About

Yun Jiang is an Associate Professor at the University of Michigan School of Nursing, with a focus on health informatics and data-driven solutions for chronic condition self-management. Her research emphasizes cancer medication adherence and symptom self-management, aiming to discover consumer health self-management behavior patterns from data and develop information technology-based support to empower and engage patients and families in health self-management. She has received training in both Nursing and Health Informatics and holds certificates in Gerontology (Gerontechnology track) and Clinical & Translational Science. Dr. Jiang's current research projects include patient engagement in medication safety event reporting, older adults’ tolerance to oral anticancer treatments, patients’ acceptance and use of mobile technology for health self-monitoring and decision support, and understanding cancer patients' toxicity self-reporting behaviors using natural language processing and machine learning approaches. She is actively involved in research funded by agencies such as AHRQ and NIH/NINR, and her work contributes to advancing informatics solutions for chronic disease management and cancer care. Additionally, she teaches graduate-level Health Informatics courses and undergraduate Nursing Leadership clinical sessions, emphasizing student-centered active learning.

Research topics

• Medicine
• Psychology
• Family medicine
• Internal medicine
• Chemistry

Selected publications

• Digital literacy and scientific research ability among nursing postgraduates: The chain-mediating role of deep learning and research self-efficacy

  Nurse Education Today · 2026-03-03

  article
  PublisherDOI

• Factors associated with family-perceived support among caregivers of hospitalized children: A cross-sectional study

  Journal of Pediatric Nursing · 2026-03-10

  article
  PublisherDOI

• AI Applications for Chronic Condition Self-Management: Scoping Review

  Journal of Medical Internet Research · 2025-02-20 · 15 citations

  articleOpen accessSenior author

  BACKGROUND: Artificial intelligence (AI) has potential in promoting and supporting self-management in patients with chronic conditions. However, the development and application of current AI technologies to meet patients' needs and improve their performance in chronic condition self-management tasks remain poorly understood. It is crucial to gather comprehensive information to guide the development and selection of effective AI solutions tailored for self-management in patients with chronic conditions. OBJECTIVE: This scoping review aimed to provide a comprehensive overview of AI applications for chronic condition self-management based on 3 essential self-management tasks, medical, behavioral, and emotional self-management, and to identify the current developmental stages and knowledge gaps of AI applications for chronic condition self-management. METHODS: A literature review was conducted for studies published in English between January 2011 and October 2024. In total, 4 databases, including PubMed, Web of Science, CINAHL, and PsycINFO, were searched using combined terms related to self-management and AI. The inclusion criteria included studies focused on the adult population with any type of chronic condition and AI technologies supporting self-management. This review was conducted following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. RESULTS: Of the 1873 articles retrieved from the search, 66 (3.5%) were eligible and included in this review. The most studied chronic condition was diabetes (20/66, 30%). Regarding self-management tasks, most studies aimed to support medical (45/66, 68%) or behavioral self-management (27/66, 41%), and fewer studies focused on emotional self-management (14/66, 21%). Conversational AI (21/66, 32%) and multiple machine learning algorithms (16/66, 24%) were the most used AI technologies. However, most AI technologies remained in the algorithm development (25/66, 38%) or early feasibility testing stages (25/66, 38%). CONCLUSIONS: A variety of AI technologies have been developed and applied in chronic condition self-management, primarily for medication, symptoms, and lifestyle self-management. Fewer AI technologies were developed for emotional self-management tasks, and most AIs remained in the early developmental stages. More research is needed to generate evidence for integrating AI into chronic condition self-management to obtain optimal health outcomes.

  PublisherDOI

• Exploring factors affecting the adoption and use of digital health technologies among older adults with cancer: A qualitative study

  Supportive Care in Cancer · 2025-08-06

  articleOpen accessSenior author

  PURPOSE: Although digital health technologies (DHTs) are promising to improve health outcomes in older adults with cancer, the low adoption and limited use remain significant gaps in their effective digital health care. Little is known about their concerns about adopting and using DHTs in routine life, particularly in the continued use phase. This study aims to explore factors affecting the initial adoption and continued use of DHTs among older adults with cancer. METHODS: A secondary analysis of qualitative data was conducted based on interviews with 21 older adults (≥ 65 years) with breast, prostate, lung, or colorectal cancer. The transcripts of interview recordings were analyzed using a thematic analysis. RESULTS: Three major themes and several subthemes were identified as potential factors affecting the (1) initial adoption, (2) continued use, and (3) limited use of DHTs. Digitalized healthcare systems and access to technology influenced the initial adoption of DHTs. Perceived ease of use, perceived usefulness, expected timely care from providers, and increased sense of control emerged as leading factors to the continued use. The limited use of DHTs was influenced by a lack of knowledge and skills, a lack of direct interaction with providers, and concerns about digital communication quality. CONCLUSIONS: Ensuring digital access and providing technology-based solutions that meet diverse patient needs is crucial to promoting the adoption and use of DHTs among older adults with cancer. Healthcare providers should address older adults' low digital literacy and uncertainty to ensure the quality of cancer care provided through DHTs.

  PublisherOA PDFDOI

• Patient-Contributed Data and Medication Safety: A Study on Self-Reporting Behaviors Among Patients with Cancer

  Studies in health technology and informatics · 2025-08-07

  articleOpen accessSenior author

  Understanding cancer patients' experiences with medication safety events at home is crucial due to their impact on health outcomes. However, patients often face barriers to sharing this information with their clinicians. This study identified key factors influencing cancer patients' self-reporting behaviors through a mixed-methods approach, analyzing survey and interview data from 41 patients diagnosed with breast, lung, prostate, or colorectal cancer. Logistic regression analysis identified predictors such as beliefs about medication and perceived system usability, achieving an AUC of 0.85. Thematic analysis indicated that patients were more likely to report safety events when medications disrupted their daily routines, with perceived severity as the key trigger. System usability and relationships with clinicians also affected reporting behaviors. Our findings highlight the need for user-friendly reporting systems and supportive communication to improve patient engagement and medication safety, offering valuable insights for designing patient-centered reporting systems to facilitate patient-contributed data.

  PublisherDOI

• Enhancing Theory-Driven Design and Evaluation of Patient-Facing Technologies

  Studies in health technology and informatics · 2025-05-15

  articleOpen accessSenior author

  Patient-facing technologies (PFTs) are essential tools for patient-centered outcome research. The current designs of PFTs need solid theoretical support and face challenges with maintenance and sustainability. This paper identifies PFT features and theoretical frameworks that support patient engagement in cancer care. Six prevailing PFT features - feedback & alert, a summary of entries, education materials, aggregated review, dedicated staff, and reminders - were identified in the primary literature, and five theoretical frameworks were adopted to guide the design and evaluation of 20 PFTs. Further research into user-centered design, personalization, and social context in PFT development is recommended to connect system design, maintenance, and sustainability. This theory-driven approach could enhance patient-reported outcomes in patient-centered research by engaging diverse patient populations and addressing their needs.

  PublisherOA PDFDOI

• Corrigendum to“ Crosstalk between reactive oxygen species and Dynamin-related protein 1 in periodontitis” [Free Radic. Biol. Med. 172 (2021) 19–23]

  Free Radical Biology and Medicine · 2025-08-05

  erratum
  PublisherDOI

• Functional Creatine Kinase Imaging (fCKI) for brain functional and metabolic imaging

  Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition · 2025-09-16

  article

  Motivation: The creatine kinase (CK) reaction rate is essential for understanding brain function. More importantly CK rates have been shown to change in various neurological conditions, including psychiatric disorders. Goal(s): In this abstract a novel functional imaging modality is introduced called functional Creatine Kinase Imaging (fCKI). Approach: Participants are exposed to a visual paradigm during CKI acquisition. Results: fCKI reveals a 15% increase in CK enzyme reaction rate in the occipital lobe, consistent with prior findings. Additionally, fCKI provides, for the first time, a 3D activation map, with activation clusters predominantly located in the visual cortex. Impact: A novel functional modality, functional Creatine Kinase Imaging (fCKI) is introduced. fCKI reveals increased CK enzyme activity in the occipital lobe and, for the first time, a 3D activation-map, with activation clusters predominantly found in the visual cortex.

  PublisherDOI

• Association Between Risk Factors and Major Cancers: Explainable Machine Learning Approach

  JMIR Cancer · 2025-03-21 · 3 citations

  articleOpen accessSenior author

  Background: Cancer is a life-threatening disease and a leading cause of death worldwide, with an estimated 611,000 deaths and over 2 million new cases in the United States in 2024. The rising incidence of major cancers, including among younger individuals, highlights the need for early screening and monitoring of risk factors to manage and decrease cancer risk. Objective: This study aimed to leverage explainable machine learning models to identify and analyze the key risk factors associated with breast, colorectal, lung, and prostate cancers. By uncovering significant associations between risk factors and these major cancer types, we sought to enhance the understanding of cancer diagnosis risk profiles. Our goal was to facilitate more precise screening, early detection, and personalized prevention strategies, ultimately contributing to better patient outcomes and promoting health equity. Methods: Deidentified electronic health record data from Medical Information Mart for Intensive Care (MIMIC)-III was used to identify patients with 4 types of cancer who had longitudinal hospital visits prior to their diagnosis presence. Their records were matched and combined with those of patients without cancer diagnoses using propensity scores based on demographic factors. Three advanced models, penalized logistic regression, random forest, and multilayer perceptron (MLP), were conducted to identify the rank of risk factors for each cancer type, with feature importance analysis for random forest and MLP models. The rank biased overlap was adopted to compare the similarity of ranked risk factors across cancer types. Results: Our framework evaluated the prediction performance of explainable machine learning models, with the MLP model demonstrating the best performance. It achieved an area under the receiver operating characteristic curve of 0.78 for breast cancer (n=58), 0.76 for colorectal cancer (n=140), 0.84 for lung cancer (n=398), and 0.78 for prostate cancer (n=104), outperforming other baseline models (P<.001). In addition to demographic risk factors, the most prominent nontraditional risk factors overlapped across models and cancer types, including hyperlipidemia (odds ratio [OR] 1.14, 95% CI 1.11-1.17; P<.01), diabetes (OR 1.34, 95% CI 1.29-1.39; P<.01), depressive disorders (OR 1.11, 95% CI 1.06-1.16; P<.01), heart diseases (OR 1.42, 95% CI 1.32-1.52; P<.01), and anemia (OR 1.22, 95% CI 1.14-1.30; P<.01). The similarity analysis indicated the unique risk factor pattern for lung cancer from other cancer types. Conclusions: The study's findings demonstrated the effectiveness of explainable ML models in assessing nontraditional risk factors for major cancers and highlighted the importance of considering unique risk profiles for different cancer types. Moreover, this research served as a hypothesis-generating foundation, providing preliminary results for future investigation into cancer diagnosis risk analysis and management. Furthermore, expanding collaboration with clinical experts for external validation would be essential to refine model outputs, integrate findings into practice, and enhance their impact on patient care and cancer prevention efforts.

  PublisherDOI

• Template-free synthesis of zeolite nanocrystalline with controllable phase and composition

  Nanotechnology · 2025-11-19

  articleCorresponding

  Abstract This study reports a template-free hydrothermal synthesis of zeolite nanocrystals with controllable phase and composition. The controlled formation of sodalite (SOD) nanocrystals was primarily achieved through temperature modulation. Phase transitions in zeolite nanocrystals exhibited significant temperature dependence, sequentially yielding zeolite a, hydroxy-SOD, and cancrinite (CAN) as hydrothermal temperatures increased from 50 °C to 160 °C. Notably, hydroxy-SOD with consistent polyhedral morphology was obtained across a broad temperature range (60 °C–150 °C). Furthermore, a two-step synthesis strategy enabled the construction of hydroxy-SOD-CAN heterojunctions with tunable phase ratios in mixed crystals, demonstrating 38% higher CAN yield compared to single-step process. The crystallization mechanisms involve temperature-dependent assembly of [AlO4] and [SiO4] structural units, with phase transformation from hydroxy-SOD to CAN initiating at surface active sites. The hydroxy-SOD nanocrystals exhibit excellent nitrogen adsorption capacity, reaching up to 266 cm 3 g −1 .

  PublisherDOI

Frequent coauthors

• Youmin Cho

  Chungnam National University

  29 shared

• Dante Anthony Tolentino

  University of California, Los Angeles

  25 shared

• Deena Kelly Costa

  Yale University

  25 shared

• Yang Gong

  The University of Texas Health Science Center at Houston

  21 shared

• Marcelline R. Harris

  University of Michigan–Ann Arbor

  17 shared

• Rumei Yang

  17 shared

• Zhenhua Zhou

  Hunan University

  16 shared

• Xiaojun Zhu

  Sun Yat-sen University

  16 shared

Labs

• Yun Jiang LabPI

Education

• PhD, School of Nursing

  University of Pittsburgh

  2015

• BSN, School of Nursing

  University of Missouri Columbia

  2008

• MS in Health Informatics, School of Medicine

  University of Missouri Columbia

  2006

Awards & honors

• Fellow of American Medical Information Association (FAMIA),…
• MD2K mHealth Training Institute Scholar, 2017
• Pauline Thompson Clinical Nursing Research Award, Nursing Fo…
• Best Abstract Award Winner, the International Transplant Nur…
• Cameos of Caring Endowed Nursing Scholarship, 2012