The CAMH Neuroinformatics Platform: A Hospital-Focused Brain-CODE Implementation

Rotenberg, David; Chang, Qing; Potapova, Natalia; Wang, Andy; Hon, Marcia; Sanches, Marcos; Bogetic, Nikola; Frias, Nathan; Liu, Tommy; Behan, Brendan; El-Badrawi, Rachad; Strother, Stephen C.; Evans, Susan Gilbert; Mikkelsen, Jordan; Gee, Tom; Fan, Dashan; Arnott, Stephen R.; Laing, Shuai; Dharsee, Moyez; Vaccarino, Anthony L.; Javadi, Mojib; Evans, Kenneth R.; Jankowicz, Damian

doi:10.3389/fninf.2018.00077

Cited by 9 publications

(8 citation statements)

References 41 publications

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“…In addition to providing clinical care, CAMH is Canada’s leading mental illness research facility. In recent years, REDCap (Research Electronic Data Capture; Vanderbilt University) has been instrumental to this research effort [ 5 ]. REDCap is a secure, Health Insurance Portability and Accountability Act–compliant, web-based application for building and managing electronic surveys and assessments [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Digitization of Measurement-Based Care Pathways in Mental Health Through REDCap and Electronic Health Record Integration: Development and Usability Study

Hawley¹,

Yu²,

Bogetic³

et al. 2021

J Med Internet Res

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Background The delivery of standardized self-report assessments is essential for measurement-based care in mental health. Paper-based methods of measurement-based care data collection may result in transcription errors, missing data, and other data quality issues when entered into patient electronic health records (EHRs). Objective This study aims to help address these issues by using a dedicated instance of REDCap (Research Electronic Data Capture; Vanderbilt University)—a free, widely used electronic data capture platform—that was established to enable the deployment of digitized self-assessments in clinical care pathways to inform clinical decision making. Methods REDCap was integrated with the primary clinical information system to facilitate the real-time transfer of discrete data and PDF reports from REDCap into the EHR. Both technical and administrative components were required for complete implementation. A technology acceptance survey was also administered to capture physicians’ and clinicians’ attitudes toward the new system. Results The integration of REDCap with the EHR transitioned clinical workflows from paper-based methods of data collection to electronic data collection. This resulted in significant time savings, improved data quality, and valuable real-time information delivery. The digitization of self-report assessments at each appointment contributed to the clinic-wide implementation of the major depressive disorder integrated care pathway. This digital transformation facilitated a 4-fold increase in the physician adoption of this integrated care pathway workflow and a 3-fold increase in patient enrollment, resulting in an overall significant increase in major depressive disorder integrated care pathway capacity. Physicians’ and clinicians’ attitudes were overall positive, with almost all respondents agreeing that the system was useful to their work. Conclusions REDCap provided an intuitive patient interface for collecting self-report measures and accessing results in real time to inform clinical decisions and an extensible backend for system integration. The approach scaled effectively and expanded to high-impact clinics throughout the hospital, allowing for the broad deployment of complex workflows and standardized assessments, which led to the accumulation of harmonized data across clinics and care pathways. REDCap is a flexible tool that can be effectively leveraged to facilitate the automatic transfer of self-report data to the EHR; however, thoughtful governance is required to complement the technical implementation to ensure that data standardization, data quality, patient safety, and privacy are maintained.

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Section: Introductionmentioning

confidence: 99%

Digitization of Measurement-Based Care Pathways in Mental Health Through REDCap and Electronic Health Record Integration: Development and Usability Study

Hawley¹,

Yu²,

Bogetic³

et al. 2021

J Med Internet Res

Self Cite

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“…Therefore, the RESIT may have better performance than current methods, such as NI and SSI. As a novel interpolation method, the RESIT has been integrated in the EEG preprocessing pipeline on the WeBrain cloud platform ( https://webrain.uestc.edu.cn/ ), and it is further expected to be integrated into common EEG tools such as EEGLAB (Delorme and Makeig 2004 ) and FieldTrip (Oostenveld et al 2011 ), as well as other EEG preprocessing pipelines and tools for simultaneous EEG-fMRI multimodal fusion (Dong et al 2014 , 2018 ) and large-scale EEG preprocessing. In addition, because no interpolation method provides completely unique data for the missing channels, the interpolated channels using the RESIT are not independent, which may reduce the spatial resolution of the EEG when there are too many bad channels.…”

Section: Discussionmentioning

confidence: 99%

Reference Electrode Standardization Interpolation Technique (RESIT): A Novel Interpolation Method for Scalp EEG

et al. 2021

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Abstract“Bad channels” are common phenomena during scalp electroencephalography (EEG) recording that arise due to various technique-related reasons, and reconstructing signals from bad channels is an inevitable choice in EEG processing. However, current interpolation methods are all based on purely mathematical interpolation theory, ignoring the neurophysiological basis of the EEG signals, and their performance needs to be further improved, especially when there are many scattered or adjacent bad channels. Therefore, a new interpolation method, named the reference electrode standardization interpolation technique (RESIT), was developed for interpolating scalp EEG channels. Resting-state and event-related EEG datasets were used to investigate the performance of the RESIT. The main results showed that (1) assuming 10% bad channels, RESIT can reconstruct the bad channels well; (2) as the percentage of bad channels increased (from 2% to 85%), the absolute and relative errors between the true and RESIT-reconstructed signals generally increased, and the correlations between the true and RESIT signals decreased; (3) for a range of bad channel percentages (2% ~ 85%), the RESIT had lower absolute error (approximately 2.39% ~ 33.5% reduction), lower relative errors (approximately 1.3% ~ 35.7% reduction) and higher correlations (approximately 2% ~ 690% increase) than traditional interpolation methods, including neighbor interpolation (NI) and spherical spline interpolation (SSI). In addition, the RESIT was integrated into the EEG preprocessing pipeline on the WeBrain cloud platform (https://webrain.uestc.edu.cn/). These results suggest that the RESIT is a promising interpolation method for both separate and simultaneous EEG preprocessing that benefits further EEG analysis, including event-related potential (ERP) analysis, EEG network analysis, and strict group-level statistics.

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“…We have minimised the privacy risks associated with accessing the EHRs by processing and analysing this data according to established protocols and safeguards, codeveloped with CAMH’s Privacy Department and Research Ethics Board. 92 Briefly, these protocols involve hospital data warehouse staff extracting numerical features from the unstructured or text data before it is shared with the study team, since textual data can contain personal and identifiable information. In this way, this sensitive data (in its raw form) never leaves dedicated hospital clinical servers and is never accessed directly by the study team.…”

Section: Ethics and Disseminationmentioning

confidence: 99%

Predictive care: a protocol for a computational ethnographic approach to building fair models of inpatient violence in emergency psychiatry

et al. 2023

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IntroductionManaging violence or aggression is an ongoing challenge in emergency psychiatry. Many patients identified as being at risk do not go on to become violent or aggressive. Efforts to automate the assessment of risk involve training machine learning (ML) models on data from electronic health records (EHRs) to predict these behaviours. However, no studies to date have examined which patient groups may be over-represented in false positive predictions, despite evidence of social and clinical biases that may lead to higher perceptions of risk in patients defined by intersecting features (eg, race, gender). Because risk assessment can impact psychiatric care (eg, via coercive measures, such as restraints), it is unclear which patients might be underserved or harmed by the application of ML.Methods and analysisWe pilot a computational ethnography to study how the integration of ML into risk assessment might impact acute psychiatric care, with a focus on how EHR data is compiled and used to predict a risk of violence or aggression. Our objectives include: (1) evaluating an ML model trained on psychiatric EHRs to predict violent or aggressive incidents for intersectional bias; and (2) completing participant observation and qualitative interviews in an emergency psychiatric setting to explore how social, clinical and structural biases are encoded in the training data. Our overall aim is to study the impact of ML applications in acute psychiatry on marginalised and underserved patient groups.Ethics and disseminationThe project was approved by the research ethics board at The Centre for Addiction and Mental Health (053/2021). Study findings will be presented in peer-reviewed journals, conferences and shared with service users and providers.

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The CAMH Neuroinformatics Platform: A Hospital-Focused Brain-CODE Implementation

Cited by 9 publications

References 41 publications

Digitization of Measurement-Based Care Pathways in Mental Health Through REDCap and Electronic Health Record Integration: Development and Usability Study

Digitization of Measurement-Based Care Pathways in Mental Health Through REDCap and Electronic Health Record Integration: Development and Usability Study

Reference Electrode Standardization Interpolation Technique (RESIT): A Novel Interpolation Method for Scalp EEG

Predictive care: a protocol for a computational ethnographic approach to building fair models of inpatient violence in emergency psychiatry

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