The Oncology Care Model is dead. Long Live the Oncology Care First Model.

The Oncology Care Model is slated to end soon. Specifically, the last set of six-month episodes would initiate no later than December 31, 2020 and thus all episodes will be completed in June 30, 2021. Nevertheless, CMS is proposing a successor to the Oncology Care Model called the Oncology Care First (OCF) Model. CMS describes the revised approach as follows:

…the payment mechanisms for the potential OCF Model would include: (1) A prospective, monthly population payment (MPP) for an OCF participant’s assigned population of Medicare FFS beneficiaries with cancer or a cancer-related diagnosis that would include payment for Evaluation and Management (E&M) services, “Enhanced Services” required under the terms of the model participation agreement, and drug administration services; and, (2) Total cost of care accountability for Medicare costs, including drug costs, incurred during a six-month episode of care triggered by a Medicare beneficiary’s receipt of a Part B or D chemotherapy drug, 6 with the opportunity to achieve a performance-based payment (PBP) or owe a repayment to CMS (PBP recoupment), depending on quality performance and costs relative to benchmark and target amounts

What are the differences between OCM and OCF? AJMC reports some key differences:

* CMS wants to shift some of the FFS payment to capitation, which “will pose challenges for OCF participants.” Evaluation and management (E/M) services and drug administration fees, which were previously outside the monthly practice transformation fee, would be folded inside it.
* Improved performance-based-payment formulas would do a better job of accommodating rapidly rising drug costs and protect oncologists from being held responsible for events that are beyond their control.
* New requirements may be added to require practices to gather patient-reported outcomes (PROs).

A Health Affairs commentary by de Brantes and co-authors is skeptical that OCF will be a significant improvement over OCM.

…today’s OCM are completely unpredictable because the actual expenses may vary from baseline depending on the specific nature of each cancer treated. Moreover, in OCF as in OCM, the treating physician is at risk for total costs of care, including services having nothing to do with cancer, like care after a car accident or for brittle diabetes. Adequately risk adjusting for each cancer type and stage across all possible cancers, including underlying patient-specific characteristics, seems all but impossible.

Research by my colleagues Jim Baumgardner and colleagues (Baumgardner et al. 2018) quantifies some of the variation in cost types across OCM episodes.

Implementation of OCF, however, has been pushed back due to COVID-19. The presence of a pandemic makes implementing payment reform problematic. As Lucio Gordan, MD–president and managing physician at Florida Cancer Specialists–mentions at AJMC:

But during COVID-19, it’s really a bad time to do new models and experimenting or pushing the envelope too quick, too hard, because we are under a tremendous amount of stress, and none of us wants to fail and not deliver and have consequences in terms of 2-sided risk for our practices that could impact access to care to our patients.
For Oncology Care First, I’m happy that they pushed back 12 months for 2022. We may need more time, but we’ll see how this plays out.

The Dawn of a European Health Data Space – Challenges

The European data strategy aims to construct common data spaces for all, create a single EU market for data, and catalyze a dynamic data economy. In a previous post, we briefly described the essence of the envisioned heath data space and pointed at opportunities and possible starting points to transform this vision into reality.

However, several questions have also emerged in exchanges with
thought leaders and collaborators. Borne from gray areas around data, systems
and mechanisms for common use, these questions serve as food for thought
to structure an open, cross-sector discussion.

What data

We need to talk honestly about data quality and data bias. In our
experience with AI, efforts to make data reliable, transparent and reusable go
a long way in catalyzing new, machine-based data exploration. Part of that
effort is to define the amount and type of bias permissible in data for a
particular purpose because there are no data without bias.

Which system

Vast amounts of data on biology, chemistry and health need to be
machine-readable on a massive scale. Manual curation is not an option, but in a
framework that spans from big data in populations to patient-centric care –
where data are personal and private by definition – how do we verify the
fidelity of automated processing and build trust in the system?

Where to guide

Central guidance is essential for unified empowerment, but
individuals should participate in a common data space because they understand
the benefits and not just because there are safeguards against risks. Robust
but flexible guidance can address seemingly insurmountable differences among
sectors in the way data are generated and valued, while ensuring that abuses
are not rewarded. We need to understand the interests and objectives of all
parties. Only so can these spaces be truly inclusionary.

It is time for a dialogue – time to openly define the needs,
interests, objectives and differences of participants in an EU health data
space. We want to hear your thoughts; not only about the nature of these data
spaces but also about the forum in which clinical researchers and data
scientists can access unified research, literature and clinical data in one
secure environment. To join the discussion, please email my colleague Xuanyan
Xu at [email protected].