Optimizing the Radiology Revenue Cycle

Contemporary physicians must not only be equipped with the essential medical skills and knowledge required to practice medicine, but must also be proficient in various factors influencing patient care, including medical coding and billing. Medicine inherently includes business elements and necessitates that hospital systems are adequately compensated for their services to maintain operational efficacy and patient care delivery. This is particularly important in radiology as many radiology services are cost-intensive, underscoring the critical nature of effective medical coding and billing processes.

Accurate medical billing and coding are pivotal in imaging administration. They ensure that the intricate services radiologists provide are translated into standardized codes, crucial for securing appropriate reimbursement. These codes convey critical information about a patient's diagnosis and treatments. In the spectrum of radiological procedures, from simple radiography exams to complex image-guided interventional therapies, coding accuracy directly influences not only the economic stability of medical facilities but also the availability of diagnostic care to patients. Therefore, precise medical billing and coding are vital for sustaining radiological services, promoting the delivery of top-notch care, and fostering the growth of medical imaging technologies.

Hospitals and physicians depend on the Resource-Based Relative Value Scale (RBRVS) payment system, which allocates a specific RVU to each CPT code. This system compensates physicians with RVUs for the medical procedures they perform. However, for these services—ranging from CT scans to central venous catheter insertions—to receive reimbursement, insurance companies must accept the submitted billing codes. Rejected claims transfer the financial responsibility to the patient or the health-care provider. A Change Healthcare study in 2016 highlighted that billing denials reached a staggering $262 billion, with each denial adding a cost of roughly $118 and culminating in an estimated $8.6 billion in lost revenue.¹

The effects of under-reimbursement extend beyond financial loss, as it undermines a hospital's ability to invest in vital resources, state-of-the-art technology, and skilled staff. This shortfall can lead to staff shortages and reliance on outdated equipment, ultimately diminishing the standard of patient care. Precise coding is essential not only for current funding but also for resource distribution for future projects. When coding is inaccurate, it not only disrupts the immediate patient care but also sets off a chain reaction that can impair future health-care management and resource allocation. Therefore, meticulous and accurate coding is fundamental to maintaining high-quality health-care delivery and the overall efficiency of medical services.

The ICD serves as a global reference for medical coding, allowing for the comparison of health data between countries. Such comparisons allow for a deeper understanding of disease characteristics by examining the incidence and prevalence of conditions as reported by different countries.² Since radiology plays a pivotal role in diagnosing these conditions, coding inaccuracies can distort the true incidence and prevalence rates, leading to flawed epidemiological studies. Reliable data derived from accurate coding is crucial for detecting disease outbreaks that may require public health responses. Additionally, this data is instrumental in pinpointing risk factors for certain diseases, thereby supporting preventive health-care measures.

Precise coding and billing practices are also vital for monitoring patient outcomes following radiological procedures and diagnoses.³ This monitoring is crucial in assessing the efficacy and quality of radiological services, including outcomes of interventions. This is particularly important in interventional radiology as evaluations of the success of new procedures depend on the availability of accurate outcome data.

Three Key Takeaways

• “Reimbursement Challenges in Radiology: an Interview with Richard Heller, MD”

• “Are You Hungry, Radiologist?”

• “Stabilizing Medicare Reimbursement: a Plan for Action”

The integrity of coding and billing directly influences the perceived quality and efficiency of radiologists. Relative value units are commonly used to gauge physician productivity. Errors in these areas can not only undervalue the work performed by radiologists, they may lead to misperceptions of lower productivity, potentially damaging their professional reputation among peers and in the eyes of potential employers.

Organizations like Healthgrades and Leapfrog rely on coded data to derive quality metrics for health-care providers. Inaccuracies in coding may lead to unfairly low quality scores, which could negatively affect patient perceptions of a radiologist. Such outcomes can prompt patients to hesitate in seeking care from radiologists who are marked by these lower scores, preferring instead to turn to alternative providers or institutions.^4,5Therefore, accurate coding is paramount to truthful representation of a radiologist's performance and ensuring his or her professional standing in the health-care community.

Several hurdles continue to complicate the coding process. These include incomplete physician documentation, a lack of standardized medical training for coders, continuous updates in coding systems, and insufficient coding education for radiology practitioners. Addressing these issues is essential for the financial sustainability of health-care providers and the broader integrity of medical billing practices.

Common Bottleneck for Claim Denials: Failing to Document All the Relevant Information

When it comes to denials or insufficient reimbursement from payors, a significant bottleneck is the proper documentation of medical procedures and conditions. A qualitative study that interviewed 28 coders identified that a prevalent issue in coding errors is the need for complete and accurate physician documentation.⁶ Coders rely heavily on the physician's notes and are constrained by the details provided. If the medical records are inconsistent or incomplete, coders are obligated to use the physician's diagnosis, even if the patient's condition doesn't fully meet the diagnostic criteria.

For instance, a physician might diagnose a patient with "pancreatitis," but without meeting the necessary criteria, the coder must report the given diagnosis. For a condition like pancreatitis that could be caused by specific factors such as alcohol consumption or gallstones, detailed documentation by the physician is crucial for full reimbursement. An "unspecified" pancreatitis diagnosis could result in a lower than expected reimbursement.

In radiology, the precision of documentation is most critical in the "impression" or diagnostic summary of the radiology report. Should the referring provider omit necessary indications for a CT scan to confirm pancreatitis, the radiologist must search medical records to extract these details, reducing the time they can dedicate to high-value diagnostic work.

Radiologists may assume that personnel involved in interpreting medical records, including medical coders, share a similar level of medical literacy. This assumption does not always align with the reality of the medical coding profession. Coders come from a broad spectrum of educational and professional backgrounds, many of which do not include direct clinical training or experience. Educational prerequisites are not uniformly regulated across the board in medical coding employment. The majority of employers require candidates to possess a coding certification, which can be obtained through various programs and courses. These certification programs may vary in the medical education they provide.

As a field heavily reliant on terminology to describe complex imaging findings, radiology requires all coders to be accredited by the Radiology Coding Certification Board. This accreditation ensures a foundational competence in the essential tasks relevant to the coding of radiology reports. However, it does not stipulate a specific level of medical training.⁷ This gap in the standardization of medical training for coders can lead to inconsistencies in the interpretation of medical terms and result in errors in the coding process.

For example, an esoteric medical term used by a radiologist in a report might be misinterpreted by a coder who lacks familiarity with a particular clinical context or high-level radiologic terminology. Recognizing the disparity in the coders' understanding of medical jargon is crucial for the integrity of medical coding, especially in the technical field of radiology.

A General Overview of the Coding and Billing Process

The coding and billing process begins with a patient encounter, which is documented by the provider in the electronic medical records. This record is then forwarded to medical coders who, utilizing the written medical information, assign the pertinent billing codes to the encounter. In the inpatient setting, two predominant code types are CPT (Current Procedural Terminology) and ICD (International Classification of Diseases) codes.

The CPT codes, developed and maintained by the American Medical Association (AMA), are allocated for specific procedures administered by a physician, and categorized into three groups based on their frequency of performance. Each CPT code is associated with a Relative Value Unit (RVU), reflecting the Work RVU (encompassing time, intensity, effort, and skill required for a service); Practice Expense RVU (costs including non-physician staff, supplies, equipment); and the Malpractice RVU (professional liability expenses).

International Classification of Diseases (ICD) codes, developed by the World Health Organization (WHO), characterize medical disease and diagnoses. For the acceptance of CPT codes, they must be correctly associated with the relevant ICD codes, which prove the medical necessity of procedures performed.

After a review of coding for accuracy, corresponding RVU values are then attributed to the codes with subsequent adjustments based on variables like geographical location. This information is then submitted as a claim to either an insurance company or another payor.

Subsequently, the claim undergoes adjudication, a process where it is either accepted or rejected. The submitting entity can resubmit rejected claims. Accepted claims are covered, leaving any uninsured portion of the visit as a financial responsibility of the patient.

Payors are categorized as either public, like Medicare and Medicaid, or private, including employer-provided or individually purchased insurance. Rates from Medicare and Medicaid are governmental mandates and are non-negotiable. Conversely, private health insurance rates undergo a negotiation process between hospitals and insurance companies, resulting in a mutually agreed upon fee for each service.

The medical system is continuously changing, with new technologies, diseases, procedures, and legal regulations being introduced constantly. Along with these changes, the codes associated with their use must be updated to reflect their introduction into medicine. The CPT codes are updated annually while the ICD codes are updated periodically when necessary. The last update of ICD codes from ICD-9 to ICD-10 in 2015 resulted in a nealy sixfold increase of diagnostic codes. Currently, the medical system is transitioning to the ICD-11, which started at the beginning of 2022.⁸

When new codes are introduced, it is imperative that both coders and providers are aware of the changes. If coders do not keep up to date on changing coding rules, they will inevitably use an old code and trigger a denial.⁹ From the perspective of medical providers, if there is a lack of awareness of coding changes, they may not provide the newly needed information required to accurately code health-care interactions.

The Lack of a Uniform Standard on Coding Education for Radiologists or Trainees

The educational framework for radiology attendings and trainee physicians currently lacks a uniform standard for coding education. The Accreditation Council for Graduate Medical Education (ACGME) milestones do not concretely define the role of coding instruction within the curriculum. The Diagnostic Radiology Residency manual by ACGME does include "non-interpretive skills" such as health-care economics, coding, billing compliance, and business as essential medical knowledge areas. However, it does not provide specific guidance on how this instruction should be implemented or the expected results of the training.

As a result, there is significant variability in coding education among new diagnostic radiology graduates.¹⁰ Typically, radiologists carry forward the reporting habits formed during residency and fellowship.11 Without detailed instruction on coding and billing processes, there's a risk that new radiologists may not be adequately prepared to document reports that ensure proper reimbursement.

Optimizing billing practices in radiology is an ongoing challenge that requires a multifaceted approach. The shifting landscape of medical coding demands constant knowledge updates to ensure accurate billing to maximize reimbursement. In order to enhance billing practices in radiology, there should be an ongoing emphasis on utilizing contemporary resources, targeted education, and the potential of innovative technology. The following strategies provide ideas to streamline billing practices, reduce errors, and secure the financial integrity of radiological services.

With the critical role of coding and billing in the health-care system, various organizations offer resources to stay current with recent updates and avoid common errors. The American Medical Association (AMA), which releases the CPT codes annually, provides online materials designed to enhance coding precision. Likewise, the Centers for Medicare and Medicaid Services (CMS), the organization behind the ICD codes, also provides support through the CMS website. This includes a "Resources for Providers" section and specialty-specific insights into the most frequently used ICD-10 codes in each medical field.¹²

Radiological societies also contribute guidance on the coding and billing process. The American College of Radiology (ACR) issues an electronic, bimonthly publication called “Coding Source,” which is prepared by the ACR Coding staff within the Economics and Health Policy Department. It also offers continuing education units for a fee with associated tests to encourage continuous learning.¹³

Regarding interventional radiology, the Society of Interventional Radiology (SIR) presents numerous resources to aid in coding education. This includes an annual "Coding Update Meeting" that shares documentation strategies and addresses common questions and scenarios encountered during the previous year. Additionally, SIR publishes a yearly review, highlighting frequently missed revenue opportunities, that is available at a reduced rate for active society members.¹⁴

Due to the vagueness in the requirements for residents learning medical coding, many programs do not require specific education. A major improvement would be detailing lecture-based requirements in the residency manuals of radiology and interventional radiology.

In a study analyzing coding errors before and after monthly resident lectures aimed at medical coding and billing education in a dermatology residency program, the results showed a significant decrease in the errors in coding and billing after the intervention.¹⁵ Another study in otolaryngology residency showed a significant increase in medical coding and billing knowledge after a single instructional session.¹⁶ These results suggest that if a similar approach was taken in radiology residency, a similar improvement would occur.

Integrating artificial intelligence (AI) into health care has opened new horizons in various sectors, including medical billing and coding. Traditionally, this domain has been marked by a manual process of going through extensive medical information to assign specific codes, a task susceptible to human error.

Artificial intelligence, mainly through deep learning models, presents a transformative advancement, promising improved accuracy, efficiency, and speed in coding processes. A recently published assessment of a deep learning model applied to ICD-10 coding found a significant enhancement in the coders' performance and efficiency.¹⁷

The employment of AI facilitates a streamlined coding process. Automating intricate tasks not only reduces the risk of errors but also alleviates the intensive workload traditionally shouldered by medical coders. This incorporation leads to an optimized workflow, enhancing productivity and allowing medical coders to focus more on accuracy.

The path to an optimized radiology revenue cycle is both necessary and attainable. With collaborative effort, innovative thinking, and a commitment to education and technological adaptation, the field of radiology can look forward to a future where financial operations are as precise and effective as the diagnostic services they support.

1. Pennic F. Change Healthcare: $262B in healthcare claims initially denied in 2016. Healthcare IT Consultant. June 2017.

3. Williams AN, Johnson RA, Bender AP. Use of coded mortality data to assess area cancer rates: impact of residence reporting and coding errors. Am J Epidemiol. 1990;132(1 Suppl):S178-S182.

6. Tang KL, Lucyk K, Quan H. Coder perspectives on physician-related barriers to producing high-quality administrative data: a qualitative study. CMAJ Open. 2017;5(3):E617-E622.

8. Hyland P, Shevlin M, Brewin CR. The memory and identity theory of ICD-11 complex posttraumatic stress disorder. Psycho Rev. 2023;130(4):1044–1065.

11. Thorwarth WT. Get paid for what you do: Dictation patterns and impact on billing accuracy. J Am Coll Radiol. 2005;2(8):665-669.

14. Society of Interventional Radiology (SIR) Health Policy and Economics Team. Health policy, economics, and coding. Society of Interventional Radiology. Available at: https://www.sirweb.org/practice-resources/policy-economics-coding/ . Updated in October 2023. Accessed January 4, 2024.

15. Owji S, Tassavor M, Han J, Golant A, Svidzinski C, Ungar J. Impact of coding curriculum on dermatology resident billing. Cureus. 2022;14(4):e24148.

16. Benke JR, Lin SY, Ishman SL. Directed educational training improves coding and billing skills for residents. Int J Pediatr Otorhinolaryngol. 2013;77(3):399-401.

17. Chen P-F, Wang S-M, Liao W-C, et al. Automatic ICD-10 coding and training system: Deep Neural Network based on supervised learning. JMIR Med Inform. 2021;9(8):e23230.