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1.
J Am Geriatr Soc ; 70(8): 2458-2461, 2022 Aug.
Article in English | MEDLINE | ID: covidwho-2001695
3.
Interface (Botucatu, Online) ; 25(supl.1): e210047, 2021. ilus
Article in Portuguese | WHO COVID, LILACS (Americas) | ID: covidwho-1855149

ABSTRACT

Considerando o cenário mundial de pandemia do coronavírus, o presente estudo objetiva apresentar um modelo de formação para educação em saúde, constituído por Equipes de Aprendizagem Ativa (EAA), integrando ensino-serviço-comunidade para prevenção da contaminação por Covid-19. Esse modelo reúne nas EAA, supervisionadas por um docente, discentes da área da Saúde, professores e estudantes do ensino médio e agentes comunitários de saúde. O modelo proposto utiliza vídeos do Projeto Homem Virtual acerca do vírus SARS-CoV2 e pode ser ofertado nas modalidades remota, presencial ou híbrida. Assim, essa proposta de formação contribui para o enfrentamento da pandemia na perspectiva da educação em saúde. Ademais, a estruturação desse modelo permite que sua aplicabilidade seja versátil no que se refere às temáticas abordadas nos cursos, bem como no que diz respeito aos integrantes das EAA. (AU)


Against the backdrop of the coronavirus pandemic, this study presents an active learning teams (ALTs) training model for health education as part of teaching-service-community for the prevention of Covid-19 infection. Supervised by an academic staff member, the teams were made up of health students, high school teachers and students, and community health workers. The model uses videos from the Virtual Man Project about the SARS-CoV2 virus and can be offered in remote, face-to-face or hybrid formats. The training model contributes to the response to the pandemic in the field of health education. In addition, the model's versatile structure means it can be applied across different topics addressed by the courses and to different members of the ALTs. (AU)


Considerando el escenario mundial de pandemia del coronavirus, el presente estudio tiene el objetivo de presentar un modelo de formación, constituido por Equipos de Aprendizaje Activos (EAA), para Educación en Salud, integrando enseñanza-servicio-comunidad para prevención de la contaminación por Covid-19. Ese modelo reúne en las EAA, supervisadas por un docente, a discentes del área de la salud, profesores y estudiantes de la enseñanza media y agentes comunitarios de salud. El modelo propuesto utiliza vídeos del Proyecto Hombre Virtual sobre el virus SARS-CoV2 y puede ofrecerse en las modalidades remota, presencial o híbrida. Por lo tanto, esta propuesta de formación contribuye al enfrentamiento de la pandemia bajo la perspectiva de la Educación en salud. Además, la estructuración de este modelo permite que su aplicabilidad sea versátil en lo que se refiere a las temáticas abordadas en los cursos, así como en lo que se refiere a los integrantes de las EAA. (AU)


Subject(s)
Humans , Health Education/methods , Problem-Based Learning , Learning Health System/methods , COVID-19/prevention & control , Educational Technology , Teleworking
5.
Appl Clin Inform ; 13(1): 315-321, 2022 01.
Article in English | MEDLINE | ID: covidwho-1721720

ABSTRACT

BACKGROUND: One key aspect of a learning health system (LHS) is utilizing data generated during care delivery to inform clinical care. However, institutional guidelines that utilize observational data are rare and require months to create, making current processes impractical for more urgent scenarios such as those posed by the COVID-19 pandemic. There exists a need to rapidly analyze institutional data to drive guideline creation where evidence from randomized control trials are unavailable. OBJECTIVES: This article provides a background on the current state of observational data generation in institutional guideline creation and details our institution's experience in creating a novel workflow to (1) demonstrate the value of such a workflow, (2) demonstrate a real-world example, and (3) discuss difficulties encountered and future directions. METHODS: Utilizing a multidisciplinary team of database specialists, clinicians, and informaticists, we created a workflow for identifying and translating a clinical need into a queryable format in our clinical data warehouse, creating data summaries and feeding this information back into clinical guideline creation. RESULTS: Clinical questions posed by the hospital medicine division were answered in a rapid time frame and informed creation of institutional guidelines for the care of patients with COVID-19. The cost of setting up a workflow, answering the questions, and producing data summaries required around 300 hours of effort and $300,000 USD. CONCLUSION: A key component of an LHS is the ability to learn from data generated during care delivery. There are rare examples in the literature and we demonstrate one such example along with proposed thoughts of ideal multidisciplinary team formation and deployment.


Subject(s)
COVID-19 , Learning Health System , COVID-19/epidemiology , Humans , Observational Studies as Topic , Pandemics , Practice Guidelines as Topic , Workflow
6.
Am J Infect Control ; 50(5): 542-547, 2022 05.
Article in English | MEDLINE | ID: covidwho-1664608

ABSTRACT

BACKGROUND: Incidence of health care personnel (HCP) with a higher-risk SARS-CoV-2 exposure and subsequent 14-day quarantine period adds substantial burden on the workforce. Implementation of an early return-to-work (RTW) program may reduce quarantine periods for asymptomatic HCP and reduce workforce shortages during the COVID-19 pandemic. METHODS: This observational quality improvement study included asymptomatic HCP of a multi-facility health care system with higher-risk workplace or non-household community SARS-CoV-2 exposure ≤4 days. The program allowed HCP to return to work 8 days after exposure if they remained asymptomatic through day 7 with day 5-7 SARS-CoV-2 nucleic acid amplification test result negative. RESULTS: Between January 4 and June 25, 2021, 384 HCP were enrolled, 333 (86.7%) remained asymptomatic and of these, 323 (97%) tested negative and were early RTW eligible. Mean days in quarantine was 8.16 (SD 2.40). Median day of early RTW was 8 (range 6-9, IQR 8-8). Mean days saved from missed work was 1.84 (SD 0.52). A total of 297 (92%) HCP did RTW ≤10 days from exposure and days saved from missed work was 546.48. CONCLUSIONS: Implementing an HCP early RTW program is a clinical approach for COVID-19 workplace safety that can increase staffing availability, while maintaining a low risk of SARS-CoV-2 transmission.


Subject(s)
COVID-19 , Learning Health System , COVID-19/prevention & control , Delivery of Health Care , Health Personnel , Humans , Pandemics , Quality Improvement , Return to Work , SARS-CoV-2
7.
J Med Internet Res ; 23(2): e23795, 2021 02 18.
Article in English | MEDLINE | ID: covidwho-1574557

ABSTRACT

BACKGROUND: It has been widely communicated that individuals with underlying health conditions are at higher risk of severe disease due to COVID-19 than healthy peers. As social distancing measures continue during the COVID-19 pandemic, experts encourage individuals with underlying conditions to engage in telehealth appointments to maintain continuity of care while minimizing risk exposure. To date, however, little information has been provided regarding telehealth uptake among this high-risk population. OBJECTIVE: The aim of this study is to describe the telehealth use, resource needs, and information sources of individuals with chronic conditions during the COVID-19 pandemic. Secondary objectives include exploring differences in telehealth use by sociodemographic characteristics. METHODS: Data for this study were collected through an electronic survey distributed between May 12-14, 2020, to members of 26 online health communities for individuals with chronic disease. Descriptive statistics were run to explore telehealth use, support needs, and information sources, and z tests were run to assess differences in sociodemographic factors and information and support needs among those who did and did not use telehealth services. RESULTS: Among the 2210 respondents, 1073 (49%) reported engaging in telehealth in the past 4 months. Higher proportions of women engaged in telehealth than men (890/1781, 50% vs 181/424, 43%; P=.007), and a higher proportion of those earning household incomes of more than US $100,000 engaged in telehealth than those earning less than US $30,000 (195/370, 53% vs 241/530 45%; P=.003). Although 59% (133/244) of those younger than 40 years and 54% (263/486) of those aged 40-55 years used telehealth, aging populations were less likely to do so, with only 45% (677/1500) of individuals 56 years or older reporting telehealth use (P<.001 and P=.001, respectively). Patients with cystic fibrosis, lupus, and ankylosing spondylitis recorded the highest proportions of individuals using telehealth when compared to those with other diagnoses. Of the 2210 participants, 1333 (60%) participants either looked up information about the virus online or planned to in the future, and when asked what information or support would be most helpful right now, over half (1151/2210, 52%) responded "understanding how COVID-19 affects people with my health condition." CONCLUSIONS: Nearly half of the study sample reported participating in telehealth in the past 4 months. Future efforts to engage individuals with underlying medical conditions in telehealth should focus on outreach to men, members of lower-income households, and aging populations. These results may help inform and refine future health communications to further engage this at-risk population in telehealth as the pandemic continues.


Subject(s)
COVID-19/diagnosis , Telemedicine/methods , Chronic Disease , Female , Humans , Internet , Learning Health System , Male , Middle Aged , Pandemics , SARS-CoV-2 , Surveys and Questionnaires
8.
Interface (Botucatu, Online) ; 25(supl.1): e210047, 2021. ilus
Article in Portuguese | WHO COVID, LILACS (Americas) | ID: covidwho-1538281

ABSTRACT

Considerando o cenário mundial de pandemia do coronavírus, o presente estudo objetiva apresentar um modelo de formação para educação em saúde, constituído por Equipes de Aprendizagem Ativa (EAA), integrando ensino-serviço-comunidade para prevenção da contaminação por Covid-19. Esse modelo reúne nas EAA, supervisionadas por um docente, discentes da área da Saúde, professores e estudantes do ensino médio e agentes comunitários de saúde. O modelo proposto utiliza vídeos do Projeto Homem Virtual acerca do vírus SARS-CoV2 e pode ser ofertado nas modalidades remota, presencial ou híbrida. Assim, essa proposta de formação contribui para o enfrentamento da pandemia na perspectiva da educação em saúde. Ademais, a estruturação desse modelo permite que sua aplicabilidade seja versátil no que se refere às temáticas abordadas nos cursos, bem como no que diz respeito aos integrantes das EAA. (AU)


Against the backdrop of the coronavirus pandemic, this study presents an active learning teams (ALTs) training model for health education as part of teaching-service-community for the prevention of Covid-19 infection. Supervised by an academic staff member, the teams were made up of health students, high school teachers and students, and community health workers. The model uses videos from the Virtual Man Project about the SARS-CoV2 virus and can be offered in remote, face-to-face or hybrid formats. The training model contributes to the response to the pandemic in the field of health education. In addition, the model's versatile structure means it can be applied across different topics addressed by the courses and to different members of the ALTs. (AU)


Considerando el escenario mundial de pandemia del coronavirus, el presente estudio tiene el objetivo de presentar un modelo de formación, constituido por Equipos de Aprendizaje Activos (EAA), para Educación en Salud, integrando enseñanza-servicio-comunidad para prevención de la contaminación por Covid-19. Ese modelo reúne en las EAA, supervisadas por un docente, a discentes del área de la salud, profesores y estudiantes de la enseñanza media y agentes comunitarios de salud. El modelo propuesto utiliza vídeos del Proyecto Hombre Virtual sobre el virus SARS-CoV2 y puede ofrecerse en las modalidades remota, presencial o híbrida. Por lo tanto, esta propuesta de formación contribuye al enfrentamiento de la pandemia bajo la perspectiva de la Educación en salud. Además, la estructuración de este modelo permite que su aplicabilidad sea versátil en lo que se refiere a las temáticas abordadas en los cursos, así como en lo que se refiere a los integrantes de las EAA. (AU)


Subject(s)
Humans , Health Education/methods , Problem-Based Learning , Learning Health System/methods , COVID-19/prevention & control , Educational Technology , Teleworking
9.
BMJ Health Care Inform ; 28(1)2021 Jul.
Article in English | MEDLINE | ID: covidwho-1503762

ABSTRACT

OBJECTIVES: Digital systems have long been used to improve the quality and safety of care when managing acute kidney injury (AKI). The availability of digitised clinical data can also turn organisations and their networks into learning healthcare systems (LHSs) if used across all levels of health and care. This review explores the impact of digital systems i.e. on patients with AKI care, to gauge progress towards establishing LHSs and to identify existing gaps in the research. METHODS: Embase, PubMed, MEDLINE, Cochrane, Scopus and Web of Science databases were searched. Studies of real-time or near real-time digital AKI management systems which reported process and outcome measures were included. RESULTS: Thematic analysis of 43 studies showed that most interventions used real-time serum creatinine levels to trigger responses to enable risk prediction, early recognition of AKI or harm prevention by individual clinicians (micro level) or specialist teams (meso level). Interventions at system (macro level) were rare. There was limited evidence of change in outcomes. DISCUSSION: While the benefits of real-time digital clinical data at micro level for AKI management have been evident for some time, their application at meso and macro levels is emergent therefore limiting progress towards establishing LHSs. Lack of progress is due to digital maturity, system design, human factors and policy levers. CONCLUSION: Future approaches need to harness the potential of interoperability, data analytical advances and include multiple stakeholder perspectives to develop effective digital LHSs in order to gain benefits across the system.


Subject(s)
Acute Kidney Injury , Learning Health System , Patient Care , Acute Kidney Injury/diagnosis , Acute Kidney Injury/therapy , Humans , Outcome Assessment, Health Care , Patient Care/instrumentation , Patient Care/methods
10.
Yearb Med Inform ; 30(1): 176-184, 2021 Aug.
Article in English | MEDLINE | ID: covidwho-1392942

ABSTRACT

OBJECTIVES: We examine the knowledge ecosystem of COVID-19, focusing on clinical knowledge and the role of health informatics as enabling technology. We argue for commitment to the model of a global learning health system to facilitate rapid knowledge translation supporting health care decision making in the face of emerging diseases. METHODS AND RESULTS: We frame the evolution of knowledge in the COVID-19 crisis in terms of learning theory, and present a view of what has occurred during the pandemic to rapidly derive and share knowledge as an (underdeveloped) instance of a global learning health system. We identify the key role of information technologies for electronic data capture and data sharing, computational modelling, evidence synthesis, and knowledge dissemination. We further highlight gaps in the system and barriers to full realisation of an efficient and effective global learning health system. CONCLUSIONS: The need for a global knowledge ecosystem supporting rapid learning from clinical practice has become more apparent than ever during the COVID-19 pandemic. Continued effort to realise the vision of a global learning health system, including establishing effective approaches to data governance and ethics to support the system, is imperative to enable continuous improvement in our clinical care.


Subject(s)
COVID-19 , Knowledge Management , Learning Health System , Medical Informatics , Data Analysis , Electronic Health Records , Humans , Information Dissemination , Information Storage and Retrieval
12.
Healthc Q ; 24(2): 7-11, 2021 Jul.
Article in English | MEDLINE | ID: covidwho-1323459

ABSTRACT

The COVID-19 pandemic has highlighted the need for a robust and nimble public health data infrastructure. ICES - a government-sponsored, independent, non-profit research institute in Ontario, Canada - functions as a key component of a resilient information infrastructure and an enabler of data co-production, contributing to Ontario's response to the COVID-19 pandemic as part of a learning health system. Linked data on the cumulative incidence of infection and vaccination at the neighbourhood level revealed disparate uptake between areas with low versus high risk of COVID-19. These data were leveraged by the government, service providers, media and the public to inform a more efficient and equitable vaccination strategy.


Subject(s)
COVID-19 Vaccines/therapeutic use , COVID-19/prevention & control , Learning Health System/organization & administration , Public Health Administration , Adolescent , Adult , Age Factors , Aged , Aged, 80 and over , COVID-19/epidemiology , COVID-19 Vaccines/supply & distribution , Health Equity/organization & administration , Humans , Immunization Programs/organization & administration , Immunization Programs/statistics & numerical data , Learning Health System/methods , Middle Aged , Ontario/epidemiology , Vaccination Coverage/organization & administration , Vaccination Coverage/statistics & numerical data , Young Adult
16.
Lancet Digit Health ; 3(6): e383-e396, 2021 06.
Article in English | MEDLINE | ID: covidwho-1221078

ABSTRACT

Health information technology can support the development of national learning health and care systems, which can be defined as health and care systems that continuously use data-enabled infrastructure to support policy and planning, public health, and personalisation of care. The COVID-19 pandemic has offered an opportunity to assess how well equipped the UK is to leverage health information technology and apply the principles of a national learning health and care system in response to a major public health shock. With the experience acquired during the pandemic, each country within the UK should now re-evaluate their digital health and care strategies. After leaving the EU, UK countries now need to decide to what extent they wish to engage with European efforts to promote interoperability between electronic health records. Major priorities for strengthening health information technology in the UK include achieving the optimal balance between top-down and bottom-up implementation, improving usability and interoperability, developing capacity for handling, processing, and analysing data, addressing privacy and security concerns, and encouraging digital inclusivity. Current and future opportunities include integrating electronic health records across health and care providers, investing in health data science research, generating real-world data, developing artificial intelligence and robotics, and facilitating public-private partnerships. Many ethical challenges and unintended consequences of implementation of health information technology exist. To address these, there is a need to develop regulatory frameworks for the development, management, and procurement of artificial intelligence and health information technology systems, create public-private partnerships, and ethically and safely apply artificial intelligence in the National Health Service.


Subject(s)
COVID-19 , Learning Health System , Medical Informatics , Artificial Intelligence/trends , Contact Tracing/methods , Health Information Interoperability , Humans , Mobile Applications , Population Surveillance/methods , Public-Private Sector Partnerships , Robotics/trends , Systems Integration , United Kingdom
17.
Am J Manag Care ; 27(3): 123-128, 2021 03.
Article in English | MEDLINE | ID: covidwho-1134755

ABSTRACT

The coronavirus disease 2019 (COVID-19) pandemic has fundamentally changed how health care systems deliver services and revealed the tenuousness of care delivery based on face-to-face office visits and fee-for-service reimbursement models. Robust population health management, fostered by value-based contract participation, integrates analytics and agile clinical programs and is adaptable to optimize outcomes and reduce risk during population-level crises. In this article, we describe how mature population health programs in a learning health system have been rapidly leveraged to address the challenges of the pandemic. Population-level data and care management have facilitated identification of demographic-based disparities and community outreach. Telemedicine and integrated behavioral health have ensured critical primary care and specialty access, and mobile health and postacute interventions have shifted site of care and optimized hospital utilization. Beyond the pandemic, population health can lead as a cornerstone of a resilient health system, better prepared to improve public health and mitigate risk in a value-based paradigm.


Subject(s)
Delivery of Health Care/organization & administration , Learning Health System/organization & administration , Population Health , COVID-19/prevention & control
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