My lab focuses on creating tools that aid clinicians to enhance their clinical skill in order to ultimately improve patient care. Towards this goal, we specialize in the development of technologies at the interface of human-computer interaction, state-of-the-art sensors, robotic technology and computational tools. Two key research questions that our lab focuses on are
1. Can clinical skill (e.g., robotic surgery, needle insertion) be accurately assessed by integrating sensors, robotic technology and computational methods?
2. Can sensor data from simulators enable improvement in clinical skill?
The underlying premise for our work is based on the fact that the skill of a clinician is critical to successful patient outcomes. It is imperative, therefore, that our clinical workforce be effectively and efficiently trained. In view of this goal, there is an ongoing movement by leading medical education stakeholders to use advanced tools to create standardized, objective and structured training. This tangible need, combined with burgeoning medical technologies like surgical robots and smart operating rooms, has generated the field of medical simulation.
Another factor that propels the need for simulation-based training is the growing problem of medical errors, now the third leading cause of death in the US alone. Simulation-based methods are currently being explored in a plethora of medical disciplines as a means of enabling a skilled workforce.
Our work is highly transdisciplinary in nature, since development of meaningful tools is possible only through close collaboration with clinicians. Further, our collaborators span the disciplines of human factors psychology, electrical and industrial engineering and computer science.
One current project we are working on is to develop a simulator for hemodialysis cannulation. Most patients in the US with end stage renal disease require dialysis multiple times a week for survival. One of the primary contributors to the inordinately high rate of complications among dialysis patients is unskilled cannulation at dialysis clinics. Training nurses and dialysis clinic technicians in locating and accurately cannulating patients holds the promise of greatly improving patient outcomes. Towards this goal, we are developing a simulator for effective palpation and needle insertion during cannulation by integrating hardware and software components.
Another project addresses the need to train suturing skills of novice surgeons by integrating video, motion and force sensor data. To facilitate the characterization of suturing skill, a novel platform was developed that captures synchronized video, motion and force data during suturing at various depths. This device permits the use of standard surgical tools during suturing and quantifies performance using sensor metrics. Several preliminary studies with residents and attending surgeons using the platform have demonstrated promising results for the viability of the system for skills assessment.
A recent exciting development is the work we have begun with Drs. Venkat Krovi and Rick Groff on using a da Vinci surgical robot to examine skills training. Clemson provides a unique environment that fosters interdisciplinary collaboration because of our relationships with Greenville Health System and other departments beyond bioengineering. And, lay people seem to “get” the significance of what we aim to achieve through our research because the goal of improved patient outcomes is universally relatable.
