Retrieving Billions in Overpayments by CMS

Amid swirling accusations that Medicare Advantage Organizations (MAOs) are overbilling the U.S. government and calls for better oversight, the Centers for Medicare & Medicaid Services announced in early February that it would investigate overbilling by those plans. They expect to recoup 4.7 billion dollars through this program.

This article focuses on the relatively young technologies that enable CMS to uncover overbillings, whether they be errors or fraud. The article is based on an interview with Kel Pults, chief clinical officer and vice president of MediQuant. A future article will explain how Medicare Advantage plans are trying to improving data collection and reporting, and how AI helps.

Challenges of Investigating Overpayments

Undeserved payments are needles lurking in the haystack of 135 million Americans enrolled in Medicare, Medicaid, and the Children’s Health Insurance Program (CHIP). But the needles pile up fast. Improper payments for Medicare alone were estimated to be $43 billion for a single year.

Conventional fee-for-service payments, which reimburse a doctor for each visit or other intervention, are hard enough to investigate. It’s even harder to determine payments for more open-ended fee-for-value plans, which include Medicare Advantage.

Medicare Advantage programs are reimbursed on the basis on how much they are expected to spend on a patient. A person who has smoked for 60 years and is diagnosed with congestive heart failure and diabetes is expected to cost the plan more money than a professional dancer who has maintained an active life style. The difference between these situations drive differences in per-patient CMS reimbursements, which are called “risk-adjusted payments.”

But how sick is a patient, really? There are lots of ways to make patients seem sicker than they really are by exaggerating parts of their histories.

Currently, Pults told me, CMS is focusing on overbillings during the height of the COVID-19 pandemic. Although the recent CMS rule doesn’t mention COVID-19 explicitly, the crisis around that pandemic provided a new loophole for Medicare Advantage plans to extract undeserved reimbursements.

Think back to the height of pandemic, especially pre-vaccine. In 2020 and 2021, doctors, nurses, and other staff risked their lives hourly to treat patients who had COVID-19, often without knowing who had it. To provide incentives for both the front-line staff and the clinical institutions employing them, CMS offered bonuses, or add-on payments, for treating patients who had the disease.

In short, a patient who died of COVID-19 won a higher reimbursement than one who died of cancer, even if the money expended by the clinician and payer on the two victims was comparable.

The add-on payment created an unfortunate incentive to mark a patient as a victim of COVID-19 when they weren’t really. If a patient was admitted to a hospital for COPD or cancer and was subsequently found to have COVID-19, they shouldn’t trigger an add-on payment because the “primary cause of death” was COPD or cancer. But often, the providers and plans reclassified the patients as COVID-19 victims in order to get the add-on payments. This was particularly ironic if the patient developed COVID-19 as a result of entering the hospital for a different condition.

How can such overpayments be uncovered? One has to intensively examine the diagnosis and decisions leading up to the original hospital admission. It’s unfeasible to review every hospital admission for 2020 and 2021, so CMS needed advanced computing tools to choose suspicious deaths.

Selecting Payments to Investigate

For 24 years, MediQuant has offered a cloud data service to healthcare organizations that it calls an “active archive.” The archive is a layer above generic services such as Amazon’s S3, providing an interface to retrieve and search old clinical data. Figure 1, for instance, shows a screen offering various kinds of information on a patient.

Figure 1: New Release of Information Request

The task of determining what really caused a patient death involves searching several histories, each of which could be made of up different sections stored in different documents. Each relevant item, such as a vital sign, might be stored separately. For instance, one red flag calling for a review is if COVID-19 is listed as the primary cause of death and another was listed as secondary.

Legacy record systems add extra fragmentation to the records. Pults said that one of their clients had 250 legacy systems.

MediQuant can use machine learning to find the relationships among all these scattered facts, look for particular diagnosis codes, and check certain parameters to narrow down what to review out of millions of records. Pults says their service can help CMS in tracking down the billions that are owed to the U.S. public in overpayments.

Amid swirling accusations that Medicare Advantage Organizations (MAOs) are overbilling the U.S. government and calls for better oversight, the Centers for Medicare & Medicaid Services announced in early February that it would investigate overbilling by those plans. They expect to recoup 4.7 billion dollars through this program.

This article focuses on the relatively young technologies that enable CMS to uncover overbillings, whether they be errors or fraud. The article is based on an interview with Kel Pults, chief clinical officer and vice president of MediQuant. A future article will explain how Medicare Advantage plans are trying to improving data collection and reporting, and how AI helps.

Challenges of Investigating Overpayments

Undeserved payments are needles lurking in the haystack of 135 million Americans enrolled in Medicare, Medicaid, and the Children’s Health Insurance Program (CHIP). But the needles pile up fast. Improper payments for Medicare alone were estimated to be $43 billion for a single year.

Conventional fee-for-service payments, which reimburse a doctor for each visit or other intervention, are hard enough to investigate. It’s even harder to determine payments for more open-ended fee-for-value plans, which include Medicare Advantage.

Medicare Advantage programs are reimbursed on the basis on how much they are expected to spend on a patient. A person who has smoked for 60 years and is diagnosed with congestive heart failure and diabetes is expected to cost the plan more money than a professional dancer who has maintained an active life style. The difference between these situations drive differences in per-patient CMS reimbursements, which are called “risk-adjusted payments.”

But how sick is a patient, really? There are lots of ways to make patients seem sicker than they really are by exaggerating parts of their histories.

Currently, Pults told me, CMS is focusing on overbillings during the height of the COVID-19 pandemic. Although the recent CMS rule doesn’t mention COVID-19 explicitly, the crisis around that pandemic provided a new loophole for Medicare Advantage plans to extract undeserved reimbursements.

Think back to the height of pandemic, especially pre-vaccine. In 2020 and 2021, doctors, nurses, and other staff risked their lives hourly to treat patients who had COVID-19, often without knowing who had it. To provide incentives for both the front-line staff and the clinical institutions employing them, CMS offered bonuses, or add-on payments, for treating patients who had the disease.

In short, a patient who died of COVID-19 won a higher reimbursement than one who died of cancer, even if the money expended by the clinician and payer on the two victims was comparable.

The add-on payment created an unfortunate incentive to mark a patient as a victim of COVID-19 when they weren’t really. If a patient was admitted to a hospital for COPD or cancer and was subsequently found to have COVID-19, they shouldn’t trigger an add-on payment because the “primary cause of death” was COPD or cancer. But often, the providers and plans reclassified the patients as COVID-19 victims in order to get the add-on payments. This was particularly ironic if the patient developed COVID-19 as a result of entering the hospital for a different condition.

How can such overpayments be uncovered? One has to intensively examine the diagnosis and decisions leading up to the original hospital admission. It’s unfeasible to review every hospital admission for 2020 and 2021, so CMS needed advanced computing tools to choose suspicious deaths.

Selecting Payments to Investigate

For 24 years, MediQuant has offered a cloud data service to healthcare organizations that it calls an “active archive.” The archive is a layer above generic services such as Amazon’s S3, providing an interface to retrieve and search old clinical data. Figure 1, for instance, shows a screen offering various kinds of information on a patient.

Figure 1: New Release of Information Request

The task of determining what really caused a patient death involves searching several histories, each of which could be made of up different sections stored in different documents. Each relevant item, such as a vital sign, might be stored separately. For instance, one red flag calling for a review is if COVID-19 is listed as the primary cause of death and another was listed as secondary.

Legacy record systems add extra fragmentation to the records. Pults said that one of their clients had 250 legacy systems.

MediQuant can use machine learning to find the relationships among all these scattered facts, look for particular diagnosis codes, and check certain parameters to narrow down what to review out of millions of records. Pults says their service can help CMS in tracking down the billions that are owed to the U.S. public in overpayments.