Cancer care delivery in the United States is often fragmented and inefficient, imposing substantial burdens on patients. Costs of cancer care are rising more rapidly than other specialties, with substantial regional differences in quality and cost. The Centers for Medicare & Medicaid Services (CMS) Innovation Center (CMMIS) recently launched the Oncology Care Model (OCM), which uses payment incentives and practice redesign requirements toward the goal of improving quality while controlling costs. As of March 2017, 190 practices were participating, with approximately 3,200 oncologists providing care for approximately 150,000 unique beneficiaries per year (approximately 20% of the Medicare Fee-for-Service population receiving chemotherapy for cancer). This article provides an overview of the program from the CMS perspective, as well as perspectives from two practices implementing OCM: an academic health system (Yale Cancer Center) and a community practice (Hematology Oncology Associates of Central New York). Requirements of OCM, as well as implementation successes, challenges, financial implications, impact on quality, and future visions, are provided from each perspective.

Purpose: Many US academic centers have acquired community practices to expand their clinical care and research footprint. The objective of this assessment was to determine whether the acquisition and integration of community oncology practices by Yale/Smilow Cancer Hospital improved outcomes in quality of care, disease team integration, clinical trial accrual, and patient satisfaction at network practice sites. Methods: We evaluated quality of care by testing the hypothesis that core Quality Oncology Practice Initiative measures at network sites that were acquired in 2012 were significantly different after their 2016 integration into the network. Clinical and research integration were measured using the number of tumor board case presentations and total accruals in clinical trials. We used Press-Ganey scores to measure patient satisfaction pre- and postintegration. Results: Mean Quality Oncology Practice Initiative scores at Smilow Care Centers were significantly higher in 2016 than in 2012 for core measures related to improvement in tumor staging ( z = 1.33; P < .05), signed consent and documentation plans for antineoplastic treatment ( z = 2.69; P < .01; and z = 2.36; P < .05, respectively), and appropriately quantifying and addressing pain during office visits ( z = 2.95; P < .05; and z = 3.1; P < .01, respectively). A total of 493 cases were presented by care center physicians at the tumor board in 2017 compared with 45 presented in 2013. Compared with 2012, Smilow Care Center clinical trial accrual increased from 25 to 170 patients in 2017. Last, patient satisfaction has remained at greater than the 90th percentile pre- and postintegration. Conclusion: The process of integration facilitates the ability to standardize cancer practice and provides a platform for quality improvement.

PURPOSE: Acute care imposes a significant burden on patients and cancer care costs. We examined whether an advanced practice provider-driven, cancer-specific urgent care center embedded within a large tertiary academic center decreased acute care use among oncology patients on active therapy. MATERIALS AND METHODS: We conducted a quasi-experimental study anchored around the Oncology Extended Care Clinic (OECC) opening date. We evaluated two parallel 4-month periods: a post-OECC period that followed a 5-month run-in phase, and the identical calendar period 1 year earlier. Our primary outcomes included all emergency department (ED) presentations and hospital admissions during the 3-month window following the index provider visit. We used Poisson models to calculate absolute pre-OECC v post-OECC rate differences. RESULTS: Our cohort included 2,095 patients in the pre-OECC period and 2,188 in the post-OECC period. We identified 32.6 ED visits/100 patients and 41.2 hospitalizations/100 patients in the pre-OECC period, versus 28.2 ED visits/100 patients and 26.1 hospitalizations/100 patients post-OECC. After adjusting for age, sex, race and ethnicity, and practice location, we observed a significant decrease of 4.6 ED visits/100 patients during the post-OECC period (95% CI, –8.92/100 to –0.28/100; P = .04) compared with the pre-OECC period. There was no significant association between the OECC opening and hospitalization rate (rate difference: –3.29 admissions/100 patients; 95% CI, –8.24/100 to 1.67/100; P = .19). CONCLUSION: Establishing a cancer-specific urgent care center was significantly associated with a modest decrease in emergency room utilization but not with hospitalization rate. Barriers included clinic capacity, patient awareness, and physician comfort with advanced practice provider autonomy. Optimizing workflow and standardizing clinical pathways can create benchmarks useful for value-based payments.

Cancer care delivery in the United States is often fragmented and inefficient, imposing substantial burdens on patients. Costs of cancer care are rising more rapidly than other specialties, with substantial regional differences in quality and cost. The Centers for Medicare & Medicaid Services (CMS) Innovation Center (CMMIS) recently launched the Oncology Care Model (OCM), which uses payment incentives and practice redesign requirements toward the goal of improving quality while controlling costs. As of March 2017, 190 practices were participating, with approximately 3,200 oncologists providing care for approximately 150,000 unique beneficiaries per year (approximately 20% of the Medicare Fee-for-Service population receiving chemotherapy for cancer). This article provides an overview of the program from the CMS perspective, as well as perspectives from two practices implementing OCM: an academic health system (Yale Cancer Center) and a community practice (Hematology Oncology Associates of Central New York). Requirements of OCM, as well as implementation successes, challenges, financial implications, impact on quality, and future visions, are provided from each perspective.

6615 Background: Oncology-specific urgent care clinics (UCC) may play a key role in reducing unscheduled emergency department (ED) visits among patients with cancer. We sought to determine if establishment of an Oncology UCC was associated with lower ED utilization among patients receiving cancer care at Yale’s Smilow Cancer Hospital (SCH) and two nearby, integrated community practices. Methods: SCH opened its UCC in April 2017 to provide supportive care and symptom management for patients with cancer who need acute medical attention outside of regular clinic visits. We identified patients who had at least one visit with an oncology provider during the Pre-UCC period (9/1/16 – 12/31/16) or Post-UCC period (9/1/17 – 12/31/17) and received chemotherapy within a year preceding their provider visit. For each patient, we captured all ED visits in a four-month window starting from their last provider visit in each study period. The ED visit rate for both periods was defined as the total number of ED visits divided by the total number of unique patients in the period. To determine the impact of the UCC on ED utilization, we evaluated the absolute difference in the ED visit rate between the Pre- and Post-UCC period using a two-sample t test. Results: There were 3,754 patients in the Pre-UCC period and 4,734 patients in the Post-UCC period. In the full study sample, the mean age was 62.9 and most common cancer types were Hematologic, Gastrointestinal, and Breast. Prior to opening the UCC, the ED visit rate was 0.27 per unique patient. After opening the UCC, we found a 13.9% relative decrease in the overall ED visit rate from 0.27 to 0.23 (p = 0.02). The SCH patient ED visit rate declined by 12.5% (p = 0.03) and the community practice rate declined by 37.1%; however, the latter decline was not statistically significant, potentially due to a small sample size (p = 0.19). Conclusions: Our study found a decrease in the ED visit rate after the opening of an Oncology UCC. An urgent care strategy for cancer centers may serve as an efficient way to manage patients while minimizing ED use. [Table: see text]

e19367 Background: The OCM is a Centers for Medicare and Medicaid Services (CMS) alternative payment model, which seeks to curb costs while improving care for patients receiving systemic cancer therapy. CMS models the expected total cost (spending target) for each 6-month episode using historical, geographic and clinical factors including CTr participation. We evaluated the relationship between CTr participation, actual cost of care and performance in the OCM. Methods: We used claims for OCM episodes attributed to the Yale Cancer Center between July 2016 and July 2018. We stratified episodes by CTr participation and used t-tests and chi-square tests to compare total cost, drug costs (Part B and D) and whether actual episode costs were above or below CMS targets. Analyses were conducted for the total sample, and among the most common cancer types. Results: Among 9,387 OCM episodes (5,270 unique patients), 815 (8.7%) episodes involved a CTr. Among non-CTr patients, the mean Medicare cost per episode ($32,909) was modestly higher than the mean episode spending target ($31,746; p < 0.001), while in the CTr group, the mean Medicare cost per episode ($36,590) was substantially lower than the mean episode spending target ($48,124 p < 0.001). Mean drug cost was lower with CTr vs without ($15,650 vs $19,587, p < 0.001). Drug costs also accounted for a lower percentage of total costs for episodes with CTr vs not (41% vs 57%). CTr episodes were more likely to meet spending targets than non-CTr episodes (66% vs 56%, p < 0.001) overall and in breast, lung, and myeloma cancers, although only statistically significant for lung cancer (76% CTr vs 48% non-CTr, p < 0.001). Mean difference between target and actual costs was greater for episodes with CTr (- $11,534) than for episodes without CTr (+ $1,163) (p < 0.001). Conclusions: On average, episodes with CTr participation had substantially lower costs compared with their spending targets, while non-CTr episodes had slightly higher costs compared with their spending targets. While total cost of care was higher for episodes with CTr (as the CMS model predicts), drug costs were significantly lower. As drugs comprise a large proportion of total cost, lower drug costs in CTr episodes likely contribute to savings. Additional research should explore whether other OCM centers with higher rates of CTr participation are more likely to meet spending targets in value-based payment models.

85 Background: Growing outpatient volume poses patient flow challenges, making it difficult to accommodate the complexities of academic medical practice. Volume increases create operational inefficiencies like delays in lab turnaround time (TAT) and limited rooming capacity resulting in delays in patient access and reduced provider productivity. These bottlenecks negatively impact patient satisfaction. Methods: Four multidisciplinary teams assessed barriers to patient flow in the lab, rooming and scheduling process. We sought to maximize the Advanced Practice Provider (APP) role within clinical programs. Each team was led jointly by an MD and RN and included subject matter experts, advisors and facilitators. The groups met regularly for 2 months to evaluate operational data, national benchmarks and surveyed staff. Monthly progress was presented during the Ambulatory Clinic Committee (ACC) meetings. Number of labs not completed on time, wait-time and APP visit volume were tracked. Results: Recommendations included provider education on lab order process, purchasing a second instrument for chemistries, APP independent visits standards, and realistic scheduling times. Preliminary findings indicate that the lab reduced their average TAT defect rate by 52.3% (CBC) and 76.1% (CMP) compared to January 2014 (baseline) and January 2015 (post implementation). This difference was statistically significant at a 95% CI, with p < .001 for both CBCs and CMPs. In addition, APP total visit volume increased by 81% from FY 2014 to FY 2015. Finally, the overall Press-Ganey mean in patient satisfaction with physician wait time increased from 81.7 to 82.7 (2014 vs. 2015). Conclusions: Multidisciplinary teams recommended valuable process improvement changes to reduce the TAT in the lab and to promote that APPs work within the full scope of their license. Implementation requires extensive project management support and continuous tracking to evaluate outcomes. Opportunities exist to maximize space and room utilization and optimize the scheduling process as the outpatient volume continues to increase.

2 Background: Value-based payment programs like the Oncology Care Model (OCM) have focused efforts to reduce costly acute care use through improvements in access and coordination rather than targeting the exponential rise in pharmaceutical pricing. We assessed how participation in OCM affected total cost of cancer care at a large academic cancer center. Methods: Using Medicare claims for Yale-Smilow Cancer Hospital, an NCI-designated cancer center with an academic hub and 10 community practices, we identified episodes for chemotherapy initiated during a historical period (pre-OCM, 2012-2015) and performance period (post-OCM, 2016-2017) following OCM criteria to identify total cost of care. We reported frequency of utilization categories, the mean cost per episode, the proportion of total cost attributed to each utilization category and compared pre- and post-participation periods. Results: There were 8,843 episodes during the historical period and 6,679 episodes during the performance period. The mean total cost per episode increased from $28,645 to $32,666, but this was less than the Medicare-defined expected increase (target price). Between the two periods, the percentage of total episodes decreased for emergency department (ED) use from 36% to 33%, inpatient care from 33% to 29%, and post-acute care from 28% to 25% (p < 0.01). Mean costs of drugs per episode increased by 27% between periods, and from 52% to 58% of total cost of care (p < 0.01). While mean cost per episode for ED, inpatient, and post-acute care remained stable, the mean cost per episode for antineoplastics increased 39% from $10,676 to $14,843. Conclusions: After implementing OCM, we beat the Medicare target largely by decreasing acute care use and stabilizing the cost of hospitalizations and ED; however, actual cost increased largely due to pharmaceutical spending. Because drug costs were the largest proportion of overall cost of care, future value-based models must address the rising cost of pharmaceuticals. [Table: see text]