End-of-life spending analysis of randomized trial of machine learning nudges to prompt serious illness communication among patients with cancer.

Authors

Ravi Parikh

Ravi Bharat Parikh

Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA

Ravi Bharat Parikh , Jinbo Chen , Jonathan Heintz , Marc LaPergola , Warren Bilker , Mitesh S Patel , Justin E. Bekelman , Christopher Manz

Organizations

Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, University of Pennsylvania, Philadelphia, PA, Ascension Health, St. Louis, MO, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA

Research Funding

U.S. National Institutes of Health
U.S. National Institutes of Health

Background: Early serious illness conversations (SICs) between oncology clinicians and patients in the outpatient setting may improve mood and quality-of-life among patients with cancer. However, the impact of early SIC “nudges” on end-of-life spending is unknown. Methods: This was a secondary analysis of a stepped-wedge randomized trial (NCT03984773) that randomized 9 medical oncology practices and their high-risk patients at a large academic institution to a behavioral intervention to increase SICs (performance reports and peer comparisons; precommitments for high-risk patients; weekly opt-out text prompts before high-risk encounters) vs. standard of care, between June 2019 and April 2020. We identified high-risk patients using a machine learning (ML) algorithm predicting 180-day mortality. This secondary analysis included 1187 (957 intervention, 230 control) patients with complete data who died by December 2020. We abstracted spending (defined as inflation-adjusted reimbursements for acute care [inpatient + ED], office/outpatient care, intravenous chemotherapy, other therapy [e.g. radiation], long-term care, and hospice) from the institution’s accounting system; we captured spending at University of Pennsylvania inpatient, outpatient, and pharmacy settings. To evaluate intervention impacts on spending, we used a two-part model: first, logistic regression to model zero versus nonzero spending, and second, generalized linear models with gamma distribution and log-link function to model daily mean spending in the last 180 days of life. Models were adjusted for clinic and wedge fixed effects and clustered at the oncologist level. Results: Median age at death was 68 years (IQR 15.5), 317 (27%) patients were non-White, and 448 (38%) patients had a SIC prior to death. The intervention was associated with lower mean spending in the last 180 days of life (mean daily spending $377.96 [intervention] vs. $449.92 [control]; adjusted mean difference -$75.33, 95% CI -$136.42, -$14.23 , p = 0.016) (Table), translating to $13,559 total adjusted savings. Intervention patients incurred lower mean daily spending for chemotherapy (adjusted difference -$51.35, p < 0.001), office/outpatient care (-$14.59, p < 0.001), and other therapy (-$10.35, p = 0.043). The intervention was not associated with differences in end-of-life spending for acute care utilization, long-term care, and hospice. Results were consistent for spending in the last 30 and 90 days of life and after adjusting for age, race, and ethnicity. Conclusions: A ML intervention to prompt SICs led to end-of-life savings, driven by decreased chemotherapy and outpatient spending. Clinical trial information: NCT03984773.

Unadjusted spending in last 180 days of life.
InterventionControl
Total Spending $69,977$82,110
Mean Daily Spending$378$450
· Acute Care $202$200
· Outpatient$24$39
· Chemotherapy$113$164
· Other Therapy$29$40

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Abstract Details

Meeting

2023 ASCO Annual Meeting

Session Type

Poster Discussion Session

Session Title

Health Services Research and Quality Improvement

Track

Quality Care/Health Services Research

Sub Track

Value/Cost of Care

Clinical Trial Registration Number

NCT03984773

Citation

J Clin Oncol 41, 2023 (suppl 16; abstr 6515)

DOI

10.1200/JCO.2023.41.16_suppl.6515

Abstract #

6515

Poster Bd #

7

Abstract Disclosures

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