Janet A. Zink

Background: Navigation programs for patients with cancer improve access and continuity of care, yet their digital transformation is often limited by poor usability and inadequate uptake. Applying user-centered and human-centered design (UCD/HCD) principles may close this gap, but the extent to which such design methods are used and evaluated in oncology navigation tools remains unclear. Objective: This scoping review identifies how UCD/HCD principles have been, and should be, applied in developing and implementing digital health tools for navigation for patients with cancer. Methods: A scoping review was conducted following PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) and Joanna Briggs Institute guidance. A total of 7 databases (PubMed/MEDLINE, Scopus, IEEE Xplore, Web of Science, Embase, ACM Digital Library, and CINAHL) were searched for English-language articles published between January 2015 and July 2025. Eligible studies reported original, peer-reviewed research on digital or mobile health interventions linked to cancer navigation and documented at least 1 UCD/HCD activity. Two reviewers independently screened records and charted data on context, target users, functions, tool modality, design phase, methods, and outcomes. Findings were synthesized descriptively and thematically. Results: A total of 36 studies met the inclusion criteria. Findings were organized into 4 domains: study characteristics, navigation functions and digital modalities, design processes and methods, and UCD/HCD application. Iterative prototyping and usability testing were the most common, while participatory design and implementation evaluation were underused. Conclusions: UCD/HCD approaches enhance usability and patient relevance of digital cancer navigation tools. However, their application remains limited across cancer types, regions, and functions. Broader stakeholder participation and evaluation beyond usability are needed to strengthen coordination, equity, and sustainability in cancer care.

Background: Artificial intelligence (AI) is rapidly reshaping oncology, offering advancements in clinical care and patient support. A growing area of interest is the integration of empathic AI: systems integrating clinical precision with emotional intelligence to support medical decision-making and the emotional and psychosocial well-being of patients and caregivers. Objective: This review aimed to explore the role of empathic AI in cancer care, with a focus on its applications in patient education, clinician support, and emotional care. It also evaluated the ethical, cultural, and implementation challenges associated with its integration into clinical practice in oncology. Methods: A systematic search of literature was conducted in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines. Peer-reviewed papers published between January 2018 and January 2025 were identified through a search of PubMed, Scopus, and IEEE Xplore, and citation tracking. Eligible studies focused on applications of empathic AI in oncology. A total of 44 studies were included and analyzed thematically across 3 core clinical domains: tailored communication and education, diagnostics and care plan optimization, and emotional and psychosocial support. Results: Empathic AI demonstrates the potential to improve cancer care by enhancing patient education, clinical decision-making, and emotional support. Common applications include personalized education for patients and providers, support for diagnostic and therapeutic decisions, and tools designed to recognize and respond to patient distress. Several studies noted improved patient engagement and reduced clinician burden. However, concerns were identified regarding overreliance on AI systems, cultural insensitivity, and patient privacy. Conclusions: Empathic AI represents a promising advancement in patient-centered oncology, integrating emotional intelligence into clinical care. Its successful implementation will require careful attention to ethical, cultural, and clinical considerations to ensure health equity, trust, and safety in AI-assisted cancer care.

BACKGROUND Navigation programs for patients with cancer improve access and continuity of care, yet their digital transformation is often limited by poor usability and inadequate uptake. Applying user-centered and human-centered design (UCD/HCD) principles may close this gap, but the extent to which such design methods are used and evaluated in oncology navigation tools remains unclear. OBJECTIVE This scoping review identifies how UCD/HCD principles have been, and should be, applied in developing and implementing digital health tools for navigation for patients with cancer. METHODS A scoping review was conducted following PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) and Joanna Briggs Institute guidance. A total of 7 databases (PubMed/MEDLINE, Scopus, IEEE Xplore, Web of Science, Embase, ACM Digital Library, and CINAHL) were searched for English-language articles published between January 2015 and July 2025. Eligible studies reported original, peer-reviewed research on digital or mobile health interventions linked to cancer navigation and documented at least 1 UCD/HCD activity. Two reviewers independently screened records and charted data on context, target users, functions, tool modality, design phase, methods, and outcomes. Findings were synthesized descriptively and thematically. RESULTS A total of 36 studies met the inclusion criteria. Findings were organized into 4 domains: study characteristics, navigation functions and digital modalities, design processes and methods, and UCD/HCD application. Iterative prototyping and usability testing were the most common, while participatory design and implementation evaluation were underused. CONCLUSIONS UCD/HCD approaches enhance usability and patient relevance of digital cancer navigation tools. However, their application remains limited across cancer types, regions, and functions. Broader stakeholder participation and evaluation beyond usability are needed to strengthen coordination, equity, and sustainability in cancer care.

Background: Electronic health records contain inconsistently structured or free-text data, requiring efficient preprocessing to enable predictive health care models. Although artificial intelligence–driven natural language processing tools show promise for automating diagnosis classification, their comparative performance and clinical reliability require systematic evaluation. Objective: The aim of this study is to evaluate the performance of 4 large language models (GPT-3.5, GPT-4o, Llama 3.2, and Gemini 1.5) and BioBERT in classifying cancer diagnoses from structured and unstructured electronic health records data. Methods: We analyzed 762 unique diagnoses (326 International Classification of Diseases [ICD] code descriptions, 436 free-text entries) from 3456 records of patients with cancer. Models were tested on their ability to categorize diagnoses into 14 predefined categories. Two oncology experts validated classifications. Results: BioBERT achieved the highest weighted macro F₁-score for ICD codes (84.2) and matched GPT-4o in ICD code accuracy (90.8). For free-text diagnoses, GPT-4o outperformed BioBERT in weighted macro F₁-score (71.8 vs 61.5) and achieved slightly higher accuracy (81.9 vs 81.6). GPT-3.5, Gemini, and Llama showed lower overall performance on both formats. Common misclassification patterns included confusion between metastasis and central nervous system tumors, as well as errors involving ambiguous or overlapping clinical terminology. Conclusions: Although current performance levels appear sufficient for administrative and research use, reliable clinical applications will require standardized documentation practices alongside robust human oversight for high-stakes decision-making.

PURPOSE: Unplanned treatment interruptions represent an important care quality shortfall for patients undergoing cancer radiotherapy. This study aimed to evaluate use of a centralized electronic health record warehouse and large language model–based data preprocessing to facilitate identification of risk factors for radiation therapy interruptions (RTI). METHODS: We analyzed demographic, behavioral, clinical, and neighborhood-level data for 2,130 patients treated with radiotherapy at the University of Tennessee Medical Center in Knoxville. Treatment interruptions were measured as missed days, adjusted for weekends and holidays. Multinomial logistic regression was used to identify factors associated with moderate (2-4 days) and severe (≥5 days) RTI. RESULTS: Moderate RTI occurred in 15.8% of patients, while 7.7% experienced severe RTI. Moderate delays were associated with genitourinary cancer (adjusted odds ratio (AOR), 3.81; 95% CI, 1.24 to 11.66), prostate cancer (AOR, 2.44; 95% CI, 1.34 to 4.46), and Medicaid coverage (AOR, 2.22; 95% CI, 1.32 to 3.73). Severe RTI was associated with marital status (AOR for divorced or separated patients, 1.86; 95% CI, 1.18 to 2.94), head and neck cancer (AOR, 2.31; 95% CI, 1.10 to 4.87), gynecologic cancer (AOR, 2.97; 95% CI, 1.30 to 6.79), Medicaid insurance (AOR, 3.43; 95% CI, 1.77 to 6.64), daily dose of ≤225 cGy (AOR, 2.55; 95% CI, 1.21 to 5.37), and a total dose of ≥6,000 cGy (AOR, 2.30; 95% CI, 1.09 to 4.88). Severe interruptions were also significantly associated with high neighborhood social vulnerability (AOR, 2.60; 95% CI, 1.32 to 5.09). CONCLUSION: Automated data preprocessing permitted efficient identification of treatment course length, marital status, disease site, Medicaid coverage, and socially vulnerable locations as significant factors associated with RTI. These findings underscore the need for data-driven risk assessment and intervention strategies to maintain cancer treatment quality at scale.