Application of Artificial Intelligence in Community-Based Primary Health Care: Systematic Scoping Review and Critical Appraisal.

BACKGROUND: Research on the integration of artificial intelligence (AI) into community-based primary health care (CBPHC) has highlighted several advantages and disadvantages in practice regarding, for example, facilitating diagnosis and disease management, as well as doubts concerning the unintended harmful effects of this integration. However, there is a lack of evidence about a comprehensive knowledge synthesis that could shed light on AI systems tested or implemented in CBPHC. OBJECTIVE: We intended to identify and evaluate published studies that have tested or implemented AI in CBPHC settings. METHODS: We conducted a systematic scoping review informed by an earlier study and the Joanna Briggs Institute (JBI) scoping review framework and reported the findings according to PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analysis-Scoping Reviews) reporting guidelines. An information specialist performed a comprehensive search from the date of inception until February 2020, in seven bibliographic databases: Cochrane Library, MEDLINE, EMBASE, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), ScienceDirect, and IEEE Xplore. The selected studies considered all populations who provide and receive care in CBPHC settings, AI interventions that had been implemented, tested, or both, and assessed outcomes related to patients, health care providers, or CBPHC systems. Risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Two authors independently screened the titles and abstracts of the identified records, read the selected full texts, and extracted data from the included studies using a validated extraction form. Disagreements were resolved by consensus, and if this was not possible, the opinion of a third reviewer was sought. A third reviewer also validated all the extracted data. RESULTS: We retrieved 22,113 documents. After the removal of duplicates, 16,870 documents were screened, and 90 peer-reviewed publications met our inclusion criteria. Machine learning (ML) (41/90, 45%), natural language processing (NLP) (24/90, 27%), and expert systems (17/90, 19%) were the most commonly studied AI interventions. These were primarily implemented for diagnosis, detection, or surveillance purposes. Neural networks (ie, convolutional neural networks and abductive networks) demonstrated the highest accuracy, considering the given database for the given clinical task. The risk of bias in diagnosis or prognosis studies was the lowest in the participant category (4/49, 4%) and the highest in the outcome category (22/49, 45%). CONCLUSIONS: We observed variabilities in reporting the participants, types of AI methods, analyses, and outcomes, and highlighted the large gap in the effective development and implementation of AI in CBPHC. Further studies are needed to efficiently guide the development and implementation of AI interventions in CBPHC settings.

Associations between psychosocial stressors at work and moral injury in frontline healthcare workers and leaders facing the COVID-19 pandemic in Quebec, Canada: A cross-sectional study.

Healthcare workers (HCWs) on the frontline of the COVID-19 pandemic exhibit a high prevalence of depression and psychological distress. Moral injury (MI) can lead to such mental health problems. MI occurs when perpetrating, failing to prevent, or bearing witness to acts that transgress deeply held moral beliefs and expectations. Since the start of the pandemic, psychosocial stressors at work (PSWs) might have been exacerbated, which might in turn have led to an increased risk of MI in HCWs. However, research into the associations between PSWs and MI is lacking. Considering these stressors are frequent and most of them are modifiable occupational risk factors, they may constitute promising prevention targets. This study aims to evaluate the associations between a set of PSWs and MI in HCWs during the third wave of the COVID-19 pandemic in Quebec, Canada. Furthermore, our study aims to explore potential differences between urban and non-urban regions. The sample of this study consisted of 572 HCWs and leaders from the Quebec province. Prevalence ratios (PR) of MI and their 95% confidence intervals (CI) were modelled using robust Poisson regressions. Several covariates were considered, including age, sex, gender, socio-economic indicators, and lifestyle factors. Results indicated HCWs exposed to PSWs were 2.22-5.58 times more likely to experience MI. Low ethical culture had the strongest association (PR: 5.58, 95% CI: 1.34-23.27), followed by low reward (PR: 4.43, 95% CI: 2.14-9.16) and high emotional demands (PR: 4.32, 95% CI: 1.89-9.88). Identifying predictors of MI could contribute to the reduction of mental health problems and the implementation of targeted interventions in urban and non-urban areas.

Predict, prevent and manage moral injuries in Canadian frontline healthcare workers and leaders facing the COVID-19 pandemic: Protocol of a mixed methods study.

Moral injuries can occur when perpetrating, failing to prevent, or bearing witness to acts that transgress deeply held moral beliefs and expectations. The COVID-19 crisis highlighted the fact that psychosocial stressors at work, such as high emotional demands, are placing Canadian healthcare workers at risk of moral injuries. Evidence linking psychosocial stressors at work to moral injuries are needed to better predict, prevent and manage moral injuries, as these stressors are frequent and modifiable occupational risk factors. This protocol presents a study aiming to: 1) understand workplace events having the potential to either cause or reduce moral injuries, 2) predict the risk and severity of moral injuries using a disease prevention model, 3) identify biological signatures (biomarkers) associated with psychosocial stressors at work and moral injuries and 4) elaborate preliminary guidelines of organizational practices for frontline healthcare workers to reduce and manage moral injuries. This study is a mixed methods research with three components: qualitative, quantitative and biological. The data collection has been completed and because of the COVID-19 pandemic, it was adjusted to allow for gathering qualitative and quantitative data remotely. Frontline healthcare workers and leaders were included. Through focus groups and individual interviews, and an online questionnaire, events and psychosocial working conditions that may increase the risk of moral injuries will be documented. In addition, blood samples which were collected from a sub-sample of volunteer participants will measure an innovative set of biomarkers associated with vulnerability to stress and mental health. Data analyses are ongoing. We anticipate to identify workplace events that may trigger moral injuries. We expect that potential predictors of moral injury risk occurrence and severity will be identified from psychosocial stressors at work that can be improved by implementing organizational practices. We also expect to observe a different mental health state and biological inflammation signature across workers exposed compared to workers not exposed to psychosocial stressors at work. Based on these future findings, we intend to develop preliminary recommendations of organizational practices for managers. This research will contribute to expand our knowledge of the events in the workplace likely to generate or lessen the impact moral injuries, to build a model for predicting the risk of moral injuries at work, all in the specific context of the COVID-19 health crisis among healthcare workers.

Évaluation de l'aptitude d'une personne âgée atteinte de déficits cognitifs àgéer sa personne et ses biens : Identification des outils disponibles.

The accelerated aging of the Canadian population is a recognized fact and leads to an increasing number of seniors with cognitive impairments (Curateur public du Québec, 2010a). This has a definite impact on health professionals who have to assess their competency to live independently and manage their finances. This decision, which has important consequences for the person, must be based on an objective and rigorous assessment. The purpose of this paper is to analyse the available tools, both in the scientific literature and in clinical settings, to better document the various components to assess seniors' competency to live independently and manage their finances. The goal is to help practitioners who work with older people with cognitive impairments to accurately assess their ability to manage themselves and their property. A review of the relevant literature and training available, as well as three group consultations, showed that there is no consensus about the tools used to assess the capacity to take care of oneself and one's property. Additional studies are thus needed to fill the gap in knowledge about specific tools used to assess competency.