Automatic Clinical Procedure Detection for Emergency Services

Heard, Jamison; Paris, Richard; Scully, Deirdre; McNaughton, Candace D.; Ehrenfeld, Jesse M.; Coco, Joseph; Fabbri, Daniel; Bodenheimer, Bobby; Adams, Julie A.

doi:10.1109/embc.2019.8856281

Cited by 7 publications

(10 citation statements)

References 7 publications

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“…Short duration procedures were difficult to differentiate (Heard et al, 2019), thus, the twenty-four clinical procedures were sorted by the mean time to complete. The sorted procedures were divided into three groups of eight procedures: 1) shortduration (< 20 seconds -s), 2) mid-duration (20-45s), 3) longduration (> 45s).…”

Section: Clinical Procedures Selectionmentioning

confidence: 99%

“…The experimental data was collected via an IRB approved human subjects evaluation, using a video enabled HAR system (Heard et al, 2019), conducted at the Vanderbilt University's medical training laboratory. Twenty-four clinical procedures were selected based on focus groups with emergency services personnel, army combat care guidelines, and commonly performed procedures in ambulances (El Sayed et al, 2015).…”

Section: Human Subjects Experimental Designmentioning

confidence: 99%

“…The sorted procedures were divided into three groups of eight procedures: 1) shortduration (< 20 seconds -s), 2) mid-duration (20-45s), 3) longduration (> 45s). A baseline classifier's, without contextual information, (Heard et al, 2019) accuracy identified the most and least accurate procedure recognitions in each group, resulting in six procedures. Each procedure, their associated subtasks, and their mean durations are provided in Table 1.…”

Section: Clinical Procedures Selectionmentioning

confidence: 99%

“…More than twenty clinical procedures are commonly performed by emergency medical technicians (El Sayed et al, 2015). Prior work (Heard et al, 2019) reduced the intraclass variance by using contextual information extracted from video data (i.e., the active body region) to classify the procedures. Although the video data provided additional contextual information that improved the procedure recognition accuracy, its use is discouraged.…”

Section: Introductionmentioning

confidence: 99%

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Emergency Clinical Procedure Detection via Wearable Sensors

Baskaran

Heard

Adams

2022

Proceedings of the Human Factors and Ergonomics Society Annual

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Communicating a patient’s state accurately during transfer from emergency medical technicians to hospital personnel is crucial for optimal care. Prior work demonstrated automated algorithms that combined wearable sensors with video data from cameras to detect clinical procedures and improve this information transfer. However, incorporating video requires task-or environment-specific installation mechanisms, raises privacy concerns, and is susceptible to occlusion and image noise. The presented approach detects clinical procedures using wearable sensors (i.e., inertial and electrophysiological) only and the procedures’ subtasks to mitigate the sensors’ signal variability to provide clinical procedure detection.

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Section: Clinical Procedures Selectionmentioning

confidence: 99%

Section: Human Subjects Experimental Designmentioning

confidence: 99%

Section: Clinical Procedures Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Emergency Clinical Procedure Detection via Wearable Sensors

Baskaran

Heard

Adams

2022

Proceedings of the Human Factors and Ergonomics Society Annual

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“…Our main research effort is to develop novel technologies to automatically generate a clinical care record without requiring the active participation of personnel in the field [ 6 ]. This automated sensing clinical documentation (ASCD) technology observes the tasks the medic performs using a combination of sensors [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Understanding the Information Needs and Context of Trauma Handoffs to Design Automated Sensing Clinical Documentation Technologies: Qualitative Mixed-Method Study of Military and Civilian Cases

Novak¹,

Simpson²,

Coco³

et al. 2020

J Med Internet Res

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Background Current methods of communication between the point of injury and receiving medical facilities rely on verbal communication, supported by brief notes and the memory of the field medic. This communication can be made more complete and reliable with technologies that automatically document the actions of field medics. However, designing state-of-the-art technology for military field personnel and civilian first responders is challenging due to the barriers researchers face in accessing the environment and understanding situated actions and cognitive models employed in the field. Objective To identify design insights for an automated sensing clinical documentation (ASCD) system, we sought to understand what information is transferred in trauma cases between prehospital and hospital personnel, and what contextual factors influence the collection, management, and handover of information in trauma cases, in both military and civilian cases. Methods Using a multi-method approach including video review and focus groups, we developed an understanding of the information needs of trauma handoffs and the context of field documentation to inform the design of an automated sensing documentation system that uses wearables, cameras, and environmental sensors to passively infer clinical activity and automatically produce documentation. Results Comparing military and civilian trauma documentation and handoff, we found similarities in the types of data collected and the prioritization of information. We found that military environments involved many more contextual factors that have implications for design, such as the physical environment (eg, heat, lack of lighting, lack of power) and the potential for active combat and triage, creating additional complexity. Conclusions An ineffectiveness of communication is evident in both the civilian and military worlds. We used multiple methods of inquiry to study the information needs of trauma care and handoff, and the context of medical work in the field. Our findings informed the design and evaluation of an automated documentation tool. The data illustrated the need for more accurate recordkeeping, specifically temporal aspects, during transportation, and characterized the environment in which field testing of the developed tool will take place. The employment of a systems perspective in this project produced design insights that our team would not have identified otherwise. These insights created exciting and interesting challenges for the technical team to resolve.

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Machine Learning in Medical Emergencies: a Systematic Review and Analysis

et al. 2021

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Despite the increasing demand for artificial intelligence research in medicine, the functionalities of his methods in health emergency remain unclear. Therefore, the authors have conducted this systematic review and a global overview study which aims to identify, analyse, and evaluate the research available on different platforms, and its implementations in healthcare emergencies. The methodology applied for the identification and selection of the scientific studies and the different applications consist of two methods. On the one hand, the PRISMA methodology was carried out in Google Scholar, IEEE Xplore, PubMed ScienceDirect, and Scopus. On the other hand, a review of commercial applications found in the best-known commercial platforms (Android and iOS). A total of 20 studies were included in this review. Most of the included studies were of clinical decisions (n = 4, 20%) or medical services or emergency services (n = 4, 20%). Only 2 were focused on m-health (n = 2, 10%). On the other hand, 12 apps were chosen for full testing on different devices. These apps dealt with pre-hospital medical care (n = 3, 25%) or clinical decision support (n = 3, 25%). In total, half of these apps are based on machine learning based on natural language processing. Machine learning is increasingly applicable to healthcare and offers solutions to improve the efficiency and quality of healthcare. With the emergence of mobile health devices and applications that can use data and assess a patient's real-time health, machine learning is a growing trend in the healthcare industry.

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Automatic Clinical Procedure Detection for Emergency Services

Cited by 7 publications

References 7 publications

Emergency Clinical Procedure Detection via Wearable Sensors

Emergency Clinical Procedure Detection via Wearable Sensors

Understanding the Information Needs and Context of Trauma Handoffs to Design Automated Sensing Clinical Documentation Technologies: Qualitative Mixed-Method Study of Military and Civilian Cases

Machine Learning in Medical Emergencies: a Systematic Review and Analysis

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