An Electronic Data Capture Tool for Data Collection During Public Health Emergencies: Development and Usability Study.

BACKGROUND: The Discovery Critical Care Research Network Program for Resilience and Emergency Preparedness (Discovery PREP) partnered with a third-party technology vendor to design and implement an electronic data capture tool that addressed multisite data collection challenges during public health emergencies (PHE) in the United States. The basis of the work was to design an electronic data capture tool and to prospectively gather data on usability from bedside clinicians during national health system stress queries and influenza observational studies. OBJECTIVE: The aim of this paper is to describe the lessons learned in the design and implementation of a novel electronic data capture tool with the goal of significantly increasing the nation's capability to manage real-time data collection and analysis during PHE. METHODS: A multiyear and multiphase design approach was taken to create an electronic data capture tool, which was used to pilot rapid data capture during a simulated PHE. Following the pilot, the study team retrospectively assessed the feasibility of automating the data captured by the electronic data capture tool directly from the electronic health record. In addition to user feedback during semistructured interviews, the System Usability Scale (SUS) questionnaire was used as a basis to evaluate the usability and performance of the electronic data capture tool. RESULTS: Participants included Discovery PREP physicians, their local administrators, and data collectors from tertiary-level academic medical centers at 5 different institutions. User feedback indicated that the designed system had an intuitive user interface and could be used to automate study communication tasks making for more efficient management of multisite studies. SUS questionnaire results classified the system as highly usable (SUS score 82.5/100). Automation of 17 (61%) of the 28 variables in the influenza observational study was deemed feasible during the exploration of automated versus manual data abstraction. The creation and use of the Project Meridian electronic data capture tool identified 6 key design requirements for multisite data collection, including the need for the following: (1) scalability irrespective of the type of participant; (2) a common data set across sites; (3) automated back end administrative capability (eg, reminders and a self-service status board); (4) multimedia communication pathways (eg, email and SMS text messaging); (5) interoperability and integration with local site information technology infrastructure; and (6) natural language processing to extract nondiscrete data elements. CONCLUSIONS: The use of the electronic data capture tool in multiple multisite Discovery PREP clinical studies proved the feasibility of using the novel, cloud-based platform in practice. The lessons learned from this effort can be used to inform the improvement of ongoing global multisite data collection efforts during the COVID-19 pandemic and transform current manual data abstraction approaches into reliable, real time, and automated information exchange. Future research is needed to expand the ability to perform automated multisite data extraction during a PHE and beyond.

Clinical Data Extraction During Public Health Emergencies: A Blockchain Technology Assessment.

OBJECTIVE: We sought to explore the technical and legal readiness of healthcare institutions for novel data-sharing methods that allow clinical information to be extracted from electronic health records (EHRs) and submitted securely to the Food and Drug Administration's (FDA's) blockchain through a secure data broker (SDB). MATERIALS AND METHODS: This assessment was divided into four sections: an institutional EHR readiness assessment, legal consultation, institutional review board application submission, and a test of healthcare data transmission over a blockchain infrastructure. RESULTS: All participating institutions reported the ability to electronically extract data from EHRs for research. Formal legal agreements were deemed unnecessary to the project but would be needed in future tests of real patient data exchange. Data transmission to the FDA blockchain met the success criteria of data connection from within the four institutions' firewalls, externally to the FDA blockchain via a SDB. DISCUSSION: The readiness survey indicated advanced analytic capability in hospital institutions and highlighted inconsistency in Fast Healthcare Interoperability Resources format utilitzation across institutions, despite requirements of the 21st Century Cures Act. Further testing across more institutions and annual exercises leveraging the application of data exchange over a blockchain infrastructure are recommended actions for determining the feasibility of this approach during a public health emergency and broaden the understanding of technical requirements for multisite data extraction. CONCLUSION: The FDA's RAPID (Real-Time Application for Portable Interactive Devices) program, in collaboration with Discovery, the Critical Care Research Network's PREP (Program for Resilience and Emergency Preparedness), identified the technical and legal challenges and requirements for rapid data exchange to a government entity using the FDA blockchain infrastructure.

Useful properties of spinal circuits for learning and performing planar reaches.

OBJECTIVE: We developed a detailed model of the spinal circuitry plus musculoskeletal system (SC + MS) for the primate arm and investigated its role in sensorimotor control, learning and storing of movement repertoires. APPROACH: Recently developed models of spinal circuit connectivity, neurons and muscle force/energetics were integrated and in some cases refined to construct the most comprehensive model of the SC + MS to date. The SC + MS's potential contributions to center-out reaching movement were assessed by employing an extremely simple model of the brain that issued only step commands. MAIN RESULTS: The SC + MS was able to generate physiological muscle dynamics underlying reaching across different directions, distances, speeds, and even in the midst of strong dynamic perturbations (i.e. viscous curl field). For each task, there were many different combinations of brain inputs that generated physiological performance. Natural patterns of recruitment and low metabolic cost emerged for about half of the learning trials when a purely kinematic cost function was used and for all of the trials when an estimate of metabolic energy consumption was added to the cost function. Solutions for different tasks could be interpolated to generate intermediate movement and the range over which interpolation was successful was consistent with experimental reports. SIGNIFICANCE: This is the first demonstration that a realistic model of the SC + MS is capable of generating the required dynamics of center-out reaching. The interpolability observed is important for the feasibility of storing motor programs in memory rather than computing them from internal models of the musculoskeletal plant. Successful interpolation of command programs required them to have similar muscle recruitment patterns, which are thought by many to arise from hard-wired muscle synergies rather than learned as in our model system. These properties of the SC + MS along with its tendency to generate energetically efficient solutions might usefully be employed by motor cortex to generate voluntary behaviors such as reaching to targets.