In busy medical offices with a lot of patients, manual claims processing is often slow and full of mistakes. Staff have to gather patient insurance information, type it in by hand, check coverage, and send claims to insurance companies. Each step can cause delays or errors. This can lead to claims getting denied, needing to be sent again, and slower payments.
Studies show that nearly 15% of claims sent to private insurers are denied at first. This causes about $10.6 billion worth of time and resources spent fixing claims that should have been approved the first time. Also, the delay between when a patient is seen and when the charges are entered can be long. Without automation, charge entry takes about 6.7 days on average.
For medical managers, slow claim submission means slow cash flow and more work. This can be especially hard during busy times like flu season or vaccination events. The extra pressure hurts daily work and can make staff feel tired and stressed.
AI automation built into Electronic Health Records (EHRs) cuts down the time it takes to enter charges and speeds up sending claims. For example, athenahealth’s Auto Claim Create tool uses AI to make claims right after a patient visit ends. This lowers the average charge entry time by 66%—from 6.7 days to 2.17 days.
Because claims are processed faster, medical offices can send more claims quickly and with fewer mistakes. Getting claims in soon after the visit helps offices get paid faster by insurance companies.
Some administrators have shared how this technology helps. Tina Kelley, Director of Operations at Mountain View Medical Center, said that automating insurance selection and claims creation took away much of the manual work for staff. This sped up the process and also lowered the number of claim denials, leading to quicker payments.
One big cause of claim denials is wrong or missing information. AI helps fix this by checking data against insurance rules and warning staff before claims are sent. Athenahealth’s AI checks information using a rules engine that studies data from over 160,000 providers. This helps reach a 98.4% clean claims submission rate, which means most claims are accepted right away.
Practices that use AI for claims see fewer denials. Using automated insurance selection alone reduces denials linked to bad insurance details by 7.4%. Fewer denials mean less time spent resubmitting claims and faster payments.
AI also guesses the best times to follow up with insurance companies and predicts which appeals might succeed. This helps staff focus on the claims that are most likely to get paid. Using AI for claim resolution has been shown to increase money collected per visit by 2.3 percentage points. Combining AI with medical coding services raises this increase to 7.6 points.
Doing claims by hand means repeating work and slow processing, especially when many staff are involved. Busy offices may need many people just to keep up with the claims.
South Texas Spinal Clinic shows how AI can cut down this workload. They used athenahealth’s AI-based prior authorization tools and cut approval time from 6-8 weeks to just five days—a drop of over 90%. Also, staff handling prior authorizations went from four full-time workers to just one, lowering costs a lot.
Likewise, automating claims creation and insurance selection saves time and effort. By speeding up data entry and cutting mistakes, staff spend less time fixing problems. This makes the whole office run better and lets staff focus more on caring for patients and other important tasks.
Faster claim submissions and better accuracy mean medical offices get money sooner. When the time between service and payment is shorter, practices have more cash to pay bills, buy equipment, or add services.
Data from athenaOne clients shows that lowering charge entry time and denials leads to faster payments. Tina Kelley said automation helped her clinic reduce the time needed for insurance selection and get paid faster. This shows how important speed and accuracy are for money flow.
Also, cutting the cost of fixing denied claims and doing follow-ups improves profit. Automation reduces stress on revenue teams, lowering chances of burnout and staff quitting, especially for those who handle billing and coding.
These improvements combine to make revenue cycles faster and better. Medical offices get lower admin work, more accurate claims, and faster money collection.
For practice managers and owners, especially those running big clinics or several locations in the U.S., AI automation offers clear benefits:
IT managers gain from AI systems that easily connect with existing EHRs. These systems need little manual work and can handle busy times well. They also offer tools that show claim status, payer patterns, and staff performance.
AI automation in claims creation helps healthcare providers by making claim submissions faster, improving cash flow, and cutting down charge entry delays, especially in busy U.S. medical offices. This technology also works with other AI tools like automated insurance selection, prior authorization, and denial management to reduce admin work and improve money outcomes. Medical managers, owners, and IT teams who use AI in revenue cycle management can improve efficiency, staff satisfaction, and revenue.
AI-native EHRs streamline clinical workflows by reducing administrative burdens on RCM tasks by 50-70%, enhancing speed, accuracy, and transparency. They automate insurance selection, claims creation, claim denial management, prior authorization, and documentation, thereby improving financial outcomes and reducing delays in payment for healthcare practices.
AI-powered insurance selection uses machine learning to analyze images of insurance cards and patient data, recommending the correct insurance. Practices using automated insurance selection saw a 7.4% decrease in insurance-related claim denials, reducing manual data entry and administrative time.
AI automates the claims creation process immediately after patient encounters, reducing charge entry lag by 66% compared to manual processes. This increases claim accuracy, speeds up submissions, and improves cash flow, especially useful during high-volume periods.
AI analyzes claim data from a large provider network to identify potential errors before submission, reducing denials. Machine learning suggests optimal follow-up times with payers and enables better appeal success prediction, contributing to higher clean claim rates (98.4%) and improved financial performance.
Physicians spend nearly two days weekly on prior authorizations, contributing to burnout. AI automates authorization management by predicting requirements, extracting clinical data, and pre-filling forms, reducing time spent by 45% and enabling faster approvals—from weeks to days—while decreasing administrative staff needs.
Athenahealth’s Authorization Management service automates prior authorization workflows with AI features like prediction and chart analysis, achieving over a 98% success rate in managing authorizations, significantly reducing administrative burden and expediting approval processes.
Using athenahealth’s AI tools, South Texas Spinal Clinic reduced prior authorization approval time from 6-8 weeks to as little as 5 days, cutting administrative overhead and improving financial outcomes by decreasing staff requirements for authorization processing.
AI agents assist by analyzing patient charts, extracting relevant clinical data, and pre-filling prior authorization forms, improving accuracy and efficiency while reducing manual data entry and errors in the authorization process.
By automating prior authorization workflows and reducing time spent on manual tasks by up to 45%, AI lessens administrative burdens, allowing physicians and staff to focus more on patient care, addressing one of the leading causes of physician burnout.
Fully AI-native EHRs will predict when prior authorizations are required, autonomously gather necessary clinical information, pre-fill forms, and expedite approvals, further streamlining workflows, decreasing delays, reducing administrative staff needs, and improving overall healthcare financial management.
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