RO Model: IT strategies to help prepare for the data demands of CMS’ new APM

Change, especially in the context of healthcare IT systems and processes, can be difficult to design and implement. But it also provides an opportunity to take a fresh look at how healthcare IT can be utilized in new ways to improve the quality and efficiency of care delivery.

The Radiation Oncology Alternative Payment Model is an advanced alternative payment model developed by the Centers for Medicare and Medicaid Services in conjunction with the Center for Medicare and Medicaid Innovation. 

Under this model, Medicare will pay providers a predetermined, site-neutral, bundled rate for most radiation therapy services provided within a 90-day episode of care, rather than paying for each service individually. The intended goal is to incentivize providers to deliver radiation therapy services more efficiently while maintaining or improving the quality of care delivered.

As currently written, the RO Model will require participants to collect clinical quality measures data and clinical data elements to have the chance to qualify for maximum CMS reimbursement for covered services. Providers will collect CDEs for prostate, breast, and lung cancers, in addition to bone and brain metastases.

These data collection requirements are just one aspect of the changes to which radiation oncology providers and practices will need to adapt to comply with and have the chance to receive maximum reimbursement under the RO Model.

Under an initial pilot program, which currently is on hold, but could begin as early as 2023, CMS will select sites that will be required to comply with RO-Model reporting requirements and will also identify sites that will serve as controls.

Fulfilling the RO Model reporting requirements will require the collection of new datasets as well as new analysis algorithms and data outputs. Designing and implementing information technology solutions that simplify compliance with these requirements is critical for reducing the burden on care providers and administrators while ensuring the accurate and timely reporting of required information and supporting maximum reimbursement for provided services.

A case study in preparing for RO Model

Oklahoma Cancer Specialists and Research Institute is a physician-owned group practice with more than 20 blood and cancer specialty physicians and 400 nurses and associates. OCSRI offers the most comprehensive cancer care in the Northwest Oklahoma region and is an important resource for cancer patients in this area.

In September 2021, CMS published a list of participating zip codes, and OCSRI  learned one but not all its sites had been selected to participate in the RO Model test program. Recognizing the significant impact that this would have on its ongoing quality control and clinical data collection processes and systems, OCSRI took immediate action to begin preparing for RO Model participation.

The goal of these preparatory activities was to identify and implement a system for automating manual reporting processes. OCSRI also sought better visibility into additional data that could inform its understanding of how well and how efficiently it delivers care, including treatment metrics, staff productivity, and machine downtime.

In preparation for RO Model implementation, OCSRI assembled a task force of team members across disciplines that met weekly. To ensure the deployment of a system that would meet the needs of diverse stakeholders across OCSRI’s operations, the task force was broad-based in its membership and included its Radiation Oncology director; leadership from the RO nursing and radiation, nursing value-based care/quality improvement, and billing teams; the billing team, the RO department billing specialist, and financial analysts.

The value-based care team also provided the task force with quality improvement data to ensure evidence-based decision-making. The task force’s members were organized into Clinical, Billing and Data/Quality divisions.

After evaluating several options, the task force determined that MOSAIQ Oncology Analytics, a component of OCSRI’s existing radiation department electronic health record was the best solution for meeting RO Model reporting requirements and achieving its care delivery and operational efficiency improvement goals.

A key factor in this decision was the ability for MOA to automatically pull data from the EHR, reducing the need for manual entry and its associated errors. It also provides flexible templates that could be tailored to OCSRI’s specific needs and made critical data accessible with a minimum number of clicks.

As the RO-Model test program only includes traditional Medicare beneficiaries with specific cancer diagnoses and is not applicable to all OCSRI patients, the EHR system was programmed to provide a pop-up alert each time the EHR of an RO Model is opened. This provides OCSRI staff with an important reminder of the patient’s status within the program.

Selecting and adopting an RO Model data collection and reporting system well in advance of the RO Model test program commencement provides the OCSRI team confidence it has a solution that supports RO Model participation. This solution provides monitoring of CDE data collection and the ability to generate the CDE submission file, RO episode management that enables anticipation of reimbursement, and the ability to calculate and attest for quality measures.

OCSRI uses MOA to develop, evaluate, and deploy a variety of reports in addition to the RO Model reports.

1. The stereotactic ablative radiotherapy treatment report includes daily treatments per day of the week, per physician, and volumes by month. These data are used to make informed staffing and equipment decisions.
2. The weekly physics quality assurance report shows RO treatment volumes by type and medical physicist for the prior week and includes multiple metrics of specific physics tasks. This ensures equitable distribution of work among OCSRI medical physicist personnel.
3. The weekly dosimetry report shows RO treatment volumes by task type, dosimetrist, and location. This has been especially important in ensuring visibility of team members’ contributions while working in a remote-onsite hybrid setting during the pandemic.
4. The daily treatment statistics and machine performance report, which used to be labor-intensive to collect, provides important insights into why patients missed treatments and facilitates optimized staffing decisions.

OCSRI also has developed reports that collect data on missed, duplicate and non-exported charges (captured daily and at the end of each month) and hypofractionation metrics by disease site, physician, and location.

The team is also developing a report that will provide information on staffing, supply, service/software agreement costs per diagnosis, type of treatment and location. Collectively, these reports will provide OCSRI’s leadership and decision-making teams with unprecedented insight into critical aspects of its operations. This is expected to enable increased efficiency in care delivery and enhanced equity in assignment and recognition of key care-related activities.

Key learnings from the OCSRI experience

While each site selected for participation in the RO Model test program has unique software and workflow that will guide selection of an optimized RO Model data collection and reporting system, several lessons learned in OCSRI’s process are broadly applicable.

Consideration of these learnings may help other RO care providers facilitate not only implementation of the RO Model test program but also provide greater insight into critical factors that drive all aspects of providing high-quality, efficient RO services that help patients achieve optimal health outcomes.

• Know where you’ve been. Work with your billing department to understand historical reimbursements for the anatomic treatment sites included in the RO Model.

• Know where you are. Use the historical data and compare it to the information CMS provided in the RO Model final rule to determine whether you’ll be ahead or behind these reimbursements. If you are behind, dive deeper to determine why.

• Know where you want to be. Assess the software and data you have so you can determine how you’re going to report the information to CMS to maximize reimbursement for each episode of care.

• Get there! Implement processes and start tracking data to see where you need to focus your efforts.

Pursuing a holistic approach to designing an IT solution can provide important insights that will allow radiation care centers to improve their care delivery processes, strengthen their bottom line, and facilitate adaptation to an evolving reimbursement landscape. All while keeping patient care at the center of what they do.