Session: 903. Health Services Research—Malignant Conditions (Myeloid Disease): Barriers to Cancer Care Delivery in Myeloid Malignancies
Hematology Disease Topics & Pathways:
Adult, Adverse Events, Study Population, Clinically relevant, Quality Improvement
Financial toxicity (FT) has been consistently demonstrated to a be a major contributor to morbidity and mortality in a variety of cancers. However, the vast majority of research examining this issue has been in solid tumors, and there has been less investigation of how this concept applies in malignant hematology and even fewer studies looking at an interventional model. This pilot study attempts to identify patients at high-risk due to FT in a busy clinical environment and improve clinical outcomes with comprehensive intervention.
All patients seen at the Malignant Hematology Clinic at the Levine Cancer Institute, a tertiary hospital-based specialty practice, were surveyed at their visits over a six-month period. All patients were aged ≥18 years and diagnosed with hematologic malignancy or bone marrow failure syndrome. The survey consisted of the PROMIS Global-10 measure and two questions from the COST measure. FT was defined as scoring 5 or less (maximum: 10) in agreement with the COST questions: “I know that I have enough money in savings, retirement, or assets to cover the costs of my treatment” and “I am satisfied with my current financial situation.” Patients with FT were entered into the interventional cohort and scheduled for a visit with a nurse navigator where they completed a standardized worksheet to identify gaps in care and opportunities for grant funding/other assistance. Patients were seen by a clinical pharmacist for copay review and discussion of assistance programs. Finally, patients were offered the services of a community pro-bono financial planner for help with budgeting, asset management, and general financial advice. Patients were tracked longitudinally for assistance provided, changes in PROMIS scores, and clinical outcomes. Categorical variables, including responses to survey questions, were summarized with frequencies and proportions, while continuous variables were summarized with medians and ranges. Correlation of FT screening scores and COST scores was assessed with Spearman’s correlation. Baseline versus post-intervention PROMIS scores were compared with paired t-tests, while McNemar tests for agreement were used to compare ER and IP utilization 3 months prior versus post intervention.
A total of 107 patients were included in the intervention. Specific characteristics of the intervention population are listed in Table 1. FT screening scores were found to correlate with the full COST measure (Spearman correlation = 0.45, p <.001). Patients in the intervention cohort had high rates of noncompliance due to inability to afford prescription medications (16.8%), OTC medications (15.9%), and doctor visits (6.5%). In order to pay for their care, patients reported reducing spending on food and clothing (48.6%), using savings to cover OOP expenses (51.4%), and partially filling prescriptions (11.2%) (Table 2).
In terms of the intervention, 37.4% of patients were found to qualify for and were helped to obtain grants from external foundations. The median value of these grants was $850 (range: $100-$17,850). Through manufacturer’s assistance and other programs, the clinical pharmacy team was able to obtain free or greatly reduced cost medications for the qualified patients at a median retail value of $197,158 (range: $29,909-$639,801). Gas cards, food pantry assistance, and transportation assistance were also supplied to patients who qualified at a median value of $300 ($100-$300). 58 patients (54.2%) expressed interest and were scheduled with a pro-bono financial counselor.
The intervention resulted in statistically significantly higher quality of life when measured by PROMIS physical and mental health scores, compared to baseline scores (Table 3) (all p <.001). There was no significant difference found when looking at patients with at least one ER visit 3 months prior and post intervention (10.3% vs 6.5% p=.317). There was no difference between inpatient visits/days pre and post intervention.
Using a quick screening method for FT in a busy clinical environment is feasible and allows identification of an extremely high-risk population. Intervening on FT in a comprehensive way including navigators, pharmacists, and financial counselors is effective and leads to increased quality of life.
Disclosures: Knight: Foundation for Financial Planning: Research Funding. Ai: Celgene: Speakers Bureau; Incyte: Speakers Bureau. Chojecki: Incyte: Research Funding; Novartis: Other: Investigator Meeting Attendance. Copelan: Amgen: Membership on an entity's Board of Directors or advisory committees. Grunwald: Forma Therapeutics: Research Funding; Premier: Consultancy; Premier: Consultancy; Celgene: Consultancy; Abbvie: Consultancy; Pfizer: Consultancy; Abbvie: Consultancy; Trovagene: Consultancy; Premier: Consultancy; Astellas: Consultancy; Astellas: Consultancy; Genentech/Roche: Research Funding; Celgene: Consultancy; Celgene: Consultancy; Janssen: Research Funding; Merck: Research Funding; Cardinal Health: Consultancy; Amgen: Consultancy; Amgen: Consultancy; Agios: Consultancy; Merck: Consultancy; Merck: Consultancy; Amgen: Consultancy; Merck: Consultancy; Cardinal Health: Consultancy; Pfizer: Consultancy; Cardinal Health: Consultancy; Daiichi Sankyo: Consultancy; Astellas: Consultancy; Daiichi Sankyo: Consultancy; Trovagene: Consultancy; Pfizer: Consultancy; Forma Therapeutics: Research Funding; Genentech/Roche: Research Funding; Genentech/Roche: Research Funding; Janssen: Research Funding; Forma Therapeutics: Research Funding; Trovagene: Consultancy; Incyte: Consultancy, Research Funding; Incyte: Consultancy, Research Funding; Incyte: Consultancy, Research Funding; Agios: Consultancy; Daiichi Sankyo: Consultancy; Agios: Consultancy; Abbvie: Consultancy.
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