Home Health Billing Fraud Study
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Home Health Billing Fraud Study Sheds Light on Growing Problem

Providers who outsource home health billing see multiple benefits, but one of the most important is access to a tool in navigating home health billing fraud. 

Healthcare billing fraud is a pressing problem, and a recent study out of Dartmouth’s Geisel School of Medicine offers a deeper understanding of how home health billing fraud has spread in recent years [1]. 

Types of Billing Fraud
To best understand the results of the survey, it’s important to first understand the type of home health care billing fraud you might encounter [2]. 

Fraud in Billing for Non-Existent Services
This is one of the most common types of fraud to look out for. In this case, services are billed but never performed. It can involve extra services being added to a legitimate patient bill or a situation that is more extreme, where someone finds a patient’s name and insurance information and uses this to create a fraudulent bill with services that were never performed. 

Medical Necessity
Another common type of fraud involves a party billing for services that weren’t medically necessary. For example, a diagnosis code like hypertension or diabetes that might not justify home health care services. 

Unbundling
In home health billing, there are some services that are often performed together that should also be billed together. In some cases, charging for these services separately can result in higher rates of reimbursement. By breaking them up, a billing party can receive higher payments. 

Durable Medical Equipment
Durable medical equipment (DME) is also an area that’s at risk for billing fraud. This can include examples like orthotics, patient lifts, walkers, glucose meters, traction equipment, pressure mattresses, and crutches. 

Upcoding Billing Issues
This type of fraud is a misrepresentation of services where a provider falsifies a claim to get paid for services that reimburse at a higher rate. 

Home Health Care Billing Fraud Study Results
The Dartmouth study took on the work of examining the network structure of home health agencies (HHAs) to identify a set of characteristics that were shared across the regions where fraud was most common. These characteristics included: 

  • Sharing patients across multiple agencies
  • Higher rates of expenditures across hospital referral regions along with fast increases in rates
  • Significant growth in the number of HHAS
  • Whether or not a region attracted a Department of Justice anti-fraud office

There was also a peer effect between local agency billing, which suggested sharing fraudulent practices between these local agencies. 

Types of Fraudulent Behavior
The study documented the most common types of fraudulent behavior, including:

  • Owners of agencies billing for services that were unnecessary or nonexistent
  • Physician kickbacks
  • Recruiting for patients
  • Staffing groups that got patients referred to an agency
  • Cross-network sharing of patient IDs in HHAs owned by organized crime

Activity Has Been Increasing
The researchers found a notable increase in home health care activity over the period between 2002 and 2009 which corresponded with expenditures doubling from $14.9 billion to $33.7 billion. This increase was mostly concentrated in just a few hospital referral regions (HRRs) which supported their findings that the regions were also areas where the Department of Justice had set up local anti-fraud offices. 

As an example, the average home health care billing per enrollee (Medicare) in Miami, FL and McAllen, TX increased by $2,422 and $2,127 respectively. In other HRRs where the DOJ wasn’t targeting, the average increase was $289. Study Lead Author, James O’Malley explains the difference in expansion rates, “it more or less makes the argument that if the benefits exceed the risks in the eyes of the perpetrators, then it’s more likely that you’ll see this type of fraudulent behavior where people are willing to risk conviction, fines, and imprisonment to make larger profits.”

While the study stands on its own, one additional outcome has been the development of tools that could be useful in the future in identifying more issues of home health billing fraud. The index (BMIX) looks promising in predicting excessive billing behavior from HHAs in the future. This means that it could be valuable for approaches based on machine learning to address billing fraud. The study authors expressed excitement at the idea of the methods being used to create generalized and alternative versions for organizations responsible for policing billing in healthcare. They look forward to the chance that the tools might be used to prosecute more violators, earlier, before fraud escalates, potentially saving the healthcare system and tax payers money. 

If you’re looking for ways to build an approach to home health billing that removes some of the internal and local risk of fraud, outsourcing should be on your short list of considerations. Outsourcing can be a path to end-to-end monitoring of your billing processes, increased efficiency, and easier response to a fast-changing regulatory environment. To learn more about how our services align with your needs, contact us today. 

References
[1] O’Malley, et al., “The diffusion of health care fraud: A bipartite network analysis,” Social Science & Medicine, 2023.
[2] Law Offices Of Robert David Malove, “Types of Healthcare Billing Fraud,” 2023. Available: https://www.robertmalovelaw.com/library/types-of-healthcare-billing-fraud-.cfm.

Hospital Billing Outsource Strategy
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Changed Your Service Lines? It’s Time to Rethink Your Hospital Billing Outsource Strategy

In the wake of COVID, budget crunches, and labor shortages, many hospitals and health systems have been making the smart decision to rethink their service lines. But what many haven’t had the space to consider is the impact on hospital billing, hospital coding, and how their decisions should impact outsourcing strategy. 

If you are a billing or coding leader in a hospital, it’s time to review your approach to outsourcing to determine whether you have an opportunity to make changes that will positively impact your profitability as well as patient and employee experiences. 

Why Service Lines Are Volatile Today
Hospitals have lost billions in recent years, resulting in threats of service line closures and even systemic collapse without support. 

One hospital, Main Line Health, saw its expense per admission jump by 26% – significantly more than their revenue per admission, which was only increasing by 14% per admission. While they had budgeted for a $6 million loss, they actually lost $20 million. Hospitals that have seen issues like these have been closing services like obstetrics, leaving mothers who were ready to deliver facing a three-hour drive to find a hospital [1]. In 2022, hospitals cut services including obstetrics, converted pediatric units to ICU, closed emergency departments, inpatient care at a Children’s hospital, behavioral health services, at least one even temporarily closed an ICU [2].

Why Evaluating Service Lines is Important to Hospital Billing
Service lines are groups or populations of patients with similar traits based on encounter attributes. This includes DRGs, ICD groups, HCPCS codes, inpatient or outpatient status, and MDCs. Planning service lines is a way of evaluating the performance of a hospital or health system, where patients are engaging with multiple departments during a visit. By focusing on service lines, hospital leaders can gain deeper insights into the contribution that each line is making to the organization’s financial health as well as community well-being. 

Consider that, in cases where a hospital is only providing services that are highly profitable, they might not be best serving their community. And if they’re only providing the services that the community prefers, they might not be meeting the long-term health needs or supporting the health of the organization. This type of question has caused hospital leadership to change their approach to service lines in recent years. But the questions can’t stop there. 

When service lines shift, finances and revenue cycle are impacted. Every change to a service line can impact stakeholder groups including patients, providers, and other administrative departments, but especially hospital coding and billing. Hospital billing leaders must also ask whether they’re receiving appropriate and market-comparable reimbursement from all payer types, including employers, managed care, and government. 

How to Offer or Cut New Service Lines
When an organization is considering changing service lines, they have to go through a few considerations [3].

What Do We Need?
Hospitals and health systems are rethinking the revenue potential of many service lines after experiencing constraints due to COVID-19. Some add lines to gain market share, increase patient volume, or even generate new revenue. When this happens, the impact on hospital billing should always be considered.

What Are Our Operational Concerns?
Any shift in service lines will impact your labor force, the physical space different departments use, and the need for medical staff specialties. Beyond this, leadership should also consider the impact on hospital billing and hospital coding. This is because new service lines could mean different coding processes, shifts to relationships with payors, and new approaches to follow up. 

What Will the Financial Impact Be?
Adding or subtracting service lines will have a direct impact on the finances of a facility. It will be crucial to plan ahead for this. This includes understanding your starting point and ensuring you have the right metrics to properly evaluate the financial impact on your organization. These measurements also apply directly to your revenue cycle processes, where you’ll need to ensure you have efficient hospital coding and billing workflows that are ready to adapt to change. 

How Service Line Changes Impact Outsourcing Hospital Billing 
Outsourcing hospital billing in particular can be a smart option for managing this type of volatility. The Healthcare Financial Management Association (HFMA) found that over one out of every five revenue cycle leaders manage their inpatient revenue cycle, but have shifted to outsourcing for ancillary and outpatient services. Meaning that if you are considering service line adjustments in these areas, outsourcing could be a smart decision [4]. 

While 22% of survey respondents who managed RCM internally reported outsourcing some RCM services, 12% of leaders were interested in taking this step in the future. And a notable number want to go even further – 10% said they want to outsource all of their ancillary or outpatient services. The most common services outsourced were:

Survey respondents were most likely to consider outsourcing services for:

The best news is that most organizations that outsourced their RCM services were satisfied with the outcomes, and that leaders who outsourced more than one function were more highly likely to outsource more. 

The main takeaway is that, if you’ve had any service line changes, outsourcing hospital billing should be on the table for your organization. If you’d like to discuss which service lines could be a good starting point for you, or what changes you should consider after dropping service lines, contact us and we can help.

References
[1] D. Muoio, “‘Unsustainable’ losses are forcing hospitals to make ‘heart-wrenching’ cuts and closures, leaders warn,” Fierce Healthcare, 16 September 2022. Available: https://www.fiercehealthcare.com/providers/unsustainable-losses-are-forcing-hospitals-make-heart-wrenching-cuts-and-closures-leaders.
[2] A. Ellison, “13 hospitals cutting services,” Beckers Hospital Review, 14 July 2022. Available: https://www.beckershospitalreview.com/care-coordination/10-hospitals-cutting-services-712.html.
[3] Center for Optimizing Rural Health, “Implementing New Service Lines: Strategies and Tips You Should Know,” 8 December 2020. Available: https://optimizingruralhealth.org/implementing-new-service-lines-strategies-and-tips-you-should-know-2/.
[4] V. Bailey, “22% of Revenue Cycle Leaders Outsource Outpatient RCM Services,” RevCycleIntelligence, 13 July 2022. Available: https://revcycleintelligence.com/news/22-of-revenue-cycle-leaders-outsource-outpatient-rcm-services.

Are You Moving Too Early on AI in Medical Coding Services?
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Are You Moving Too Early on AI in Medical Coding Services?

Artificial Intelligence (AI) solutions have been hyped up in everything from customer service to disease diagnosis – and the healthcare revenue cycle hasn’t been exempt.

You’ve likely heard about how AI is a great fit for the healthcare revenue cycle – revenue cycle is a treasure trove of data and workflows that require manual input. Many healthcare leaders have looked specifically to medical coding solutions, asking whether AI could be beneficial there.

While AI is a tempting solution, anyone considering applying AI to their medical coding services should take a step back and consider the idea that while AI might show amazing promise, it might be a bit too early to consider for the healthcare revenue cycle. This means that making a decision too soon might be difficult to reverse.

Data Quality Is a Challenge
An ancient truth of data applies even more to AI – “garbage in, garbage out”. This lesson has emerged clearly in clinical analytics, where organizations have learned that their results depend on clinicians feeding clean and comprehensive data into their EHRs. They’ve learned that if information was entered incorrectly, it flows to other people using the data, comprising data quality and record integrity [1].

This means that the same issues with inaccuracies that you might have had in the past will persist even under an AI solution. This includes inaccurate information, but also gaps in coding data you might not yet have resolved.

No matter how good your AI models are, you won’t get the results that you’re looking for if your data quality isn’t where it needs to be – no matter what quantity of data you feed into it. Any organization evaluating AI will need to answer some tough questions about their data governance policies and strategies before even considering investing in an AI solution. Skip this step at your own risk.

Trust Should Be a Priority
AI has a particular problem with trust and it’s especially acute in healthcare. Consider some recent commentary on “the black box problem” in AI and machine learning.

“There is much confusion about the black box problem of AI. Many AI algorithms are not explainable, even by the programmers who created them, as the code evolves over several virtual generations and ends up as a complex code whose working is opaque to us humans. We are unable to see the ‘rough work,’ only the final answer. Thus, especially in the critical field of healthcare, there is a big doubt whether we can trust AI.” [2]

It’s worth asking if you can find a solution you trust enough to implement in your revenue cycle.

Security Is an Issue
Healthcare as an industry has led the pack in identity theft for years now, and while many organizations have made significant progress in their cybersecurity efforts, hackers and cyber criminals still find healthcare extremely appealing. For any organization that is still struggling with security risks or that has just found their footing, AI solutions for medical coding could derail present or future progress.

AI runs on your data networks, which means it’s inherently vulnerable to security risks. Consider its differences from traditional software. Vulnerabilities in traditional software generally flow back to issues with design and source code. In AI, this extends to images, text, audio files, and whatever data that was used to train and run models. Your security team will need to be aware of an entirely new level of threat to keep your data safe from malactors [3].

You’ll Still Need People
The way some people discuss AI in the revenue cycle, you’d think it was as simple as flipping a switch and enjoying the benefits of increased efficiency and reduced long-term costs. The truth about any AI solution, though, is that while they’ve come a long way, AI still needs some level of human surveillance. While AI can reduce your need for certain positions, it will also launch you into a new phase of hiring and recruitment.

You will need an AI team to manage your projects. This team will require a range of skills and backgrounds and need to operate at high levels of collaboration for you to get the results you’re looking for. Gartner has estimated that half of IT leaders will struggle with any AI project they have beyond the conceptual level – partly because the data scientists they have are doing too much [4]. This is largely because of a talent shortage in other roles. Consider that AI itself is dealing with a labor, skills, expertise, and knowledge shortage – an issue that stands out as the number one barrier to adoption for many organizations.

Ethical Issues Still Exist
Ethics are paramount at all levels in healthcare, revenue cycle included. Unfortunately, incorporating ethics and morality into AI is still a struggle. This is especially true as the importance of social determinants of health (SDoH) continues to grow for healthcare decision makers. AI still might not be able to make considerations that are specific to certain situations.

Risk of Failure
Perhaps the greatest risk of jumping too early into AI for a medical coding solution is what happens if the solution doesn’t work out.

As things stand now, if your AI solution fails, you’re left with little recourse, either experiencing a severe interruption in your revenue cycle while you rebuild or having to quickly hire and train new human staff or possibly even scrambling to sort through medical coding companies and medical coding solutions to help you get moving again.

While we believe in the potential for AI in medical coding services, we also believe that now might be too early for most organizations to take a step. To learn what medical coding services could be a smarter choice for you as AI finds its footing, start here.

References
[1] J. Bresnick, “What are the barriers to clinical analytics, big data success?,” Health IT Analytics, 30 July 2014. Available: https://healthitanalytics.com/news/what-are-the-barriers-to-clinical-analytics-big-data-success.
[2] J. D. Akkara and A. Kuriakose, “Commentary: Is human supervision needed for artificial intelligence?,” Indian J Ophthalmol, vol. 70, no. 4, p. 1138–1139, 2022.
[3] B. Dickson, “Machine learning security vulnerabilities are a growing threat to the web, report highlights,” PortSwigger Ltd., 30 June 2021. Available: https://portswigger.net/daily-swig/machine-learning-security-vulnerabilities-are-a-growing-threat-to-the-web-report-highlights.
[4] L. Goasduff, “How to Staff Your AI Team,” Gartner, 15 December 2020. Available: https://www.gartner.com/smarterwithgartner/how-to-staff-your-ai-team.

Accounts Receivable Management Tips for Healthcare: It’s Time to Standardize Your Metrics for Denial Management
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Accounts Receivable Management Tips for Healthcare: It’s Time to Standardize Your Metrics for Denial Management

Claim denial rates are still a major concern for accounts receivable management in 2022. For example, denial rates for marketplace payers have reached rates as high as 80% according to the Kaiser Family Foundation [1]. But this is only the beginning. COVID has put upward pressure on denial rates for a while now. All of this means that revenue cycle leaders should be taking a fresh look at their denial management practices, not only considering accounts receivable management services but also seeing this as an opportunity to investigate new and more effective approaches to denial management.

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CMS Updates DMEPOS Requirements

The Centers for Medicare & Medicaid Services (CMS) has added 31 items and deleted 5 items on the Durable Medical Equipment, Prosthetics, Orthotics, & Supplies (DMEPOS) Master List. Additionally, effective April 13, 2022, CMS is selecting items beyond Power Mobility Devices (PMDs) that require a face-to-face encounter and written order prior to delivery as a condition of payment.

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