Clinical Perspectives on Actionable Risk Factors for Deterioration in Acute Myeloid Leukemia: Implications for
Sena Chae1, Alaa Harb, Nayung Youn
1Authors' Affiliation: College of Nursing, University of Iowa, Iowa City, Iowa.
Background:
Acute myeloid leukemia (AML) is associated with high complication rates following induction chemotherapy, yet clinicians lack tools that anticipate deterioration and support proactive care. Preliminary predictive modeling using structured electronic health record data and symptoms extracted through natural language processing identified key risk factors for deterioration within 30 days of discharge. How clinicians interpret these risk factors in practice remains unclear.
Objective:
This study aimed to explore how hematology clinicians understand actionable risk factors for deterioration in AML and how these factors should inform the design of clinical decision support.
Interventions/Methods:
Seven hematology clinicians-including hematologist-oncologists, a nurse practitioner, and physician assistants-at a Midwestern academic medical center participated in individual or group semistructured interviews. Interview questions addressed perceived risk factors, actionability, and needs for clinical decision support. Data were analyzed using conventional content analysis.
Results:
Three themes emerged: (1) generic tools do not adequately support the complexity of AML care; (2) actionability is context‑dependent, with both nonmodifiable and modifiable factors influencing discharge decision‑making; and (3) clinical decision support must integrate predictive risk scoring with user‑centered design features such as intuitive displays, timely alerts, and transparency about contributing factors.
Conclusions:
Clinicians expressed strong support for a clinical decision support tool that anticipates deterioration, provides individualized and interpretable risk alerts, and aligns with existing workflows to reduce preventable complications and readmissions.
Implications For Oncology Nursing Practice:
Clinical decision support tools that integrate dynamic clinical data with intuitive, workflow‑aligned interfaces may enhance risk communication, strengthen discharge decision‑making, and support proactive monitoring in AML care.
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