Estimating cost implications of potentially avoidable hospitalizations among Oncology Care Model patients with prostate cancer.

PURPOSE/OBJECTIVES: We sought to estimate the expected cost savings generated if a set of potentially avoidable hospitalizations (PAHs) among oncology care model (OCM) patients with prostate cancer were shifted to an acute care model in the outpatient setting. METHODS: We previously identified a set of 28 PAHs among OCM prostate cancer patients. Outpatient management costs for a characteristically similar cohort of cancer patients were obtained from our institution's ambulatory acute-care Oncology Care Unit (OCU). We excluded OCU visits resulting in hospitalization, involving non-cancer diagnoses, and those missing clinical/financial information. Exact-matching based on the strata of age, categorically-defined presenting complaint, and systemic disease was used to match PAHs to OCU acute care visits. PAH costs obtained from OCM data were compared to costs from matched OCU visits. RESULTS: We identified 130 acute care OCU visits, of which 47 met inclusion criteria. Twenty-four PAHs (89%) matched to 26 of these OCU visits. PAHs accounted for 5.8% of OCM expenditures during our study period. The mean inpatient cost among matched PAHs was $15,885 compared to $6,227 for matched OCU visits. Boot strapping within each match stratum produced a mean estimated cost savings of $12,151 (95% CI $10,488 to $13,814) per PAH. We estimate this per event savings to yield a 4.4% (95% CI 3.8% to 5.0%) an overall spending decrement for OCM prostate cancer episodes. CONCLUSIONS: PAHs contribute meaningfully to costs of care in oncology. Investment in specialized ambulatory acute care services for oncology patients could lead to substantial cost savings.

Multiple Myeloma Cost of Care Under the Oncology Care Model: The Influence of High-Cost Therapies.

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.

Comparison of statistical and machine learning models for healthcare cost data: a simulation study motivated by Oncology Care Model (OCM) data.

BACKGROUND: The Oncology Care Model (OCM) was developed as a payment model to encourage participating practices to provide better-quality care for cancer patients at a lower cost. The risk-adjustment model used in OCM is a Gamma generalized linear model (Gamma GLM) with log-link. The predicted value of expense for the episodes identified for our academic medical center (AMC), based on the model fitted to the national data, did not correlate well with our observed expense. This motivated us to fit the Gamma GLM to our AMC data and compare it with two other flexible modeling methods: Random Forest (RF) and Partially Linear Additive Quantile Regression (PLAQR). We also performed a simulation study to assess comparative performance of these methods and examined the impact of non-linearity and interaction effects, two understudied aspects in the field of cost prediction. METHODS: The simulation was designed with an outcome of cost generated from four distributions: Gamma, Weibull, Log-normal with a heteroscedastic error term, and heavy-tailed. Simulation parameters both similar to and different from OCM data were considered. The performance metrics considered were the root mean square error (RMSE), mean absolute prediction error (MAPE), and cost accuracy (CA). Bootstrap resampling was utilized to estimate the operating characteristics of the performance metrics, which were described by boxplots. RESULTS: RF attained the best performance with lowest RMSE, MAPE, and highest CA for most of the scenarios. When the models were misspecified, their performance was further differentiated. Model performance differed more for non-exponential than exponential outcome distributions. CONCLUSIONS: RF outperformed Gamma GLM and PLAQR in predicting overall and top decile costs. RF demonstrated improved prediction under various scenarios common in healthcare cost modeling. Additionally, RF did not require prespecification of outcome distribution, nonlinearity effect, or interaction terms. Therefore, RF appears to be the best tool to predict average cost. However, when the goal is to estimate extreme expenses, e.g., high cost episodes, the accuracy gained by RF versus its computational costs may need to be considered.

Identifying Potentially Avoidable Hospitalizations in Medicare Patients With Prostate Cancer: A Retrospective Analysis.

PURPOSE:: If identifiable, potentially avoidable hospitalizations (PAHs) can serve as an important target for cost containment efforts in oncology. METHODS:: PAHs among a cohort of Medicare patients with prostate cancer were identified using a two-stage consensus-driven review process. In stage 1, two clinicians independently evaluated admissions records using a case review form, which we modified from a previous study to assess for PAHs. In stage 2, any admissions that the reviewers disagreed on or were unsure of were re-examined in a larger group of clinicians to yield a consensus determination regarding avoidability. Univariable and multivariable regression analyses were performed to identify factors predictive of PAH. RESULTS:: There were 160 admissions among this cohort of 210 patients from January 2012 to June 2015, of which 99 were evaluable. Consensus-driven clinical review yielded an overall PAH rate of 28.3%. Factors associated with increased PAH risk were admission for symptoms related to cancer (odds ratio [OR], 7.33; P < .001), presence of a social contributor to admission (OR, 4.40; P = .014), and history of alcohol or drug abuse (OR, 4.93; P = .025). Admission for a noncancer condition was associated with decreased PAH risk (OR, 0.32; P = .011). On multivariable analysis, presence of a social contributor to admission (OR, 9.35; P = .002) and admission as a result of a noncancer condition (OR, 0.16; P = .038) remained predictive of PAH risk. CONCLUSION:: A significant proportion of hospitalizations among patients with prostate cancer are potentially avoidable. Understanding factors predictive of risk for PAH can help inform programs aimed at avoiding such admissions to improve overall care quality and value.

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

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.

Pharmacoeconomic impact of up-front use of plerixafor for autologous stem cell mobilization in patients with multiple myeloma.

BACKGROUND AIMS: Stem cell collection can be a major component of overall cost of autologous stem cell transplantation (ASCT). Plerixafor is an effective agent for mobilization; however, it is often reserved for salvage therapy because of its high cost. We present data on the pharmacoeconomic impact of the use of plerixafor as an up-front mobilization in patients with multiple myeloma (MM). METHODS: Patients with MM who underwent ASCT between January 2008 and April 2011 at the Mount Sinai Medical Center were reviewed retrospectively. In April 2010, practice changes were instituted for patients with MM to delay initiation of granulocyte-colony-stimulating factor (G-CSF) support from day 0 to day +5 and to add plerixafor to G-CSF as an up-front autologous mobilization. Targets of collection were 5-10 × 10(6) CD34(+) cells/kg. RESULTS: Of 50 adults with MM who underwent ASCT, 25 received plerixafor/filgrastim and 25 received G-CSF alone as an up-front mobilization. Compared with the control, plerixafor mobilization yielded higher CD34(+) cell content (16.1 versus 8.4 × 10(6) CD34(+) cells/kg; P = 0.0007) and required fewer sessions of apheresis (1.9 versus 3.1; P = 0.0001). In the plerixafor group, the mean number of plerixafor doses required per patient was 1.8. Although the overall cost of medications was higher in the plerixafor group, the cost for blood products and overall cost of hospitalization were similar between the two groups. CONCLUSIONS: Up-front use of plerixafor is an effective mobilization strategy in patients with MM and does not have a substantial pharmacoeconomic impact in overall cost of hospitalization combined with the apheresis procedure.

Economic survivorship stress is associated with poor health-related quality of life among distressed survivors of hematopoietic stem cell transplantation.

BACKGROUND: Hematopoietic stem cell transplantation is a demanding cancer treatment associated with enduring physical and psychological complications. Survivors' well-being may be further compromised by exposure to chronic stressors common to this population, including difficulties arising from costly medical care, changes in employment status, and health insurance coverage. Thus, we hypothesized that financial, employment, and insurance stressors (collectively referred to as economic survivorship stressors) would be associated with poorer health-related quality of life (HRQOL) among hematopoietic stem cell transplantation survivors. METHODS: Survivors (n = 181; M = 640 days post-transplant) completed the measures of study variables through mailed questionnaires and telephone interviews. Hierarchical regression analyses were conducted to test the hypothesized associations between economic survivorship stressors and HRQOL, and to examine whether social and situational factors interact with survivors' stress perceptions to predict HRQOL. RESULTS: Greater financial and employment stress were associated with poorer functioning across multiple HRQOL domains, even after controlling for the effects of possible confounding sociodemographic and medical variables. Insurance stress was not associated with HRQOL. Some associations were moderated by situational factors including timing of the current financial crisis and portion of the transplant paid for by health insurance. CONCLUSIONS: Hematopoietic stem cell transplantation survivors can face serious economic challenges during recovery. Results suggest the value of viewing these challenges as chronic stressors capable of reducing survivors' mental and physical well-being. Identifying resources and skills that help survivors cope with these demands is an important goal for clinicians and researchers.