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McPhee PG, Vaccarino AL, Naska S, Nylen K, Santisteban JA, Chepesiuk R, Andrade A, Georgiades S, Behan B, Iaboni A, Wan F, Aimola S, Cheema H, Gorter JW. Harmonizing data on correlates of sleep in children within and across neurodevelopmental disorders: lessons learned from an Ontario Brain Institute cross-program collaboration. Front Neuroinform 2024; 18:1385526. [PMID: 38828185 PMCID: PMC11141168 DOI: 10.3389/fninf.2024.1385526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 04/19/2024] [Indexed: 06/05/2024] Open
Abstract
There is an increasing desire to study neurodevelopmental disorders (NDDs) together to understand commonalities to develop generic health promotion strategies and improve clinical treatment. Common data elements (CDEs) collected across studies involving children with NDDs afford an opportunity to answer clinically meaningful questions. We undertook a retrospective, secondary analysis of data pertaining to sleep in children with different NDDs collected through various research studies. The objective of this paper is to share lessons learned for data management, collation, and harmonization from a sleep study in children within and across NDDs from large, collaborative research networks in the Ontario Brain Institute (OBI). Three collaborative research networks contributed demographic data and data pertaining to sleep, internalizing symptoms, health-related quality of life, and severity of disorder for children with six different NDDs: autism spectrum disorder; attention deficit/hyperactivity disorder; obsessive compulsive disorder; intellectual disability; cerebral palsy; and epilepsy. Procedures for data harmonization, derivations, and merging were shared and examples pertaining to severity of disorder and sleep disturbances were described in detail. Important lessons emerged from data harmonizing procedures: prioritizing the collection of CDEs to ensure data completeness; ensuring unprocessed data are uploaded for harmonization in order to facilitate timely analytic procedures; the value of maintaining variable naming that is consistent with data dictionaries at time of project validation; and the value of regular meetings with the research networks to discuss and overcome challenges with data harmonization. Buy-in from all research networks involved at study inception and oversight from a centralized infrastructure (OBI) identified the importance of collaboration to collect CDEs and facilitate data harmonization to improve outcomes for children with NDDs.
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Affiliation(s)
- Patrick G. McPhee
- Department of Psychiatry and Behavioural Neurosciences, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
- Offord Centre for Child Studies, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
- School of Rehabilitation Science, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
- CanChild Centre for Childhood Disability Research, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
| | | | - Sibel Naska
- Ontario Brain Institute, Toronto, ON, Canada
| | - Kirk Nylen
- Ontario Brain Institute, Toronto, ON, Canada
| | - Jose Arturo Santisteban
- Ontario Brain Institute, Toronto, ON, Canada
- The Centre for Addiction and Mental Health, Toronto, ON, Canada
| | | | - Andrea Andrade
- Department of Paediatrics, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Stelios Georgiades
- Department of Psychiatry and Behavioural Neurosciences, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
- Offord Centre for Child Studies, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
| | | | - Alana Iaboni
- Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada
| | - Flora Wan
- Ontario Brain Institute, Toronto, ON, Canada
| | | | | | - Jan Willem Gorter
- CanChild Centre for Childhood Disability Research, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
- Department of Pediatrics, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
- Center of Excellence for Rehabilitation Medicine, University Medical Center Utrecht, Utrecht, Netherlands
- Brain Center, University Medical Center Utrecht, Utrecht, Netherlands
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Yu J, Shen N, Conway S, Hiebert M, Lai-Zhao B, McCann M, Mehta RR, Miranda M, Putterman C, Santisteban JA, Thomson N, Young C, Chiuccariello L, Hunter K, Hill S. A holistic approach to integrating patient, family, and lived experience voices in the development of the BrainHealth Databank: a digital learning health system to enable artificial intelligence in the clinic. FRONTIERS IN HEALTH SERVICES 2023; 3:1198195. [PMID: 37927443 PMCID: PMC10625404 DOI: 10.3389/frhs.2023.1198195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 10/04/2023] [Indexed: 11/07/2023]
Abstract
Artificial intelligence, machine learning, and digital health innovations have tremendous potential to advance patient-centred, data-driven mental healthcare. To enable the clinical application of such innovations, the Krembil Centre for Neuroinformatics at the Centre for Addiction and Mental Health, Canada's largest mental health hospital, embarked on a journey to co-create a digital learning health system called the BrainHealth Databank (BHDB). Working with clinicians, scientists, and administrators alongside patients, families, and persons with lived experience (PFLE), this hospital-wide team has adopted a systems approach that integrates clinical and research data and practices to improve care and accelerate research. PFLE engagement was intentional and initiated at the conception stage of the BHDB to help ensure the initiative would achieve its goal of understanding the community's needs while improving patient care and experience. The BHDB team implemented an evolving, dynamic strategy to support continuous and active PFLE engagement in all aspects of the BHDB that has and will continue to impact patients and families directly. We describe PFLE consultation, co-design, and partnership in various BHDB activities and projects. In all three examples, we discuss the factors contributing to successful PFLE engagement, share lessons learned, and highlight areas for growth and improvement. By sharing how the BHDB navigated and fostered PFLE engagement, we hope to motivate and inspire the health informatics community to collectively chart their paths in PFLE engagement to support advancements in digital health and artificial intelligence.
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Affiliation(s)
- Joanna Yu
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Health and Technology, Vector Institute for Artificial Intelligence, Toronto, ON, Canada
| | - Nelson Shen
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada
- AMS Healthcare, Toronto, ON, Canada
| | - Susan Conway
- Centre for Addictions and Mental Health, Toronto, ON, Canada
| | - Melissa Hiebert
- Centre for Addictions and Mental Health, Toronto, ON, Canada
| | - Benson Lai-Zhao
- Centre for Addictions and Mental Health, Toronto, ON, Canada
| | - Miriam McCann
- Centre for Addictions and Mental Health, Toronto, ON, Canada
| | - Rohan R. Mehta
- Centre for Addictions and Mental Health, Toronto, ON, Canada
| | - Morena Miranda
- Centre for Addictions and Mental Health, Toronto, ON, Canada
| | - Connie Putterman
- Centre for Addictions and Mental Health, Toronto, ON, Canada
- CanChild, Hamilton, ON, Canada
- CHILD-BRIGHT Network, Montreal, QC, Canada
- Kids Brain Health Network, Burnaby, ON, Canada
- Province of Ontario Neurodevelopmental (POND) Network, Toronto, ON, Canada
| | - Jose Arturo Santisteban
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Nicole Thomson
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada
| | - Courtney Young
- Centre for Addictions and Mental Health, Toronto, ON, Canada
| | | | - Kimberly Hunter
- Centre for Addictions and Mental Health, Toronto, ON, Canada
| | - Sean Hill
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Health and Technology, Vector Institute for Artificial Intelligence, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
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Sikstrom L, Maslej MM, Findlay Z, Strudwick G, Hui K, Zaheer J, Hill SL, Buchman DZ. Predictive care: a protocol for a computational ethnographic approach to building fair models of inpatient violence in emergency psychiatry. BMJ Open 2023; 13:e069255. [PMID: 37185650 PMCID: PMC10151964 DOI: 10.1136/bmjopen-2022-069255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/17/2023] Open
Abstract
INTRODUCTION Managing 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 ANALYSIS We 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 DISSEMINATION The 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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Affiliation(s)
- Laura Sikstrom
- The Krembil Centre for Neuroinformatics, The Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Anthropology, University of Toronto, Toronto, Ontario, Canada
| | - Marta M Maslej
- The Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Zoe Findlay
- The Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Gillian Strudwick
- Information Management Group, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
| | - Katrina Hui
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Juveria Zaheer
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
- Gerald Sheff and Shanitha Kachan Emergency Department, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Sean L Hill
- The Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Daniel Z Buchman
- Office of Education, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
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4
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Redolfi A, Archetti D, De Francesco S, Crema C, Tagliavini F, Lodi R, Ghidoni R, Gandini Wheeler-Kingshott CAM, Alexander DC, D'Angelo E. Italian, European, and international neuroinformatics efforts: An overview. Eur J Neurosci 2022. [PMID: 36310103 DOI: 10.1111/ejn.15854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 10/18/2022] [Accepted: 10/18/2022] [Indexed: 12/15/2022]
Abstract
Neuroinformatics is a research field that focusses on software tools capable of identifying, analysing, modelling, organising and sharing multiscale neuroscience data. Neuroinformatics has exploded in the last two decades with the emergence of the Big Data phenomenon, characterised by the so-called 3Vs (volume, velocity and variety), which provided neuroscientists with an improved ability to acquire and process data faster and more cheaply thanks to technical improvements in clinical, genomic and radiological technologies. This situation has led to a 'data deluge', as neuroscientists can routinely collect more study data in a few days than they could in a year just a decade ago. To address this phenomenon, several neuroimaging-focussed neuroinformatics platforms have emerged, funded by national or transnational agencies, with the following goals: (i) development of tools for archiving and organising analytical data (XNAT, REDCap and LabKey); (ii) development of data-driven models evolving from reductionist approaches to multidimensional models (RIN, IVN, HBD, EuroPOND, E-DADS and GAAIN BRAIN); and (iii) development of e-infrastructures to provide sufficient computational power and storage resources (neuGRID, HBP-EBRAINS, LONI and CONP). Although the scenario is still fragmented, there are technological and economical attempts at both national and international levels to introduce high standards for open and Findable, Accessible, Interoperable and Reusable (FAIR) neuroscience worldwide.
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Affiliation(s)
- Alberto Redolfi
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Damiano Archetti
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Silvia De Francesco
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Claudio Crema
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Fabrizio Tagliavini
- Scientific Directorate, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Raffaele Lodi
- Functional and Molecular Neuroimaging Unit, IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy.,Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, Italy
| | - Roberta Ghidoni
- Molecular Markers Laboratory, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Claudia A M Gandini Wheeler-Kingshott
- NMR Research Unit, Queen Square MS Center, Department of Neuroinflammation, UCL Institute of Neurology, London, UK.,Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Daniel C Alexander
- Centre for Medical Image Computing, University College London, London, UK.,Department of Computer Science, University College London, London, UK
| | - Egidio D'Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
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Liang S, Beaton D, Arnott SR, Gee T, Zamyadi M, Bartha R, Symons S, MacQueen GM, Hassel S, Lerch JP, Anagnostou E, Lam RW, Frey BN, Milev R, Müller DJ, Kennedy SH, Scott CJM, Strother SC. Magnetic Resonance Imaging Sequence Identification Using a Metadata Learning Approach. Front Neuroinform 2021; 15:622951. [PMID: 34867254 PMCID: PMC8635782 DOI: 10.3389/fninf.2021.622951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Accepted: 10/21/2021] [Indexed: 11/29/2022] Open
Abstract
Despite the wide application of the magnetic resonance imaging (MRI) technique, there are no widely used standards on naming and describing MRI sequences. The absence of consistent naming conventions presents a major challenge in automating image processing since most MRI software require a priori knowledge of the type of the MRI sequences to be processed. This issue becomes increasingly critical with the current efforts toward open-sharing of MRI data in the neuroscience community. This manuscript reports an MRI sequence detection method using imaging metadata and a supervised machine learning technique. Three datasets from the Brain Center for Ontario Data Exploration (Brain-CODE) data platform, each involving MRI data from multiple research institutes, are used to build and test our model. The preliminary results show that a random forest model can be trained to accurately identify MRI sequence types, and to recognize MRI scans that do not belong to any of the known sequence types. Therefore the proposed approach can be used to automate processing of MRI data that involves a large number of variations in sequence names, and to help standardize sequence naming in ongoing data collections. This study highlights the potential of the machine learning approaches in helping manage health data.
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Affiliation(s)
- Shuai Liang
- Rotman Research Institute, Baycrest Health Center, Toronto, ON, Canada
- Indoc Research, Toronto, ON, Canada
| | - Derek Beaton
- Rotman Research Institute, Baycrest Health Center, Toronto, ON, Canada
| | - Stephen R. Arnott
- Rotman Research Institute, Baycrest Health Center, Toronto, ON, Canada
| | - Tom Gee
- Indoc Research, Toronto, ON, Canada
| | - Mojdeh Zamyadi
- Rotman Research Institute, Baycrest Health Center, Toronto, ON, Canada
| | - Robert Bartha
- Robarts Research Institute, Western University, London, ON, Canada
| | - Sean Symons
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Glenda M. MacQueen
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Stefanie Hassel
- Department of Psychiatry, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Jason P. Lerch
- Mouse Imaging Centre, Hospital for Sick Children, Toronto, ON, Canada
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Evdokia Anagnostou
- Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada
- Department of Pediatrics, University of Toronto, Toronto, ON, Canada
| | - Raymond W. Lam
- Department of Psychiatry, University of British Columbia, Vancouver, BC, Canada
| | - Benicio N. Frey
- Department of Psychiatry and Behavioral Neurosciences, McMaster University, Hamilton, ON, Canada
- Mood Disorders Program, St. Joseph’s Healthcare, Hamilton, ON, Canada
| | - Roumen Milev
- Departments of Psychiatry and Psychology, Providence Care Hospital, Queen’s University, Kingston, ON, Canada
| | - Daniel J. Müller
- Molecular Brain Science, Centre for Addiction and Mental Health, Campbell Family Mental Health Research Institute, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Sidney H. Kennedy
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, Krembil Research Centre, University Health Network, Toronto, ON, Canada
- Department of Psychiatry, St. Michael’s Hospital, University of Toronto, Toronto, ON, Canada
- Keenan Research Centre for Biomedical Science, St. Michael’s Hospital, Li Ka Shing Knowledge Institute, Toronto, ON, Canada
| | - Christopher J. M. Scott
- L.C. Campbell Cognitive Neurology Research Unit, Toronto, ON, Canada
- Heart & Stroke Foundation Centre for Stroke Recovery, Toronto, ON, Canada
- Sunnybrook Health Sciences Centre, Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Stephen C. Strother
- Rotman Research Institute, Baycrest Health Center, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
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6
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Sherin A, Rajeswari R. Computer-Aided Diagnosis System for Alzheimer's Disease Using Positron Emission Tomography Images. Interdiscip Sci 2021; 13:433-442. [PMID: 33811602 DOI: 10.1007/s12539-020-00409-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Revised: 11/24/2020] [Accepted: 12/03/2020] [Indexed: 11/28/2022]
Abstract
Alzheimer's disease (AD) is a kind of neurological brain disease. It is an irretrievable, neurodegenerative brain disorder. There are no pills or drugs to cure AD. Therefore, an early diagnosis may help the physician to make accurate analysis and to provide better treatment. With the advent of computational intelligence techniques, machine learning models have made tremendous progress in brain images analysis using MRI, SPECT and PEI. However, accurate analysis of brain scans is an extremely challenging task. The main focus of this paper is to design a Computer Aided Diagnosis (CAD) system using Long-Term Short Memory (LSTM) to improve classification rate and determine suitable attributes that can differentiate AD from Healthy Control (HC) subjects. First, 3D PET images are preprocessed, converted into many groups of 2D images and then grouped into many subsets at certain interval. Subsequently, different features including first order statistical, Gray Level Co-occurrence Matrix and wavelet energy of all sub-bands are extracted from each group, combined and taken as feature vectors. LSTM is designed and employed for classifying PET brain images into HC and AD subjects based on the feature vectors. Finally, the developed system is validated on 18FDG-PET images collected from 188 subjects including 105 HC and 83 AD subjects from ADNI database. Efficacy of the developed CAD system is analyzed using different features. Numerical results revealed that the developed CAD system yields classification accuracy of 98.9% when using combined features, showing outstanding performance.
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Affiliation(s)
- A Sherin
- Department of Computer Applications, Bharathiar University, Coimbatore, India.
| | - R Rajeswari
- Department of Computer Applications, Bharathiar University, Coimbatore, India
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7
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Hawley S, Yu J, Bogetic N, Potapova N, Wakefield C, Thompson M, Kloiber S, Hill S, Jankowicz D, Rotenberg D. Digitization of Measurement-Based Care Pathways in Mental Health Through REDCap and Electronic Health Record Integration: Development and Usability Study. J Med Internet Res 2021; 23:e25656. [PMID: 34014169 PMCID: PMC8176343 DOI: 10.2196/25656] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 02/03/2021] [Accepted: 03/16/2021] [Indexed: 01/20/2023] Open
Abstract
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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Affiliation(s)
- Steve Hawley
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Joanna Yu
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Nikola Bogetic
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Natalia Potapova
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Chris Wakefield
- Clinical Applications, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Mike Thompson
- Clinical Applications, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Stefan Kloiber
- General Adult Psychiatry and Health Systems Division, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Sean Hill
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Damian Jankowicz
- Clinical Applications, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - David Rotenberg
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada
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8
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Busatto G, Rosa PG, Serpa MH, Squarzoni P, Duran FL. Psychiatric neuroimaging research in Brazil: historical overview, current challenges, and future opportunities. REVISTA BRASILEIRA DE PSIQUIATRIA (SAO PAULO, BRAZIL : 1999) 2021; 43:83-101. [PMID: 32520165 PMCID: PMC7861184 DOI: 10.1590/1516-4446-2019-0757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Accepted: 02/03/2020] [Indexed: 11/23/2022]
Abstract
The last four decades have witnessed tremendous growth in research studies applying neuroimaging methods to evaluate pathophysiological and treatment aspects of psychiatric disorders around the world. This article provides a brief history of psychiatric neuroimaging research in Brazil, including quantitative information about the growth of this field in the country over the past 20 years. Also described are the various methodologies used, the wealth of scientific questions investigated, and the strength of international collaborations established. Finally, examples of the many methodological advances that have emerged in the field of in vivo neuroimaging are provided, with discussion of the challenges faced by psychiatric research groups in Brazil, a country of limited resources, to continue incorporating such innovations to generate novel scientific data of local and global relevance.
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Affiliation(s)
- Geraldo Busatto
- Laboratório de Neuroimagem em Psiquiatria (LIM 21), Departamento e Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil
| | - Pedro G. Rosa
- Laboratório de Neuroimagem em Psiquiatria (LIM 21), Departamento e Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil
| | - Mauricio H. Serpa
- Laboratório de Neuroimagem em Psiquiatria (LIM 21), Departamento e Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil
| | - Paula Squarzoni
- Laboratório de Neuroimagem em Psiquiatria (LIM 21), Departamento e Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil
| | - Fabio L. Duran
- Laboratório de Neuroimagem em Psiquiatria (LIM 21), Departamento e Instituto de Psiquiatria, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo, SP, Brazil
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