Objectives: Predictions estimate supplies of filtering facepiece respirators (FFRs) would be limited in the event of a severe influenza pandemic. Ultraviolet decontamination and reuse (UVDR) is a potential approach to mitigate an FFR shortage. A field study sought to understand healthcare workers' perspectives and potential logistics issues related to implementation of UVDR methods for FFRs in hospitals. Methods: Data were collected at three hospitals using a structured guide to conduct 19 individual interviews, 103 focus group interviews, and 285 individual surveys. Data were then evaluated using thematic analysis to reveal key themes. Results: Data revealed noteworthy variation in FFR use across the sample, along with preferences and requirements for the use of UVDR, unit design, and FFR reuse. Based on a scale of 1 (low) to 10 (high), the mean perception of safety in a high mortality pandemic wearing no FFR was 1.25 of 10, wearing an FFR for an extended period without decontamination was 4.20 of 10, and using UVDR was 7.72 of 10. Conclusions: In addition to technical design and development, preparation and training will be essential to successful implementation of a UVDR program. Ultraviolet decontamination and reuse program design and implementation must account for actual clinical practice, compliance with regulations, and practical financial considerations to be successfully adopted so that it can mitigate potential FFR shortages in a pandemic.
Introduction The electronic medical record (EMR) is presumed to support clinician decisions by documenting and retrieving patient information. Research shows that the EMR variably affects patient care and clinical decision making. The way information is presented likely has a significant impact on this variability. Well-designed representations of salient information can make a task easier by integrating information in useful patterns that clinicians use to make improved clinical judgments and decisions. Using Cognitive Systems Engineering methods, our research team developed a novel health information technology (NHIT) that interfaces with the EMR to display salient clinical information and enabled communication with a dedicated text-messaging feature. The software allows clinicians to customize displays according to their role and information needs. Here we present results of usability and validation assessments of the NHIT. Materials and Methods Our subjects were physicians, nurses, respiratory therapists, and physician trainees. Two arms of this study were conducted, a usability assessment and then a validation assessment. The usability assessment was a computer-based simulation using deceased patient data. After a brief five-minute orientation, the usability assessment measured individual clinician performance of typical tasks in two clinical scenarios using the NHIT. The clinical scenarios included patient admission to the unit and patient readiness for surgery. We evaluated clinician perspective about the NHIT after completing tasks using 7-point Likert scale surveys. In the usability assessment, the primary outcome was participant perceptions about the system’s ease of use compared to the legacy system. A subsequent cross-over, validation assessment compared performance of two clinical teams during simulated care scenarios: one using only the legacy IT system and one using the NHIT in addition to the legacy IT system. We oriented both teams to the NHIT during a 1-hour session on the night before the first scenario. Scenarios were conducted using high-fidelity simulation in a real burn intensive care unit room. We used observations, task completion times, semi-structured interviews, and surveys to compare user decisions and perceptions about their performance. The primary outcome for the validation assessment was time to reach accurate (correct) decision points. Results During the usability assessment, clinicians were able to complete all tasks requested. Clinicians reported the NHIT was easier to use and the novel information display allowed for easier data interpretation compared to subject recollection of the legacy EMR. In the validation assessment, a more junior team of clinicians using the NHIT arrived at accurate diagnoses and decision points at similar times as a more experienced team. Both teams noted improved communication between team members when using the NHIT and overall rated the NHIT as easier to use than the legacy EMR, especially with respect to finding information. Conclusions The primary findings of these assessments are that clinicians found the NHIT easy to use despite minimal training and experience and that it did not degrade clinician efficiency or decision-making accuracy. These findings are in contrast to common user experiences when introduced to new EMRs in clinical practice.
Introduction The emergence of more complex Prolonged Field Care in austere settings and the need to assist inexperienced providers’ ability to treat patients create an urgent need for effective tools to support care. We report on a project to develop a phone-/tablet-based decision support system for prehospital tactical combat casualty care that collects physiologic and other clinical data and uses machine learning to detect and differentiate shock manifestation. Materials and Methods Software interface development methods included literature review, rapid prototyping, and subject matter expert design requirements reviews. Machine learning algorithm methods included development of a model trained on publicly available Medical Information Mart for Intensive Care data, then on de-identified data from Mayo Clinic Intensive Care Unit. Results The project team interviewed 17 Army, Air Force, and Navy medical subject matter experts during design requirements review sessions. They had an average of 17 years of service in military medicine and an average of 4 deployments apiece and all had performed tactical combat casualty care on live patients during deployment. Comments provided requirements for shock identification and management in prehospital settings, including support for indication of shock probability and shock differentiation. The machine learning algorithm based on logistic regression performed best among other algorithms we tested and was able to predict shock onset 90 minutes before it occurred with better than 75% accuracy in the test dataset. Conclusions We expect the Trauma Triage, Treatment, and Training Decision Support system will augment a medic’s ability to make informed decisions based on salient patient data and to diagnose multiple types of shock through remotely trained, field deployed ML models.
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Tactical combat casualty care (TCCC) involves care for casualties in armed conflict from one’s own service (e.g., U.S. Marine Corps), other services (i.e., U.S. Army, Air Force,), allied forces, adversaries, and civilians. To minimize injury and preserve life, medics perform TCCC which includes casualty retrieval, stabilization and documentation, transport, triage, and treatment. In future scenarios, delays in evacuation are expected to require extended care including prolonged field care (PFC) over hours to days, increasing the potential for complications such as bloodstream infection (sepsis). Most medics have only simple equipment and essential medications and will need assistance at point of care to make decisions on how to treat more complex cases and perform procedures in an austere setting.We describe a project for the Defense Health Agency (DHA) over 3 years to develop and evaluate the Trauma Triage Treatment and Training Decision Support (4TDS), a real-time decision support system (DSS) to monitor casualty health. The operating 4TDS prototype uses the Samsung smart phone and tablet certified for use in the Department of Defense (DoD) Nett Warrior program. Connection to a simple VitalTag (Pacific Northwest National Laboratory, Richland, WA) vital signs monitor placed on a casualty at point of injury (PoI) will stream patient data including heart rate, respiration rate, peripheral oxygen saturation (SpO2), and diastolic and systolic blood pressure. Nurses, technicians, and physicians can use the tablet to display an expanded data set including lab values while providing care at a Battalion Aid Station (BAS) and Field Hospital (FH).4TDS includes a Machine Learning (ML) model to indicate shock probability, risk of internal hemorrhage, and probability of the need for a massive transfusion. The shock model was trained on Mayo Clinic Intensive Care Unit (ICU) patient data, then evaluated in a 6-month “silent test” comparing shock prediction with actual clinician diagnoses. The model only uses 6 vital signs, which is suited to battlefield care, while other published results include lab tests (e.g., lactate), and produces a Receiver Operator Characteristic Curve (ROC) of 0.83 for shock detection. The model only decreases by 0.05 90 minutes, identifying shock probability well before its onset. Medic reviews indicate a 30-minute advanced warning would be more than sufficient to initiate treatment.Medics who provide PFC may need to perform life-critical procedures such as shock management, cricothyroidotomy intubation, and transfusion that may not have been used for an extended period. 4TDS includes refresher training in how to perform such a procedure, as well as whether to perform the procedure. Usability assessments with healthcare providers from the Army, Navy, and Air Force at Joint Base San Antonio, TX have demonstrated 4TDS and its capabilities align with TCCC practice. This work is supported by the US Army Medical Research and Materiel Command under Contract No. W81XWH‐15‐9‐0001.
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