Large-Scale Telemedicine Implementation for Outpatient Clinicians: Results From a Pandemic-Adapted Learning Collaborative.

Learning collaboratives are seldom used outside of health care quality improvement. We describe a condensed, 10-week learning collaborative ("Telemedicine Hack") that facilitated telemedicine implementation for outpatient clinicians early in the COVID-19 pandemic. Live attendance averaged 1688 participants per session. Of 1005 baseline survey respondents, 57% were clinicians with one-third identifying as from a racial/ethnic minoritized group. Practice characteristics included primary care (71%), rural settings (51%), and community health centers (28%). Of three surveys, a high of 438 (81%) of 540 clinicians had billed ≥1 video-based telemedicine visit. Our learning collaborative "sprint" is a promising model for scaling knowledge during emergencies and addressing health inequities.

Facilitating Real-Time, Multidirectional Learning for Clinicians in a Low-Evidence Pandemic Response.

As COVID-19 was declared a health emergency in March 2020, there was immense demand for information about the novel pathogen. This paper examines the clinician-reported impact of Project ECHO COVID-19 Clinical Rounds on clinician learning. Primary sources of study data were Continuing Medical Education (CME) Surveys for each session from the dates of March 24, 2020 to July 30, 2020 and impact surveys conducted in November 2020, which sought to understand participants' overall assessment of sessions. Quantitative analyses included descriptive statistics and Mann-Whitney testing. Qualitative data were analyzed through inductive thematic analysis. Clinicians rated their knowledge after each session as significantly higher than before that session. 75.8% of clinicians reported they would 'definitely' or 'probably' use content gleaned from each attended session and clinicians reported specific clinical and operational changes made as a direct result of sessions. 94.6% of respondents reported that COVID-19 Clinical Rounds helped them provide better care to patients. 89% of respondents indicated they 'strongly agree' that they would join ECHO calls again.COVID-19 Clinical Rounds offers a promising model for the establishment of dynamic peer-to-peer tele-mentoring communities for low or no-notice response where scientifically tested or clinically verified practice evidence is limited.

Virtual Peer-to-Peer Learning to Enhance and Accelerate the Health System Response to COVID-19: The HHS ASPR Project ECHO COVID-19 Clinical Rounds Initiative.

Tasked with identifying digital health solutions to support dynamic learning health systems and their response to COVID-19, the US Department of Health and Human Services Office of the Assistant Secretary for Preparedness and Response partnered with the University of New Mexico's Project ECHO and more than 2 dozen other organizations and agencies to create a real-time virtual peer-to-peer clinical education opportunity: the COVID-19 Clinical Rounds Initiative. Focused on 3 "pressure points" in the COVID-19 continuum of care-(1) the out-of-hospital and/or emergency medical services setting, (2) emergency departments, and (3) inpatient critical care environments-the initiative has created a massive peer-to-peer learning network for real-time information sharing, engaging participants in all 50 US states and more than 100 countries. One hundred twenty-five learning sessions had been conducted between March 24, 2020 and February 25, 2021, delivering more than 58,000 total learner-hours of contact in the first 11 months of operation.

Use of out-of-hospital variables to predict severity of injury in pediatric patients involved in motor vehicle crashes.

STUDY OBJECTIVE: We sought to create a clinical decision rule, on the basis of variables available to out-of-hospital personnel, that could be used to accurately predict severe injury in pediatric patients involved in motor vehicle crashes as occupants. METHODS: We analyzed the National Automotive Sampling System database, a national probability sample, using pediatric patients up to 15 years old (occupants only) involved in motor vehicle crashes from January 1993 to December 1999. The National Automotive Sampling System database includes patients from regions throughout the country, weighted to represent a nationwide sample. Twelve out-of-hospital variables were used in classification and regression tree analysis to create a decision rule separating children with severe injuries (Injury Severity Score [ISS] > or =16) from those with minor injuries (ISS < 16). Misclassification costs and complexity parameters were selected to yield a decision tree with appropriate sensitivity and specificity for the identification of severely injured patients, while also being simple and practical for out-of-hospital use. Probability weights were used throughout the analysis to account for the sampling design and sampling weights. RESULTS: Using a sample size of 8,392 children, we constructed a decision rule using 3 out-of-hospital variables (Glasgow Coma Scale score, passenger space intrusion > or =6 in [> or =15 cm], and restraint use) to predict those patients with an ISS of 16 or more. Internal cross-validation was used to determine the sensitivity and specificity, yielding values of 92% and 73%, respectively, for the prediction of patients with an ISS of 16 or more. CONCLUSION: Out-of-hospital variables available to field personnel could be used to effectively triage pediatric motor vehicle crash patients using the decision rule developed here. Prospective trials would be needed to test this decision rule in actual use.

National Conference on Medical Indication for Air Bag Disconnection-July 16-18, 1997.

[National Highway Traffic Safety Administration: National Conference on Medical Indication for Air Bag Disconnection-July 16-18, 1997. Ann Emerg Med June 1998;31:782-783.].