Background: End-of-life management is a difficult aspect of cancer care. With the oncology care model (OCM), we have data to assess both clinical outcomes and total cost of care (TCOC). Objective: To measure and characterize the TCOC for those who received less than three days of hospice care (HC) at the end of life compared with those who received three days or more. Design: Assess data on costs and site and date of death from Medicare claims on patients identified in the OCM who received chemotherapy in the six months before death. Standard statistical methods were used to characterize both populations. Setting/Subjects: Subjects were Medicare patients with cancer who died while managed by U.S. oncology practices in the OCM. Measurements were TCOC in 30-day intervals for the last months of life, cost by site of care at the end of life, and demographic characteristics of the population and association with HC. Results: There were 7329 deaths. Dying in the hospital was twice the cost of dying at home under HC ($20,113 vs. $10,803). Of demographic groups measured, only black race and a lymphoma diagnosis had <50% hospice enrollment for three days or more before death. Conclusions: This study reinforces previous studies regarding costs in the last 30 days of life. The graphic representation highlights the dollar cost and the costs of lost opportunity. Using these data to improve communication, addressing socioeconomic support, and formal palliative care integration are potential strategies to improve care.

11534 Background: Simply put, cancer care in the last 30 days of life (30DAY) is often aggressive and expensive without best meeting the wishes and needs of patients and caregivers. In this study, we evaluated total and 30DAY cost data for patients who died while enrolled from July 1, 2016 to June 30, 2017 in the Oncology Care Model (OCM), a Medicare (CMS) program initiated by the Center for Medicare and Medicaid Innovation. Methods: CMS provided claims data on 16 OCM-participating US Oncology Network practices. We measured 30DAY OCM episode expenditures (“EXP30”) for n = 5017 deceased patients, as well as patient demographics, clinical outcomes (hospital, ICU, and ER visits) and 30DAY treatments . The population was divided into two cohorts: those with ≥ 3 days of hospice care (“HC”) and those without (“NOHC”). EXP30 was compared between HC and NOHC at the univariate level using Wilcoxon Rank-sums; categorical variables were compared using Chi-squared tests. Multivariate regression was used to determine the effect of HC on EXP30 adjusted for demographic and disease factors. Results: HC had mean EXP30 reduction of $7,192 vs. NOHC [95%CI -$7,628, -$6,730] adjusted for demographics and disease. HC had lower rates of death in hospital (0.03% vs. 43.3%, p < .0001). Mean expense by days before death: NOHC values 0-30 days = $20,701; 31-60 days = $12,962; 61-90 days = $9,952 and HC values 0-30 days = $10,877; 31-60 days = $10,376; 61-90 days = $9,064. Conclusions: All categories of care except HC are associated with significantly higher cost in the last 30 days of life. The dramatic and steep trajectory in the last 30 days suggests high expense but also loss of choices as to how best to live the end of one’s life. The results will be updated as new data become available. [Table: see text]

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181 Background: The recommendations of the Choosing Wisely campaign are evidence –based strategies to reduce cost without sacrificing outcomes. Yet tying the recommendations to indicators of use at the physician and case level has been challenging. As practices become responsible for total cost of care, an easy to use analytic method to determine appropriate use will be critical. We here describe a tool for rapid assessment of individual cases to achieve that objective. Methods: The population was a payer defined cohort of lung cancer patients treated at Texas Oncology (TxO). TxO maintains a payer patient list, updated daily with demographics and diagnosis details for all enrolled patients. The list of lung cancer patients was cross-referenced against billing data from TxO's financial data warehouse (FDW). The FDW data is generated monthly, based on billing details from TxO's practice management system, and includes procedure codes and dates of service. Radiation therapy, chemotherapy, and GCSF administrations for each enrolled lung cancer patient were identified in the FDW data based on CPT codes. SAS software (version 9.4 for Windows) was used to generate a time series plot for each patient, based on date of service for each procedure. The time series plots were inserted into an Excel report template, along with general patient information, using a Visual Basic script, for review by TxO's medical director and quality committees. Results: Each patient-specific time series schematic displays elapsed weeks on the x-axis, beginning with week 1 to end of treatment. Three variables are displayed on the y-axis, using distinct colors and symbols: dates of radiation therapy (orange #), dates of GCSF administration (red x), and dates of chemotherapy administration (green ^). Each of the three y-axis variables is assigned a constant value that is plotted along a straight line. A graphic representation for a patient with stage III lung cancer could look as shown in the Table. Conclusions: Treatment episodes can be distilled into a meaningful format that allows rapid case review and the opportunity for continuous learning. Additional diseases and graphics will be available for presentation. [Table: see text]

33 Background: Texas Oncology (TO) is a community-based practice. with over 250 physicians (MD) who order chemotherapy. In 2015, pegfilgrastim (pgcsf) was the highest individual drug in billed claims. Concurrently, TO was participating in the United Healthcare (UHC) Episode of Care (EOC) program and anticipating the Oncology Care Model. Patient and program expense dictated understanding pgcsf utilization. Methods: Data were obtained in 9/2015 from EOC claims to identify overall and individual pgcsf use in solid tumor metastatic disease (MDx). Chart review was done for the top prescribers. In 3/16, ASCO pgcsf guidelines and TO education materials were emailed to all physicians. In 3/17 appropriate non-use recommendations were communicated and a real-time internal approval process begun. On 10/1/2017, UHC instituted a pgcsf prior authorization (PA) program. Retrospective data from the iKnowMed EHR system was collected in 3-month intervals from April 2016 through March 2018 and evaluated for number of pgcsf administrations at TO for MDx, as well as, administrations/provider/quarter for TO and The US Oncology Network. MDx was defined by any of the following criteria: stage IV at diagnosis (with certain disease exceptions); TNM M value of “1” or “+”; a metastatic line of therapy; evidence of metastatic disease documented within the EHR. Results: Initial survey of EOC claims indicated 16% of patients received pgcsf for MDx. Rates per MD ranged from 0 to 57%. Review of the top users indicated 90% of uses could have been addressed with dose reduction. In the first measured quarter of April to June 2016 to the last, October to December 2017, pgcsf use dropped by 50%. Changes for some diseases were dramatic: Use in colon cancer fell from 132 uses/quarter to a low of 46, and for non-small lung cancer – 80 to 23 respectively. Conclusions: The deadopting of low value care can be challenging. In the case of pgcsf substantial reductions in use were achieved using data and guidelines for education in the setting of value based contracts. Ultimately, the most change was achieved by an internal real time approval process. This was reinforced by a payer PA requirement.