With paying for value ascendant, diagnostic coding accuracy grows more crucial

Jennifer SwindleCoding

Value-based payment has been steadily growing in healthcare in the past few years, thanks to programs such as the Hospital Inpatient Value-based Payment Program and the Merit-based Incentive Payment System (MIPS, part of the Quality Payment Program for physicians). More recently, this movement has gained new agency due to risk-based programs in the commercial payer market. For providers, these initiatives, while promoting innovation and safety in clinical care, also put diagnosis coding under the microscope.

Coding at the highest level of accuracy and specificity has always been instrumental in supporting claims of medical necessity and painting a picture of the clinical condition of the patient. Too often, however, providers have bumped along with incomplete diagnosis coding, leaving a bare sketch of the patient’s condition, not truly a finished picture. With value-based payment apparently here to stay – and perhaps to become a much bigger amount of reimbursement at risk – providers that want to stay in business must be exacting in diagnostic coding accuracy.

Outpatient providers have learned this lesson faster, because the regulations are clearer in regard to capturing definitive diagnoses. They already know that documentation with phrases such as “consistent with,” “compatible to,” “possibly,” etc., likely will be kicked back to them with a denied claim.

Providers face many areas of risk in documentation; here are just a few:

  • Every encounter, regardless of setting, needs to have a clearly stated or easily inferred chief complaint. Even on an inpatient facility record, where a provider or possibly multiple providers see the same patient daily, each individual provider who sees that patient must document a chief complaint every day to show that what he or she was doing was medically necessary.
  • When you open a patient’s electronic health record, some documentation is auto filled, so you need to go in and edit those fields for the current visit.
  • Voice recognition/natural language processing systems may misinterpret something, causing documentation to be incorrect. Careful proofreading and editing is required by the provider.Problem lists are often not well maintained and providers hesitate to remove things they did not add; however, if the problem list pulls into a current note and is left unedited and the note is authenticated, that provider is indicating that problem list is true and accurate on that date.

Patients often present, particularly in the office or emergency department setting, with one complaint, but at the end of the visit, there are multiple conditions listed. These may be problems the patient has had historically; however, if there is no evidence they were addressed or impacted the care, a list does not support coding those conditions.

Providers are the only individuals who can diagnose a patient, so elements in the chart that are clinically abnormal cannot be coded until the provider makes the diagnosis. Abnormally low pulse oximetry, abnormal lab values and abnormal echocardiogram tracings may be evident to the provider of a condition, but until that condition is documented, this is just additional data in the chart. Coders or clinical documentation improvement specialists may use these reports to formulate queries for clarification, but they do not support coding.

Examples of some common scenarios:

Patient presents with uncontrolled diabetes. The coder can only code E11.9, diabetes, not otherwise specified. There is no code for uncontrolled diabetes. There are separate and distinct codes for diabetes with hyperglycemia (E11.65) or diabetes with hypoglycemia (E11.64X, last digit depending on with or without coma), but the condition must be captured by the provider. The coder cannot look at the lab results and code anything more than unspecified until it is clarified by the provider. It matters: Both E11.65 and E11.64X are in a higher weighted risk adjustment category, which impacts the patient’s risk adjustment factor, which will impact an individual provider MIPS score, as well as potentially impact payment.

Patient has urosepsis. The coder can only code N39.0 for unspecified urinary tract infection. Is that what is meant, or does the patient have septicemia or sepsis? Without clarification, serious conditions may not be able to be coded, not only impacting the risk adjustment factor, but also failing to reflect the true acuity of the patient being treated.

GI bleed and anemia. The coder is able to code K92.2 for gastrointestinal hemorrhage and D64.9 for unspecified anemia. There is a more specific code, D50.0, for iron deficiency anemia secondary to blood loss; however, this cannot be chosen unless the provider captures the cause and effect relationship of the two conditions, i.e., anemia secondary to GI bleed.

Diagnosis coding is crucial first and foremost to accurately reflect the severity of the patients being treated and served. However, accurate and complete diagnosis coding also can significantly impact the revenue cycle and financial health of the organization. As value-based purchasing grows, so will the importance of coding accuracy.

While AI in healthcare matures, finding the right data is a starting point

Jesse FordRevenue Management

In our fall newsletter, I asked, “Has AI really arrived in healthcare revenue cycle?” I shared that as a company born in a digital age, Salud recognizes the need to collect, store, and analyze clinical and financial data that can help predict outcomes and improve processes that affect cash.

However, I questioned then and continue to question whether the industry does revenue cycle artificial intelligence well – yet. The pathway to meaningful AI and machine learning requires smart design, lots of data and continuous focus, and I am not sure how many are hitting that trifecta. With all of the pressure on revenue cycle today, we need to be taking steps now to ensure we are ready when the tools and processes of AI that are common in other industries make their way into everyday use.

You might start by completing this question: “Wouldn’t it be great if we could use artificial intelligence to …?” How many revenue cycle professionals would answer almost reflectively, “… to get the patient’s bill paid.” Unfortunately, that is not possible today, but the promise of AI does apply to individual steps in the end-to-end cycle that over time will come together so payment starts to “figure itself out” – automatically, accurately and in a timely manner.

AI has near-term potential throughout the revenue cycle, including areas such as patient access, health information management and utilization review. It has applications in value-based reimbursement systems, consumer-driven care models and population health. Efficient revenue management is the common thread through all of this. Salud’s commitment to assist in the health of the communities that our clients call home, as well as our calling and mission align well with our clients’.

While you may have a robust and aggressive plan for applying AI throughout your organization, Salud’s vision is to be the national model for the delivery of revenue cycle services, so we focus on improving efficiency, accuracy and the patient experience. We seek the information we need from electronic health information and from outside the healthcare space and then apply analytics and trending data to bring meaning and quality execution to this vision.

Rich data for analytics in electronic transactions
Consider dissecting and expanding on the data contained in the 837 transaction set, established to meet HIPAA requirements for the electronic submission of healthcare claim information that broadly falls into four categories:

  • Patient demographics
  • Patient conditions/reasons for treatment
  • Services provided
  • Prices for services

Depending on the problem you are trying to solve, the 837 is rich with data for healthcare analytics (and AI). This data also reside in your EMR, but we find most providers struggle to extract and store data as concisely as it can be found in electronic data interchange 837 files.

The 835 transaction set, aka the Health Care Claim Payment and Remittance Advice, is the electronic transmission of healthcare payment/benefit information. It contains some of your data, but adds payer information that may inform advanced analytics; most importantly, payments and denials. Once again, you may be storing some 835 data in your EMR system.

If you have trouble aggregating demographic, service, and payer data and extracting it from your systems, Salud recommends you begin now to store these electronic files, as they are rich with data you likely need for your future work with AI.

Data not captured in your information systems
Salud looks within and outside of the industry to find information that our providers don’t have, but that should be part of our AI framework. We’ve been impressed with providers partnering to share data in health information exchanges.

Of course, the industry still lags way behind banking, social media, pharmaceutical companies, and even pizza chains, all of which already collect and understand our habits, struggles and capability/propensity to pay. Earlier this month it was reported by Atlantic magazine that one political party has “3,000 data points on almost every voter in America, and they use those data points to determine how exactly to pitch their message.” Salud sees parallels in utilizing data points in determining how exactly to “pitch” care and the account resolution process most seamlessly.

AI requires a dedication to collecting and archiving meaningful data. Those who would like to move the needle on the delivery of revenue cycle services need to advance current capabilities in this area. From this rich(er) base of data, what can then be mined and designed are the most efficient workflows and exception processing systems for your operation.

Salud’s digital infrastructure already helps simplify processes, drives results and enhances patient experience. With the proper balance of data and human ingenuity, we look forward to building on our platforms so that sophisticated AI will soon connect data, predict outcomes and prescribe real world, powerful solutions. Until such advanced capabilities become available, Salud is focused on pre-AI, akin to machine learning analytics, while simultaneously acquiring and storing the most meaningful data from which to mine optimal workflow and liquidity.

We are excited to partner with providers looking for the right revenue cycle model for the digital age where many or all of these AI and machine learning concepts may already be in the discussion or planning stage.

Salud connects the dots to help providers use the rev cycle to connect with patients as consumers across the enterprise

Frank MassiRevenue Management

In our fall newsletter we talked about how self-pay can be turned in to a provider advantage through “consumerization.” We also discussed how Salud organizes around the patient-as-consumer via an organization-wide commitment to provide revenue cycle managed services that are “connective, predictive and prescriptive.”

This consumerist outlook was bolstered by the work of two major organizations, the Healthcare Financial Management Association (HFMA) and the Health Information and Management Systems Society (HIMSS). HIMSS has painted consumerism with a broad brush as a bold restatement of industry mission, while HFMA has adopted a more focused, actionable strategy. These approaches align, but we at Salud see a need to go further to ultimately satisfy the requirements and promises that both organizations have made to adopt a patient-centric approach in revenue cycle.

HIMSS’ ‘digital front door’
At HIMSS’ 3rd Annual Patient Experience Forum last year, sponsored by its Southern California chapter, “the patient is the center of everything” was the bold keynote statement that became the theme throughout. As an active member in both HIMSS and HFMA local and national organizations, I attended all three HIMSS patient experience conferences and was able to contrast its take with how HFMA is framing the issue.

HIMSS articulated a cumulative strategy for a “digital front door” for patient self-directed capabilities.

Multiple presentations dissecting the topic proved there’s not just a single patient engagement ecosystem, but an “ecosystem of patient engagement ecosystems.” Those include patient financial self-service, clinical self-service, wellness self-service, convenience self-service and proactive self-service. Application programming interfaces (APIs) and Fast Healthcare Interoperability Resources, or FHIR, arguably facilitate a virtual best-of-breed and potentially a self-funding patient engagement ecosystem. Also embraced were “convenience apps” such as Uber “orders” built into physician order sets, and ultra-realistic WayFinder tools so patients and family can navigate the provider premises smoothly and smartly.
More aspirational, “proactive” wellness tools such as real-time wearable monitors, patient-specific nutrition “pushes” and chronic illness interventional capabilities are available, but similar initiatives over the last decade haven’t shown much ROI.

Monetizing the path to consumerism
HFMA just recently produced a whitepaper and introduced an online tool that makes the aspiration of a patient-centric digital future “actionable.” This 34-page document, entitled “Maturity Model Measurement Tools for Consumerism in Healthcare,” includes tools to benchmark a provider’s or an entire health system’s consumerism Maturity Model Index Score (MMIS).
Where HIMSS described holistic capabilities for an end-to-end, tech-enabled, and patient-centric, self-service future, HFMA has organized its Consumerism Maturity Model into four components that will have great leverage for providers from a self-pay perspective: consumer interaction channels, quality and accuracy, consumer experience and measurement.
Salud’s services delivery model – connective, predictive, prescriptive – is a strategy that ties both to HFMA and to HIMSS, and thus satisfies the tactical as well as the strategic for our clients.

Leveraging the surge in patient pay
Salud’s hypothesis is to deploy the “patient as self-service payer” strategy in such a manner that is sustainable financially but funds in part or in whole the complete patient engagement/patient loyalty strategy for the larger enterprise.

Rev cycle innovators should be at the table in all discussions and to act as the greatest funding source and development lab for further patient engagement advancements.

Salud intersects with the four pillars of HFMA’s consumerism maturity model, exemplified as follows:

  • Consumer interaction channels. Connect into current highly utilized patient channels (e.g., scheduling, results, physician communications). Add capabilities to make patient payment more understandable (estimates), convenient and affordable (self-risk-adjusted prescriptive payment plans).
  • Quality and accuracy. Focus on integrity of patient data from sources of truth, deliver efficient bill generation and produce clean claims. Robust feedback should propel a self-improving cycle.
  • Consumer experience. Incorporate quality ratings utilization, implement consumer feedback methods, and consider guarantees on price estimates provided as well as overall patient satisfaction.
  • Measurement. Utilize HFMA MAP keys, to include insurance verification rate, service authorization rate, cash collected as a percentage of patient service revenue, aged A/R over 90 days, and discharged not submitted to payer. Factor in HCAHPS “would recommend” score. Add Net Promoter Score.

Salud improves the overall digital patient engagement experience our clients are seeking, by starting in self-pay, but with the goal of “funding as we go” for broader engagement strategies.

Salud connects the dots of the tactical urgencies of exploding patient pay balances, to the outer ring of HFMA’s consumerism maturity model, to ultimately achieving the retail experience contemplated by HIMSS’ “patient at the center of everything” superstructure of the future.

E/M coding changes require education and technology updates

Jennifer SwindleCoding, E/M

E/M Coding changes

Evaluation and management (E/M) services occur in the hospital as inpatient or observation visits. They also occur in nursing homes, physicians’ offices, emergency departments and even in the home. While there have been guidelines since 1995 and guidelines updated in 1997, both of which are still used, E/M services still have been vulnerable to fraud and abuse.

In 2021, major changes will be implemented for new and established patient office visits. However, there are some immediate concerns because the changes only apply to new and established office visits.

Has AI really arrived in healthcare revenue cycle?

Jesse FordRevenue Management

It’s safe to say that healthcare has come a long way from its low-tech past. Until just a few years ago, paper was still the foundation for most healthcare processes. We spent tens of billions of dollars, much of it government money, to adopt electronic medical records, for better or, in all too many cases, for worse. Interpersonal communications went from face-to-face to email to instant messaging, seemingly in an instant. I can still remember the awe of holding my first Blackberry and the dread of that first pager.

Today, tech is ubiquitous, with applications in almost every nook and cranny of a healthcare organization. In most cases, there has been no strategy to it all, so we have tech that can’t communicate or is so poorly implemented that it isn’t solving the problems it was meant to solve. To help make that technology work for us, and to conquer expense and reimbursement challenges, we’re trying Lean, Six Sigma, CQI, or even TQM, and some of it is helping, though there are often too many new projects to implement all of them, no matter the rapid improvement approach being used.

Another problem is how to finally take advantage of the massive amount of data being generated by all of this tech. Now the flavor of the month is artificial intelligence. It seems like every vendor marketing to healthcare providers claims its technology uses it, and some health systems are buying off-the-shelf software and/or databases to try some of it on their own. One big problem is what “it” is. Is AI merely advanced data analytics, machine learning, predictive modeling, prescriptive solutions, or all of the above? What can we expect? Will it ease transitions to value-based care? What does it mean to use AI to manage revenue cycle in a digital age?

Salud recently celebrated its eighth anniversary. As a company that was born in a digital age, we recognize the need to collect, store, and analyze clinical and financial data that can help us predict outcomes and improve processes. We’ve embraced efficiency gained through robotic processes. And we comfortably present insights to clients using the knowledge we’ve gleaned from our analytics, from payers and from our staff.

And yet, I am still not comfortable with what I hear about artificial intelligence for revenue cycle. The pathway to meaningful AI and machine learning requires smart design, lots of data, and continuous focus. I’m not sure that our industry is doing this well yet; many technologies have failed to meet their promises in the past.

On the other hand, I am excited about Salud’s foundation, its current adoption of technology, and its vision for the future. Our company recognizes that revenue cycle impacts patient, jobs, and community health. Our technology utilizes data from client, payer, and internal sources to ensure that we properly serve patients, empower our staff with meaningful work, and help us share opportunities for our clients to improve their own processes. We aren’t just dangling our feet in AI; we’re wading out into the stream, but the real value will be when we can swim with it.

How self-pay can be turned into a provider advantage

Frank MassiRevenue Management

Consumer? Consumerism? Consumerization? In the context of healthcare, all three evoke different perspectives (and emotions) regarding the healthcare continuum and the end-to-end patient experience.

From a revenue cycle perspective, for what is known as the “patient financial experience,” the word we use is consumerization. This implies action, and lots of it; an ongoing effort universally acknowledged and urgently needed to close the considerable gap with e-commerce norms.

Patients are likely to remember the billing experience as their last interaction of an episode of care and extrapolate that memory to the entire encounter (often negative). Revenue cycle’s role – and by extension Salud’s – is therefore huge. Salud’s charge is to satisfy the consumer, live up to the expectations of consumerism, and aggressively undertake with our provider partners the magnitude of the ongoing systems- and process-build for the digital patient engagement experience. Taken together, this is process of consumerization.

The urgency can be measured by declining patient satisfaction scores, eroding net promoter scores, the exploding volume of patient balances, a spike in “uncollectibles” (bad debt), and an unsettling rise in patients avoiding care altogether due to the expense.

Where to start? As a revenue cycle services outsourcing company with a culture of doing what’s best for the patient, Salud is committed to helping shape this future with innovative, intelligent solutions, beginning with arguably the most remunerative process, self-pay account follow-up.
A technology-enabled and blended revenue cycle services approach, constructed and implemented properly, consumerization can pay not just for itself, but also fund enterprise-wide patient loyalty and patient engagement initiatives, in part or in whole.

The quickest and least expensive access to new operating capital was and still is in a provider’s own revenue cycle. This means that “self-funding patient loyalty” is achievable, and thus rev cycle’s and Salud’s potentially huge contribution, not only to financial health, but to the overall health of the community.

The process of consumerization
Salud has amassed the expertise and vision critical to what it takes to operate a revenue cycle in the digital age, where “rev cycle meets consumerism.” With mobile. With AI. With psychology-based machine learning. With a relentless pursuit of “narrowing work” and therefore narrowing labor expense to only that which is “meaningful.” Meaningful work enables highly engaged employees. Highly engaged employees enable highly engaged patient consumers.

Salud has organized its innovations around the patient as payer in three major ways: Connective. Predictive. Prescriptive.

  • Connective. How and when revenue cycle digitally “connects” to patients and in what balance versus live contact, interactive live chat and interactive automated chat (natural language processing).
  • Predictive. How revenue cycle proactively and in real-time captures intelligence to, for example, predict denials, to predict best use of “psychology of commitment” analytics.
  • Prescriptive. How revenue cycle can “prescribe” payment plan options custom-fit to actual checking account “behavioral analytics” (versus less reliable FICO scores). Also, “prescribing” contact call windows fed by “authoritatively accurate” data on caller device ownership and precise times of device use.

Affordability chokes off access
Access to healthcare has long been a hot topic, but from the standpoint of helping patients obtain physical access or admission to a health facility. Today, affordability is the true barrier to access due to increasingly higher-cost health plans and exploding out-of-pocket costs.

It’s a harsh fact that 64% of patients avoid care due to high costs. Some of our neighbors, coworkers, and family members are urgently in need of care but can’t afford it. In far too many scenarios people are dying due to their medical expense burden. Healthcare bankruptcy filings more than tripled in 2017 and now represent the No. 1 reason for bankruptcy filings.Reading deeper however, it’s the “access to affordability” that is the issue for a vast majority.

Progressive revenue cycle professionals are aware of the multitude of tools and services to interact with the patient consumer to determine a level and method that is “affordable.” But without a trusted partner, it’s an arduous task for a provider alone to evaluate, integrate, and operate all the tech and services elements into a single workflow.

Salud’s perspective is that revenue cycle management in the digital age can be the “bridge to affordability. Interacting on the patient’s terms, applying advanced psychology-based processes to prompt payment, and deploying algorithms to predict and literally “prescribe” financial clearance tasks and call windows that generate increased promises-to-pay and enormous leaps in right party contact.

Answering the call to action
Revenue cycle operators are literally on the front lines as we face patients at their most vulnerable, from patient access to patient account resolution. Revenue cycle operators are also among the most technically astute and the most empathetic, making them the best people to address the task of consumerization.

Salud is excited to be working with our partner clients to facilitate this journey.

Opioids and coding: An important new program to meet funding needs for treatment

Jennifer SwindleCoding

When you think of America’s opioid crisis, your thoughts probably don’t quickly turn to medical codes. And yet, the fact is that when medical crises arise, code changes are made to accurately capture and support needed services. Opioid addiction, with millions of people affected and horrendous loss of life as well as broken lives, is certainly such a crisis, perhaps the biggest public health challenge of the 21st century. In order to treat it, you need to get paid for it. The SUPPORT (Substance Use-Disorder Prevention that Promotes Opioid Recover and Treatment for Patients and Communities) Act, which was enacted on Oct. 24, 2018, established a benefit category to begin on or after Jan. 1, 2020.

Final SUPPORT Act regulations are due soon from the Centers for Medicare and Medicaid Services, and there are many elements to it from a coding and billing perspective. A proposed rule published in July 2019 expanded the types and places of service for addiction treatment and defines a new Opioid Treatment Programs designation. Some services had previously been allowed in physicians’ offices and acute-care facilities, but the new law allows for other facilities to use evidence-based care, including methadone, for opioid addictions. These proposed changes are a new CMS Part B benefit; the statute defines who would be covered, as well as the scope and frequency of the services, and there will be additional revenues available to support these services.

There are three Healthcare Common Procedure Coding System (HCPCS) procedure codes being created to report services for opioid use disorder. Office-based treatment, which includes the development of a treatment plan, care coordination, individual and/or group therapy and counseling, is based on the initial month and subsequent months and has time requirements. CMS is proposing that the individual psychotherapy, group psychotherapy, and substance use counseling included in these codes could be furnished as Medicare telehealth services using communication technology as clinically appropriate. The codes are as follows:

  • GYYY1 is office-based treatment for opioid use disorder, including development of the treatment plan, care coordination, individual therapy, and group therapy and counseling. This must include at least 70 minutes of therapy in the first calendar month.
  • GYYY2 would then cover the same services in subsequent months and has a time component of at least 60 minutes.
  • GYYY3 is an add-on code for each additional 30 minutes and begins after 120 minutes and can be reported with either of the above services.

A new place of service has been proposed for Opioid Treatment Programs, since they will potentially be a covered location and then additional new G codes have been proposed for these services. The G codes vary, but are based on a weekly bundle which includes all services for the week and codes vary based on the medication being utilized for treatment; there will be a drug component and a non-drug component payment amount. Some examples (not an all-inclusive list) would include:

  • GXXX1: Medication-assisted treatment (methadone); a weekly bundle including dispensing and/or administration, substance use counseling, individual and group therapy, and toxicology testing, if performed (provision of the services by a Medicare-enrolled Opioid Treatment Program).
  • GXXX2: Medication-assisted treatment, buprenorphine (oral); weekly bundle including dispensing and/or administration, substance use counseling, individual and group therapy, and toxicology testing if performed (provision of the services by a Medicare-enrolled Opioid Treatment Program).

There also is a proposed add-on code for additional time that may need to be captured.

While this is a proposed rule at this time and has not been finalized, it is always recommended to monitor closely the changes that may be coming and the impact they may have either on an individual organization or on referrals of patients who can receive help for this debilitating disease in a new setting previously not approved for payment. Certainly the healthcare industry realizes the significance of getting this crisis managed and helping all those who suffer from opioid abuse and dependence. Allowing for payment for additional services may help patients receive the help they need.

Cracking a tough nut in accounts receivable: Balance-due paid claims

Jesse FordRevenue Management

I am speaking about balances due on paid claims. Among our clients, we see as much as 63% of accounts already have payments on them, and those with a balance due ranging from just a few dollars to several thousand dollars. The question is whether you have the savvy and the technology to work the accounts cost-effectively enough to make the effort worthwhile.

Read the full article.