Artificial intelligence is a vast area to explore! In the present scenario, AI in healthcare is building bigger opportunities for the healthcare workers in healthcare industry. Not every area of healthcare requires AI assistance, but majority of them depend on AI to make their day easier than ever before. Automated innovations have proclaimed the necessity of implementing them in healthcare industry to experience greater profits and successful business.

AI in healthcare resolves a number of issues connected to revenue reimbursements. Today, AI has adopted automated technologies in multiple areas including disease diagnosis, imaging analytics, virtual telehealth services, and most recently, EMR integration. There are endless possibilities associated with AI which have become a common process in medical billing and coding.

AI in healthcare works effectively for healthcare professionals. Passive collections are long gone and with the help of AI, we’ve entered an era where even traditional strategies won’t be enough to expedite cash collections, reduce denials, and improve receivables. Let’s see the impact of AI in billing and collections.

AI in Healthcare adds Value and reduces the cost:

  • The present phenomenon is associated with expectations on cost reduction and value added business. Building such strategies to accomplish the above requires support from other medical billing and coding companies or third parties.
  • Healthcare professionals will have a tough time championing the cost reduction if they don’t start counting and collecting each dollar. That is why there’s even more reason to be excited about AI in healthcare and its ability to reduce manual collection intervention while enabling efficient, automated processes.

Technology that Drives Value:

  • Healthcare professionals and other healthcare billing companies are showing keen interest in modernizing their systems.
  • It automatically will reduce costs by eliminating manual processes, streamline support of infrastructure, and drive digital transformation at the enterprise level.
  • Achieving such goal in a sustainable way is impossible without AI in healthcare, and specifically, with deep learning technologies. They can learn and evolve to keep up with the increasingly complex needs of the healthcare revenue cycle.

Achieving Automated Remittance Posting with AI:

  • A survey from a market research firm had revealed that almost 33% of hospitals did not have a technology solution in place to handle automated remittance posting. This means they’re shouldering the cost of manual workflows and the unnecessary risk inherent in human processing.
  • Solutions that automate insurance payment processing through targeted AI and Deep Learning models empower healthcare professionals and revenue cycle management companies to deliver a straight-through process for posting payments.

Stopping the Leakage:

  • According to a healthcare report in 2012, patients were only responsible for 8% of their medical bill. Five years later, the percentage doubled to 12.2% and commercial insurance patients saw patient balance after insurance (PBAI) jump to 67%, leading to an 88% increase in overall hospital revenue attributable to PBAI.
  • This stands as a major problem. Many IT systems were designed around documentation of care and submitting bills, largely forgetting patients. This means these systems are a liability in bringing the revenue cycle in sync with patient experience.
  • With the help of large data extracted from EOB healthcare providers, RCM companies can perform deeper analysis of the transactional details and situational factors that lead to patient payment leakage.

How is artificial intelligence used in healthcare?

  • AI-assisted robotic surgery: Using data contained in medical records to guide surgeons through surgery.
  • Nurse-like chatbots: Navigating basic questions with natural language interpretation, processing requests and notifying the appropriate staff member to navigate patient concerns.
  • Diagnosis: Analyzing symptoms and producing objective, diagnostic possibilities for medical professionals.
  • Medical billing: AI has the capacity to automatically conduct audits, self-adjusting known values to the audit results.

AI in Healthcare Streamlines Medical Billing Workflow:

  • AI in healthcare streamlines the revenue cycle management processes by providing accurate data at the right time based on its priority.
  • One of the most significant impacts on a medical billing and coding team’s everyday tasks might result from the bottomless learning of users’ interaction with EHR and billing software.
  • By using AI, it’s easy to retrieve the patient’s information and manipulate it automatically. This in return decreases the time consumption on the manual billing task and also helps the medical billing experts to make better decisions about the Medical claim denials

Deeper Denial Analysis:

  • AI helps the medical billing and coding experts to analyze the reasons for the denials. If the denials are caused due to medical coding errors, lack of medical necessity, the AI tool will analyze and find out the actual cause for the denial and update the information within the EHR to resolve the problems.
  • This is an advantage which increases efficiency and profitability and moreover provides an opportunity to increase revenue.
  • Apart from healthcare professionals and medical billing companies, the insurance companies also benefit from AI in healthcare.

Healthcare Industry Adaptation of AI:

  • Each year, CPT and HCPCS coding undergoes a lot of changes. In fact many codes are removed and replaced with the new ones. Medical billing experts should concentrate on coding updates that reflect on the billing charges. For these changes, the medical billing service providers must adjust and take more time to learn new codes. In-house medical billing staff can make coding errors due to new coding updates.
  • AI in healthcare can do a better job by accelerating the progress of these adjustments in various ways. For instance, healthcare professionals could be recommending new codes or forestalling entering codes that don’t exist. It also helps the users to remember any changes a code has gone through. Guided solutions can accelerate the process of the coders adapting to whatever changes may take place in the future.

AI to assist with Medical Billing and Coding:

  • With the help of machine learning technologies to process the data, it’s possible to deploy an application that can process medical codes and billing codes, by identifying errors and making appropriate corrections.
  • AI could be integrated as an assistant that allows medical billers and coders to work more efficiently and accurately by flagging mistakes and suggesting possible fixes.

AI in healthcare can do a lot more. Its assistance can keep the business in profits. Choosing AI is always a better option than struggling with human errors.

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