 industry leading RCM solutions in one secure, intelligent, automated platform
Live demos can be scheduled during HIMSS21, in Las Vegas from August 9 - 13

NEW YORK, Aug. 9, 2021 /PRNewswire/ -- BUDDI AI, the leading provider of artificial intelligence (AI)-powered healthcare solutions, today announced the expansion of their revenue cycle management (RCM) automation applications with a new comprehensive end-to-end RCM suite: Practice.AI.  Powered by the first and only healthcare contextual lake, Practice.AI goes beyond robotic process automation with AI that understands context and the complexities of healthcare data to optimize RCM workflows and take the administrative burden of registration, coding, documentation, billing and more, off healthcare workers so they can focus on value based care.

"Until now, the healthcare RCM industry has been largely stuck in a cycle of management—managing claims, denials and appeals as well as patients, providers and payers. With Practice.AI, we're disrupting that pattern and giving healthcare workers an improved, automated and truly intelligent RCM experience to go beyond management to actively predicting, preventing and solving your most pressing healthcare challenges," said Ram Swaminathan, Co-Founder and CEO of BUDDI AI. "With Practice.AI, our goal is to help ensure healthcare workers from practices big and small can get back the time and resources needed to take care of what matters most—their patients."

Front-end, mid-cycle and back-end automated RCM solutions are all included within the Practice.AI suite. Fully customizable using our state of the art "drag & drop" workflow, which customizes features to the facility level and can be integrated within your existing workflows. Practice.AI encompasses the following:

  • Smart Patient Registration: Simplifies the front-end patient intake process by capturing relevant information and documentation electronically with pre-reg options, including real-time insurance eligibility verification, to avoid delays and provide the best possible patient experience with mobile based appointments and payments.
  • Prior Auth Identification: Applies natural language processing (NLP) and graph technology to autonomously identify certain clinical procedures which require prior authorization from the respective payer and kick starts the respective prior auth approval process, thereby cutting down significant labor time on a daily-basis and denials.
  • Medical Coding Automation: Automates structured and unstructured coding volumes—often the most burdensome function of RCM—with 95% or greater coding accuracy contractually guaranteed, to help reduce denials and increase reimbursements.
  • Denial Prediction & Prevention | Claims Automation: Analyzes historical denials, approvals, patterns or other payer behavior based on both BUDDI AI and the institution's experience to proactively predict and prevent errors with claim submission, leading to reduced denials, faster A/R times and higher payer reimbursement. This real-time automation of the claims submissions process cuts down A/R times by 3X to 6X depending on the provider organization.
  • AI Driven Denials Root Cause Analysis: Analyzes revenue leakage and reduces the lead time to get reimbursed for services rendered by eliminating the potential of manual error across the billing process, including explanation of benefits (EOB) root cause analysis. Based on RARC/CARC denial codes and automatically classified into respective denial work queues, an automated first-pass analysis is completed and then dropped into respective queues for experts, if needed, to conduct manual review prior to reappeal.
  • Payer Contract Management & Under Payment Analysis: Onboards payer contracts and applies NLP and graph algorithms on the unstructured contract languages, mines the legal language—including pricing, expiries, sub-contract language, value-based care or Fee-For-Service contracts—and then creates a "Contextual Contract Graph" to analyze each incoming explanation of benefits (EOB) /electronic remittance advice (ERA) to identify under payments by respective payers and help reappeal those claims. Additionally, payer specific adjudication reports are generated in detail to re-negotiate contractual terms at the time of renewal.
  • Web-based Patient Portal and BUDDI PAY app: Improves the patient journey and helps minimize drop-off by offering one-stop web- and app-based portals for patients to see and engage in their care continuum, from registration to bill payment. On an upcoming version, patients can take snapshots of their invoice and make payments for any provider bill across all 50 states in America. BUDDI Pay could dramatically improve patience experience by being the one-stop-payment app for patients around the country irrespective of the provider's EMR or billing system.

Learn how BUDDI AI's end-to-end revenue cycle automation suite can help you go from managing to maximizing healthcare workflows by scheduling a demo here: Attendees can meet the BUDDI AI team at Sands Expo, Level 2, MP5065.

BUDDI AI is a leading provider of AI-powered healthcare solutions, including clinical and revenue cycle automation, automating more than 2.6 million medical records per month. Fueled by an unmatched 'contextual lake' platform and subject matter experts with over 250+ years of cumulative clinical experience, BUDDI AI helps healthcare organizations make sense of unstructured data, simplify workflows and do it all with guaranteed speed, security and accuracy. A buddy to all involved in end-to-end revenue cycle management, BUDDI AI easily integrates into existing healthcare workflows to increase efficiencies, automate processes and reduce administrative burden, resulting in improved patient care, enhanced clinical documentation, streamlined medical coding accuracy and improved reimbursements.

BUDDI AI's vision is to contextualize all healthcare data—from medical records and claims to clinical trials and socio-economic determinants— and, via a simple API, empower data scientists to build the lifesaving applications of tomorrow.

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