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One step closer: CMS finalizes TEAM updates

On July 31, the Centers for Medicare and Medicaid Services (CMS) finalized updates to the Transforming Episode Accountability Model (TEAM) in the federal fiscal year 2026 Inpatient Prospective Payment System (IPPS) final rule .  TEAM, a mandatory Medicare bundled payment initiative launching Jan. 1, 2026, was first introduced in the FFY 2025 IPPS proposed rule. 745 hospitals will advance into TEAM in the beginning of the year – 735 hospitals located in a Core-Based Statistical Area (CBSA) chosen for mandatory participation and 10 hospitals that have voluntarily opted into the model.    Earlier this year, CMS proposed changes to TEAM and sought feedback from hospitals, associations and other stakeholders. The final rule now cements those updates — changes that will directly impact TEAM participation requirements, quality measurement, payment methodologies and care delivery strategies. Any future model changes will go through the same rulemaking process.    Key T...

TEAM moves forward: What hospitals must do now with CMS baseline data [updated]

New updates as of July 2  CMS has released Transforming Episode Accountability Model (TEAM) performance year (PY) 1 baseline period data to a second wave of participating hospitals. Hospitals included in this wave had their Data Request and Attestation Form (DRA) and Data Sharing Agreement (DSA) submitted to CMS through the TEAM participant portal by May 26.  Any hospital that submitted their DRA and DSA to CMS after May 26 will not receive their TEAM PY1 baseline period data until fall 2025.  Important note:  If your hospital won’t receive its TEAM PY1 baseline data until fall 2025, don’t let that delay your preparation. Simulated TEAM episodes of care can still offer valuable insight into how your hospital is positioned ahead of the model’s launch.   All hospitals should also expect an update to their baseline data, including preliminary target prices, in the fall of 2025. This will account for the additional 2024 data run-out and pending methodological upda...

Your introduction to CMS TEAM Model financial risks

What are the financial risks in TEAM?  Hospitals are preparing for the launch of the Centers for Medicare and Medicaid Services’ (CMS) Transforming Episode Accountability Model (TEAM) in 2026.   To help you understand both the opportunities and financial risks for participants, DataGen put together a comprehensive white paper, CMS Transforming Episodes Accountability Model Financial Risk Guide.   [Access it Now for Key Updates]  Keep reading for highlights of what’s inside.   Exploring the goals and structure of CMS TEAM Model  CMS TEAM Model is designed to enhance care quality and cost-efficiency. It aims to revolutionize how hospitals manage patient care over 30-day episodes for specific surgical procedures by holding them accountable for the quality and cost of care delivered.   As seen on CMS’ Transforming Episode Accountability Model overview webcast , this model not only seeks to improve patient outcomes, but encourages hospit...

CJR ends, TEAM to begin: 5 ways CJR evolved and what’s ahead

The end of the CJR model: A look back at its evolution  In October 2024, the final episodes of the Comprehensive Care for Joint Replacement (CJR) model were initiated, with all episodes ending by Dec. 31, 2024. This was the final performance year of CJR, which spanned eight years overall.   CJR began Apr. 1, 2016. It was CMS’ first mandatory bundled payment model. Hospitals were held financially accountable for lower extremity joint replacement (LEJR) episodes of care and were incentivized to improve care coordination for patients across the continuum.    Key insights from CJR: Successes, adjustments and challenges  CJR had numerous ups and downs over the years, as its scope was adjusted over several administrations through the rule-making process. Here are the five main highlights.  1. CJR’s mandatory participation: A changing landscape  When the CJR model was introduced, participation was mandatory for hospitals in 67 metropolitan statistical a...