ChatGPT is Awesome but Did You Know How AI Can Automate Your Medical Revenue Cycle?

An automated revenue cycle has become a benchmark for healthcare organizations nowadays to elevate their services. It has been proven to be one of the ideal methods to optimize the processes involved in collecting and managing patient revenue.

This blog will focus on the ways in which you can automate your medical revenue cycle through prominent AI technologies. We will also see how important an automated revenue cycle can be for the successful execution of medical procedures and treatments.

What Is A Medical Revenue Cycle?

A medical revenue cycle is a series of processes implemented by healthcare organizations to collect and manage their revenue from patient service. It involves several administrative and clinical processes initiated with the creation of patient accounts.

You should be able to predict your medical revenue cycle and AI can help you with that. It enables you to automate your processes so they can be executed correctly. An automated medical revenue cycle can streamline payments and prevent billing errors.

How Can AI Automate Your Medical Revenue Cycle?

AI can automate the operations executed to manage your medical revenue cycle by optimizing data validation and identifying patterns. There are various time-consuming healthcare tasks that can be automated like insurance verification, patient registration, and coding. 

An automated medical revenue cycle prevents a buildup of workload on your administrative staff and enables them to prioritize value-added activities. In this way, you can utilize AI to improve accuracy and automate revenue cycle.

    1. Denial Management

Denial Management Services in Medical Billing

You can use AI for denial management as it can automate revenue cycle to predict future claim denials. Hospitals and clinics can utilize such predictions to mitigate the relevant problems and prevent denials from insurance providers.

AI analyzes past trends in claim denials and uses that analysis to alert healthcare organizations about potential denials. You can prepare for them in advance and fill any gaps before insurers deny your reimbursement claims.

Medical practitioners can also create automated dashboards and track their claim denials to devise valuable insights and improve workflows. They can also integrate a financial assistance AI technology to ensure price transparency and verify patient eligibility for financial aid.

    2. Provider Credentialing

Provider Credentialing in Medical Billing

You can automate revenue cycle to improve the provider credentialing process for your healthcare professionals. AI provides the speech recognition feature with which you can directly manage their notes and integrate them into your formatted documentation.

In this way, you can place specific dictations into specific details of their provider credentials. This integration allows you to increase the submission rate of reimbursement claims and effectively manage your accounts receivable. 

These benefits lead to a minimum amount of coding issues caused by your medical coders. Less issues result in less expenditure spent for mitigation which can effectively automate revenue cycles.

    3. Charge Entry

Charge Entry Services in Medical Billing

Healthcare organizations can implement a branch of AI named natural language processing in their charge entry process. With this, they can automate revenue cycle and ensure that their documented charges are accurately verified before the relevant claims are submitted.

You can also set up specific criteria to enable your medical coders to verify their complete records and documented queries. This criteria can also allow insurance companies to carefully investigate and record your submitted claims through its AI functionalities.

    4. Computer-Assisted Coding

Computer-Assisted Coding

AI can also create a computer-assisted coding experience for your medical coders by integrating natural language processing into your system. It revamps key processes like assigning CPT and procedure codes to your medical services with ICD-10-CM diagnosis.

Such coding experience can significantly reduce the working time of your coding accounts. You can attach your codes to those accounts so that you can review them for accuracy. In this way, you can automate revenue cycle as you or your coders do not have to manually enter them.

There are specific AI technologies in the healthcare sector with which you can completely eliminate the coding effort through autonomous coding. These benefits enhance the quality and efficiency of your revenue cycle which prevents additional resource allocation.

    5. Prior Authorization Requirements

Prior Authorization Requirements

The fulfillment of prior authorization requirements is an important process for any healthcare organization. However, it can exert an additional burden on your administrative staff that can hinder your patient care.

You can use AI to automate revenue cycle and ease out that burden through an AI-driven handling of your prior authorizations. Healthcare organizations can use machine learning and natural language processing to analyze and validate the details of those authorizations.

These details usually comprise clinical notes, patient demographics, and plan details. You can use those details to determine whether your current standing is sufficient to approve those prior authorizations.

    6. Insurance Eligibility

Insurance Eligibility Services in Medical Billing

Insurance eligibility is another vital step to automate revenue cycle of hospitals and clinics. There has not been much involvement of AI in eligibility verification before. This has minimized the impact of reimbursement claims to automate revenue cycles as well.

As a result, many healthcare organizations are unable to receive sufficient reimbursement for their procedures and treatments. You can use AI to automate the insurance eligibility verification process and implement its checks on either a weekly or daily basis.

In this way, you can save time in identifying the relevant errors and minimize claim denials from insurance providers.

    7. Claims Processing

Claims Processing in Medical Billing

Moreover, you can automate the claims processing step in your revenue cycle by using AI tools for medical billing. These tools can optimize the processing and submission of claims with which you can eliminate the practice of manually submitting them.

Healthcare organizations can automate revenue cycles with the usage of machine learning models and other AI scripts. These models and scripts can create valuable editing rules for claims processing with which you can prevent claim denials and rejections.

CPT code modifiers are another aspect of revenue cycle management automation. Telehealth claims are an example in which you have to attach the “95” Medicare modifier for the appropriate CPT code. You can also create a workflow to add modifiers to their specific claims.

    8. Payment Estimates

Payment Estimates

Hospitals and clinics can also automate revenue cycles to improve their payment estimates. Most of their patients are more likely to return so that they can avail themselves of future medical procedures and treatments. You can also ensure this if you make accurate payment estimates for your patients.

Healthcare organizations can use those estimates to follow the compliance policies of the medical sector. In addition, you can focus more on your patients and enhance the fulfillment process for administrative tasks with automated payment estimates.

In this way, you can elevate patient satisfaction with your medical services. You are also in a better place to successfully retain most of your patients.

    9. Payment Collections

Payment Collections

You also have to manage payment collections for the healthcare services availed by your patients. For this, your billing staff may face difficulties as a result of manually processing the relevant statements.

You can eliminate those difficulties by completely automating that process with which you can save ample time and resources. Automated software can be set up to identify patient balances and send their invoices after insurers complete their reimbursement processes.

    10. Cost Reduction

Cost Reduction

AI can automate revenue cycles to significantly reduce the costs of organizational processes and functions. The reduction has a positive impact to improve revenue cycle management of healthcare organizations.

Automated tasks are more likely to improve their accuracy and optimize the overall medical revenue. In this way, you can drive more profitability for your treatments and procedures. This automation in RCM usually involves the analysis and identification of trends and patterns.

You have to validate your data so that it can be analyzed for that purpose with a rise in your reimbursements. Moreover, you can use AI in revenue cycle management to identify various aspects of medical billing for your services.

Importance of Medical Revenue Cycle Automation

Healthcare organizations have a range of different regulations and billing codes that have to be regularly updated. You can find it difficult to record all those updates and keep track of the changes. In this regard, an automated revenue cycle is a solution for your hospital or clinic.

The solution enables you to carefully cleanse your data and eliminate any human error made during claims submission. You have to identify them first before AI solves all of the questions and reduces claim denials. You can also guarantee a swift processing of your payments in time.

The implementation of an automated revenue cycle is highly beneficial for healthcare organizations considering the overall decrease in market profitability. Current market trends have caused increased costs and AI can counter that by boosting medical cash flow.

The Bottom Line

This blog dissected key aspects of automated revenue cycle management enabled by AI and its implementation to drive revenue cycles. We explored the automated states of different processes and their importance in healthcare organizations.

Our automated revenue cycle experts are well versed in utilizing several AI technologies to enable that. Contact us today to follow up on all the latest trends and practices to automate your medical revenue cycle.

Frequently Asked Questions (FAQs)

    1. How is AI used in revenue cycle management?

AI is able to analyze a large amount of data related to the revenue cycles of healthcare organizations. Those hospitals and clinics can utilize that analysis to develop key insights about their revenue cycle management processes.

    2. How does AI help the medical industry?

AI helps the medical industry through its machine learning technology that can be used for drug development and disease diagnosis. Medical practitioners can utilize this technology to effectively diagnose diseases and customize the relevant treatments.

    3. What is RCM automation?

Healthcare revenue cycle automation involves an automation of transactional management and rules-based exchanges. It enables healthcare organizations to optimize their workflows and processes which subsequently reduces their labor costs.

    4. How does AI affect medical coding?

AI affects medical coding by making it more accurate and efficient to streamline the revenue cycle of hospitals and clinics. In this way, these organizations are able to reduce their workload and rid their healthcare professionals of unnecessary administrative burdens.

    5. How does AI improve productivity and revenue?

AI improves productivity and revenue by driving the economic growth of healthcare organizations. It does that by automating their operational processes which frees them up so that they can prioritize more creative and complex tasks.

    6. How does robotic process automation influence the revenue cycle process?

Robotic process automation (RPA) streamlines the revenue cycle process by automating repetitive tasks, which allows health systems to manage claim submissions more efficiently. By reducing manual intervention, RPA ensures quicker and more accurate processing, leading to improved patient experience.

    7. What role does human intervention play in an AI-driven revenue cycle solution?

While AI and intelligent automation handle the bulk of repetitive tasks, human intervention remains crucial for oversight, decision-making, and addressing complex scenarios. It ensures that the automated systems function within the right context and provides a personal touch in patient interactions.

    8. How do health systems benefit from integrating AI into their revenue cycle solutions?

Health systems can achieve cost savings, enhance patient experience, and optimize the revenue cycle process through AI integration. It minimizes manual tasks, streamlines claim submissions, and provides real-time insights, leading to better practice management.

    9. Can AI improve the patient experience in the revenue cycle process?

Absolutely! AI-driven revenue cycle solutions can provide real-time cost estimates, expedite claim submissions, and reduce errors. This leads to a more transparent and efficient process, enhancing the overall patient experience.

    10. What’s the connection between intelligent automation and manual tasks in healthcare?

Intelligent automation is designed to reduce or eliminate manual tasks, especially those repetitive in nature. By automating these tasks, healthcare providers can focus on value-added activities, ensuring that the electronic health record (EHR) system is used to its fullest potential.

    11. How do repetitive tasks impact the effectiveness of revenue cycle solutions?

Repetitive tasks, if done manually, can lead to inefficiencies, errors, and increased operational costs. Automating these tasks with AI and robotic process automation ensures a streamlined, accurate, and efficient revenue cycle solution.

    12. How do real-time AI capabilities influence cost estimates and practice management?

Real-time AI capabilities allow for instantaneous data analysis and feedback, enabling accurate cost estimates based on current data. For practice management, this means quicker decision-making, reduced human errors, and a more streamlined revenue cycle process.