Evaluating proficiency in combat casualty training includes the assessment of hands-on training with mannequins through instructor observation. The evaluation process can suffer due to the subjective nature of the assessment: differences between instructor rating schemas, student to instructor ratios, and time to observe individual student performance. Because combat casualty care requires timely and accurate assessment for medical interventions, evaluators can look at the trainees’ physical actions (e.g., hand motion) to assess proficiency, as seen in suturing literature. The Lempel Ziv (LZ) complexity index is then used to assess proficiency. The LZ algorithm reduces complex strings of data (i.e., hand motion) to a string of 1’s and 0’s. The string is then broken into small “unique� strings that are grouped together. The pattern formed is a measure of performance with more complex patterns per unit of time indicating expertise. Expanding the current state of the art, experimentation occurs using several different precision tracking devices that are unobtrusive and require limited setup. During this effort, student hand motion is tracked and digitally stored as participants complete multiple tasks part of a cricothyroidotomy (emergency airway procedure in the neck). Motion data is subsequently processed using an algorithm adapted for text compression (LZ algorithm). Data has been gathered from nearly 100 military combat medic trainees at Joint Base Lewis McChord (JBLM) Medical Simulation Training Center (MSTC). Participant hand acceleration data from an emergency surgical cricothyroidotomy reveals a statistically significant difference in ability among different expertise levels. The higher the LZ score and self reported expertise level, the better the participant performed. The results show that when presented with demographic and video performance-based data, it is possible to gauge experience using LZ scores.