Straight-Through Billing
Medical billing complexity and massive volumes of daily claims render manual claims processes incapable of protecting both the provider and the payer from underpayments, overpayments, and billing compliance violations. Straight-Through Billing (STB) addresses complexity and volume processing problems by automating the majority of the claim flow and focusing the billing follow-up specialists on exceptions only. An STB process flags problems, routes them for follow-up, and enables online correction and resubmission. The STB methodology implements billing service transparency and focuses management on strategic process improvement opportunities. Straight-Through Billing integrates the billing process into the practice management workflow, automates the vast majority of transactions, focuses manual labor on exceptions, and establishes a process for continuous improvement. Remember: Straight-Through Billing offers a comprehensive approach to improving the billing process, integrating various components, and promoting continuous improvement. Practice Management Integration First, integrated practice management and billing workflow connects patient scheduling, medical record management, and billing into a single process. Every participant in the practice management workflow receives a unified and coherent picture of the practice workload, patient and provider location, resource availability, and cash flow. However, integrated with Electronic Health Records, practice management systems are more beneficial. Electronic health records (EHR) are digital formats of a patient’s chart. They contain all the information about a patient’s health. This includes medical history, allergies, immunizations, previous treatments, medication history, past diagnoses, history of substance abuse (if any), and so forth (Shah, 2021). Transaction Automation Transaction automation streamlines and expedites the billing process by automating claim validation, payer message reconciliation, and billing workflow management: Automated claim validation eliminates errors downstream and reduces processing time because it flags errors before submitting the claim to the payer. Automated claim message reconciliation eliminates the costly search for the original claim and standardizes message communication, eliminating the need to decipher the (often cryptic) payer’s message. Automated billing workflow management drives the follow-up discipline required for the resolution of claim denial and underpayment incidents, and it establishes a high degree of process transparency for all billing process participants, resulting in full and timely payments. Automated billing increases the net collection rate due to quick claim turnaround and efficient follow-up. Respond to your denials within 5-21 business days of receiving them, using our Daily Denial Email Alerts (Qureshi, 2022). Focus on Exceptions Focusing manual labor on exceptions requires timely exception identification, routing to follow-up personnel, online error correction, and rigorous follow-up tracking. Again, process transparency enables tracking exception follow-up as implemented in ClinicMind-like systems. Another significant benefit of automated medical billing is the ability to track and analyze financial data. With this, healthcare facilities can monitor their revenue cycles, identify growth opportunities, and generate detailed financial reports (Polo, 2023). Continuous Process Improvement Finally, a process for continuous improvement requires continuous observability of every process attribute and a modification methodology for both automated claim processing and manual exception follow-up tracking. Straight-Through Billing implements billing transparency by design because billing transparency is an integral attribute of every component of the STB process. It also enables businesses to streamline their billing operations, reduce errors, enhance efficiencies, and improve the customer experience (Ward, 2023). Straight-Through Billing Architecture The Straight-Through Billing systems architecture mirrors the architecture of general Straight-Through Processing (STP) systems developed for the financial services industry. Such systems require effective workflow management, a knowledge-based validation system, connectivity to all process participants (including online data reconciliation), and tracking of problem resolution. Therefore, a typical ClinicMind-like STB system has a three-tiered architecture: Back-end processing engine, designed for a high-volume transaction processing environment Middle tier, using Java Servlet technology Front end, using an HTML-JavaScript, zero-footprint client Did You Know? The STB architecture is inspired by the systems used in the financial services industry, showcasing the transferability of advanced processing concepts across different domains. An STB system (e.g., ClinicMind) based on the methodology outlined here implements rich functionality, which allows the following to be automated: Computer-aided preferential patient scheduling Integrated electronic medical records Online computer-aided coding Real-time claim validation and patient eligibility testing Electronic claim submission Payment posting, reconciliation, and verification of meeting contractual obligations Monitoring of audit risk and billing compliance Tracking of denial appeal process Quantitative STB Management Straight-Through Billing methodology allows for quantitative management since the likelihood of the entire process failing can be estimated as the product of such items for each individual workflow step. A ClinicMind-like STB system tracks the percentage of clean claims (claims paid in full, and within the allocated time frame, without any manual intervention) and focuses the management on those process aspects that yield the greatest potential improvement. Thus, STB methodology focuses on exceptions at both the tactical and strategic management levels and can help to improve cash flow and reduce outstanding invoices by providing real-time visibility into billing and payment status (Mielnicki, 2022). Modern Insights and Research In the ever-evolving field of medical billing, staying ahead of the curve is crucial for achieving financial excellence in the healthcare industry. Let’s embark on an exhilarating journey into the future of medical billing, where the convergence of electronic health records (EHRs), artificial intelligence (AI), real-time analytics, and collaborative efforts reshapes the revenue cycle landscape. Brace yourself for a transformative exploration that revolutionizes processes, enhances data accuracy, maximizes financial outcomes, and ushers in an era of unparalleled efficiency and effectiveness in the dynamic realm of medical billing. Role of Blockchain Technology in Billing Systems The seamless integration of electronic health records (EHRs) and billing systems is revolutionizing the field of medical billing. Gone are the days of fragmented medical records scattered across various healthcare organizations. With blockchain at the helm, a distributed EHR ecosystem emerges, ensuring a smooth flow of information between providers. By eliminating manual data entry and ensuring accurate documentation, this innovative technology guarantees accurate and secure documentation, eliminating errors and speeding up reimbursement processes (Cerchione et al., 2022). But that’s not all. Blockchain brings an unparalleled level of data integrity and security, employing cryptographic techniques to safeguard
AI in Medical Billing
AI has revolutionized many different industries, and healthcare is no exception. In recent years, medical billing has benefited greatly from using Artificial Intelligence. Where are the major pain points in healthcare today? 1-Patients: A major challenge today is long wait times. In 2022, the average wait time for a physician appointment in the 15 largest U.S. metro markets is 26 days [1]. The longer someone has to wait, the higher the risk of complications or possibly more serious health issues arising. Some possible solutions to address this problem include use of telemedicine involves providing medical consultations and services remotely using technology such as video conferencing. increasing the number of healthcare professionals in practice, streamlining administrative processes and improving patient communication and education to help prevent avoidable illnesses and hospital visits. 2-Physicians: Physician burnout is affecting a high percentage of physicians. 62.8% of physicians experienced at least one symptom of burnout in 2021 [2]. Reasons for burnout include outdated technology and inefficient workflows, which contribute to increased work stress and frustration even for skilled and experienced professionals. Additionally, a shortage of skilled workers puts extra pressure on those in the workforce, leading to burnout and poor job satisfaction. One potential way to address these challenges is to invest in updating technology and improving workflows, which can streamline processes and reduce workload. Additionally, increasing access to training and education for both current and future workers could help alleviate the skill shortage issue. It’s essential to take proactive steps to address these issues to ensure that our healthcare workforce can continue to provide top-quality care to patients without experiencing burnout. 3-Payers: Payers recognize the importance of delivering better experiences to their customers. To meet these expectations, payers are focusing on several critical areas: A-Improving ease of use Improving ease of use can be achieved through various initiatives, such as clear communication to help customers understand their network, status updates on claims, and easy-to-use portals and tools. B-Ensuring the availability of services on-demand. Having around-the-clock access to support and information is essential to ensuring customers can get the help they need when they need it. To support these initiatives, payers need to leverage data-driven insights to create value for their customers. This can be achieved through technologies such as artificial intelligence and machine learning, which can help to identify trends and patterns in customer behavior and preferences, enabling payers to provide targeted and personalized support proactively. Overall, payers must continue to innovate and adapt to meet the evolving needs and expectations of their customers, and taking a data-driven approach to improve ease of use and availability on demand could be a critical step forward. C-Reducing hospital readmission rates Payers are leveraging machine learning to gain actionable insights from healthcare data sets. By analyzing claims data, payers can identify trends in patient outcomes and determine the most effective treatments for specific patient populations. They can also predict which patients are at a higher risk of complications or readmissions, e.g., inflammation and blood clotting occurs most following surgery, and provide this information to providers to help them take preventative measures. This kind of data-driven approach is valuable because it enables healthcare providers to deliver more personalized care to patients, leading to improved patient outcomes and reduced costs. By sharing these insights, payers can demonstrate the value of their contributions to patient care while simultaneously empowering providers to make better decisions and improve healthcare delivery. Improved Medical Coding Accuracy One way AI has been used in medical billing is through automated billing and coding. The technology can analyze electronic health records and notes made by healthcare providers and use that information to generate codes that accurately bill for specific services. This reduces the risk of errors, which can result in denied claims and lost revenue. Improved Insurance Cash Flow Prediction and Denial Management AI is also used for claims prediction. By analyzing past claims data, AI identifies patterns and predicts which claims will likely be denied. This allows billing teams to proactively address issues and avoid denials, which saves time and money. Also, when denials are increasing due to a lack of medical necessity, lacking documentation, or coding mistakes, AI can analyze the denials to find the cause and then create tasks within the EHR to correct the likely causes for denials. Improved Medical Billing Workflow To automate the claims follow-up process, AI analyzes claims data and identifies the claims that are most likely to require follow-up. AI automatically generates follow-up tasks for billing teams. This reduces the administrative burden on billing teams and ensures that claims are followed up on in a timely manner. The deep learning of users’ interaction with EHR and billing software allows the learning of users’ habits, needs anticipation, and the display of the right data at the right time. Automatically retrieving and displaying all of the required data and just at the right time drastically reduces the amount of labor spent on manual billing tasks and allows staff to make better decisions about the next steps for denial resolution. Improved and Expedited Pre-Authorizations The current pre-authorization process can be a real headache for patients and healthcare providers. A streamlined, automated system that can quickly analyze a patient’s health data and determine the medical necessity of a procedure would be a game changer. Imagine how much time, stress, and resources could be saved if medical billers no longer had to worry about pre-authorization denials or chasing down authorization numbers. It’s exciting to think about how technology can continue to improve and simplify healthcare processes like this. Improved patient customer service With the rise of technology, we’re seeing a lot of improvements in healthcare that can benefit patients and providers alike. Using bots for patient interactions like appointment scheduling and payment collection is one way to streamline processes and reduce frustration for patients and billing staff. By standardizing these tasks, there’s less room for error and confusion, leading to smoother, more efficient
Claim Denial Management
Partial denials cause the average medical practice to lose as much as 11% of its revenue (Capko, 2009). Payers are known for denying claim payments for legitimate reasons (provider-generated errors) and arbitrary reasons, motivated by the inherent benefits of controlling the float for the maximal time (Stahl). Systematic denial management must address both kinds of errors. Denial management is difficult because of the (intentional) complexity of denial causes, payer variety, and claim volume. Systematic denial management requires measurement, early claim validation, comprehensive monitoring, and customized tracking of the appeals process. According to a survey by the Medical Group Management Association (MGMA), 69% of organizations reported a significant increase in denials, averaging 17%, in 2021 alone. These findings are further supported by additional alarming statistics (Zipple, 2023): In 2021, claim denials surpassed 48 million (Kaiser 2023). On average, nearly 20% of all claims are denied, and shockingly, up to 60% of these denied claims are never resubmitted (Poland and Harihara, 2022) Certain payers exhibit denial rates as high as 80% (Revenue Cycle Intelligence, 2022) However, there is some hope as, on average, approximately 40% of denials can be overturned through appropriate appeals processes (Kaiser 2023). According to a HIMSS Analytics study, here are some key findings on how hospital executives manage claim denials: 44% of hospital executives rely on vendor solutions to manage denials. 31% of executives still handle denials manually, without any specific tool or software. 18% of hospitals have developed their own in-house tools for denial management. 7% of executives are unsure about the method they use for denial management. Among respondents without a vendor-provided solution, 60% plan to purchase one within the next 7-12 months. (Regulsky, 2023) Denial Risk Classification The denial risk is not uniform across all claims. Certain classes of claims run significantly higher denial risks, depending on six factors: Claim complexity Modifiers, e.g., incorrect modifier used Multiple line items Temporary constraints Claim not filed on time Patient constraints, e.g., claim submission during global periods (see below) Payer constraint (e.g., claim submission timing proximity to the start of the fiscal year) Procedure constraint (e.g., experimental services) Payer idiosyncrasies Bundled services, e.g., services incorrectly bundled or Unbundling and upcoding Disputed medical necessity, e.g., Not a medical necessity Non-covered services Other Patient data Patient deductible Plan benefits exhausted Provider data, e.g., Out-of-network (OON) provider Process Compliance Incorrect insurance ID number Duplicate claim submitted Prior authorization not attached Typo errors in patient information Note that for complex claims, most payers pay the full amount for one line item but then pay only a percentage of the remaining items. This payment approach creates two opportunities for underpayment: The order of paid items The payment percentage of the remaining items Next, temporary constraints often cause payment errors because of the misapplication of constraints. For instance, claims submitted during the global period for services unrelated to the global period are often denied. A global period is a period of time before and after a surgical procedure during which related services are bundled into the initial procedure’s payment. It helps streamline billing by including pre-operative visits, post-operative follow-up care, and related services within a single payment (Master, 2020). Similar mistakes may occur at the start of the fiscal year due to misapplying rules for deductibles or outdated fee schedules. Additionally, payers often vary in their interpretations of Correct Coding Initiative (CCI) bundling rules or coverage of certain services. Developing sensitivity to such idiosyncrasies is a key to full and timely payments. CMS contractors conduct medical reviews on certain claims and prior authorizations to ensure that Medicare payments are made only for services that comply with all Medicare regulations. Suppose a review leads to a denial or non-affirmation decision. In that case, the contractor responsible for the review provides the provider or supplier with a comprehensive explanation detailing the reasons for the denial or non-affirmation. For example, the code AM300 is used when the provided documentation lacks evidence to substantiate the provision of Basic Life Support services during an emergency response. Please refer to 42 CFR § 410.40 (c), 42 CFR § 414.605, Internet Only Manual (IOM), Publication 100-02, Medicare Benefit Policy Manual, Chapter 10, Section 20, and Section 30.1.1 for further clarification and guidelines on this matter. (Reason Statements and Document (EMDR) Codes | CMS). Payers can also separate the Claim Processing and Denial Management departments to add complexity and improve the likelihood of underpayments and delays. In this scenario, the provider may be forced into a deadlock by having to deal with two separate departments for the same claim, where each of the two departments “waits” for the decision of the other. Denial Risk Management Stages In a high-volume clinic, the only practical way to manage denials is to use computer technology and follow a four-step procedure: 1. Prevent mistakes during claim submission This can be accomplished with a built-in claim validation procedure that includes payer-specific tests and EHR integration. Such tests (“pre-submission scrubbing”) compare every claim with Correct Coding Initiative (CCI) regulations, diligently review modifiers used to differentiate between procedures on the same claim, and compare the charged amount with the allowed amount, according to previous experience or the previous contract, to avoid undercharging. Integrating EHR and claims management systems allows for the seamless transfer of patient data and encounter information from the EHR to the claims system. This eliminates the need for manual data entry or transcription, reducing the chances of errors or omissions that may occur during the claims submission process. EHR systems often include built-in templates and structured documentation features that guide providers to capture complete and accurate information. These templates help ensure that all necessary information for claims submission, such as procedure details, diagnoses, and supporting documentation, is appropriately recorded. 2. Identify underpayments Identifying underpayments in the claims process is crucial for healthcare organizations to ensure accurate reimbursement and maximize revenue. This involves comparing the payment with the allowed amount, identifying zero-paid items, and evaluating payment timeliness. The
Computer-aided Patient Scheduling
Without a computerized scheduler, a practice has less than a 2% chance of earning the title of a “better-performing practice,” according to the Medical Group Management Association. Computerized scheduling helps decrease service costs, provide fairness in service delivery, increase patient satisfaction, and reduce waiting times (Zhang et al., 2019). A massive investment in scheduling features across a wide spectrum of billing products indicates the importance of computerized scheduling. Convenience and front office efficiencies are two obvious benefits of a computerized scheduling system; without them, the only manual way to find out if a specific patient has a scheduled appointment is to flip through the appointment book page by page. Worse, manual scheduling hurts both patient satisfaction and practice financial performance because of scheduling inconsistencies and unbilled (and therefore unpaid) visits. But the benefits of integrated computerized scheduling stretch far beyond convenience, front office efficiencies, and better charge follow-up of stand-alone, albeit computerized, scheduling. A well-designed and integrated scheduler allows preferential patient scheduling, which, along with improved controls, helps revenue optimization and practice compliance. Next, we review key aspects of computerized scheduling and demonstrate the important benefits of integrated scheduling, billing, and compliance management. Scheduling Policies Computerized schedulers allow a combination of single- or multiple-interval scheduling, with open-access scheduling subject to various priority constraints. Such priority-constraint-driven, open-access scheduling creates preferential appointments based on patient demographics or insurance coverage. Typical time-slot-based appointment systems essentially divide a physician’s schedule into finite slots in a day, which can be allocated according to appointment requests. However, such systems are limited by the risks of schedule fragmentations in late shows or no-shows (Chen et al., 2019). Examples of time-slots-based scheduling include single-interval scheduling and multiple-interval scheduling. Single-interval scheduling allocates appointments at regular intervals of 5 to 15 minutes, depending on the specialty. The downside of single-interval scheduling is that as soon as one appointment takes longer than the allocated slot, all subsequent patients must wait. Multiple-interval scheduling also sets appointments at regular intervals; however, unlike single-interval scheduling, it allocates the appointment length depending on the chief complaint. Such scheduling requires up-front categorization of key appointment types and their projected lengths. For instance, an initial appointment might take 30 minutes, while a routine injection might take only 5 minutes. According to the CAHPS survey database, about 12% of patients who called in did not get appointments for urgent care that they needed at the time. Forjuo et al., (2001) also showed that inadequate access to primary care providers was a leading cause of patient dissatisfaction. These challenges are mitigated by open-access scheduling. Open-access scheduling requires holding several appointments open every day. These open appointments are filled only within 48 hours of the appointment, catering to same-day or last-minute patient requests. Open-access scheduling improves access to the physician, reduces no-shows, and eliminates patient screening time. The downside of open-access scheduling is, of course, the potential for longer patient waiting lines or physician idle time because of the inability to maintain a predictable patient flow. A novel scheduling variant is the overlapping appointment scheduling (OLAS) model (Huet et al., 2020). OLAS model refers to deciding the optimal overlapping periods between the patient appointment and allocated service times. The model is formulated as an optimization problem to minimize the total cost of patients waiting and doctors’ idle time. One way to balance the practice workload is to schedule group, routine, or repeat appointments during slow hours. For instance, pediatric well-child visits or patients with a particular chronic disease—such as congestive heart failure or diabetes—could be scheduled for early mornings when there are typically fewer patients waiting in line. These scheduled visits include educational components and often involve multidisciplinary teams. It also helps save time since standard advice need not be repeated to individuals, improving on the efficiency of care delivery (Jones et al., 2019). Patients also benefit from the socialization aspect of group visits; members encourage one another, exercise together, and so forth. A good scheduler allows a repeat appointment schedule subject to total frequency and time slot constraints. Compliance Process An integrated scheduler verifies the filing of a signed patient consent form—and, in certain cases, a signed ABN form. An ABN (Advance Beneficiary Notice) serves three goals: To protect the beneficiaries from liability for services denied as not reasonable (depends on the frequency or duration) and necessary (depends on the diagnosis and the provider’s specialty) To protect the provider’s revenue by shifting financial liability for denied services to the patient To provide documentation for a Medicare audit For more complex procedures, the scheduler warns the front office about the need to obtain all required diagnostic test results and clearances up front. Billing Interface The integrated scheduler avoids unbillable patient encounters and reconciles visits with patient balances. It checks outstanding patient balances and verifies coverage and eligibility at the point of scheduling before the appointment. AI-driven computerized coding and billing systems can accurately provide the code for a particular disease condition and help with appropriate automated billing (Venkatesh et al., 2023). In many cases, such a test discovers data entry errors too, reducing the payment cycle at later stages. Additionally, the insurance company may require referrals or separate pre-authorization/certification for certain procedures, refusing the payment if the procedure was performed without a referral or preauthorization. The integrated scheduler can access medical records to supply necessary background and diagnosis information to obtain pre-authorization. Finally, without the ability to reconcile visits with payments, the practice owner cannot be sure that every visit resulted in a payment. Practice Flow Interface The integrated scheduler manages the entire patient flow, continuously updating arrival lists, checkoffs, and office/room tracking. Further, the scheduler tracks no-shows and follow-up actions. Detailed reports include daily schedules, load reports, missed appointments, free time, canceled appointments, etc. With AI-integrated schedulers, different color codes and status flags can be used for different appointment types, whether emergencies or routine or based on the specialist to be seen by the patient. This offers a good visually appealing summary by just glancing over the