About

John A. Vozenilek is a Clinical Professor in the Department of Bioengineering at the University of Illinois College of Engineering and a Professor of Clinical Emergency Medicine at the University of Illinois College of Medicine, Peoria. His educational background includes a Bachelor of Science in Microbiology from the University of Florida, a Doctor of Medicine with Research Distinction from the University of Miami School of Medicine, and training at the McGaw Medical Center, Northwestern University, where he completed Emergency Medicine residency and served as Chief Resident. His professional career encompasses roles in healthcare administration, innovation, and digital health, including positions such as Chief Medical Officer for Innovation and Digital Health at OSF Healthcare System and Vice President of OSF Healthcare System. He has also been involved in outside service as a Special Advisor to the Dean at Carle Illinois College of Medicine and a Board Member at the Illinois Innovation Network's Distillery Lab. His research focuses on medical simulation, team performance, patient safety, and the application of mixed reality and digital tools in healthcare training and practice. Vozenilek has contributed to the development of evidence-based approaches to medical team training, risk assessment in emergency care, and communication strategies in healthcare settings.

Research topics

• Computer Science
• Virology
• Environmental health
• Pathology
• Medicine
• Nursing
• Telecommunications

Selected publications

• A quantitative and qualitative assessment of differential privacy’s ability to support collaborative research using a real-world data analysis

  JAMIA Open · 2025-11-03

  articleOpen access

  Objective: Sharing clinical data for research that is both collaborative and privacy-preserving remains a challenge. Differential privacy (DP) offers a solution by introducing noise to query results. Using the PrivateSQL DP platform, this study assesses the resulting utility of differentially private data at different levels of aggregation through analyses of COVID-19 pandemic associations with new cancer diagnosis counts (NCCs). Materials and Methods: Data from a multi-hospital system for adult (18-90 years) encounters from 2019-2021 with apparently new cancer diagnoses were extracted, then queried using standard SQL ("original") and DP, each with 1-week and 4-week aggregations. Analyses on the 4 datasets included NCCs by year and multivariate regression models of associations between COVID-19 positivity rates (by county) and change in NCCs between pre- and post-COVID-19 start. Results: NCCs dropped in 2020, rebounding in 2021. This same pattern was demonstrated in the 4-week, but not the 1-week, DP dataset. Confidence intervals were substantially narrower in regressions using original datasets compared to those using DP datasets, and narrower in DP dataset regressions using 4-week rather than 1-week aggregation. Post-hoc sensitivity analyses found significant associations with 2 variables of interest on the original datasets (though these have methodologic limitations), but not the DP datasets. Discussion: DP reduces analytic accuracy to protect data privacy, but aggregation mitigated this tradeoff. Strategies for using DP in healthcare research and potential opportunities to enhance the DP platform were identified. Conclusion: DP platform enhancements for hypothesis-driven medical studies may expand DP's ability to support fruitful, cross-institutional research collaborations.

  PublisherOA PDFDOI

• Teaching the Communication of Diagnostic Uncertainty at Scale: Leveraging a Mobile App for Just-in-Time Instruction

  Cureus · 2025-07-20

  editorialOpen access

  Diagnostic uncertainty is a reality of clinical care, particularly in emergency medicine. The ability to communicate this uncertainty to patients and families, however, remains underdeveloped in medical education curricula. Traditional simulation training is regarded as the gold standard for teaching difficult conversations, but resource constraints can limit access, especially for larger cohorts of learners. Faced with this challenge, we turned to a tool our team developed: the Uncertainty Communication mobile application (app). With over 200 medical students entering their fourth year of training, we used this app to deliver real-time, large-scale, skill-based learning, without simulation rooms, standardized patients, or small-group role-play. Students practiced communicating diagnostic uncertainty to patients, received immediate feedback, and reflected on patient-centered communication strategies. Their responses were generally positive. While not a formal study or intended to replace the role of traditional simulation training, our experience reaffirms how the intentional integration of mobile tools into medical training can introduce complex skills, like communicating diagnostic uncertainty, into scalable and practical learning solutions. As we train the next generation of physicians, such tools may offer educators some degree of flexibility when training large cohorts of students.

  PublisherOA PDFDOI

• Applications of mixed reality with medical imaging for training and clinical practice

  Journal of Medical Imaging · 2024-12-26 · 6 citations

  reviewOpen access

  Purpose: This review summarizes the current use of extended reality (XR) including virtual reality (VR), mixed reality, and augmented reality (AR) in the medical field, ranging from medical imaging to training to preoperative planning. It covers the integration of these technologies into clinical practice and within medical training while discussing the challenges and future opportunities in this sphere. This will hopefully encourage more physicians to collaborate on integrating medicine and technology. Approach: The review was written by experts in the field based on their knowledge and on recent publications exploring the topic of extended realities in medicine. Results: Based on our findings, XR including VR, mixed reality, and AR are increasingly utilized within surgery both for preoperative planning and intraoperative procedures. These technologies are also promising means for improved education at every level of physician training. However, there are still barriers to the widespread adoption of VR, mixed reality, and AR, including human factors, technological challenges, and regulatory issues. Conclusions: Based on the current use of VR, mixed reality, and AR, it is likely that the use of these technologies will continue to grow over the next decade. To support the development and integration of XR into medicine, it is important for academic groups to collaborate with industrial groups and regulatory agencies in these endeavors. These joint projects will help address the current limitations and mutually benefit both fields.

  PublisherOA PDFDOI

• Simulation-Debriefing Enhanced Needs Assessment to Address Quality Markers in Health Care: An Innovation for Prospective Hazard Analysis

  The Joint Commission Journal on Quality and Patient Safety · 2024-10-30 · 1 citations

  articleOpen accessSenior author

  Simulation-Debriefing Enhanced Needs Assessment (SDENA) is a simulation-based approach to prospective hazard analysis that uses simulation and debriefing as a unit-level diagnostic tool. Scenarios address failure modes for health care improvement targets, and debriefing explores unit-specific barriers and resiliencies. Debriefing guides are structured to explore how six drivers of a behavior engineering framework (data, tools/resources, incentives, knowledge/skills, capacity, motivation) influence clinical behaviors. Illinois Hospital Association members who deployed SDENA to address specific hospital-acquired conditions found motivation to be a more significant barrier than anticipated before deployment. SDENA represents a novel approach to improving safety and may refine intervention targets.

  PublisherDOI

• Can a 5-to-90-day Mortality Predictor Perform Consistently Across Time and Equitably Across Populations?

  Journal of Medical Systems · 2023-07-03 · 2 citations

  articleOpen accessSenior author

  Advance care planning (ACP) facilitates end-of-life care, yet many die without it. Timely and accurate mortality prediction may encourage ACP. However, performance of predictors typically differs among sub-populations (e.g., rural vs. urban) and worsens over time ("concept drift"). Therefore, we assessed performance equity and consistency for a novel 5-to-90-day mortality predictor across various demographies, geographies, and timeframes (n = 76,812 total encounters). Predictions were made for the first day of included adult inpatient admissions on a retrospective dataset. AUC-PR remained at 29% both pre-COVID (throughout 2018) and during COVID (8 months in 2021). Pre-COVID-19 recall and precision were 58% and 25% respectively at the 12.5% certainty cutoff, and 12% and 44% at the 37.5% cutoff. During COVID-19, recall and precision were 59% and 26% at the 12.5% cutoff, and 11% and 43% at the 37.5% cutoff. Pre-COVID, compared to the overall population, recall was lower at the 12.5% cutoff in the White, non-Hispanic subgroup and at both cutoffs in the rural subgroup. During COVID-19, precision at the 12.5% cutoff was lower than that of the overall population for the non-White and non-White female subgroups. No other significant differences were seen between subgroups and the corresponding overall population. Overall performance during COVID was unchanged from pre-pandemic performance. Although some comparisons (especially precision at the 37.5% cutoff) were underpowered, precision at the 12.5% cutoff was equitable across most demographies, regardless of the pandemic. Mortality prediction to prioritize ACP conversations can be provided consistently and equitably across many studied timeframes and sub-populations.

  PublisherOA PDFDOI

• Communicating Diagnostic Uncertainty at Emergency Department Discharge: A Simulation-Based Mastery Learning Randomized Trial

  Academic Medicine · 2022-10-04 · 22 citations

  article

  PURPOSE: There are no standardized approaches for communicating with patients discharged from the emergency department with diagnostic uncertainty. This trial tested efficacy of the Uncertainty Communication Education Module, a simulation-based mastery learning curriculum designed to establish competency in communicating diagnostic uncertainty. METHOD: Resident physicians at 2 sites participated in a 2-arm waitlist randomized controlled trial from September 2019 to June 2020. After baseline (T1) assessment of all participants via a standardized patient encounter using the Uncertainty Communication Checklist (UCC), immediate access physicians received training in the Uncertainty Communication Education Module, which included immediate feedback, online educational modules, a smartphone-based application, and telehealth deliberate practice with standardized patients. All physicians were retested 16-19 weeks later (T2) via in-person standardized patient encounters; delayed access physicians then received the intervention. A final test of all physicians occurred 11-15 weeks after T2 (T3). The primary outcome measured the percentage of physicians in the immediate versus delayed access groups meeting or exceeding the UCC minimum passing standard at T2. RESULTS: Overall, 109 physicians were randomized, with mean age 29 years (range 25-46). The majority were male (n = 69, 63%), non-Hispanic/Latino (n = 99, 91%), and White (n = 78, 72%). At T2, when only immediate access participants had received the curriculum, immediate access physicians demonstrated increased mastery (n = 29, 52.7%) compared with delayed access physicians (n = 2, 3.7%, P < .001; estimated adjusted odds ratio of mastery for the immediate access participants, 31.1 [95% CI, 6.8-143.1]). There were no significant differences when adjusting for training site or stage of training. CONCLUSIONS: The Uncertainty Communication Education Module significantly increased mastery in communicating diagnostic uncertainty at the first postintervention test among emergency physicians in standardized patient encounters. Further work should assess the impact of clinical implementation of these communication skills.

  PublisherDOI

• Can a “goldilocks” Mortality Predictor Perform Consistently Across Time and Equitably Across Populations?

  Research Square · 2022-10-10

  preprintOpen accessSenior author
  PublisherOA PDFDOI

• Mitigation of SARS-CoV-2 transmission at a large public university

  Nature Communications · 2022 · 33 citations

  • Computer Science
  • Medicine
  • Environmental health

  In Fall 2020, universities saw extensive transmission of SARS-CoV-2 among their populations, threatening health of the university and surrounding communities, and viability of in-person instruction. Here we report a case study at the University of Illinois at Urbana-Champaign, where a multimodal "SHIELD: Target, Test, and Tell" program, with other non-pharmaceutical interventions, was employed to keep classrooms and laboratories open. The program included epidemiological modeling and surveillance, fast/frequent testing using a novel low-cost and scalable saliva-based RT-qPCR assay for SARS-CoV-2 that bypasses RNA extraction, called covidSHIELD, and digital tools for communication and compliance. In Fall 2020, we performed >1,000,000 covidSHIELD tests, positivity rates remained low, we had zero COVID-19-related hospitalizations or deaths amongst our university community, and mortality in the surrounding Champaign County was reduced more than 4-fold relative to expected. This case study shows that fast/frequent testing and other interventions mitigated transmission of SARS-CoV-2 at a large public university.

  PublisherOA PDFDOI

• Access to Care: End-to-End Digital Response for COVID-19 Care Delivery

  The Journal for Nurse Practitioners · 2021-09-27 · 6 citations

  articleOpen accessSenior author
  PublisherOA PDFDOI

• Mitigation of SARS-CoV-2 Transmission at a Large Public University

  medRxiv · 2021-08-05 · 14 citations

  preprintOpen access

  In the Fall of 2020, many universities saw extensive transmission of SARS-CoV-2 among their populations, threatening the health of students, faculty and staff, the viability of in-person instruction, and the health of surrounding communities. 1, 2 Here we report that a multimodal “SHIELD: Target, Test, and Tell” program mitigated the spread of SARS-CoV-2 at a large public university, prevented community transmission, and allowed continuation of in-person classes amidst the pandemic. The program combines epidemiological modelling and surveillance (Target); fast and frequent testing using a novel and FDA Emergency Use Authorized low-cost and scalable saliva-based RT-qPCR assay for SARS-CoV-2 that bypasses RNA extraction, called covidSHIELD (Test); and digital tools that communicate test results, notify of potential exposures, and promote compliance with public health mandates (Tell). These elements were combined with masks, social distancing, and robust education efforts. In Fall 2020, we performed more than 1,000,000 covidSHIELD tests while keeping classrooms, laboratories, and many other university activities open. Generally, our case positivity rates remained less than 0.5%, we prevented transmission from our students to our faculty and staff, and data indicate that we had no spread in our classrooms or research laboratories. During this fall semester, we had zero COVID-19-related hospitalizations or deaths amongst our university community. We also prevented transmission from our university community to the surrounding Champaign County community. Our experience demonstrates that multimodal transmission mitigation programs can enable university communities to achieve such outcomes until widespread vaccination against COVID-19 is achieved, and provides a roadmap for how future pandemics can be addressed.

  PublisherOA PDFDOI

Frequent coauthors

• James A. Gordon

  Swedish Medical Center

  249 shared

• Ernest Wang

  Penn State Milton S. Hershey Medical Center

  247 shared

• Stephen McLaughlin

  Heriot-Watt University

  222 shared

• William F. Bond

  170 shared

• Yasuharu Okuda

  University of South Florida

  165 shared

• Rosemarie Fernandez

  University of Florida

  162 shared

• Emily M. Hayden

  Massachusetts General Hospital

  122 shared

• Moira Davenport

  Allegheny Health Network

  121 shared

Labs

• BioengineeringPI

Education

• Chief Resident, Emergency Medicine

  Northwestern University

  2000

• MD, School of Medicine

  University of Miami

  1996