Article 1 in a series to help anesthesia providers survive and thrive using the new value-based payment models.
The reality of the ambulatory anesthesia value-based world is that Medicare inadvertently set up the ambulatory anesthesiology practice for failure. Most of the non-hospital based practices were unable to achieve meaningful use which eliminated them from the Medicare incentive payment model. Medicare provided practitioner incentives for the implementation of a solid EHR platform. The application of such a model is extremely difficult at best due to the nature of the technical component of providing anesthesia and monitoring vitals. Systems are rarely integrated which makes it difficult and costly for anesthesia providers to pull patient data from center systems.
By necessity, manually adding data to the non-EHR practice management system increases the number of staff dramatically as providers leverage surgery center system documentation to pass through to the business office for billing activities. Therefore, anesthesia providers entering into the EHR space would be subjected to a higher initial investment with lower gains than other specialty practices. For that reason, most of the ambulatory anesthesia community has not invested in EHR technology.
Medicare has further inhibited anesthesiology practices from readily reporting by allowing (i.e. “requiring”) claims-based value identifier reporting. Most, if not all, ambulatory anesthesia providers record their identifiers on individual claims to Medicare. This requires an experienced biller working closely with the clinical staff to properly attach the right identifier codes to each claim billed to Medicare. Some physicians determine the current identifiers, also known as measures, and the biller just adds it to the claim. Other physicians provide access to ambulatory surgery center (ASC) systems and the biller has to search, identify, set policy and then add the measure to the claim.
Either way is cumbersome, time-consuming and usually requires a matrix of sorts to accomplish the task, thereby adding cost to the business model despite only very few measures being required for reporting. As the additional cost has been minimal due to the small number of identifiers reported, few providers have seen a need for initial investments in technology or resources. Consequently, a real dilemma emerges.
Recently in 2016, Medicare added many measures that could be reported but only via a qualified reporting registry. Claims-based reporting is no longer available to capture enough measures to pass Medicare’s minimum requirements in order to prevent revenue based penalties or individual practice measure auditing. Medicare calls this the MAV process (Measure Applicability Validation). The goal is to avoid the MAV process which leaves the final outcome up to Medicare, potentially resulting in the loss of 2%, 4% or up to 6+% of a provider’s Medicare revenue.
To bypass MAV, it is required that at least nine measures across three domains or categories be reported in 2016. That is the minimum. More measures are available to report, and should be, to maximize a practice’s efficiency. Herein lies the problem: operational costs will skyrocket when providers utilize manual processes and resources to capture data/measures.
Studies have shown that claims-based or manual-based reporting has only been 40% successful at capturing all the needed quality measures. Technology must be incorporated to effectively capture measures and link them to billing data. This can be accomplished without using a full blown EHR system. With a minimal upfront investment, physician buy-in and about one extra minute per patient at the time of service, ambulatory anesthesia providers can succeed in this new value-based world in which they practice. If implemented right, a solid solution that has minimal financial investment can be rewarded by receiving a Medicare positive financial adjustment instead of a penalty or remaining neutral. Keep in mind, Medicare is not going to be the only payer looking at value-based models.
The next article in this series will inform as to how practices are solving this problem.