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The coded inpatient medical record drives more than reimbursement. It shapes Case Mix Index, severity of illness and risk of mortality scores, mortality comparisons, public reporting, and how service lines are viewed and evaluated. Once that record is finalized, it becomes part of the organization’s permanent reporting history. 

Most health systems treat the record as final once coding is complete. But complex inpatient cases contain clinical signals that are subtle, evolving, and spread across multiple encounters and providers. Two qualified professionals reviewing the same chart can reasonably differ in how they assess severity or interpret clinical relationships. Therefore, organizations need a structured way to catch what that variability leaves behind. 

The Cost of an Incomplete Medical Record 

When the coded record doesn’t fully capture the clinical picture, the effects don’t announce themselves. There’s no denial letter, no flagged claim. Instead, the impact shows up quietly across the organization: 

  • CMI has the potential to understate the true complexity of your patient population 
  • Severity of illness and risk of mortality scores fall below what the clinical picture supports 
  • Mortality comparisons and performance benchmarks are skewed 
  • Service lines appear less acute than they actually are 
  • Downgrades and denials become harder to defend — not because the care was wrong, but because the documentation didn’t clearly support the coded result 

Because these rarely trigger a formal alert, many organizations don’t realize how much value is eroding until the pattern is well established. 

AI Doesn’t Solve The Issue On its Own  

As documentation volumes grow, manually reviewing every inpatient case at multiple levels isn’t sustainable. AI has stepped in to help, and for good reason. A 2024 HIMSS/Medscape survey found that AI is most commonly used in healthcare for transcription and patient-related recordkeeping, including clinical documentation and coding functions.  

AI systems can process large volumes of structured and unstructured data quickly. They can surface documentation patterns, flag inconsistencies, and identify the cases most likely to benefit from a closer look. That targeting helps teams focus their time where the impact of interpretation is greatest. But there are limits.  

AI can identify signals. It cannot determine how those signals should ultimately be represented in the coded medical record. 

Decisions about sequencing, clinical relationships, and severity determination still require trained clinical-coding expertise. Those decisions carry compliance implications and affect long-term reporting.  

When organizations rely on automation without a governance layer to guide interpretation, they risk embedding ambiguity at greater speed and scale. 

What a Structured Second Review Looks Like  

A structured second-level review isn’t a vote of no confidence in CDI or coding teams. It’s a checkpoint, and it becomes more important as documentation standards and regulatory expectations evolve.  

When done well, it operates within a clearly defined governance framework: 

The result is a more defensible record, stronger alignment between clinical documentation and coded outcomes, and less downstream rework.  

Organizations protect revenue, but they also protect the integrity of the data they report on, benchmark against, and are publicly evaluated by.  

The Human Center of an AI-Enabled Workflow 

AI will continue to reshape documentation and coding workflows. Does it diminish the role of HIM and CDI professionals? Nope. Instead, as routine case identification moves toward technology, the value of oversight, governance, and interpretive expertise grows. 

HIM and CDI professionals serve as stewards of clinical data integrity, ensuring the medical record reflects the full complexity of the care delivered.  

At Accuity, we build this kind of structured review into how we work with health systems — combining AI-driven case identification with expert clinical-coding interpretation to ensure the right cases get the right level of attention before the medical record is finalized.  

For more on how CDI review strategy shapes documentation accuracy and reimbursement, read my related post: What’s Your CDI Review Strategy?

There have been many attempts to redefine sepsis and septic shock to create the most consistent diagnoses, which would help physicians to diagnose early and code for the disease appropriately. It is difficult to define and diagnose, as the symptoms that can trigger a diagnostic test are similar to many other infections. The challenging nature of the condition highlights the need for a consistent definition accepted by all physicians. In its simplest form of the CDC’s definition, “sepsis is the body’s extreme response to an infection. It is a life-threatening medical emergency.”

Sepsis has placed an immense toll on the people it affects and the healthcare system as a whole. Sepsis was the cause of hundreds of millions of hospitalizations in the United States between 1979 through 2000, with an estimated cost of $17 billion annually during that time, and “killing 20 to 50 percent of severely affected patients.” According to the CDC, in a typical year, 1.7 million adults in America develop sepsis, with nearly 350,000 dying of the disease. Sepsis makes up one in three patient deaths in a hospital. Sepsis makes up 1 in 3 patient deaths in a hospital.

Sepsis makes up 1 in 3 patient deaths in a hospital.

Despite the prevalence of sepsis in America, fewer than half of Americans have heard of this disease, according to the CDC. The need for more awareness is why, in 2011, the Sepsis Alliance designated September as Sepsis Awareness Month. The month is not to just bring attention to the adults suffering from this disease, but also the children who develop it. After a change in the definition in 2016, a separate task force was set up to establish guidelines for sepsis in pediatric patients. This was the plan set forth by the Society of Critical Care Medicine (SCCM) and the European Society of Intensive Care Medicine (ESICM), because as SCCM notes:

“Population-based studies of the prevalence of pediatric sepsis estimate 72-89 cases per 100,000 pediatric population in the United States, with over 50,000-75,000 hospitalizations for pediatric sepsis and an associated cost near $5 billion annually… Over 4% of all hospitalized patients younger than 18 years and 8% of pediatric intensive care unit (PICU) patients in the United States have sepsis.”

The goal of the guidelines set up by the SCCM was to allow for earlier detection of sepsis in children, many of whom are very vulnerable as their immune systems have not fully developed. The SCCM released a new set of guidelines and best practices for diagnosing and treating sepsis in children in February 2020.

The guidelines provided by the SCCM focused on creating better outcomes for children through early detection and protocols to focus treatment and recognition. Pediatricians must understand these guidelines and the diagnostic tools necessary to diagnose the problem earlier, as there can be an improvement in mortality overall.

Consistent protocols also have the added benefit, when pediatricians are familiar with coding and clinical documentation improvement (CDI) with sepsis, of diagnosing sepsis at an earlier stage in its development and creating a better patient outcome through this knowledge. Then, with the added help of an experienced coding and clinical documentation integrity partner, like Accuity, the coding plan for sepsis can be standardized within an organization.

Protecting the integrity of your care with AI that thinks clinically

Accuity’s AI-driven clinical model analyzes every record to identify gaps, signals, and discrepancies between the care delivered and the final coded record. 

By aligning both documentation and coding with the reality of care, Accuity improves clinical quality metrics and financial accuracy — ensuring the final coded record reflects the true clinical reality.

This standardization will help keep healthcare professionals aligned with the diagnosis, treatment, and coding of sepsis to focus on healing the vulnerable children in the world.

In healthcare’s increasingly value-based landscape — where provider income is tied to outcomes, hospital prices are accessible and transparent, and consumers play a larger role in managing their care — it is essential that health systems correctly record and bill for patient services to maintain revenue cycle integrity.

Mistakes or variations in this data can lead to compliance violations, rejected claims, and over- or underpaying for services. It also affects the patient experience and perceived quality of care, impacting consumer satisfaction and health systems’ bottom line.

Accurate code capture — the process doctors follow when recording information on patient encounters — safeguards organizations from these hiccups. Hospitals can support code capture through coding compliance, which ensures health systems are:

Building a Culture of Compliance 

Promoting accurate code capture also helps create a culture of compliance within healthcare organizations by increasing productivity, strengthening employee engagement, and boosting staff morale. Combined, this contributes to the success and health of your organization, giving you a competitive edge as healthcare becomes increasingly fragmented and consumer-centric.

Improving Code Compliance in Your Health System 

Code compliance is the bedrock of complete and correct coding capture. However, many organizations struggle to maintain full compliance due to the sheer workload providers take on, ineffective processes for collecting and interpreting data, and ever-changing laws and regulations. Still, your health system can put strategies in place to improve coding compliance and code capture.

AI and Coding Compliance

With healthcare transitioning to a value-based model, accurate code capture is essential in maintaining revenue integrity, preventing government fines, and maximizing ROI in hospital systems. Coding compliance ensures that code capture is accurate, complete, and throughout every level of a healthcare system. Hospitals can set their providers up for successful code capturing and compliance by putting the right teams and technology in place.

Accuity’s AI-driven clinical model reviews every chart prior to billing, identifying clinical and coding opportunities that might otherwise be missed. Clinical Integrity Teams then evaluate those insights to ensure each record is accurate, compliant, and fully reflects the care provided.

By combining clinical expertise with intelligent automation, Accuity enables health systems to secure the revenue they’ve earned today while strengthening the infrastructure for lasting accuracy.

Hospitals that partner with Accuity shift from uncertainty to confidence — and from missed revenue to measurable financial performance. Instead of leaving earned dollars on the table, they can focus on what matters most: delivering exceptional care that is clearly documented, accurately valued, and appropriately reimbursed.

Learn more about Accuity’s solution and how it strengthens your hospital’s overall coding and documentation compliance and integrity.

In 2011, the Sepsis Alliance designated September as Sepsis Awareness Month to elevate public understanding of this life-threatening condition.

Three years later, the Centers for Disease Control and Prevention (CDC) formally recognized the month, highlighting the urgent need for awareness: “Despite the fact that sepsis affects more than a million Americans each year and kills up to half of them, a new survey published by Sepsis Alliance found that fewer than half of all Americans have ever even heard of the term ‘sepsis.’”

The contrast is stark: sepsis impacts over a million Americans annually, yet public awareness remains alarmingly low.

Sepsis is a serious condition that happens when the body overreacts to an infection. Instead of just fighting the infection, the immune system sets off a chain reaction throughout the body. This can lead to tissue damage, organ failure, and even death if it isn’t treated quickly.

Without prompt treatment in the first few hours, sepsis can quickly progress to a point where recovery becomes unlikely, and the damage may be irreversible.

The CDC’s concern about low public awareness becomes even more alarming when you look at the numbers:

Despite how common and deadly it is, many people still don’t recognize the signs—or even know what sepsis is.

The overwhelming prevalence of sepsis led to Accuity’s commitment to helping our hospitals and their communities raise sepsis education and awareness. The first step is to look at the difficulties hospitals face in the fight against sepsis. One of the biggest challenges with sepsis is that it’s not always easy to define. The signs can be subtle or overlap with other conditions, which makes it harder for clinicians to recognize and diagnose quickly.

In 2016, an international task force came together to create a clearer, more consistent definition of sepsis. They defined it as “life-threatening organ dysfunction caused by a dysregulated response to infection.”

In simple terms, sepsis happens when the body’s response to an infection spirals out of control and begins damaging its own organs.

The group also clarified the difference between sepsis and septic shock. Septic shock is a more severe form of sepsis, marked by serious problems with circulation, cells, and metabolism that dramatically increase the risk of death.

In other words, while sepsis is already life-threatening, septic shock represents an even more critical stage — with a significantly higher likelihood of mortality.

But this creates a two-fold problem. First, not all physicians adopt new definitions at the same pace. Some may begin using the updated criteria right away, while others continue diagnosing sepsis based on older standards. That inconsistency can lead to confusion in how cases are identified and documented.

Second, when definitions aren’t applied consistently, coding challenges follow. If sepsis isn’t clearly recognized and documented early — or isn’t diagnosed until a patient becomes critically ill — it can create gaps in coding accuracy, severity capture, and overall clinical clarity.

These two problems can cause inconsistencies in billing. To address this issue, clinicians must look at each case holistically, ensuring the clinical documentation relates to the final bill after DRG reconciliation.

Health Information Associates explains: “This [discrepancy] can lead to case denials unless the physician documentation fully describes the severity of the patient’s condition and supports the clinical diagnosis of sepsis. Coders will have to take this into consideration when trying to decide if a query is appropriate. If the documentation is not adequate, a query would not be recommended.”

AI that Supports Your Expertise 

Accuity’s AI-driven clinical model reviews every inpatient chart before billing, surfacing the clinical and coding opportunities that are often lost in translation between medicine and coding.

Our Clinical Integrity Team determines and applies the most accurate clinical-coding updates directly in your system — with full visibility and no added workload for your CDI or coding teams. 

Complete & compliant documentation plus accurate coding mean better outcomes. 

With the help of an experienced partner like Accuity, the coding and clinical documentation plan for sepsis can be standardized within an organization to alleviate the issues facing hospitals. This way, with all healthcare professionals on the same page, the focus of sepsis can be early diagnosis and treatment.

Coronary artery bypass — also known as CABG — is the most common form of open-heart surgery in the United States. Each year, approximately 500,000 of these potentially life-saving procedures are done in hospitals around the country.

However, a patient who is a candidate for this procedure may present with comorbidities — such as diabetes, chronic anemia, or kidney disease — that increase their risk. They may develop an acute kidney injury or other complications that can impact the postoperative course and extend their hospital stay beyond what was initially expected.

How are you handling these and other complex clinical scenarios in terms of clinical documentation integrity (CDI)?

Equally important: Are you being reimbursed for the care your clinical teams provide? And do your hospital’s quality scores accurately reflect that work? 

It all starts with the codes. 

I like to think of it like this: The DRG is the title of the story and the codes make up the sentences; they are the nouns, the adjectives, and the action verbs describing how sick a patient is. 

Without an accurate, descriptive patient story, you’re not going to have the right coding–and that means you’re not going to capture the proper data or get paid the appropriate reimbursement. 

Let’s think through two ways that concurrent review helps capture the complexity of your patients while also supporting teamwork. 

  1. Concurrent review captures clarification while the patient is still in the hospital–and the care team is still engaged in their care. This means your CDI team can use a variety of nudges and alerts to help the physician while they’re documenting. The end result: The physician captures all the necessary information for coding. Also, CDI queries are often less disruptive to the physician’s workflow because there’s no struggle to remember a patient who has already been discharged. 
  2. Concurrent review supports relationship-building between physicians and CDI teams. CDI specialists who conduct concurrent reviews typically work at the same facility with physicians and share the same goals of wanting to deliver high-quality patient care. Also, the key stakeholders–from the CEO to the CFO to the VP of revenue cycle management to the VP of quality–share the same understanding of the role of clinical documentation integrity. 

This is important in terms of buy-in from physicians and their acceptance of CDI queries. It’s also important because key stakeholders are aware of and support the concurrent review program. 

The reality: Without an accurate, descriptive patient story, you’re not going to have the right coding, and that means you’re not going to capture the proper data or get paid the appropriate reimbursement. 

This is where AI comes in.

AI technology can help you work smarter and with more confidence — knowing your documentation is accurate and that your role truly matters to both clinical care and the bottom line. 

Using a hybrid approach that combines AI precision with multidisciplinary clinical–coding expertise to interpret the gray areas between documentation and coding — can reveal opportunities that would otherwise be lost. 

Here’s a breakdown of how AI technology can improve accuracy in documentation and coding: 

So, what about the patient who had the CABG procedure? Each patient is different. A patient with a preexisting condition, such as chronic kidney disease, is at a greater risk and may stay in the hospital longer than expected. During the postoperative period they may develop conditions that need to be addressed, and these need to be included in the story — as do the codes. 

Capturing the severity of the patient’s chronic kidney disease is a must; it translates into an accurate risk adjustment for that patient. It’s just as important to identify the underlying etiology causing altered mental status — such as metabolic encephalopathy — as it is to document the condition itself. That information must be captured in the patient chart before it’s sent to billing. 

You must have the answer to this single question: Why was the patient in your hospital longer than expected?  

Physician-led AI technology can interpret these types of findings. It translates clinical reality into precise, defensible codes that reflect the full story of care and strengthen accuracy across all reported metrics. And clinical integrity means better financial performance.  

Missed opportunities 

The fact is, it’s hard to get staff. Generally, people don’t want to work weekends. Add to that, weekends are usually busy. That means all those cases sit in the CDI queue on Monday. Here’s what’s going to happen: Even with the best of intentions, something will be missed.

I offer an example from my own experience from concurrent CDI work: When a patient was admitted on a Thursday, we reviewed their chart on a Friday. But what happened if the patient was discharged on a Sunday?

The chart would probably drop out of the queue on Monday, which is often the day with the highest volume of charts to review. In general, approximately 20% of charts are lost to final review. That translates to a lot of dollars.

Then there’s your hospital’s reputation. If you don’t code patients’ charts appropriately, it’s going to look like it took you longer to take care of patients than the other hospital down the road. That’s a tough pill to swallow if the only difference is that the other hospital accurately captured the complexity of its patients, and you didn’t.

Tell the real story 

AI-driven clinical models review every inpatient chart before billing, surfacing the clinical and coding opportunities that are often lost in translation between medicine and coding. The result is more certainty, fewer unnecessary queries, and a final coded record that accurately reflects actual care.

When you combine clinical integrity with smart automation, AI helps health systems capture the revenue they’ve already earned — and sets them up for greater accuracy and innovation down the road.

AI allows CDI teams to fully understand the patient story and close the gap between the clinical picture and the codes used to represent it. It can be a welcome partner to support — not replace — your expertise.

Clinical documentation is a necessary process for every hospital’s bottom line. It’s the foundation for payer reimbursement and drives the best patient care outcomes throughout the episode of care. Mistakes in clinical documentation are a real problem for hospitals and have led to millions of dollars in missed revenue.

Even when the documentation is complete, the accuracy and complexity of actual care can get lost. Small disconnects can hide clinical nuance, which means payers can quietly keep the difference. These hidden losses cost the average health system $2–$6 million per 10,000 inpatient discharges.

The source of clinical documentation starts with the physician. The purpose of this work is to document the true clinical picture of care and can put a health system at risk if done improperly.

In a 2013 paper, Adele L. Towers, MD, MPH, said, “Physicians are not taught how to complete the documentation to accurately assign codes, and physician billing does not require a high degree of specificity…however, the lack of specificity on a hospital record can affect payment. The key is to engage physicians to correlate how clinical documentation provides an opportunity to demonstrate the quality of care that was provided.”

While this is an important aspect for any hospital, for the physician, the most common response to why a physician may feel the effects of burnout is that they are buried in administrative processes and tasks. Physician burnout is a work-related syndrome involving emotional exhaustion, depersonalization and a sense of reduced personal accomplishment. This problem represents a public health crisis with negative impacts on individual physicians, patients and healthcare organizations.

According to the AMA, “Physician burnout is costing the U.S. about $4.6 billion annually when you conservatively estimate the costs related to physician turnover and reduced clinical hours.”

However, in the age of AI technology, physician burnout and its related costs can be significantly reduced. Clinically driven AI engines redefine documentation by combining expert judgement with machine precision to deliver a more accurate picture of care.

Physician-led AI has the ability to analyze every chart to surface clinical signals, codable clues, and missed opportunities where clinical reality is most often lost in translation between medicine and coding. Put simply, these AI engines reveal where clinical reality isn’t fully reflected in the coded record.

AI finds the signals, interprets the complexity, and revenue cycle teams sustain the results.

AI technology does not ask physicians to document more — on the contrary, it helps reduce administrative burden and empowers physicians. Through transparent updates and trend-based education, AI reduces unnecessary queries over time and makes remaining queries more clinically meaningful. The result is a final coded record that accurately reflects the actual care delivered.

The strength of a physician’s clinical documentation not only helps ensure that a hospital will be reimbursed for the accurate clinical scenario, but it also benefits the health of the patient beyond the encounter in which it was made. Physician education in AI clinical documentation is critical to realizing better health outcomes and hospital performance.

Learn more about Amplifi, Accuity’s customized, physician-led documentation program.

Leveraging clinical perspectives to power the middle revenue cycle—insights that drive impact.

In today’s fast-moving digital healthcare landscape, staying ahead of clinical updates, coding changes, and revenue cycle trends is more critical—and more accessible—than ever. Chart Checkup, Accuity’s new podcast, brings you fresh perspectives and expert insights that make a difference where it counts: the middle revenue cycle.

Join us as we explore the latest developments with physicians, CDI professionals, and industry leaders—including Accuity’s own clinical and revenue integrity experts. Each episode dives into timely topics designed to inform and empower healthcare professionals.

Topics include:

Whether you’re a CDI specialist, coder, physician advisor, clinician or healthcare executive, Chart Checkup will keep you informed, inspired, and ahead of the curve.

Listen on your favorite podcast platform or at https://accuitychartcheckup.buzzsprout.com/

Encephalopathy is not a single disease but a disorder of cellular metabolism. Whether it is a lack of oxygen, a chemical imbalance, a metabolic dysfunction, dysregulation, or a toxic environment, the brain cells cannot function, leading to neurological symptoms.

Although most cases are temporary, the capture of encephalopathy is critical for documentation accuracy and to capture the complexity of the patient’s encounter. To accurately translate cases involving this diagnosis into coding and avoid cumbersome denials, coders and CDI specialists must thoroughly understand what to look for in clinical documentation. Physicians can help by using precise and codable terminology in their documentation.

Unraveling a diagnosis such as encephalopathy can be a daunting task due to the numerous types and the many twists and turns it can take clinically. I’ve outlined five core concepts to help anyone navigate the complexity of an encephalopathy case.

5 types of encephalopathy

  1. Metabolic Encephalopathy is an acute condition arising from a metabolic disturbance within the body that alters mental status.
  2. Toxic Encephalopathy can appear as a result of a reaction from a prescribed medication, illicit drugs, over-the-counter drugs, or a toxin such as vapors or toxic solutions and is also considered an acute condition.
  3. Hepatic Encephalopathy arises from a form of liver dysfunction such as cirrhosis or hepatitis. It is usually accompanied by an elevated ammonia level, which is often responsible for the acute alteration of mental status.The other scenario that can occur is when a patient has a progression of their underlying liver disease, resulting in an acute alteration of mental status that is remedied by increasing specific medication regimens.
  4. Hypertensive Encephalopathy occurs as a result of an acute hypertensive episode and can serve as an end-organ dysfunction in a hypertensive emergency or crisis.
  5. Static Encephalopathy is a chronic permanent state of a patient suffering from chronic epilepsy. This is not to be confused with transient (acute) alteration in mental status (AMS) during the post-ictal state that follows seizure activity, as this is considered integral to the seizure.

Acute forms of encephalopathy occur more frequently than chronic. There are numerous terms used in medical documentation for an encephalopathic process. Still, one should also consider coding rules, clinical symptoms, and regulatory enforcement of clinical validation, which comes from the False Claims Act, meaning that there must be sufficient clinical indicators to support billing for any encephalopathy.

5 key concepts for navigating encephalopathy documentation

Encephalopathy is diffuse by nature.

Per the National Institute of Neurological Disorders and Stroke, Encephalopathy is considered a ‘diffuse’ condition, indicating that the problem occurs as a widespread pathology within the brain that can’t be pinpointed.

Imaging results are expected to be negative.

Because encephalopathy is defined as a diffuse condition, an abnormality should not be identified on imaging via CT scan or MRI.

One exception we must consider is the AHA Coding Clinic fourth quarter, 2018, page 16, which states that encephalopathy can be due to a cerebrovascular accident (CVA). CVAs are identifiable on imaging except for embolic showers, which require more time to build up the density needed to be identified on a head CT scan or MRI. Although this coding clinic’s direction appears to be the opposite of the medical definition, we cannot ignore the AHA Coding Clinic.

Many interpret it as: If the symptoms are due to direct damage (i.e. dysarthria due to fronto/temporal stroke) encephalopathy is not appropriate. However, global diffuse altered mental status due to the general dysfunction of the steady state of the brain, which dissipates after time and treatment, may be captured as encephalopathy.

Identifying the cause is necessary.

Encephalopathy is always due to an underlying etiology, so the next step after imaging is to follow working differentials to identify the underlying cause.

The underlying etiology must improve with treatment.

Once the underlying etiology is identified, the next step is to determine if the treatment for the underlying etiology improves the encephalopathic process that resulted in an altered mental state.

If the patient’s alteration in mental status (AMS) does not improve once treatment for the underlying condition is implemented, there are two possibilities. Either the underlying etiology is incorrect, and the treating providers must return to the drawing board and work up the clinical differentials again, or the patient doesn’t have encephalopathy, and something else is happening.

The patient must return to mental status baseline.

The last core concept is that if the patient’s mental status has improved once treatment for the underlying cause was treated, the patient should return to their normal mental status baseline.

When a patient with dementia is admitted for AMS, a dementia baseline should be documented to allow for a CDI specialist to measure the patient’s return to baseline. This core concept is the clinical validating piece that supports the diagnosis of whichever type of acute encephalopathy is being addressed.

Conundrum cases

I can’t speak about encephalopathy without addressing a few twists and turns that make this diagnosis challenging to capture accurately.

One particularly challenging scenario is when a patient with dementia is admitted with an alteration in mental status. Often, the patient resides in a nursing home and wakes up altered, making it necessary to transport the patient to the emergency room, where a UTI is identified. For this class of patients, the only way to measure the return to baseline is to have a documented mental status baseline for dementia.

Another problematic scenario is when two different forms of encephalopathy are superimposed on each other. This gets tricky as one would need to identify an underlying etiology for each one to validate the diagnoses clinically.

Conclusion and additional resources

Regardless of the scenario, if the five core concepts outlined above are considered, along with referring to applicable AHA coding clinics and coding conventions, processing these cases will be more straightforward. Accuity’s clinical capture experts have created this tip sheet as an additional support tool.

The healthcare industry faces an invisible yet significant challenge: the “silent payer discount.”

The silent payer discount is a hidden revenue loss, where hospitals and health systems miss out on millions. The average health system’s silent payer discount can equal $2-$5 million for every 10,000 inpatient discharges.

Revenue “leaks” contributing to the silent payer discount are hard to identify, quantify and resolve. Even the best-performing revenue cycle teams are losing out on millions in potential revenue.

What is the Silent Payer Discount?

To provide an official definition, the silent payer discount is earned revenue, typically between 3% and  5% of the hospital’s annual net revenue, that is lost due to the complexity of accurately documenting and coding complex inpatient cases.

At a mid-size health system, this can mean a staggering $22 million to $38 million in lost revenue annually. Even hospitals with strong Clinical Documentation Improvement (CDI) programs aren’t immune to these losses, leaving health systems struggling to be fully reimbursed for the critical care they provide.

Why does this happen?

Unfortunately, the silent payer discount is a natural consequence of the status quo. It’s caused by unavoidable challenges in today’s healthcare system that are largely out of providers’ control. The three main causes of the silent payer discount are clinical documentation issues, payer challenges, and a massive increase in patient data.

Clinical documentation

Today’s insurance system is designed to reimburse providers based on documented care rather than actual care provided. One of the primary culprits of the silent payer discount is the disconnect between physician documentation and the coding process.

Clinical documentation and coding are two different “languages.” Physicians use free-text documentation to describe the complexity of cases, but when this information is translated into codes for billing, critical details can get lost.

Physicians are focused on describing a patient’s condition, not necessarily on how their documentation impacts coding and reimbursement. In contrast, coders work within a strict framework that doesn’t always align with the nuances of clinical care.

Take, for example, a physician’s note about a “frozen mediastinum,” a situation where scar tissue makes surgery more difficult. This complexity doesn’t translate neatly into a code, and coders don’t always have the luxury of talking to the physician directly for clarity. Instead, it might be ultimately coded as simple pneumonia, missing the intricacies of the case. The result? The hospital misses out on the full reimbursement it’s entitled to.

Physicians often believe their clinical judgment should be enough to justify a diagnosis, but that’s not how the system works. Coding and documentation need to match perfectly to secure reimbursement. Queries from CDI teams can help bridge the gap, but they are often seen by physicians as challenges to their expertise rather than a tool to ensure proper coding.

Payer created challenges

The complexity of coding patient care is largely driven by payers, who benefit from the system’s intricacies. Payers control the rules for how patient care is coded and reimbursed, leading to an uneven playing field. The payers and auditors make the rules and can change them anytime, as well as audit providers for compliance. These rules also do not always take the physician’s clinical judgment into account, even though physicians are the ones experiencing face-to-face encounters with patients.

A 2021 survey showed a 20% increase in claim denial rates over the prior five years. Many of these are clinical validation denials—where a payer rejects a diagnosis based on its own clinical criteria. When a payer’s definition of a diagnosis, like sepsis or respiratory failure, doesn’t align with a physician’s clinical judgment, hospitals lose potential reimbursement.

To gain back this revenue, hospitals must dedicate resources to managing denials and navigating payer guidelines that often differ by payer and state. Documentation requirements for audits, appeals, and claims are also constantly increasing. Hospitals must invest in internal training for CDI teams and physicians to enhance documentation for complex patient cases.

This reimbursement structure causes excess stress for the healthcare systems balancing their work between patient care and avoiding the silent payer discount. It puts a lot of responsibility on the provider’s shoulders.

Increase in patient data

High-complexity cases with longer stays and more expensive treatments are on the rise. These cases generate massive amounts of both structured and unstructured data in a patient’s medical file.

Structured data is quantifiable, like a patient’s vital signs, lab results, and basic personal information like an address or zip code. This data can be easily automated and formatted into a standardized database.

Unstructured data, however, is the way the care teams communicate within a hospital. It involves free-written notes from multiple people based on their assessments of the patient’s situation. Think of how a radiologist might describe an image or how a physician will describe the intensity of a patient’s illness.

While structured data is easily quantified and coded, the majority of medical records—about 80%—are unstructured, making them harder to translate into billable codes. This increase in data requires more collaboration between CDI teams, coders, and physicians.

However, not all teams have access to a physician’s input in the coding process to bridge the gap. Without effective communication, critical information can slip through the cracks, leading to further revenue loss.

Solving the silent payer discount

These factors work together to create a silent payer discount, which benefits payers and costs providers, leaving a major impact on a hospital’s bottom line. The margins for hospitals have already been continuously decreasing due to the high cost of healthcare. One of the only ways to combat this is to avoid revenue leakage at all costs. If hospitals and health systems stay idle, there’s no chance of ending the silent payer discount.

Long-term success requires systematic efforts to bridge the gap between physicians and CDI teams. Until providers can plug the leaks mid-revenue cycle, they risk losing millions each year in lost reimbursement.

To learn more about how the silent payer discount may be affecting your health system and how you can stop revenue from slipping through the cracks, contact the Accuity team. Let’s talk.

In clinical documentation, cases often arise that are commonly difficult to accurately diagnosis and document. Coagulation happens to be a condition that is a documentation challenge for physicians and CDI teams alike. 

Accuity’s education team, Dr. Lynn Miller and Kelly Burns, CCS, were featured on the ACDIS Podcast to examine how hospitals can better identify coagulation pathways, which can manifest in multiple forms, from traumatic DIC to thrombophilia in weight loss surgery to new board coagulopathy. 

“Coagulopathy is incorrectly assumed to be a problem with clotting and increased risk of bleeding usually due to impaired clot formation,” said Dr. Miller. “But really it’s any derangement of hemostasis, which is the true definition of coagulopathy.” 

Listen to the ACDIS Podcast now.

Listen to the ACDIS Podcast to hear Dr. Miller and Kelly offer tips to help your CDI teams comb through charts for coagulopathy clinical indicators. They also dive into the importance of examining the big picture of a patient’s history and social determinants of health so that CDI teams have strong clinical background knowledge to capture this difficult diagnosis. They also offer tips to writing effective queries surrounding a coagulation diagnosis. 

Dr. Lynn Miller, a Board-Certified Adult and Pediatric Neurosurgeon, left a full-time surgical practice to join Accuity and is now leads Accuity’s education team as Director of Education. Dr. Miller earned her undergraduate and graduate degrees from Michigan State University and has Fellowship status in the American College of Osteopathic Surgeons. She also holds Board Certification in Integrative Medicine and continues to work on her Fellow status within Wilderness Medicine. This creates a nice blend of professional growth with exciting travel opportunities and family adventure, both additional favorite pastimes.

Pertinent to her work at Accuity, Dr. Miller has developed and implemented educational events and programs within academic arenas, medical facilities, and medical device corporations prior to joining Accuity’s education team. She also has extensive knowledge regarding the revenue cycle particularly from a surgical and implant perspective.