Organizational interventions improving access to community-based primary health care for vulnerable populations: a scoping review

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.

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

Journal of Medical Internet Research ◽

10.2196/29839 ◽

2021 ◽

Vol 23 (9) ◽

pp. e29839

Author(s):

Samira Abbasgholizadeh Rahimi ◽

France Légaré ◽

Gauri Sharma ◽

Patrick Archambault ◽

Herve Tchala Vignon Zomahoun ◽

...

Keyword(s):

Artificial Intelligence ◽

Neural Networks ◽

Health Care ◽

Primary Health Care ◽

Scoping Review ◽

Risk Of Bias ◽

Cochrane Library ◽

Community Based ◽

Primary Health ◽

The Given

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.

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A study of current primary health care in rural district, Japan - Problmes of community-based school health.

JOURNAL OF THE JAPANESE ASSOCIATION OF RURAL MEDICINE ◽

10.2185/jjrm.35.165 ◽

1986 ◽

Vol 35 (2) ◽

pp. 165-171

Author(s):

Kenji ABE ◽

Tetsuhito FUKUSHIMA ◽

Akio NAKAGAWA ◽

Nobuo YOSHIDA ◽

Tomoko TAGAWA ◽

...

Keyword(s):

Health Care ◽

Primary Health Care ◽

School Health ◽

Rural District ◽

Community Based ◽

Primary Health

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Disaster Management in Rural and Remote Primary Health Care: A Scoping Review

Prehospital and Disaster Medicine ◽

10.1017/s1049023x21000200 ◽

2021 ◽

Vol 36 (3) ◽

pp. 362-369

Author(s):

Katie A. Willson ◽

Gerard J. FitzGerald ◽

David Lim

Keyword(s):

Mental Health ◽

Health Care ◽

Primary Health Care ◽

Mental Health Care ◽

Disaster Management ◽

Scoping Review ◽

Health Care Professionals ◽

Disaster Preparedness ◽

Rural And Remote ◽

Primary Health

AbstractObjective:This scoping review aims to map the roles of rural and remote primary health care professionals (PHCPs) during disasters.Introduction:Disasters can have catastrophic impacts on society and are broadly classified into natural events, man-made incidents, or a mixture of both. The PHCPs working in rural and remote communities face additional challenges when dealing with disasters and have significant roles during the Prevention, Preparedness, Response, and Recovery (PPRR) stages of disaster management.Methods:A Johanna Briggs Institute (JBI) scoping review methodology was utilized, and the search was conducted over seven electronic databases according to a priori protocol.Results:Forty-one papers were included and sixty-one roles were identified across the four stages of disaster management. The majority of disasters described within the literature were natural events and pandemics. Before a disaster occurs, PHCPs can build individual resilience through education. As recognized and respected leaders within their community, PHCPs are invaluable in assisting with disaster preparedness through being involved in organizations’ planning policies and contributing to natural disaster and pandemic surveillance. Key roles during the response stage include accommodating patient surge, triage, maintaining the health of the remaining population, instituting infection control, and ensuring a team-based approach to mental health care during the disaster. In the aftermath and recovery stage, rural and remote PHCPs provide long-term follow up, assisting patients in accessing post-disaster support including delivery of mental health care.Conclusion:Rural and remote PHCPs play significant roles within their community throughout the continuum of disaster management. As a consequence of their flexible scope of practice, PHCPs are well-placed to be involved during all stages of disaster, from building of community resilience and contributing to early alert of pandemics, to participating in the direct response when a disaster occurs and leading the way to recovery.

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