Low-value care and excess out-of-pocket expenditure among older adults with incident cancer – A machine learning approach

Chibuzo Iloabuchi, Nilanjana Dwibedi, Traci LeMasters, Chan Shen, Amit Ladani, Usha Sambamoorthi

Research output: Contribution to journalArticlepeer-review

Abstract

Objective: To evaluate the association of low-value care with excess out-of-pocket expenditure among older adults diagnosed with incident breast, prostate, colorectal cancers, and Non-Hodgkin's Lymphoma. Methods: We used a retrospective cohort study design with 12-month baseline and follow-up periods. We identified a cohort of older adults (age ≥ 66 years) diagnosed with breast, prostate, colorectal cancers, or Non-Hodgkin's lymphoma between January 2014 and December 2014. We assessed low-value care and patient out-of-pocket expenditure in the follow-up period. We identified relevant low-value services using ICD9/ICD10 and CPT/HCPCS codes from the linked health claims and patient out-of-pocket expenditure from Medicare claim files and expressed expenditure in 2016 USD. Results: About 29 % of older adults received at least one low-value care procedure during the follow-up period. Low-value care differed by gender, and rates were higher in women with colorectal cancer (32.7 %) vs. (28.8 %) and NHL (40 %) vs. (39 %) compared to men. Individuals who received one or more low-value care procedures had significantly higher mean out-of-pocket expenditure ($8,726 ± $7,214) vs. ($6,802 ± $6,102). XGBOOST, a machine learning algorithm revealed that low-value care was among the five leading predictors of OOP expenditure. Conclusion: One in four older adults with incident cancer received low-value care in 12-months after a cancer diagnosis. Across all cancer populations, individuals who received low-value care had significantly higher out-of-pocket expenditure. Excess out-of-pocket expenditure was driven by low-value care, fragmentation of care, and an increasing number of pre-existing chronic conditions. Policy Statement: This study focuses on health policy issues, specifically value-based care and its findings have important clinical and policy implications for Centers for Medicare and Medicaid Services (CMS) which has issued a roadmap for states to accelerate the adoption of value-based care, with the Department of Health and Human Services (HHS) setting a goal of converting 50 % of traditional Medicare payment systems to alternative payment models tied to value-based care by 2022.

Original languageEnglish
Article number100312
JournalJournal of Cancer Policy
Volume30
DOIs
StatePublished - Dec 2021

Keywords

  • Cancer
  • Healthcare expenditure
  • Healthcare utilization
  • Low-value care
  • Machine learning
  • Medicare
  • Valuebased care

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