Critical Steps in Data Management During a Crisis.

Data management is essential in a flow cytometry (FCM) shared resource laboratory (SRL) for the integrity of collected data and its long-term preservation, as described in the Cytometry publication from 2016, ISAC Flow Cytometry Shared Resource Laboratory (SRL) Best Practices (Barsky et al.: Cytometry Part A 89A(2016): 1017-1030). The SARS-CoV-2 pandemic introduced an array of challenges in the operation of SRLs. The subsequent laboratory shutdowns and access restrictions brought to the forefront well-established practices that withstood the impact of a sudden change in operations and illuminated areas that need improvement. The most significant challenges from a data management perspective were data access for remote analysis and workstation management. Notably, lessons learned from this challenge emphasize the importance of safeguarding collected data from loss in various emergencies such as fire or natural disasters where the physical hardware storing data could be directly affected. Here, we describe two data management systems that have been successful during the current emergency created by the pandemic, specifically remote access and automated data transfer. We will discuss other situations that could arise and lead to data loss or challenges in interpreting data. © 2020 International Society for Advancement of Cytometry.

Solutions for Shared Resource Lab Remote Quality Control and Instrument Troubleshooting during a Pandemic.

The COVID-19 pandemic has dramatically affected shared resource lab (SRL) staff in-person availability at institutions globally. This article discusses the challenges of ensuring reliable instrument performance and quality data output while facility staff and external service provider on-site presence is severely limited. Solutions revolve around the adoption of remote monitoring and troubleshooting platforms, provision of self-service troubleshooting resources specific to facility instruments and workflows, development of an assistance contact policy, and ensuring efficiency of limited in-person staff time. These solutions employ software and hardware tools that are already in use or readily available in the SRL community, such as remote instrument access tools, video hosting and conferencing platforms, and ISAC shared resources. © 2020 International Society for Advancement of Cytometry.

DeepLMS: a deep learning predictive model for supporting online learning in the Covid-19 era.

Coronavirus (Covid-19) pandemic has imposed a complete shut-down of face-to-face teaching to universities and schools, forcing a crash course for online learning plans and technology for students and faculty. In the midst of this unprecedented crisis, video conferencing platforms (e.g., Zoom, WebEx, MS Teams) and learning management systems (LMSs), like Moodle, Blackboard and Google Classroom, are being adopted and heavily used as online learning environments (OLEs). However, as such media solely provide the platform for e-interaction, effective methods that can be used to predict the learner's behavior in the OLEs, which should be available as supportive tools to educators and metacognitive triggers to learners. Here we show, for the first time, that Deep Learning techniques can be used to handle LMS users' interaction data and form a novel predictive model, namely DeepLMS, that can forecast the quality of interaction (QoI) with LMS. Using Long Short-Term Memory (LSTM) networks, DeepLMS results in average testing Root Mean Square Error (RMSE) [Formula: see text], and average correlation coefficient between ground truth and predicted QoI values [Formula: see text] [Formula: see text], when tested on QoI data from one database pre- and two ones during-Covid-19 pandemic. DeepLMS personalized QoI forecasting scaffolds user's online learning engagement and provides educators with an evaluation path, additionally to the content-related assessment, enriching the overall view on the learners' motivation and participation in the learning process.

Trauma Data Quality Improvement: One Center's Experience With Telecommuting and Paperless Data Management.

The American College of Surgeons requires that trauma centers collect and enter data into the National Trauma Data Registry in compliance with the National Trauma Data Standard. ProMedica supports employment of 4 trauma data analysts who are responsible for entering information in a timely manner, validating the data, and analyzing data to evaluate established benchmarks and support the performance improvement and patient safety process. Historically, these analysts were located on-site at ProMedica Toledo Hospital. In 2017, a proposal was developed including modifications to data collection to streamline processes, move toward paperless documentation, and allow for the analysts to telecommute. To measure the effect of these changes, the timeliness of data entry, rate of data validation, productivity, and staff satisfaction were measured. After the transition to electronic data management and home-based workstations, registry data were being entered within 30 days and 100% of cases were being validated, without sacrificing effective and efficient communication between in-hospital and home-based staff. The institution also benefitted from reduced expense for physical space, employee turnover, and decreased employee absenteeism. The analysts appreciated benefits related to time, travel, environment, and job satisfaction.It is feasible to transition trauma data analysts to a work-from-home situation. An all-electronic system of data management and communication makes such an arrangement possible and sustainable. This quality improvement project solved a workspace issue and was beneficial to the trauma program overall, with the timeliness and validation of data entry vastly improved.