About

The Injury Biomechanics Research Center (IBRC) at The Ohio State University is a multi-disciplinary research center dedicated to investigating the relationships between human injury and physical mechanical properties. The IBRC has completed research in the field of automobile safety since 2004 and brings together an interdisciplinary team of engineers, anatomists, anthropologists, physicians, computer modelers, and technicians. The center focuses on mechanisms of injury and injury thresholds of the human body, testing biomechanical loading and impact scenarios with anthropomorphic test devices, post-mortem human subjects, and research volunteers. The IBRC works closely with Ohio State departments and industry partners to offer diverse testing facilities and conducts research on injury prevention, including skeletal and soft tissue response, advanced medical imaging, development of biofidelic response corridors, validation of human body models, pediatric biomechanics, and child restraint system testing. The center also launched the Forensic Anthropology Skeletal Trauma (FAST) database, which includes data from experimental tests on human skeletal elements with known loading mechanisms, providing valuable resources for trauma interpretation. The IBRC offers research opportunities for students across various majors, fostering collaboration and practical experience in injury biomechanics.

Research topics

• Computer Science
• Artificial Intelligence
• Knowledge management
• Medicine
• Political Science
• Physical therapy
• Business
• Engineering
• Human–computer interaction
• Systems engineering
• Risk analysis (engineering)
• Nursing
• Family medicine
• Psychology
• Process management
• Internal medicine
• Applied psychology

Selected publications

• Directly engaging patients with glucose management: integrated visualization of personal device data for use between traditional visits

  Human Factors in Healthcare · 2026-03-21

  articleOpen access1st author

  Keeping glucose below a threshold, as well as minimizing variation, is critical for individuals living with type 2 diabetes to avoid harm to organs and increase length and quality of life. Wearable devices provide patients with the opportunity to gain insight into how their physical activity and sleep quality relate to glucose levels monitored on a continuous basis. With integrated displays combining data from multiple wearable devices as well as self-reported data and assessment, there is an opportunity to see how related choices interact together to affect glucose levels on a continuous basis and averaged over the day, including documenting more information about diet, sleep, mood, and other activity choices. We conducted interviews with patients to assess their understanding of and anticipated strategies to use a personalized integrated display in-between traditional visits without the synchronous support of a clinician. We aimed to: 1) Reveal what information and uses for an integrated report are valuable for patients and 2) Elicit formative suggestions for improvement of the design of the report. An interview protocol was employed to investigate the reactions of patients to an integrated report ( N = 11). The results underscore that integrated visualizations primarily support patients in making sense of their own data obtained from multiple, independent wearable devices. With an integrated report, they can link their health behaviors to glucose variation, motivating self-reflection and behavioral change, and enhancing self-efficacy. Further, participants valued access to their own data and made recommendations to improve the usability of diet and mood information display. We discuss how integrated visualizations can enhance supervisory control and shared decision-making by empowering patients to act as informed partners in shared decision making with clinicians and diabetes self-management between visits.

  PublisherDOI

• Developing a human factors-informed onboarding workflow for digital health programs: lessons from a virtual type 2 diabetes self-management program

  Human Factors in Healthcare · 2026-05-05

  articleOpen access

  Participation in digital health programs often depends on successful setup and early use of digital tools, yet individuals with lower digital familiarity may encounter barriers during virtual onboarding. To support engagement in Closing The Loop (CTL), a virtual self-management program for type 2 diabetes (T2D), we developed and refined a structured approach to remote setup of devices and mobile applications. This paper describes the onboarding process, the barriers encountered during virtual setup, and how these observations informed a more systematic workflow for guided setup, troubleshooting, and verification of data-sharing during early program use. Individuals with T2D receiving care at the VA Connecticut Healthcare System were mailed a smartwatch and user guide before a scheduled virtual onboarding session. During onboarding, participants configured devices and mobile applications required for program participation. Participants completed a survey assessing demographic characteristics and self-reported digital health literacy using the eHealth Literacy Scale (eHEALS). Field notes from onboarding sessions were synthesized into case summaries to characterize onboarding experiences, identify common barriers, and inform iterative refinement of a more structured onboarding workflow. All 12 participants completed onboarding. Participants were 25% women, and 58% were aged 60 years or older. Common onboarding barriers included device syncing (42%), application permissions (42%), difficulty locating downloaded applications (25%), and inaccessible accounts (25%). Session duration ranged from under 20 minutes to over 60 minutes, with two-thirds of participants completing onboarding in 20 to 60 minutes. Case summaries suggested that participants with lower self-reported digital health literacy generally required more stepwise guidance and longer onboarding sessions. Observations from these sessions informed refinement of the onboarding process into the Structured Onboarding Session (SOS) and TechList, a more systematic workflow for guided setup, troubleshooting, and verification of data-sharing during early program use. This quality improvement project identified recurring barriers encountered during remote onboarding and informed development of a more systematic workflow for early setup of digital health tools. Findings underscore the importance of anticipatory troubleshooting, structured branching, and verification of actual data flow or task completion rather than installation alone. The SOS and TechList formalize onboarding as a structured implementation component within a virtual T2D self-management program. The resulting workflow may offer transferable design principles for digital health interventions that require device setup, account creation, data-sharing, or coordination across multiple apps and platforms.

  PublisherDOI

• Hybrid-Delivered Cognitive Behavioral Symptom Management and Activity Coaching Intervention for Patients Following Hematopoietic Stem Cell Transplant or CAR T-Cell Therapy: Protocol of a Randomized Controlled Trial

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access
  PublisherDOI

• An assessment of skill erosion on high‐dose rate brachytherapy treatment planning

  Journal of Applied Clinical Medical Physics · 2025-10-01

  articleOpen accessSenior author

  PURPOSE: To assess skill erosion for physicists after leaving brachytherapy service and contributors to variability during brachytherapy treatment planning. METHODS: Medical physicists simulated planning for nine patients by creating treatment plans twice, 2 months apart. The physicists were stratified as "On-Service" if they were assigned to and actively participating in the brachytherapy service (including treatment planning) during the study or "Off-Service" if they were not assigned, were not actively engaged in the service, and had not performed treatment planning the 6 months prior to the study commencement or during the study. A mixed effects model with Bonferroni correction was used to test for statistical significance between the stratified groups and Cohen's D was used to compare the effect of skill erosion. ANOVA analysis of a crossed Gauge Repeatability and Reproducibility (Gauge R&R) study quantified three contributors to variability for eight segments of the planning process: [1] repeatability (intra-observer), [2] reproducibility (inter-observer), and [3] patient-to-patient variation. Process capability was deemed acceptable when the Total Gauge R&R was less than 10%, marginally acceptable between 10% and 30%, and unacceptable when greater than 30%. RESULTS: The group of On-Service study participants had lower variability on 10 of 19 measures in the brachytherapy planning process than Off-Service study participants with the effect size being small, Cohen's D between 0.15 and 0.30 for significant measures. This indicates skill erosion does affect physicists after leaving the brachytherapy service for a period of 6 months or more. For both groups, only the bladder D2cc metric had a controlled amount of variability other than applicator reconstruction. CONCLUSIONS: Brachytherapy treatment planning suffers from skill erosion. Physicists should seek to mitigate its effects and evaluate competency on an ongoing basis.

  PublisherOA PDFDOI

• Using Machine Learning to Develop a Predictive Model of Infant Hypoglycemia Based on Maternal and Infant Variables in an Electronic Health Record

  UNC Libraries · 2025-03-18

  articleOpen access

  All newborns experience low blood glucose levels when they first initiate carbohydrate metabolism. Some levels remain low, with potential seizures and severe brain injury. Predicting newborns at higher risk is clinically useful because newborns can have their blood sugar raised with breastfeeding, donor milk, formula, or oral dextrose gels. Additionally, informing parents of this higher risk can enhance shared decision-making in the first 48 hours after birth. To address this, we propose three predictive models using binary logistic regression for newborns receiving treatment with oral dextrose gels for hypoglycemia. The first is a parsimonious model, where a high-risk newborn's first blood glucose value is highly predictive of requiring an oral dextrose gel treatment. The second model can be used earlier in the clinical workflow. It is based on the most predictive variables that are also electronically available for all newborns and do not change much in the electronic health record. The third model explores the most predictive variables based on a conceptual model of factors associated with health disparities. These three models are informed from insights gleaned by an exploratory analysis of alternative outcome measures, variables, and threshold cutoffs using a standard heuristic of greedily finding the highest average difference for records on both sides of partitions. We discuss how the dynamics of when data are available during a hospital stay in the postnatal care unit for all patients impact the selection of useful variables for electronically-based decision support. We plan to modify handouts for postnatal care nurses that detail treatment guidance and support shared decision-making. We plan to embed stratified guidance, recommended scripts for high and low-risk cohorts, orientation materials for float and junior nurses, and patient-facing educational materials.

  PublisherDOI

• Short-duration infusion obinutuzumab with venetoclax in chronic lymphocytic leukemia: a prospective observational study

  Blood Neoplasia · 2025-01-27 · 1 citations

  articleOpen access
  PublisherDOI

• 10-Item Questionnaire: Assessing Clinician User Experience with a Technology or Process Change

  Proceedings of the Human Factors and Ergonomics Society Annual Meeting · 2025-09-01

  article1st authorCorresponding

  We propose a 10-item questionnaire to assess the impact of a technology or process change on clinician user experience. Questions are slightly modified from peer-reviewed, validated questionnaires that assess the aspects of usability, efficiency, cognitive demand, workflow, alignment with intended work practice, and workarounds. Twenty-eight projects implementing a technology or process change rated the applicability of the questionnaire. The questionnaire was judged applicable and useful for the majority (80%) of the projects, with some questions not relevant to interventional radiology.

  PublisherDOI

• Collecting User Experience Data from Clinicians Without Increasing Burnout: Lessons Learned from the Department of Veterans Affairs

  Proceedings of the Human Factors and Ergonomics Society Annual Meeting · 2025-09-01 · 1 citations

  articleOpen accessCorresponding

  Many organizations, including health care systems, offer employee satisfaction and experience surveys. We examine what the literature says in response to the question, “How do health care organizations balance user experience (UX) measurement with knowledge that such collection processes might contribute to participant burnout?” and discuss how the Department of Veterans Affairs (VA) applied the results for our recurring measurement. We report the outcome of our evidence synthesis to describe how VA used the results to balance the collection of satisfaction and experience data with the need to minimize excessive clinician tasks. Based on the evidence synthesis, input from VA leadership and feedback from sites transitioning to a new electronic health record (EHR), we developed an enterprise-wide experience evaluation strategy. The strategy has four components: (1) Coordination with project teams to include questions relevant to their efforts; (2) Development of a dashboard, making survey results available to anyone needing data; (3) Proposal of a randomized sampling approach for future surveys; (4) A pilot project to explore correlation between survey results and EHR use metrics. Our approach serves as a model for human factors practitioners and other professionals interested in collecting clinician satisfaction and experience metrics while minimizing perceived impact on workload.

  PublisherOA PDFDOI

• From Development, Disease, and Decline: A Review of What Defines an Osteoclast Progenitor

  International Journal of Molecular Sciences · 2025-10-31

  reviewOpen access

  Our understanding of the different developmental origins of osteoclast progenitors and their respective roles during homeostatic bone remodeling at different skeletal sites as well as their roles within bone regeneration and degenerative conditions is evolving. In this narrative review article, we summarize what is known about the developmental origins, anatomical sources, and markers of osteoclast progenitors. We touch on how osteoclast progenitors vary during different disease contexts, including periodontitis, rheumatoid arthritis, and osteoarthritis. In addition, we also characterize osteoclast progenitors that contribute to bone healing and define changes observed with advancing age. In this regard, we offer a critical review of gaps within our understanding and opportunities for future development within the field. Because of their diverse nature under different contexts, identifying and characterizing osteoclast progenitors may help to advance condition-specific therapies.

  PublisherOA PDFDOI

• Ethical Implications of AI in Human Resource Management

  ITSI Transactions on Electrical and Electronics Engineering · 2025-04-15

  articleOpen access1st authorCorresponding

  The integration of Artificial Intelligence (AI) technologies into Human Resource Management (HRM) practices has generated significant interest due to its potential to streamline processes, enhance decision-making, and improve organizational efficiency. However, alongside the benefits, the ethical implications of AI in HRM have come under scrutiny. This abstract explores the ethical considerations surrounding the use of AI in HRM and its impact on employees, organizations, and society at large. The abstract begins by examining the ethical principles and values relevant to HRM, such as fairness, transparency, accountability, privacy, and equality. It highlights how the use of AI algorithms for recruitment, performance evaluation, employee monitoring, and decision-making can raise concerns related to bias, discrimination, and autonomy. Furthermore, the abstract delves into specific ethical challenges posed by AI in HRM, including algorithmic bias, data privacy and security, employee surveillance, job displacement, and the erosion of human judgment and empathy in decision-making processes. It discusses real-world examples and case studies to illustrate the potential risks and consequences of unethical AI practices in HRM. Moreover, the abstract explores strategies and frameworks for addressing ethical concerns in AI-powered HRM. It emphasizes the importance of designing and implementing AI systems that prioritize fairness, accountability, and transparency, while also respecting individual rights and dignity. It discusses the role of stakeholders, including HR professionals, policymakers, regulators, and technology developers, in promoting ethical AI practices in HRM. In conclusion, the abstract underscores the need for a holistic approach to addressing the ethical implications of AI in HRM, balancing the potential benefits of AI-driven innovation with the protection of employee rights and well-being. It calls for interdisciplinary collaboration, ethical guidelines, and regulatory frameworks to ensure that AI technologies in HRM are used responsibly, ethically, and in alignment with human values and societal norms.

  PublisherOA PDFDOI

Frequent coauthors

• Laura G. Militello

  Applied Decision Science (United States)

  119 shared

• Steven M. Asch

  115 shared

• Jason J. Saleem

  University of Louisville

  104 shared

• Robert L. Wears

  102 shared

• Marta L. Render

  Cincinnati VA Medical Center

  86 shared

• Dickson Cheung

  University of Colorado Denver

  83 shared

• Shilo Anders

  Vanderbilt University Medical Center

  82 shared

• Christopher Beach

  University of Missouri–Kansas City

  82 shared

Labs

• Injury Biomechanics Research CenterPI

Education

• PhD, Integrated Systems Engineering

  Ohio State University

  1999