Patients undergoing outpatient infusion systemic therapy for cancer are at risk for potentially preventable, unplanned acute care in the form of emergency department (ED) visits and hospitalizations. These events impact patient outcomes, treatment decisions, and healthcare costs. To address this need, the Centers for Medicare & Medicaid Services developed the chemotherapy measure (OP-35). Recent randomized controlled studies indicate that electronic health record (EHR)-based machine learning (ML) approaches accurately direct supportive care to reduce acute care during radiotherapy. This study aims to develop and prospectively validate ML approaches to predict the risk of OP-35 qualifying, potentially preventable, acute care events within 30 days of infusion systemic therapy.

OBJECTIVES:

I. Develop and retrospectively validate electronic health record-based machine learning models using routinely collected clinical data from patients receiving systemic therapy to predict risk of potentially preventable OP-35 qualifying acute care events. (Phase 1: Retrospective)

II. Prospectively validate machine learning models across distinct time periods. (Phase 2: Prospective)

III. Understand patterns of care by stratifying and analyzing model performance by treatment type, cancer diagnosis, and race/ethnicity to assess bias and disparities in outcomes.

OUTLINE:

Retrospective and prospective clinical data obtained from medical records will be used to develop and validate predictive machine learning models. Prospective data will be divided into 2 phases: Prospective validation (PV) 1 and PV 2.

Inclusion Criteria:

• Patients 18 years or older diagnosed with cancer who receive care at UCSF and/or one of the UCSF affiliate locations.

Exclusion Criteria:

• Patients under the age of 18.
• Patients receiving care as part of a clinical trial.

Adult patients receiving care for cancer

De-identified data may be shared with study collaborators during the course of the study.