PURPOSE: As expenditures for cancer care continue to grow substantially, value-based payment models are being tested to control costs. The Oncology Care Model (OCM) is the largest value-based payment program in oncology. The primary objective of this analysis was to determine the impact of high-cost novel agents on total cost of care for multiple myeloma (MM) episodes of care in the OCM. METHODS: This was a retrospective analysis using Medicare claims data for 258 MM OCM episodes initiated between July 1, 2016, and July 1, 2017. Patients were organized into 3 cohorts: those who received pomalidomide (cohort A), those who received lenalidomide (cohort B), and those who did not receive either drug but had received another chemotherapy agent (cohort C). We compared the actual episode expenditures and the Centers for Medicare and Medicaid target price to create an observed versus expected (O/E) ratio. RESULTS: The average O/E for cohort A (n = 73) was 1.73, compared with 1.31 for cohort B (n = 84) and 1.01 for cohort C (n = 101). The difference the in O/E ratio among the groups was statistically significant ( P < .001). The average episode target price for cohorts A, B, and C was $66,149, $63,483, and $63,937, respectively. Despite the high cost of pomalidomide and lenalidomide, there was no significant difference in the average episode target prices of the cohorts. CONCLUSION: The O/E ratio and target prices of the cohorts demonstrate a lack of adequate adjustment to the OCM target price for episodes in which pomalidomide and lenalidomide were used to treat patients with MM.
Background: In July of 2016, CMMI started a five year bundled payment program called OCM. Beneficiaries are attributed to practices providing their cancer care for a 6 month episode triggered by the administration or distribution of specified cancer drugs. The model provides risk adjustments to the target price based on risk factors such as age, chemotherapy and the receipt of certain treatments (radiation or bone marrow transplant). Target prices are adjusted by geographic region, novel therapy use, and a trend factor. Multiple Myeloma was identified in our practice as a cancer type with high variance on expenditures after the first Performance Period within the model (July 2016-December 2016). Chemotherapy represented 52% of total episode expenditures with oral chemotherapy and hormone therapy representing 24%. The average cost of lenalidomide for one year is $115,000. The model adjusts for novel therapies, including new drugs approved by the FDA after December 31, 2014 with status lasting two years. However, literature demonstrates that this does not fully adjust for the high costs of novel therapies (Muldoon et at., Health Affairs, 2018). Unlike solid tumors, Multiple Myeloma staging may not improve risk adjustment and target price. Methods: We analyzed the total cost of care of beneficiaries who triggered an OCM episode for Performance Period 1 (PP1). Beneficiaries were identified by diagnosis of Multiple Myeloma, and then segregated into cohorts of those who received lenalidomide and/or pomalidomide and those who did not. Observed vs. Expected (O/E) target price for each episode was determined for both cohorts comparing the actual episode expenditures and the target price per episode calculated by the Oncology Care Model. A two sample t-test was conducted followed by a linear regression to determine relation between drug days prescribed and O/E. Results: There were 125 attributed beneficiaries with a Multiple Myeloma diagnosis who triggered an episode during PP1. The average O/E of the cohort which received the chemotherapy, Cohort A, was 1.624 compared to 0.986 for those that did not, Cohort B. The difference in average O/E in the two cohorts was 39% higher in Cohort A, p<0.001. There were no significant differences in the amount of inpatient claims, ED visits, or Bone Marrow Transplants between the two cohorts (Table 1). Figure 1 demonstrates the positive linear relationship (p<0.01, r=.40) between number of days supplied and O/E. Discussion: This is the first report on the impact of lenalidomide and pomalidomide on the total cost of care in an OCM practice. The results demonstrate the lack of adequate adjustment to the CMS target price for episodes in which one or both of these drugs were prescribed. Lenalidomide and pomalidomide are first and second line drugs used both for induction and maintenance. Both drugs are frequently used for prolonged periods of time in patients and trigger more than one episode in OCM. Therefore, the use of these agents greatly affects the total cost of care against a target price that is not adequately adjusted. Academic Medical Centers that care for larger populations of multiple myeloma patients may be disproportionately affected and this will impede their success under the OCM methodology. Additional analysis similar to this will inform CMMI as to further refinements to the OCM adjusters. Disclosures No relevant conflicts of interest to declare.
308 Background: Reducing variation in care can improve outcomes and decrease costs. Evidence based medicine drives cancer guidelines and adherence promotes quality cancer care. Value based programs are based on adherence to pathways. Most institutions adopt costly cloud based clinical pathways products but none are mature products that fully integrate with the EHR and they require additional data entry. We present our simple Clinical Decision Support (CDS) tool for identifying best practice treatment protocols driven by the cancer diagnosis in the EMR for our large, multi-site, mixed academic and community cancer system. Methods: Our chemotherapy council must approve all protocols that are published in the system’s Epic Beacon library using a rigorous scoring system based on level of evidence and FDA or NCCN approval. Then each protocol is “tagged” appropriately: “Tier 1A”: Preferred Regimens/NCCN Approved; “Tier 1B”: Preferred Regimens/Chemo Council Approved (but not NCCN Approved); Tier 2: Specific Disease Management Team approved regimens; and finally “Other” or research protocols. When the oncologist enters the treatment plan in EPIC, a list of protocols are suggested, ordered by level of evidence, based on the cancer diagnosis and with the easily visible level of evidence or “tag” to allow data driven decision making. Results: We implemented our CDS tool December 12, 2019. As of mid-June, 2020 a total of 1637 treatment plans have been implemented. Of those, 1323 (81%) are Tier 1A, 310 (2%) are Tier 1B and 4 (.2%) are Tier 2. Thus demonstrating 81% adherence to NCCN approved regimens across the system, regardless of the line of treatment. GI and breast cancers were responsible for the most plans with the highest adherence to Tier 1A plans, specifically 92% among the breast cancer group. Multiple Myeloma and Sarcoma were tied for the lowest adherence rate of 58%. This data can be further stratified by medical oncologist. Interestingly, Multiple Myeloma had the highest utilization of Tier 1B protocols perhaps reflecting the rapidly changing literature that is ahead of the guidelines. Conclusions: We demonstrated adherence to NCCN protocols 81% of the time over a 6 month period and over multiple cancer types. Protocol tagging and reporting utilizing the EMR alone could be used as a powerful model for value based care. We identified disease areas that will require further education regarding evidence based treatment and can consider interventions including real time feedback to clinicians and/or best practice advisories or quality based incentives.
197 Background: Cancer survivors have physical, psychosocial, emotional and financial needs that vary in prevalence and may differ from needs of patients on active cancer treatment. Distress screening is mandated for cancer program accreditation and identifies, addresses and monitors the needs of patients. There is a paucity of data on the clinical application of distress screening among survivors. We used a validated distress screening tool to conduct a needs assessment of cancer survivors in the solid oncology clinic. Methods: The Cancer Support Source Distress Screening tool is an 18-item questionnaire given to patients on their 2nd and every 3 month medical oncology clinic visit to assess depression and distress. We merged patient survey data (patients completing ≥ 2) with the cancer registry to identify cancer survivors from July 2015 to October 2018. We performed bivariate and multivariate analysis evaluating change in depression and distress scores over time. Results: 92 patients were identified. The table indicates changes in depression and distress scores by cancer. Depression scores improved for most cancer types with an improvement in distress scores across all cancers. Emotional/mental health, communication, provider relationship, system of care, body image and social support were associated with significant changes in survivorship concern. We are completing a multivariate analysis controlling for sociodemographic factors to evaluate change in depression and distress scores across the survivorship trajectory. Conclusions: A distress screening survey may be a useful tool in assessing the unmet needs of cancer survivors. Identifying prevalent domains of survivorship issues can highlight areas of greatest perceived need and can guide quality improvement initiatives within a cancer program. [Table: see text]
53 Background: Hospitalists have been practicing alongside oncologists to provide high quality care for hospitalized cancer patients. We examined the differences in hospital utilization and outcomes among solid tumor patients admitted to oncologist-led teams (OT) versus hospitalist-led teams (HT). Methods: We performed a retrospective cohort study of patients with solid tumors admitted to the OT or HT at Mount Sinai Hospital from July to December 2019. We excluded patients less than 18 years of age, primary hematologic malignancies, or admission to intensive care or surgical units. We used the Activity Measure for Post Acute Care (AMPAC) and Charlson Comorbidity Index as a measure of functional ability and illness severity, respectively. We performed bivariate and multivariate analyses comparing differences in length of stay, ICU transfers, palliative care consults, healthcare proxy (HCP) decision, new DNR decision, readmission within 30 days and inpatient mortality by type of admitting service (OT vs HT). Results: A total of 544 patients were identified; 61% (334) admitted to HT. There were significant differences according to race and cancer type (p= 0.001 for both). HT patients had more functional impairment (p<0.0001) and poorer prognosis (p=0.0002). In bivariate analysis, HT had significantly more ICU transfers (OT, 2% vs HT, 8%; p=0.008), new DNR decisions (OT, 7% vs HT, 16%; p=0.002), and inpatient mortality (OT, 4% vs HT, 9%; p=0.02) while OT had significantly more palliative care consults (OT, 45% vs HT, 20%; p<0.0001). Multivariate analysis (Table) demonstrates HT had significantly more new DNR decisions (odds ratio [OR]: 0.46, 95% confidence interval [CI]: 0.23-0.93) and OT had significantly more palliative care consults (OR: 4.01, 95% CI: 2.51-6.43). Conclusions: At our academic hospital, inpatient cancer care led by hospitalists is comparable to that of oncologists despite HT caring for more severely ill oncology patients. From a value perspective, hospitalists facilitating care for hospitalized cancer patients will allow oncologists to maximize ambulatory time and focus on active cancer treatment. [Table: see text]
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.