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Clinical Challenges and AI Opportunities in Decision-Making for Cancer Treatment-Induced Cardiotoxicity

Siyi Wu, Weidan Cao, Shihan Fu, Bingsheng Yao, Ziqi Yang, Changchang Yin, Varun Mishra, Daniel Addison, Ping Zhang, Dakuo Wang

TL;DR

This study investigates decision-making for cancer treatment-induced cardiotoxicity and the role of digital health tools. Through semi-structured interviews with seven clinical experts, it identifies a three-step decision-making paradigm: 1) symptom identification, 2) diagnostic testing and specialist collaboration, and 3) clinical decision-making and intervention. It reveals significant challenges in diagnosis due to overlapping toxicities, symptom variability, and testing limitations, along with monitoring barriers such to underreporting and inconsistent follow-up, underscoring the need for early-detection and remote-monitoring solutions. The authors propose design considerations for clinician-centered AI and remote-monitoring tools that integrate with evolving cardio-oncology workflows to improve risk management and patient outcomes.

Abstract

Cardiotoxicity induced by cancer treatment has become a major clinical concern, affecting the long-term survival and quality of life of cancer patients. Effective clinical decision-making, including the detection of cancer treatment-induced cardiotoxicity and the monitoring of associated symptoms, remains a challenging task for clinicians. This study investigates the current practices and needs of clinicians in the clinical decision making of cancer treatment-induced cardiotoxicity and explores the potential of digital health technologies to support this process. Through semi-structured interviews with seven clinical experts, we identify a three-step decision-making paradigm: 1) symptom identification, 2) diagnostic testing and specialist collaboration, and 3) clinical decision-making and intervention. Our findings highlight the difficulties of diagnosing cardiotoxicity (absence of unified protocols and high variability in symptoms) and monitoring patient symptoms (lacking accurate and timely patient self-reported symptoms). The clinicians also expressed their need for effective early detection tools that can integrate remote patient monitoring capabilities. Based on these insights, we discuss the importance of understanding the dynamic nature of clinical workflows, and the design considerations for future digital tools to support cancer-treatment-induced cardiotoxicity decision-making.

Clinical Challenges and AI Opportunities in Decision-Making for Cancer Treatment-Induced Cardiotoxicity

TL;DR

This study investigates decision-making for cancer treatment-induced cardiotoxicity and the role of digital health tools. Through semi-structured interviews with seven clinical experts, it identifies a three-step decision-making paradigm: 1) symptom identification, 2) diagnostic testing and specialist collaboration, and 3) clinical decision-making and intervention. It reveals significant challenges in diagnosis due to overlapping toxicities, symptom variability, and testing limitations, along with monitoring barriers such to underreporting and inconsistent follow-up, underscoring the need for early-detection and remote-monitoring solutions. The authors propose design considerations for clinician-centered AI and remote-monitoring tools that integrate with evolving cardio-oncology workflows to improve risk management and patient outcomes.

Abstract

Cardiotoxicity induced by cancer treatment has become a major clinical concern, affecting the long-term survival and quality of life of cancer patients. Effective clinical decision-making, including the detection of cancer treatment-induced cardiotoxicity and the monitoring of associated symptoms, remains a challenging task for clinicians. This study investigates the current practices and needs of clinicians in the clinical decision making of cancer treatment-induced cardiotoxicity and explores the potential of digital health technologies to support this process. Through semi-structured interviews with seven clinical experts, we identify a three-step decision-making paradigm: 1) symptom identification, 2) diagnostic testing and specialist collaboration, and 3) clinical decision-making and intervention. Our findings highlight the difficulties of diagnosing cardiotoxicity (absence of unified protocols and high variability in symptoms) and monitoring patient symptoms (lacking accurate and timely patient self-reported symptoms). The clinicians also expressed their need for effective early detection tools that can integrate remote patient monitoring capabilities. Based on these insights, we discuss the importance of understanding the dynamic nature of clinical workflows, and the design considerations for future digital tools to support cancer-treatment-induced cardiotoxicity decision-making.
Paper Structure (36 sections, 1 figure, 1 table)

This paper contains 36 sections, 1 figure, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Cardiotoxicity from Cancer Treatments
  4. Cardiotoxicity Risk Management and Challenges
  5. Digital Health Technologies for Cancer and Cardiotoxicity Risk Management
  6. Methods
  7. Participants
  8. Procedures
  9. Data Analysis
  10. Findings
  11. Current Clinical Practices of Cancer Treatment-Induced Cardiotoxicity Risk Management
  12. Symptom Identification
  13. Diagnostic Testing and Collaboration among Specialists
  14. Clinical Decision-Making and Intervention
  15. Challenges in the Current Clinical Practices of Cardiotoxicity Risk Management
  16. ...and 21 more sections

Figures (1)

  • Figure 1: Current Workflow of Cardiotoxicity Diagnosis. First, cancer patients' self-reports or the presentation of acute cardiac symptoms lead clinicians to identify symptoms and suspect cardiotoxicity. Next, clinicians perform diagnostic tests and collaborate with other specialists to diagnose cardiotoxicity. Finally, clinical decisions regarding immediate intervention, treatment modifications, and continuous monitoring are made.