Variations in hospitalization and emergency department/observation stays using the oncology care model methodology in Medicare data

PURPOSE: With the introduction of the Oncology Care Model and plans for the transition to Oncology Care First, alternative payment models (APMs) are an increasingly important piece of the oncology care landscape. Evidence is mixed on the Oncology Care Model's impact on utilization and costs, but as policymakers consider expansion of similar models, it is critical to understand the characteristics of hospitals that may be differentially affected. METHODS: We used 2007-2016 SEER-Medicare data to identify patients with breast and prostate cancer receiving chemotherapy, endocrine therapy (breast), or androgen deprivation therapy (prostate). For each hospital, we calculated 6-month expected mortality, emergency department (ED) visits, inpatient admissions, and costs, all commonly collected APM outcomes. After calculating observed-to-expected rates for each outcome by hospital, we estimated the association between observed-to-expected rates and characteristics of each hospital to understand hospital characteristics that might be associated with higher- or lower-than-expected rates of each outcome. RESULTS: Hospitals with > 15% rural patients had significantly higher-than-expected mortality (0.31 points higher, P < .001) and ED visit rates (0.10 points higher, P = .029) as well as significantly lower costs (0.06 points lower, P = .004). Hospitals unaffiliated with a medical school also experienced significantly higher-than-expected mortality and ED visits. Hospitals eligible for disproportionate share hospital payment experienced significantly higher ED visits but lower costs. For-profit hospitals experienced higher-than-expected mortality. CONCLUSION: Rural hospitals and those unaffiliated with a medical school may require special consideration as APMs expand in oncology care. Designated cancer centers and larger hospitals may be advantaged.

Download Full-text

The association between hospitals’ risk-adjusted emergency department visits and survival and costs in kidney cancer patients.

Journal of Clinical Oncology ◽

10.1200/jco.2019.37.7_suppl.609 ◽

2019 ◽

Vol 37 (7_suppl) ◽

pp. 609-609

Author(s):

Joel E Segel ◽

Eric W. Schaefer ◽

Jay D. Raman ◽

Christopher S. Hollenbeak

Keyword(s):

Emergency Department ◽

Cancer Patients ◽

Kidney Cancer ◽

Mortality Rates ◽

Emergency Department Visits ◽

Medicare Data ◽

Initial Surgery ◽

Oncology Care ◽

Ed Visits ◽

The Cost

609 Background: As payers turn to alternative payment models, including the CMS Oncology Care Model, risk-adjusted emergency department (ED) visits are being incorporated as a quality. Yet little is know about this metric compares to existing metrics such as risk-adjusted mortality rates and costs. Methods: Using 2007-2012 SEER-Medicare data, we used logistic regression to model occurrence of an ED visit within 30 and 365 days for all kidney cancer patients receiving initial surgery. Our model controlled for demographics, stage, histology, systemic targeted therapy, and comorbidities. Based on model predictions, we created a ratio of actual versus predicted ED visits for hospitals to identify hospitals with higher and lower than predicted ED visit rates. We estimated the association between the hospitals’ ED visit ratio and hospitals’ risk-adjusted 365-day mortality rates, and 6- and 12-month total costs and total costs (less ED visits). Results: In our sample of 6,078 patients, 15.5% had an ED visit within 30 days of surgery and 43.5% within 365 days. For hospitals with ≥10 patients, we found no statistically significant association between 30-day or 365-day risk-adjusted ED visit rate and their 365-day risk-adjusted mortality rate. While hospitals’ 30-day ED visit rates were significantly associated with 6- and 12-month costs, the association was largely driven by the cost of the ED visit itself. Conversely, hospitals’ 365-day ED visit rates were significantly associated with 12-month costs after excluding the cost of the ED visit. Conclusions: Our results suggest hospitals’ risk-adjusted ED visit rates capture a qualitatively different measure of quality than the more commonly reported mortality rates and is significantly associated with patient cost.

Download Full-text

Frontline oncology care team primary palliative symptom guideline education, the oncology care model, and emergency department visits.

Journal of Clinical Oncology ◽

10.1200/jco.2018.36.34_suppl.143 ◽

2018 ◽

Vol 36 (34_suppl) ◽

pp. 143-143 ◽

Cited By ~ 1

Author(s):

Susan McInnes ◽

Cheryl M Carrino ◽

Laura Shoemaker

Keyword(s):

Emergency Department ◽

Cancer Patients ◽

Cleveland Clinic ◽

Care Quality ◽

Emergency Department Visits ◽

Care Model ◽

Main Campus ◽

Palliative Care Teams ◽

Oncology Care ◽

Ed Visits

143 Background: The Oncology Care Model (OCM) is a novel 5-year quality-based Oncology payment and care delivery program established by the Centers for Medicare & Medicaid Service in 2016. OCM prioritizes high-quality, coordinated care for patients undergoing chemotherapy (chemo pts.) Participating centers provide augmented services to enhance care and meet quality goals. Challenging symptoms (sxs) are common among chemo pts and may lead to hospitalization and decreased quality of life. Specialist palliative care teams are not able to see all chemo pts with active sxs. Front line oncology care teams (FLC) need education on primary palliative sx management. Methods: Cleveland Clinic Taussig Cancer Institute is one of 181 practices voluntarily participating in OCM. Locations include main campus and 5 regional cancer offices with 100 oncologists caring for about 4,000 chemotherapy patients annually. Our OCM team engaged Oncology (Onc) and Palliative Medicine (PM) providers to standardize sx management. Education was provided to FLC of all disciplines. Electronic record analytics were used to determine emergency department (ED) utilization. Results: A multidisciplinary team of Onc and PM experts developed guidelines for 4 common sxs (chemotherapy-induced neuropathy, persistent cancer pain, nausea/vomiting and constipation. Guidelines were approved by key Onc and PM staff and made available to all providers online. There were 4 educational sessions for FLCs to all sites in 2017. Urgent sx outpatient appointment slots were created in oncology offices to address uncontrolled sx. From Dec 2017 to May 2018, ED visits for all cancer patients at main campus decreased from 500/month to 453/month (9.4%.) Reductions in ED visits were also seen at 2 hospitals adjacent to regional cancer centers (16% and 6%.) Conclusions: OCM participation provided an opportunity to improve care quality at our institution. Primary palliative sx guidelines were successfully developed by an interdisciplinary team and disseminated to FLC. Urgent sx management appointments were made available in oncology offices. These interventions coincided with a reduction in ED visits for all cancer patients.

Download Full-text

Interpreting Oncology Care Model Data to Drive Value-Based Care: A Prostate Cancer Analysis

Journal of Oncology Practice ◽

10.1200/jop.18.00336 ◽

2019 ◽

Vol 15 (3) ◽

pp. e238-e246 ◽

Cited By ~ 4

Author(s):

Ronald D. Ennis ◽

Anish B. Parikh ◽

Mark Sanderson ◽

Mark Liu ◽

Luis Isola

Keyword(s):

Prostate Cancer ◽

Emergency Department ◽

Claims Data ◽

Hospital Utilization ◽

Care Model ◽

Castration Resistant ◽

Episodes Of Care ◽

Oncology Care ◽

Hormone Sensitive ◽

Value Based Care

PURPOSE: The Oncology Care Model (OCM) must be clinically relevant, accurate, and comprehensible to drive value-based care. METHODS: We studied OCM data detailing observed and expected expenses for 6-month-long episodes of care for patients with prostate cancer. We constructed seven disease state–treatment dyads into which we grouped each episode on the bases of diagnoses, procedures, and medications in OCM claims data. We used this clinical-administrative stratification model to facilitate a comparative cost analysis, and we evaluated emergency department and hospital utilization and drug therapy as potential drivers of cost. RESULTS: We examined 377 episodes of care, pertaining to 210 patients, that took place within our health system from January 2012 to June 2015. Ninety-six percent of episodes were assigned to clinically meaningful dyads. Excessive expenses were seen in metastatic, castration-resistant dyads containing second-line hormone therapy (ratio of observed to expected expenses [O/E], 2.66), chemotherapy (O/E, 2.09), and radium-223/sipuleucel-T (O/E, 3.01). An OCM update correcting for castration-resistant prostate cancer led to small differences in observed expenses (0% to +2%) but large changes in expected expenses (−17% to −27% for hormone-sensitive dyads and +136% to +141% for castration-resistant dyads). O/E increased up to 38% for hormone-sensitive dyads and decreased up to 58% for castration-resistant dyads. Emergency department and hospital utilization seems to drive cost for castration-resistant dyads but not for hormone-sensitive dyads. In the revised OCM model, overall O/E for all episodes improved by 22%, from 1.48 to 1.15. CONCLUSION: Our experience with OCM highlights the limitations of administrative claims data within this model and illustrates a method of translating these data into clinically meaningful information to improve value.

Download Full-text

Association between Spending in Oncology Care Model-Defined Episodes and Overall Survival in Multiple Myeloma Using 2012-2017 Medicare Data

Blood ◽

10.1182/blood-2020-136284 ◽

2020 ◽

Vol 136 (Supplement 1) ◽

pp. 22-23

Author(s):

Ruben Niesvizky ◽

Zoe Clancy ◽

Ronda Copher ◽

Ryan B. Thomas ◽

Cynthia Z. Qi ◽

...

Keyword(s):

Drug Costs ◽

Specific Drug ◽

Care Model ◽

Analysis Group ◽

Medicare Data ◽

Primary Driver ◽

Oncology Care ◽

Comorbidity Index ◽

The Cost ◽

Over Time

Introduction: The Oncology Care Model (OCM) is an innovative payment model introduced by the Centers for Medicare & Medicaid Services in 2016, which aims to improve quality and reduce the cost of cancer care. Under this framework, practices are incentivized to reduce spending in chemotherapy-centered episodes. Previous studies using data from pre-OCM periods (i.e. before July 1, 2016) suggested that reducing OCM episode costs, particularly disease-specific drug costs, may adversely affect overall survival (OS) in patients with multiple myeloma (MM). Using more recent data that covers 1.5 years after OCM implementation, the current study aimed to evaluate trends in OCM-defined episode costs and OS over time. Additionally, the association between OCM-defined MM episode costs and OS in MM, as well as changes in the association between the pre- and post-OCM periods, were evaluated. Methods: Patients with newly diagnosed MM (NDMM) and ≥ 1 qualifying OCM-defined MM episode between 2012 and 2017 were selected from the 100% Medicare data. OCM episodes were defined as the 6-month period following a triggering MM treatment claim. Each episode was linked to a practice and classified based on participation in the OCM and occurrence of pre- versus post-OCM implementation. Regression models were developed, based on the OCM algorithm, to adjust for case severity mix at the practice level. The models evaluated the impact of patient and episode characteristics on total episode costs, and episode subcomponents (e.g. MM-specific drugs, other medical treatment). Based on the regression outputs, standardized costs were calculated for each practice, adjusting for differences in patient and episode characteristics. All costs were inflated to 2017 US dollars (USD). From initial MM diagnosis, mean unadjusted episode costs, mean standardized episode costs, and 1-year OS rate were described over time by the year of first episode initiation. Two Cox proportional hazards models were constructed for OS with adjustment for key patient covariates (i.e. age, gender, comorbidity index, race, number of OCM episodes, and disability entitlement). Model 1 evaluated the association between OS and standardized total episode costs and the proportion of episode costs attributed to MM-specific drugs. Model 2 evaluated the effect on OS of the interaction between the time period (i.e. pre-OCM vs post-OCM) and total standardized episode costs. The analyses were repeated for key patient subgroups stratified by comorbidity status (i.e. low Charlson Comorbidity Index [CCI] vs high CCI) and practice type (i.e. OCM vs non-OCM). Results: A total of 17,363 patients with NDMM (51.0% male) were included in the analysis. Mean age at diagnosis was 74.8 years (30.0-102.0). Patient characteristics were comparable between pre-OCM and post-OCM periods. There were a total of 41,972 OCM episodes during the mean 2.2 years (standard deviation [SD] 1.4) of follow-up. Average (SD) MM-drug costs, other medical and drug costs, and total costs per OCM episode were USD 51,482 (USD 31,752), USD 22,625 (USD 28,452), and USD 74,107 (USD 38,606), respectively. From 2012 to 2017, average total episode costs, MM-drug costs, and survival all increased (Table). Model 1 indicated that a USD 10,000 increase in standardized total costs was associated with a 27.5% lower risk of death (hazard ratio [HR] 0.725; P < 0.05) and that MM-drug costs were the primary driver for the improved OS. The hazard of death decreased by 21.3% (HR 0.787; P < 0.05) for every 10% increase in the proportion of costs attributed to MM-drug costs versus other medical costs. Model 2 showed the association was similar during pre-OCM and post-OCM periods. Consistent results were observed between subgroups of high versus low CCI and OCM versus non-OCM practices. Conclusions: The analysis identified a positive correlation between average spending within OCM-defined episodes and OS among patients with NDMM, demonstrating observable clinical value for patients. This correlation remained consistent across pre-OCM and post-OCM periods. A closer evaluation of the cost subcomponents suggested that MM-specific drug costs are a primary driver for the observed OS benefit. Patient benefit from the innovative, albeit higher cost, therapies is noteworthy given the improved survival of MM. Careful evaluation is warranted for healthcare providers when attempting to reduce spending in response to new payment models. Disclosures Niesvizky: BMS: Consultancy, Honoraria; Amgen: Consultancy, Honoraria; Takeda: Consultancy, Honoraria; Karyopharm: Consultancy, Honoraria; GSK: Consultancy, Honoraria; Janssen: Consultancy, Honoraria. Clancy:BMS: Current Employment, Current equity holder in publicly-traded company. Copher:Bristol Myers Squibb: Current Employment. Thomas:BMS: Current Employment. Qi:BMS: Other: Employee of Analysis Group Inc., which received consulting fees; Astellas Pharma, Inc.: Research Funding. Zhou:BMS: Other: Employee of Analysis Group Inc., which received consulting fees. Zichlin:BMS: Other: Employee of Analysis Group Inc., which received consulting fees. Koenigsberg:BMS: Other: Employee of Analysis Group Inc., which received consulting fees. Signorovitch:BMS: Other: Employee of Analysis Group Inc., which received consulting fees.

Download Full-text

Commentary: Quality nutrition care is integral to the Oncology Care Model

Supportive Care in Cancer ◽

10.1007/s00520-021-06436-x ◽

2021 ◽

Author(s):

Mary Beth Arensberg ◽

Beth Besecker ◽

Laura Weldishofer ◽

Susan Drawert

Keyword(s):

Best Practice ◽

Treatment Guidelines ◽

Nutrition Therapy ◽

Care Model ◽

Nutrition Care ◽

Ambulatory Oncology ◽

Oncology Care ◽

Improvement Programs ◽

And Performance ◽

Registered Dietitian Nutritionists

AbstractThe Oncology Care Model (OCM) is a US Centers for Medicare & Medicaid Services (CMS) specialty model implemented in 2016, to provide higher quality, more highly coordinated oncology care at the same or lower costs. Under the OCM, oncology clinics enter into payment arrangements that include financial and performance accountability for patients receiving chemotherapy treatment. In addition, OCM clinics commit to providing enhanced services to Medicare beneficiaries, including care coordination, navigation, and following national treatment guidelines. Nutrition is a component of best-practice cancer care, yet it may not be addressed by OCM providers even though up to 80% of patients with cancer develop malnutrition and poor nutrition has a profound impact on cancer treatment and survivorship. Only about half of US ambulatory oncology settings screen for malnutrition, registered dietitian nutritionists (RDNs) are not routinely employed by oncology clinics, and the medical nutrition therapy they provide is often not reimbursed. Thus, adequate nutrition care in US oncology clinics remains a gap area. Some oncology clinics are addressing this gap through implementation of nutrition-focused quality improvement programs (QIPs) but many are not. What is needed is a change of perspective. This paper outlines how and why quality nutrition care is integral to the OCM and can benefit patient health and provider outcomes.

Download Full-text

Impact of cancer- and patient-level factors on provider risk in the Oncology Care Model.

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.e18852 ◽

2021 ◽

Vol 39 (15_suppl) ◽

pp. e18852-e18852

Author(s):

Basit Iqbal Chaudhry ◽

Andrew Yue ◽

Shuchita Kaila ◽

Kay Sadik ◽

Lisa Tran ◽

...

Keyword(s):

Domain Knowledge ◽

Research Literature ◽

Risk Exposure ◽

Progression Rate ◽

Cell Transplant ◽

Care Model ◽

Data Set ◽

Patient Level ◽

Oncology Care ◽

The Impact

e18852 Background: Transferring financial risk from payers to providers to align incentives is central to value-based payment (VBP) reform, including Medicare’s Oncology Care Model (OCM). We simulated the impact of selected cancer- and patient-level factors on providers’ risk in OCM for multiple myeloma (MM), due to its clinical complexity. We hypothesize that risk exposure is sensitive to factors extrinsic to the OCM methodology, including clinical phenotype, disease state and progression rate. Methods: Simulation was used to address omitted variable bias in payer data. We developed 9 key clinical MM scenarios to examine provider risk, based on conceptual frameworks that included patient- and cancer-level factors. The model was parameterized using the Medicare limited data set, research literature and domain knowledge. Twenty factors were varied for each model, e.g. age, autologous stem cell transplant (ASCT). Results: Simulations results showed MM risk for providers depended highly on cancer and patient level factors (see table). For example, high-risk patients were on average $21.5K over target while undergoing ASCT (despite risk adjustment for ASCT) and $18-28K under target for follow on maintenance (maint.) episodes. Conclusions: Provider exposure to risk in OCM is highly sensitive to factors at the cancer and patient level. The distribution of clinical phenotypes, state of disease, and rate of disease progression can significantly impact risk exposure for providers in OCM. New methodologies that model risk in more clinically granular ways are needed to improve VBP in oncology. [Table: see text]

Download Full-text

Centers for Medicare & Medicaid Services' Oncology Care Model: Delivering Higher Value Cancer Care

Clinical Pharmacology & Therapeutics ◽

10.1002/cpt.638 ◽

2017 ◽

Vol 101 (5) ◽

pp. 569-571 ◽

Cited By ~ 4

Author(s):

LK Mortimer ◽

LM Strawbridge ◽

EW Lukens ◽

A Bassano ◽

PH Conway ◽

...

Keyword(s):

Cancer Care ◽

Care Model ◽

Oncology Care

Download Full-text

Breast Cancer Stage Is Associated With Exceeding Target Price in the Oncology Care Model

JCO Oncology Practice ◽

10.1200/op.21.00270 ◽

2021 ◽

Author(s):

Ryan B. Thomas ◽

Vittorio Maio ◽

Anna Chen ◽

Seojin Park ◽

Dexter Waters ◽

...

Keyword(s):

Breast Cancer ◽

Academic Medical Center ◽

Stage Iv ◽

Cost Of Care ◽

Mean Difference ◽

Cancer Stage ◽

Care Model ◽

Target Price ◽

Old Patients ◽

Oncology Care

PURPOSE: To explore mean difference between Oncology Care Model (OCM) total costs and target price among breast cancer episodes by stage under the Centers for Medicare and Medicaid Services OCM payment methodology. METHODS: Breast cancer episodes from OCM performance period 1-4 reconciliation reports (July 1, 2016-July 1, 2018) were linked with health record data from a large, academic medical center. Demographics, total cost of care (TCOC), and target price were measured by stage. Adjusted differences between TCOC and target price were compared across cancer stage using multivariable linear regression. RESULTS: A total of 539 episodes were evaluated from 252 unique patients with breast cancer, of which 235 (44%) were stage I, 124 (23%) stage II, 33 (6%) stage III, and 147 (27%) stage IV. About 37% of episodes exceeded target price. Mean differences from target price were –$1,782, $2,246, –$6,032, and $11,379 all in US dollars (USD) for stages I through IV, respectively. Stage IV episodes had highest mean TCOC ($44,210 USD) and mean target price ($32,831 USD) but also had higher rates of chemotherapy, inpatient admission, and novel therapy use. After adjusting for covariates, stage IV and ≥ 65-year-old patients had the highest mean difference from target price ($17,175 USD; 95% CI, $12,452 to $21,898 USD). CONCLUSION: Breast cancer episodes in older women with distant metastases most frequently exceeded target price, suggesting that target price did not adequately account for complexity of metastatic cancers. A metastatic adjustment introduced in PP7 represents a promising advancement in the target price methodology and an impact evaluation will be needed.

Download Full-text