Evaluating the Addition of Clinical and Staging Data to Improve the Pricing Methodology of the Oncology Care Model.

PURPOSE: The Oncology Care Model (OCM) is the largest value-based care model focusing on oncology, but the current pricing methodology excludes relevant data on the cancer stage and current clinical status, limiting the precision of the risk adjustment. METHODS: This analysis evaluated 15,580 episodes of breast cancer, lung cancer, and multiple myeloma, starting between July 1, 2016, and January 1, 2020, with data from a cohort of OCM practices affiliated with academic medical centers. The authors merged clinical data with claims for OCM episodes defined by the Center for Medicare and Medicaid Innovation to identify potential quality improvement opportunities. The regression model evaluated the association of the cancer stage at initial diagnosis and current clinical status with variance to the OCM target price. RESULTS: Cancer stage at the time of initial diagnosis was significant for breast and lung cancers, with stage IV episodes having the highest losses of -$6,700 (USD) for breast cancer (P < .001) and -$18,470 (USD) for lung cancer (P < .001). Current clinical status had a significant impact for all three cancers in the analysis, with losses correlated with clinical complexity. Breast cancer and multiple myeloma episodes categorized as recurrent or progressive disease had significantly higher losses than stable episodes, at -$6,755 (USD) for breast (P < .001) and -$19,448 (USD) for multiple myeloma (P < .001). Lung cancer episodes categorized as initial diagnosis had significantly fewer losses than stable episodes, at -$3,751 (USD) (P = .001). CONCLUSION: As the Center for Medicare and Medicaid Innovation designs and launches new oncology-related models, the agency should adopt methodologies that more accurately set target prices, by incorporating relevant clinical data within cancer types to minimize penalizing practices that provide guideline-concordant cancer care.

No Exit: Identifying Avoidable Terminal Oncology Intensive Care Unit Hospitalizations.

PURPOSE: Terminal oncology intensive care unit (ICU) hospitalizations are associated with high costs and inferior quality of care. This study identifies and characterizes potentially avoidable terminal admissions of oncology patients to ICUs. METHODS: This was a retrospective case series of patients cared for in an academic medical center's ambulatory oncology practice who died in an ICU during July 1, 2012 to June 30, 2013. An oncologist, intensivist, and hospitalist reviewed each patient's electronic health record from 3 months preceding terminal hospitalization until death. The primary outcome was the proportion of terminal ICU hospitalizations identified as potentially avoidable by two or more reviewers. Univariate and multivariate analysis were performed to identify characteristics associated with avoidable terminal ICU hospitalizations. RESULTS: Seventy-two patients met inclusion criteria. The majority had solid tumor malignancies (71%), poor performance status (51%), and multiple encounters with the health care system. Despite high-intensity health care utilization, only 25% had documented advance directives. During a 4-day median ICU length of stay, 81% were intubated and 39% had cardiopulmonary resuscitation. Forty-seven percent of these hospitalizations were identified as potentially avoidable. Avoidable hospitalizations were associated with factors including: worse performance status before admission (median 2 v 1; P = .01), worse Charlson comorbidity score (median 8.5 v 7.0, P = .04), reason for hospitalization (P = .006), and number of prior hospitalizations (median 2 v 1; P = .05). CONCLUSION: Given the high frequency of avoidable terminal ICU hospitalizations, health care leaders should develop strategies to prospectively identify patients at high risk and formulate interventions to improve end-of-life care.