As a clinician, I understand the importance of palliative care radiation therapy as it relates to pain alleviation and quality of life. The study indicated that as many as 1 in 5 patients who receive RT in their final 30 days of life spent greater than 10 of those receiving treatment. End of life treatment, hospice, or a combination has and continues to be an important topic for patients and providers.
This study is timely and relevant as we continue to focus more on value-based care and quality of life of our patients. Access to care and expertise to make informed decisions are areas of interest for all.
The data from the performance status of the patients were collected via KPS, ECOG and Lansky. The patient status inpatient or outpatient was also tracked too. These data points in the study are critical as they provide insight into end of life decision weighting factors and resource allocations.
When analyzing this study for a true value-based care perspective and relationship, there are several elements and aspects that were not tracked, but through the EMR/EHR records of the patients would be available. These elements include:
The study did identify that 231(14%) of these palliative treatments involved stereotactic approaches. There was mention of Hypofractionation for certain disease sites too. The direct correlation between disease site and treatment approach is needed for accurate financial correlation and prediction modeling.
One of the discussion points in the study was: The high proportion of patients dying during or shortly after palliative RT initiation at this single institution is consistent with prior reports of RT at EoL,6–11,13,14 a pattern that stems from difficulties in predicting life expectancies accurately for terminally ill patients.
End of life discussions and when/how long to pursue with treatment vs. initiating the Hospice earlier in the patient’s life cycle continues to be a struggle for sure. These are individual decisions, but data helps.
Here are some of the financial correlations that can be made and predicted based on some of the study’s data. The rest are inferred from clinical radiation oncology practice and financial expertise. This data is derived from the price transparency data as required by CMS January 1 requirement for Price Transparency as well as Medicare allowable numbers for respective CPT® codes. (*note the sims were left out for 3D treatments)
Alternative payment models and direct payment models seek to encompass a single reimbursement based on disease site and treatment modality. The financial component has an impact on patients, provider, vendors, and payers. These models are the future of medicine and the data must be relational as below.
From Average (across the 3)- 201% Variation From Max (across the 3)- 139% Variation
From Medicare Allowable – average mid 549% Max Mid- 796% above Medicare Allowable
From Average (across the 2)- 59% Variation From Max- (across the 2)- 52% Variation
From Medicare Allowable – (average of 2) - 429%% Max (average of 2)- 702% above Medicare Allowable
From Average (across the 2)- 54% Variation From Max- (across the 2)- 50% Variation
From Medicare Allowable – (average of 2) - 373% Max (average of 2)- 633% above Medicare Allowable
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