Background: We are proposing to develop a novel hands free clinical documentation system for use in the operational environment that leverages a combination of off-the-shelf sensors, accelerometers, and cameras to build a software system to automatically detect the motion signatures associated with key clinical tasks and generate an abbreviated care record which can be transmitted upstream in real-time. Clinical documentation during both the point-of-injury and en route phases of care in the theater and operational environments continues to be incomplete, inaccurate, and detrimental to the goal of ensuring that receiving providers at Role 1, 2, or 3 facilities are able to rapidly gain situational awareness of the patients moving through the system. Major limitations that currently prevent the creation of timely and accurate clinical documentation include time pressure, the unique stress of providing care under fire, the use of personal protective equipment, limited visibility, and constrained working spaces. Additionally, even when documentation is generated, it is rarely transmitted either timely, clearly, or effectively.
Objectives/Hypothesis: The automatic identification, documentation, and communication of key clinical concepts (i.e. injury patterns or clinical interventions) which occur during the initial phase of care (i.e. point-of-injury and en-route care) will satisfy the information needs of upstream care providers and facilitate better care coordination and resource utilization. The overall objective of this study is to create a novel hands free system using wearable technology and cameras that can improve care by automatically sensing, documenting, and transmitting clinical events with little or no end-user input.
Project Design: Sensor technologies have evolved to the point where it is now possible to use a combination of off-the-shelf products to generate a clinical record that contains key elements. Off-the-shelf devices such as Apple Watches contain accelerometers to track motion, determine elevation change and duration of activity. Video cameras can provide valuable information about personnel activity and hand position over the patient’s body. We plan to detect key clinical events passively using data from these sensors, without active user input. This project aims to build on previous work to leverage these widely available sensors to automatically capture a broader class of clinical data.
Results:
- Bloos, S., McNaughton, C., Coco, J., Novak, L., Adams, J., Bodenheimer, R., Ehrenfeld, J., Heard, J., Paris, R., Simpson, C., Scully, D., Fabbri, D. (2019). Feasibility Assessment of a Pre-Hospital Automated Sensing Clinical Documentation System. In AMIA Summits on Translational Science Proceedings.
- Heard, J., Paris, R. A., Scully, D., McNaughton, C., Ehrenfeld, J. M., Coco, J., … Adams, J. A. (2019). Automatic Clinical Procedure Detection for Emergency Services. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 337–340). Berlin, Germany: IEEE. https://doi.org/10.1109/embc.2019.8856281
- Paris, R. A., Sullivan, P., Heard, J., Scully, D., McNaughton, C., Ehrenfeld, J. M., … Bodenheimer, R. (2019). Heatmap generation for emergency medical procedure identification. In SPIE Medical Imaging (Vol. 1095130, p. 110). San Diego, California. https://doi.org/10.1117/12.2513122