Space travel places people far from clinics and specialists. A toothache on orbit can escalate. Pressure shifts may aggravate dental pain. Microgravity can affect oral health, and a chipped filling can complicate a mission. In that setting, a clear plan for what to do and when to do it supports safety and training. A collaboration with UTHealth School of Dentistry and UTMB has translated expert procedures for dental emergencies into computable workflows so software can guide care in real time.
The project sits at the intersection of clinical practice, informatics, and space operations. The team modeled common dental emergencies step by step, then converted those diagrams into knowledge a computer can interpret. The work targets real-world use, with a digital guide that understands the procedure, prompts the right action, surfaces the necessary tools, and adapts when conditions change. That kind of support helps in spacecraft, polar stations, and other remote sites where clinicians work with limited resources.
Why Dental Workflows Matter
Emergency care already follows processes, whether written or tacit. When those processes become explicit and shareable, training becomes easier and variation narrows. That clarity matters when the person handling the problem may be a flight surgeon, a generalist, or a clinician new to austere environments. It also matters for rehearsals and simulations, which are routine in aerospace settings.
Dr. Muhammad “Tuan” Amith, Assistant Professor in the Department of Biostatistics and Data Science, says the team focused on turning clinical knowledge about “what to do next” into a structured model that software can use to assist clinicians during time pressure.
What BPMN and OWL2 Do
The team began with Business Process Model and Notation, often called BPMN. BPMN diagrams capture tasks, decision points, timing, and resources in a visual language that subject matter experts can review. After that, the group translated each diagram into an OWL2 knowledge graph. In practice, this gives a computer a way to “know” the steps, branches, and prerequisites in a procedure. That makes automated checks possible. It also enables links to medical terminologies and other data sources so the workflow carries clinical context that static checklists lack.
Dr. Amith explains that this form of modeling is a standard approach in a subfield of AI called knowledge representation, which encodes facts and rules so machines can reason about procedures and the information those procedures require.
The Role of Clinical Workflows
Clinical workflow represents the series of tasks conducted to complete clinical care, including the sequence of actions and the personnel responsible for each step. Clinicians rely on these workflows to implement procedures effectively within the clinical environment. Modeling these workflows can provide several benefits including enhanced communication and training capabilities, standardization of practices, analytical opportunities, and potential for automation.
When clinical workflows lack proper modeling and standardization, workarounds and circumvention of care can emerge. These deviations may disrupt patient care rather than streamline processes to ensure better quality outcomes. Formalizing clinical workflows in a representational format enables quality analysis, resource optimization, and opportunities for health care integration.
Given the challenges and risks associated with deep space travel, modeling clinical workflows becomes essential to optimize procedures and ensure astronaut safety for future analysis. The constraints of space flight—limited equipment resources and specialized crew expertise—make structured workflow planning particularly valuable for mission success.
Four Scenarios, One Pipeline
The study produced models for four high-priority scenarios in aerospace dentistry. These include dental abscess, extraction, a dislodged restoration, and minor dental trauma. Each scenario was drawn as a BPMN diagram and then passed through a processing pipeline to create a machine-readable graph. The result is a structured resource that teams can reuse, extend, and test in training environments.
Through his ongoing collaboration with the UTHealth School of Dentistry, Dr. Amith was introduced to a faculty member who trains astronauts in emergency dental procedures. At the same time, both collaborators were advising pre-dental undergraduates from the University of Houston and Emory University who wanted a summer project. The team conceptualized a study and invited Dr. Ronak Shah, UTMB’s Director of Aerospace Medicine, to join. Weekly sessions shaped the clinical content, while Dr. Amith coached the group on conceptual modeling and BPMN. Those working meetings moved the models off the whiteboard and into computable artifacts.
Keeping These Models Updatable
Clinical guidance evolves, and a practical model needs maintenance without a rebuild. The team addressed that with an end-to-end pipeline that reads BPMN and generates the corresponding knowledge graph. If a diagram changes, the updated graph can be regenerated. That keeps the work aligned with current practice and reduces manual rework.
Dr. Amith notes that the group plans to develop additional tools that make updates easier for clinicians to translate procedural knowledge into computational formats. The intent is to refine the pathway that takes an expert-reviewed diagram and yields a validated, computable model that stays current as guidelines change.
Broader Use at UTMB and Beyond
Although the study centers on aerospace dentistry, the approach fits a wide range of clinical and public health workflows. Many teams at UTMB teach and deliver care in settings that benefit from clear, computable processes. Modeling those processes supports consistency, analytics, and simulation. It also helps newcomers understand the “why” behind each branch and the information needed to move safely to the next step.
As humanity prepares for sustained presence on the Moon and eventual journeys to Mars, this research helps ensure that even in the vastness of space, quality healthcare remains within reach.
Read the paper “Rendering knowledge graphs from aerospace dentistry processes for clinical decision support systems” in Acta Astronautica.