Retrospective Chart Review Raises Concerns with Regulators
Over the past few years Medicare Advantage (MA) plans have been involved in disputes with government auditors regarding alleged Medicare fraud and abuse. Medicare overbilling audits typically contend that a plan overstated how sick some patients are. If allegations made by government auditors were ever finalized, the penalties could be significant.
Medicare Advantage is a value-based reimbursement alternative to standard Medicare. In 2020 Medicare Advantage enrollment accounted for nearly four in ten (39%) of all Medicare beneficiaries or 24.1 million people out of 62.0 million Medicare beneficiaries overall. The Congressional Budget Office (CBO) projects that the share of all Medicare beneficiaries enrolled in Medicare Advantage plans will rise to about 51 percent by 2030.
Medicare Advantage plans have been repeatedly targeted by government auditors looking for Medicare fraud and abuse, but in most cases, efforts to recover alleged Medicare overbilling have failed when appealed by the health plans. These initial failures have not deterred the government, and we can expect continuing scrutiny of the coding practices of MA plans and different types of healthcare abuse overall.
These audits typically are tied to alleged overbilling of a random sample of patient medical charts to make sure the codes that were submitted to CMS matched the diseases that the patients actually had. Any inconsistencies are then extrapolated across a larger population to determine the alleged amount of Medicare abuse. This extrapolation method is controversial and is opposed by AHIP (America’s Health Insurance Plans) industry trade group as a method of determining payment errors, but it’s still commonly used in different types of healthcare abuse investigations.
Current MA plan workflows could be the cause of some of the purported Medicare abuse accusations related to the validity of codes submitted to CMS. The MA payment system is tied closely to the use of certain ICD codes, that when attributed to a patient, are meant to reflect the patient’s disease burden. The system initially depends on ICD codes submitted by physicians upon the completion of an encounter. Since physician ICD coding may sometimes be incomplete, the plans typically retrospectively review medical records in an attempt to find any “missing codes”. The thesis is that in order for plans to offer high quality care they must have the financial resources to match the disease burden of their population of patients. If codes are under-reported by providers, then a mismatch of resources to disease burden is inevitable. To avoid this mismatch and Medicare underpayment, MA plans invest billions of dollars in retrospective review processes and some of those very processes create the slippery slope that may trigger a federal Medicare fraud and abuse audit. In fact, an Office of the Inspector General (OIG) report published in December 2019 questioned the retrospective chart processes and per the OIG, “Billions of estimated risk-adjusted payments supported solely through chart reviews raise potential concerns about the completeness of payment data submitted to CMS, the validity of diagnoses on chart reviews, and the quality of care provided to beneficiaries.”
There is a better way! Do it right the first time. Now, with computer assisted coding software that uses artificial intelligence in healthcare, physicians are better equipped to determine the disease burden of their patient population with clinical decision support at the point-of-care. The selection of ICD codes upon the conclusion of an encounter can be more comprehensive and the hassle related to coding is reduced. Linking codes to the text in medical records makes it easier for an auditor to understand why a code was submitted. Concurrent and prospective review processes reduce the need for costly retrospective review and could greatly reduce liability exposure for MA plans.
ForeSee Medical is a specialized software platform designed to increase the profitability of Medicare Advantage risk contracts. Using AI including natural language processing and machine learning, our disease detection algorithms rationalize patient data across the healthcare system. It’s simple, using AI we discover diseases from text notes and EHR data you already have. Then, we empower you with insightful HCC risk adjustment support, at the point of care, and integrated seamlessly with your EHR.
Blog by: The ForeSee Medical Team