Key Revenue Cycle Management Metrics to Track in 2025 (and How to Improve Them)

Denial Rate is the percentage of submitted claims that are denied by payers on the first submission. It’s calculated as (Number of claims denied on first pass) ÷ (Number of claims submitted) × 100%. For example, if you submit 100 claims and 15 come back denied initially, your denial rate is 15%. This Revenue Cycle Management metric captures how frequently payers are refusing to pay you the first time around.

(Denied Claims / Total Claims Submitted) × 100

Industry data indicate that approximately 10–15% of healthcare claims are initially denied on average. World-class performers try to keep denial rates in the single digits, often under 5%. Common reasons for denials include coding errors, missing documentation, patient ineligibility (insurance issues), lack of prior authorization, or billing the wrong payer. A rising denial rate can signal issues in front-end processes (such as insurance verification or authorization), mid-cycle issues (coding or charge capture errors), or back-end issues (billing edits). It’s essentially a quality check on your entire revenue cycle. Beyond the financial hit, consider that hospitals spent nearly $19–25 billion a year fighting denials, so a high denial rate also means high administrative costs.

AI can proactively analyze large volumes of claim history to uncover denial trends and high-risk services. When integrated into the pre-submission process, AI can flag claims that are likely to be denied and provide feedback for correction. RPA complements this process by verifying claims for completeness, checking payer-specific requirements, and attaching all necessary documentation, which leads to fewer denied claims.

While not a single-number metric, tracking denials by category involves breaking down your overall denial rate into specific reasons (such as authorization-related, eligibility, coding, medical necessity, etc.) and examining the percentage each category contributes to total denials. For example, you might find that 30% of your denied claims are due to authorization not being obtained, 20% due to coding errors, and 15% due to the patient not being covered, etc. It’s essentially a profile of why you’re getting denials.

Number of Denials per Category

Many organizations use denial category tracking as a KPI: for instance, aiming to reduce the percentage of denials due to avoidable front-end issues, quarter over quarter. Payers often supply reason codes on EOBs (such as CO-197 for authorization, CO-11 for coding, etc.), and grouping them into categories yields valuable insight. Knowing, for example, that “medical necessity” denials are 10% of your denials might prompt you to ensure better documentation or use of correct diagnosis codes to justify services.

AI automatically tags denials based on root cause and visualizes trends across departments or service lines. RPA routes these insights to managers for training or workflow adjustments and tracks their resolution.

Days in A/R is the average number of days it takes for your claims to get paid – effectively the average age of your receivables. It’s calculated by dividing your current accounts receivable by the average daily charges (or collections) over a period. For example, if you have $600,000 in receivables and average $20,000 in charges per day, your Days in A/R is 30. It answers, “On average, how many days from the time you provide a service (or bill it) until you collect the payment?”

Total Accounts Receivable / Average Daily Charges

A/R Days is a classic liquidity metric. Lower is better – it means you’re converting services into cash faster. Higher A/R days mean cash is tied up and may indicate problems such as slow payer processing, high denial levels (which delay payment), or lagging follow-ups. As a general rule, aiming for a 30-50 day average in accounts receivable (A/R) is common, although this timeframe varies by payer mix and specialty. Many physician practices shoot for <40 days, whereas hospitals with a heavier insurance mix might be in the 40-50 range. If you’re creeping upwards (say 60 days, 80 days), it’s a sign of trouble – possibly lots of aged receivables that might turn into bad debt.

RPA bots can conduct daily follow-ups on unpaid claims, automatically retrieve claim statuses from payer portals, and trigger reminders or escalations. AI enhances this by predicting which claims are most likely to delay and guiding proactive interventions, optimizing team efforts, and improving collection speed.

This metric looks at the proportion of your Accounts Receivable that is aged beyond 90 days (or sometimes 120 days). It’s often expressed as a percentage of total A/R. For instance, if you have $1,000,000 in receivables and $150,000 of that is more than 90 days old, then 15% of your A/R is >90. It essentially measures the older, likely harder-to-collect portion of your receivables. Many reports will break aging into 0-30, 31-60, 61-90, >90 day buckets.

(A/R > 90 Days / Total A/R) × 100

The older a receivable becomes, the less likely you are to collect it. A/R over 90 days is at high risk of becoming bad debt. In healthcare, timely follow-up is critical because payer contracts often have deadlines for reconsiderations, and patient bills go cold as time passes. Providers strive to keep the >90 days A/R as low as possible, ideally under 15-20% of total A/R, though top performers can be much lower. In fact, some aim for virtually 0% of A/R over 90 days aside from truly difficult cases. A high percentage over 90 days indicates bottlenecks or neglect in your collections process – perhaps denials aren’t being worked, or patient bills are not followed up. It’s also an indicator of whether you might face write-offs soon, as balances age out.

RPA helps by identifying and routing aged claims to dedicated resolution teams, escalating where needed. AI evaluates each account’s likelihood of recovery, allowing your staff to focus on high-probability cases. This targeted strategy prevents unnecessary write-offs and improves cash recovery.

Net Collection Rate measures the effectiveness of collecting the revenue that you’re legally and contractually entitled to collect. It’s often defined as payments received divided by the net charges (charges after contractual adjustments) for a given period, expressed as a percentage. In simpler terms, “out of all the money you should have gotten paid (after write-offs for insurance contracts), how much did you actually collect?”. For example, suppose after adjusting for insurance discounts, your clinic had $100,000 in net billable charges last month. If you collected $92,000 of that, your net collection rate is 92%.

(Payments / Adjusted Charges) × 100

A high NCR (close to 100%) means you’re collecting nearly all possible revenue. A lower NCR indicates that revenue is being lost due to factors such as denials that were never recovered, patient bills that went unpaid, or other write-offs. Typically, an NCR above 95% is considered excellent, while anything significantly lower (say 85-90%) signals that money is being left on the table. Even a few percentage points drop in NCR can translate into huge dollars for a hospital. This metric excludes approved contractual adjustments (since those aren’t collectible by design), focusing only on collectible revenue.

AI can identify systemic underpayments, missed charges, and instances where payers are not reimbursing according to the contract. RPA bots can automate the secondary claims process, underpayment appeals, and even the generation of itemized billing corrections, helping you recover more of the revenue already earned.

Claim Rejection Rate is the percentage of claims that are rejected by clearinghouses or payers before they are accepted into the adjudication system. Rejections differ from denials: a rejection typically means the claim didn’t pass initial automated checks (perhaps due to invalid data or formatting issues) and must be corrected and resubmitted, whereas a denial means the payer adjudicated the claim and refused payment (often for policy reasons, medical necessity, etc.). If you submit 100 claims and 8 come back almost immediately as rejected (never received by the payer’s processing system), your rejection rate is 8%.

(Rejected Claims / Total Claims Submitted) × 100

High rejection rates mean your team is essentially doing double work on a lot of claims. Common causes of rejections include missing patient info, invalid insurance ID numbers, incorrect claim form fields, or technical format errors. A healthy revenue cycle will have a very low rejection rate, often in the low single digits (ideally <2-3%). If you’re seeing something like 10%+ rejection rates, there’s significant room for improvement. Rejections also tie up cash and can contribute to longer A/R days. The difference between a claim rejected in 1 day vs. paid in 14 days is huge for cash flow.

RPA ensures claims are scrubbed using payer-specific rules and that all required data is present. AI enhances this by continuously learning from past rejections and surfacing patterns, so issues like code mismatches or eligibility errors can be corrected before the claim is submitted.

The Authorization Approval Rate represents the percentage of cases or services requiring prior authorization that are ultimately approved by payers. For instance, if 100 MRI orders required pre-authorization and 90 were approved (initially or after some back-and-forth), the approval rate is 90%. You can also consider the flip side – the authorization denial rate (how many requested authorizations are denied). This metric zooms in on the front-end insurance approval process.

(Approved Auths / Total Auth Requests) × 100

A high authorization approval rate indicates that your team excels at navigating payer requirements and securing approvals. A low rate indicates that either you’re not obtaining necessary approvals or payers are frequently denying requested services. Both are problems: services performed without auth can mean no reimbursement or burdensome retro-authorizations. Tracking this metric is an early warning indicator: if auth approvals drop, you can expect denial rates to rise down the line.

RPA bots submit authorization requests the moment orders are placed, attach all required documentation, and track responses. AI predicts which services are at higher risk for denial and suggests alternatives when appropriate, reducing denials and delays at the front end.

This metric tracks the percentage of patients whose insurance coverage is verified before the time of service (or at least at the time of service). Essentially, out of all scheduled patients, how many did we run an eligibility check on and confirm active coverage and benefits? For example, if 1,000 patients are seen in a month and 950 had their insurance verified in advance, the verification rate is 95%.

(Verified Patients / Total Scheduled Patients) × 100

A high eligibility verification rate means your front-end process is solid – you’re catching problems like inactive policies or the need for referrals before the patient is treated. A low rate means a lot of patients are coming in without checks, which is risky – you may only discover coverage issues after submitting a claim (too late). Essentially, 100% is the goal here, though emergencies or walk-ins can make that challenging. Every scheduled encounter should ideally have an eligibility confirmation at least one to two days in advance.

RPA can automatically verify eligibility during scheduling and at the time of service using payer APIs. AI enhances the process by tracking verification failures over time and identifying recurring issues (e.g., data entry errors, outdated payer info), so they can be corrected systemically.

Coding Accuracy Rate measures the correctness of medical coding, often determined by coding audits. For example, if an internal or external audit reviews 100 coded encounters and finds 5 with errors, the coding accuracy rate might be 95%. Alternatively, it can be tracked as an error rate (# of coding errors per number of charts coded). It encompasses elements such as accurate ICD-10 diagnosis codes, CPT procedure codes, modifiers, and adherence to coding guidelines.

(Accurate Codes / Total Codes Audited) × 100

A high coding accuracy rate means your coders are assigning codes correctly, which should correlate with fewer coding-related denials and less compliance exposure. It also means you’re capturing full reimbursement (not missing codes that could have been billed). For compliance, many organizations aim for coding accuracy rates in the high 90s.

AI can interpret clinical notes and suggest the most accurate CPT and ICD codes, even flagging discrepancies in real time. RPA ensures that codes are properly formatted and that the claim package matches documentation before submission.

The Patient Collection Rate measures the percentage of patient-responsible balances that you collect overall. Unlike the POS rate (which is specifically at the point of service), this metric examines all patient due amounts, whether collected at service or after, versus what was billed to patients. For example, if patients owe $100,000 (after insurance payments) and you ultimately collect $80,000 from them, your patient collection rate is 80%. This metric may also be referred to as the Self-Pay Collection Rate or Patient Pay Rate.

(Patient Payments / Patient Responsibility) × 100

If your patient collection rate is low, it directly contributes to the high bad debt rate (#1). It might also indicate issues in the patient billing experience or insufficient follow-up. There isn’t a one-size-fits-all benchmark for this, since specialties vary, but of course, 100% would be ideal (no patient ever defaults). Realistically, hospitals might collect somewhere in the range of 50-70% of patient balances, whereas a well-run practice could achieve a higher rate if it aggressively manages its accounts.

Automation plays a huge role in improving patient payment completion. AI scores patient payment behavior and segments users based on likelihood to pay. RPA then delivers personalized reminders, enables self-service payment options, and monitors follow-through. Combined, they streamline patient engagement and increase collection rates with less friction.

This Revenue Cycle Management metric measures the cost incurred by your organization to process a single claim from start to finish. It’s essentially the cost to collect on a per-claim basis. It factors in all the expenses of the revenue cycle – billing staff salaries, billing software costs, clearinghouse fees, printing/mailing for patient bills, collection agency fees, etc., divided by the number of claims (or encounters) processed. For example, if your clinic spends $50,000 on all billing-related costs in a month and processes 5,000 claims, your cost per claim is $10.

Total RCM Costs / Number of Claims Processed

A lower cost per claim means you’re collecting money efficiently, with minimal expense. A higher cost might indicate inefficiencies – perhaps overly manual processes or overstaffing, or it could be due to dealing with too many denied claims (which take more effort). According to industry benchmarks, the cost to collect in healthcare can range roughly from 2% to 5% of collections for hospitals (it varies widely). On a per-claim basis, the cost will vary by specialty and complexity, but the goal is always to reduce it without compromising effectiveness.

Automation is one of the most direct ways to lower this metric. RPA eliminates the need for manual processing across multiple stages of the claim lifecycle (e.g., data entry, validation, follow-up), thereby reducing labor costs. AI helps identify bottlenecks or redundant steps, guiding continuous improvement toward a leaner, more cost-effective revenue cycle.

Cost per Step is similar to the above, but measures the financial cost associated with each step of the RCM process, rather than time. It involves allocating expenses (primarily staff salaries, but also systems and overhead) to each function – registration, coding, billing, follow-up, etc. – and computing a cost per unit for that function. For example, how much does each eligibility verification cost in terms of staff time and systems? How much does it cost to handle an appeal on average? It’s a granular look at the cost to collect by segment.

RCM Labor + Tech for Step / Volume of Task

Breaking down the cost by step allows you to see where your revenue cycle is “spending” its resources. Perhaps you discover that the pre-authorization process is particularly expensive per case, maybe because it’s so labor-intensive with lots of phone calls and faxing. Or you find that denial management takes a big chunk of your budget. Understanding this can guide investments; if one area is very costly, that might be your prime candidate for process re-engineering or automation to get a better ROI.

Granular cost tracking by RCM step reveals hidden inefficiencies. AI helps identify which steps have the highest resource utilization and why. RPA reduces these costs by streamlining operations and allowing teams to accomplish more with fewer resources.

Staff Productivity in RCM can be measured in various ways, but a common one is the number of claims (or encounters or accounts) processed per full-time equivalent (FTE) staff in a given time period. For instance, a medical billing office might measure the number of claims each billing specialist processes per day or per hour. You can also track productivity by sub-function, such as claims coded per coder per day or payments posted per poster per hour, etc. Essentially, it gauges how efficiently your team is working.

(Claims Processed / FTEs) or (Net Collections / FTEs)

Productivity metrics help ensure you have the right staffing levels and that your team is performing well. If one person can handle 100 claims a day and another similar person handles only 60, that’s a disparity to investigate (is it due to training, the complexity of work, or an issue?). Additionally, as you implement process improvements or automation, you can expect productivity per staff member to increase, which provides a way to quantify those gains.

With automation handling routine tasks, staff are empowered to focus on high-impact areas such as denial resolution and complex billing issues. AI identifies where productivity dips occur (e.g., specific teams or times of day), while RPA streamlines workflows and reduces burnout, ultimately enhancing overall team output.

Bad Debt Rate is the percentage of revenue that you had to write off as uncollectible. In simple terms, it’s the portion of patient bills or insurance balances that you could not collect and ultimately “give up” on as bad debt. This metric is a direct indicator of revenue leakage. A rising bad debt rate is a major red flag that more patients aren’t paying their bills or that internal collection efforts are faltering.

(Bad Debt / Gross Patient Revenue) × 100

An industry benchmark for bad debt is around 2–3% of net patient revenue. In other words, for every $100 in revenue, a well-performing hospital might only write off $2–3 as bad debt. If your bad debt rate is higher than that, it means real dollars are slipping away. A high bad debt rate can result from ineffective collection processes, a lack of financial counseling, or economic factors impacting patients. It’s also tightly linked to patient satisfaction – surprise bills or confusing billing can lead patients to ignore bills altogether.

Automate pre-service eligibility checks using RPA to validate insurance in real time. Use AI to predict payment risks based on patient history and behavior, triggering proactive payment plan offers, charity screenings, or financial counseling workflows.

Charge Lag Days (also called Billing Lag Days or Days to Bill) measure the number of days between the date a service is provided and the date the charges are entered or the claim is submitted for that service. Essentially, it’s how long it takes your organization to get a bill out the door after seeing a patient.

Date of Charge Entry – Date of Service

A short charge lag (one or two days) indicates an efficient process where clinical documentation, coding, and charge entry are happening quickly. A long lag (e.g., a week or more) often signals process bottlenecks – perhaps providers are late signing charts, coders have a backlog, or there are manual workflow inefficiencies. Best practice is to submit claims within 24–48 hours of service whenever possible. Some organizations even strive for same-day billing for outpatient encounters.

Reducing the time between service delivery and charge entry directly improves cash flow. RPA bots can be configured to automatically extract charge data from EHRs and submit it to the billing system on the same day. AI helps pinpoint root causes of delays — such as incomplete documentation or slow physician sign-off — allowing administrators to target training or process changes effectively.

First Pass Resolution Rate (FPRR) measures the percentage of claims that get paid after the first submission without requiring any edits, re-submissions, or appeals. It is essentially the inverse of having denials or rejections. If out of 100 claims you submit, 85 are paid in full on the first try and the rest needed intervention, your FPRR is 85%. Sometimes this is also called First Pass Payment Rate or First Pass Yield.

(Clean Paid Claims / Total Claims) × 100

A high FPRR indicates that your team is getting things right the first time: claims are clean, complete, and compliant, ensuring that payers pay promptly. A low FPRR (and correspondingly high initial denial/rejection rates) means your staff is having to chase fixes and play catch-up on a significant portion of claims, which delays cash and drives up labor costs. Improving FPRR has a direct correlation with faster collections and lower Accounts Receivable. For context, many organizations aim for FPRR in the 90%+ range. In fact, with advanced automation, some organizations have achieved first-pass accuracy rates of 98–99% on claims. Every point increase in FPRR translates to fewer staff touches and a smoother revenue cycle.

RPA ensures claims are built and submitted accurately the first time, while AI continuously learns from payer behavior to adapt claim preparation accordingly. Improvements here mean faster revenue recognition and lower administrative costs.

Clean Claim Rate is the percentage of claims submitted that have no errors or issues and are accepted by the payer (or clearinghouse) on first submission. A “clean claim” meets all payer criteria and includes all necessary information so that it doesn’t bounce back as a rejection. For example, if you submit 100 claims and 95 are accepted without any edits, your clean claim rate is 95%. This metric typically focuses on initial submission quality – even before the payer adjudicates for payment, it checks whether your claim passes basic validations.

(Clean Claims / Total Claims Submitted) × 100

A high clean claim rate means your billing team (and systems) are doing an excellent job preparing claims. You avoid the time-consuming back-and-forth of corrections and resubmissions. A low clean claim rate indicates frequent claim rejections – perhaps due to missing information (such as patient DOB or insurance ID), incorrect coding, or other errors. Rejected claims usually never even entered the payer’s system for processing; they’re kicked back by clearinghouses or front-end edits. This is wasted effort and time. Clean claim rate is critical because it directly affects cash flow – the cleaner your claims, the faster you get paid. Industry-leading clean claim rates typically exceed 90%.

RPA bots validate that all necessary patient, provider, and insurance fields are completed and formatted correctly. Meanwhile, AI tools can identify patterns of missing codes, documentation gaps, or inconsistent inputs, helping your billing team avoid costly errors before the claim even leaves your system.

Denial Overturn Rate is the percentage of denied claims that your team successfully overturns and ultimately gets paid. For example, if you had 100 denied claims last month and, through appeals or resubmissions, you got 60 of them paid, your denial overturn (or appeal success) rate is 60%. It essentially measures how effective your back-end team is at recovering revenue from initially denied claims.

(Overturned Denials / Denials Appealed) × 100

A high overturn rate (closer to 100%) is great – it means you fight for and win payment on most denials. A low rate means a lot of denials stay unpaid, which could indicate poor follow-up or perhaps that many denials are not appealable (which points back to needing to fix root causes). Interestingly, studies have shown that a large share of denied claims can be overturned. For instance, one analysis found that 60–80% of insurance denials were overturned on appeal in California. Improving this rate directly boosts revenue without increasing volume – it’s about capturing what you’ve earned. It also highlights the effectiveness of your denial management team or vendor.

Every overturned denial is reclaimed revenue. AI supports this by generating personalized appeal letters tailored to the denial reason, using successful language from historical appeal wins. RPA submits these appeals with supporting documentation and tracks their resolution, speeding up turnaround time and maximizing recovery.

Point-of-Service (POS) Collection Rate refers to the percentage of patient payments that are collected at the time of service. This typically includes co-pays, deductibles, co-insurance, or self-pay amounts that the patient is responsible for, which are collected during check-in or check-out, before the patient leaves the facility. For example, if a clinic had $50,000 in total patient-responsible charges in a month and they collected $20,000 of that at the visits, their POS collection rate is 40%.

(POS Collections / Total Patient Payments) × 100

Industry benchmarks suggest that aiming for around 35% of patient payments to be collected at the point of service is a good target (though some organizations strive for even higher rates). This Revenue Cycle Management metric has grown in importance with the rise of high-deductible health plans; patients now have larger out-of-pocket liabilities, so capturing payments early is crucial. A high POS collection rate indicates effective financial counseling and front-end processes; a low rate might indicate staff reluctance to ask for payment or a lack of price transparency tools.

AI generates accurate patient responsibility estimates based on benefits data and common reimbursement patterns. RPA delivers these estimates via SMS or web portal and facilitates real-time payments, increasing the likelihood of collecting upfront.

This measures the share of claims that flow through the entire revenue cycle without any human intervention – from charge capture to claim submission to payment posting. For example, if out of 5,000 claims, 1,000 were processed end-to-end by automation (no manual touches), that’s 20% no-touch claims. It’s a newer metric that reflects the success of RPA and system integration.

(No-Touch Claims / Total Claims) × 100

A higher percentage means your tech systems (EHR, billing, clearinghouse, etc.) are well-integrated and you’ve automated key steps. For instance, charges auto-flow from clinical systems, edits auto-fix common issues, claims auto-submit, and payments auto-post to the ledger. If a claim can go from service to paid with zero human clicks, that’s as efficient as it gets. It’s challenging to establish a benchmark since this is an evolving field, but some leading systems boast extremely high automation rates – for example, some claim that 80-90% of claims can be processed without human involvement using advanced RPA and AI solutions.

With fully integrated RPA solutions, claims can be created, scrubbed, submitted, and even followed up on without manual input. AI ensures this workflow adapts to payer policy changes and detects anomalies that may require human oversight.

This is an internal efficiency metric that examines the amount of staff effort (time) allocated to each step of the revenue cycle per unit of work. For example, how many minutes does it take to register a patient, or to code a chart, or to work a denial on average? It can be measured in minutes per claim for each sub-process, or FTEs dedicated to certain tasks relative to volume. Essentially, it’s breaking down the revenue cycle into steps and assessing productivity at each stage.

Hours Spent / Task Volume

This Revenue Cycle Management metric aligns closely with cost-per-claim, but it’s measured in time, which can help highlight workflow issues that pure dollar figures might not. It’s especially useful for pinpointing where automation or process improvement could save time. Executives and managers can use it to justify changes: “If we reduce the effort per claim on coding from 5 minutes to 3 minutes through better software, that’s a 40% productivity gain.”

RPA eliminates low-value, high-frequency tasks (e.g., data entry, status checks), while AI provides insight into time and resource usage. With both, organizations can continuously improve workflows, reduce team workload, and maintain performance even during staffing fluctuations.

This metric assesses how well your billing and documentation withstand compliance audits, whether internal or external. It could be measured as the percentage of audited records that have no compliance issues. For instance, if an internal compliance audit reviews 50 encounters and finds 45 were billed and documented correctly with no major issues, the pass rate is 90%. It covers aspects such as proper documentation to support codes, adherence to billing rules (e.g., no unbundling or modifier misuse), and compliance with regulations, including Medicare’s guidelines.

(Passes / Total Audits) × 100

A high pass rate suggests your organization is doing things right – you’re likely safe in the event of a payer or government audit. A low pass rate is alarming; it means you are potentially overbilling or otherwise not following rules in a number of cases, which could lead to clawbacks or penalties if caught. Many compliance programs aim for >95% accuracy in documentation and billing compliance on internal audits.

AI cross-checks submitted claims with payer rules and documentation requirements, while RPA preps audit packets, logs changes, and ensures that documentation is filed and accessible, making audits smoother and more successful.

DNFB stands for “Discharged Not Final Billed.” It refers to patient encounters (usually hospital stays) that have been discharged (the patient left), but the claim hasn’t been billed yet. DNFB Days is often the average number of days accounts remain in that state. Sometimes it’s measured in terms of dollars – how much revenue is tied up in DNFB – or as a count of charts pending billing. Essentially, it’s a measure of your unbilled claims backlog. For example, if a hospital’s average DNFB is 5 days, it means that on average, it takes 5 days after discharge to process the claim.

Days from Discharge to Final Bill

A low DNFB indicates a tight ship: patients are discharged promptly, charts are coded efficiently, and claims are sent out quickly. A high DNFB (say 10+ days) signals bottlenecks – often in coding or documentation completion. It can also represent missed revenue if things fall through the cracks. Many aim for DNFB under a week. I’ve seen best practice targets around 4–6 days for DNFB in many institutions. Some break it down by inpatient vs outpatient. It also correlates with revenue: if you have a spike in DNFB dollars, that’s a lot of money waiting to be billed.

A high DNFB rate delays billing and clouds revenue projections. RPA tracks discharge events and monitors whether all documentation and coding are complete. If gaps exist, AI identifies the likely cause and routes tasks to appropriate staff, accelerating the time from discharge to billing.

Late Charge Rate is the proportion of total charges that were not included on the initial claim and had to be billed later (usually via a corrected claim or subsequent billing). These are charges that came in after the bill was initially dropped. It can be expressed as a percentage of total charges or the number of encounters with late charges. For example, if you billed $10 million in charges in a month and $200,000 of additional charges had to be added after the initial billing, that’s a 2% late charge rate.

(Late Charges / Total Charges) × 100

High late charge rates might mean revenue is leaking (if you miss the window to bill those charges, you lose money) or that billing is delayed because you wait to get all charges in. Keeping late charges minimal is part of a healthy revenue cycle. A best practice is often <1-2% of total charges as late charges, but it varies. The goal is to catch everything on the first claim whenever possible.

RPA continuously monitors for missed or delayed entries and alerts responsible departments. AI analyzes trends behind late charges — such as certain departments or times of day — and supports targeted interventions.

The Patient Financial Satisfaction Score measures how satisfied patients are with the billing and payment aspects of their care experience. This could be measured via patient surveys specifically about billing (for example, on a scale or via Net Promoter Score questions related to billing), or metrics like the number of billing complaints vs. total visits. Essentially, it’s the “patient experience” metric for the revenue cycle. Some organizations include questions in post-visit surveys like “Were you satisfied with the clarity of the billing and payment process?” or “Rate your satisfaction with how your billing was handled.”

Post-visit survey score (e.g., NPS or custom billing satisfaction survey)

Satisfied patients are more likely to pay promptly. This metric, though somewhat qualitative, is crucial for the hospital’s reputation and even revenue protection. A high patient financial satisfaction score indicates that your revenue cycle is not detracting from the overall care experience, but rather supporting it.

AI generates accurate cost estimates and tailored payment plans before treatment begins. RPA supports instant payment processing, automated statements, and real-time updates, making the experience clear, convenient, and confidence-building.

RCM success is about transitioning from a reactive to a proactive approach. With the right KPIs, your organization gains visibility to identify and address leaks, enhance collections, and improve both staff and patient experiences.

Don’t drown in reports. Focus on the metrics that tie directly to cash, compliance, and care. And when you’re ready to free your team from manual grind, Flobotics is here to help you build an automated RCM engine that works 24/7.

Want to see how RPA and AI can improve your Revenue Cycle Management metrics? Reach out to us.