Ravi B. Parikh

Effect of Integrating Machine Learning Mortality Estimates With Behavioral Nudges to Clinicians on Serious Illness Conversations Among Patients With Cancer

JAMA Oncology · 2020-10-15 · 177 citations

IMPORTANCE: Serious illness conversations (SICs) are structured conversations between clinicians and patients about prognosis, treatment goals, and end-of-life preferences. Interventions that increase the rate of SICs between oncology clinicians and patients may improve goal-concordant care and patient outcomes. OBJECTIVE: To determine the effect of a clinician-directed intervention integrating machine learning mortality predictions with behavioral nudges on motivating clinician-patient SICs. DE…

Digital Health Applications in Oncology: An Opportunity to Seize

JNCI Journal of the National Cancer Institute · 2022 · 78 citations

Digital health advances have transformed many clinical areas including psychiatric and cardiovascular care. However, digital health innovation is relatively nascent in cancer care, which represents the fastest growing area of health-care spending. Opportunities for digital health innovation in oncology include patient-facing technologies that improve patient experience, safety, and patient-clinician interactions; clinician-facing technologies that improve their ability to diagnose pathology and…

Performance Drift in a Nationally Deployed Population Health Risk Algorithm in the US Veterans Health Administration

JAMA Health Forum · 2025-08-15 · 3 citations

Importance: Clinical risk algorithms inform clinical decision support and system-level quality metrics. However, algorithm performance can drift over time and possibly promote misinformed decision-making and resource allocation. The Veterans Health Administration (VA) Care Assessment Needs (CAN) algorithm is a nationally deployed population risk algorithm used to predict risk of 90-day hospitalization and/or mortality and to allocate resources for more than 5 million veterans annually. However,…

A prospective study comparing AI-based clinical trial eligibility screening with traditional EMR-based screening.

Journal of Clinical Oncology · 2024 · 3 citations

e13636 Background: For clinical trials of novel oncology therapies, screening patients for eligibility relies on research staff manually abstracting complex eligibility criteria, which is time-consuming and subject to inconsistent interpretations. AI algorithms based on structured data fields may facilitate automated eligibility criteria extraction, assure more complete screening for the benefit of all patients, improve workflow for research staff, and assure no bias in who is reviewed. AI algor…

Derivation and External Validation of Objective Performance Status Among Patients With Metastatic Cancer

JCO Oncology Practice · 2025-07-25 · 1 citations

PURPOSE Performance status (PS) assessment is used to determine clinical trial eligibility among patients with cancer, but may be inaccurately assessed by oncology clinicians. Wearable accelerometers may allow objective assessment of physical activity, a proxy for PS. In this analysis of two prospective studies, we derive and externally validate objective PS (OPS) by measuring the association between daily physical activity and overall survival among patients with metastatic cancer. MATERIALS AN…