scholarly journals Research on Artificial Intelligence and Primary Care: A Scoping Review

2019 ◽  
Author(s):  
Jacqueline K. Kueper ◽  
Amanda L. Terry ◽  
Merrick Zwarenstein ◽  
Daniel J. Lizotte
Keyword(s):  
Artificial Intelligence ◽  
Primary Care ◽  
Scoping Review ◽  
Full Text ◽  
Interdisciplinary Research ◽  
The Body ◽  
Supervised Machine Learning ◽  
List Type ◽  
End User ◽  
Research Teams

ABSTRACTObjectiveThe purpose of this study was to assess the nature and extent of the body of research on artificial intelligence (AI) and primary care.MethodsWe performed a scoping review, searching 11 published and grey literature databases with subject headings and key words pertaining to the concepts of 1) AI and 2) primary care: MEDLINE, EMBASE, Cinahl, Cochrane Library, Web of Science, Scopus, IEEE Xplore, ACM Digital Library, MathSciNet, AAAI, arXiv. Screening included title and abstract and then full text stages. Final inclusion criteria: 1) research study of any design, 2) developed or used AI, 3) used primary care data and/or study conducted in a primary care setting and/or explicit mention of study applicability to primary care; exclusion criteria: 1) narrative, editorial, or textbook chapter, 2) not applicable to primary care population or settings, 3) full text inaccessible in the English Language. We extracted and summarized seven key characteristics of included studies: overall study purpose(s), author appointments, primary care functions, author intended target end user(s), target health condition(s), location of data source(s) (if any), subfield(s) of AI.ResultsOf 5,515 non-duplicate documents, 405 met our eligibility criteria. The body of literature is primarily focused on creating novel AI methods or modifying existing AI methods to support physician diagnostic or treatment recommendations, for chronic conditions, using data from higher income countries. Meaningfully more studies had at least one author with a technology, engineering, or math appointment than with a primary care appointment (57 (14%) compared to 217 (54%)). Predominant AI subfields were supervised machine learning and expert systems.DiscussionOverall, AI research associated with primary care is at an early stage of maturity with respect to widespread implementation in practice settings. For the field to progress, more interdisciplinary research teams with end-user engagement and evaluation studies are needed.SUMMARY BOXESSection 1: What is already known on this topicAdvancements in technology and the availability of health data have increased opportunities for artificial intelligence to be used for primary care purposes.No comprehensive review of research on artificial intelligence associated with primary care has been performed.Section 2: What this study addsThe body of research on artificial intelligence and primary care is driven by authors without appointments in primary care departments and is focused on developing artificial intelligence methods to support diagnostic and treatment decisions.There is a need for more interdisciplinary research teams and evaluation of artificial intelligence projects in ‘real world’ practice settings.

scholarly journals Influence of organisational attributes on registered nurse contributions to well-child care: a scoping review protocol

BMJ Open ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. e052634
Author(s):  
Suzanne Braithwaite ◽  
Julia Lukewich ◽  
Danielle Macdonald ◽  
Joan Tranmer
Keyword(s):  
Primary Care ◽  
Child Care ◽  
Scoping Review ◽  
Full Text ◽  
Registered Nurse ◽  
Universal Access ◽  
Secondary Analysis ◽  
Inclusion Criteria ◽  
Well Child Care ◽  
Primary Care Settings

IntroductionUniversal access to preventative healthcare is essential to children’s health. Registered nurses (RN) are well positioned to deliver well-child care within primary care settings; however, RN role implementation varies widely in this sector and the scope of literature that examines the influence of organisational attributes on nursing contributions to well-child care is not well understood. The aim of this scoping review is to identify the scope and characteristics of the literature related to organisational attributes that act as barriers to, or facilitators for RN delivery of well-child care within the context of primary care in high-income countries.Methods and analysisThe Joanna Briggs Institute scoping review methodology will be used to conduct this review. Databases that will be accessed include Cumulative Index to Nursing and Allied Health Literature (CINAHL), MEDLINE and Embase. Inclusion criteria includes articles with a focus on RNs who deliver well-child care in primary care settings. Literature that meets this inclusion criteria will be included in the study. Covidence software platform will be used to review citations and full-text articles. Titles, abstracts and full-text articles will be reviewed independently by two reviewers. Any disagreements that arise between the reviewers will be resolved through discussion, or with an additional reviewer. Data will be extracted and organised according to the dimensions outlined in the nursing care organisation conceptual framework (NCOF). Principles of the ‘best fit’ framework synthesis will guide the data analysis approach and the NCOF will act as the framework for data coding and analysis.Ethics and disseminationThis scoping review will undertake a secondary analysis of data already published and does not require ethical approval. Findings will be disseminated via peer-reviewed publications and conference presentations targeting stakeholders involved in nursing practice and the delivery of well-child care.Trial registration detailsBraithwaite, S., Tranmer, J., Lukewich, J., & Macdonald, D. (2021, March 31). Protocol for a Scoping Review of the Influence of Organisational Attributes on Registered Nurse Contributions to Well-child Care. https://doi.org/10.17605/OSF.IO/UZYX5.

scholarly journals Diabetic macular thickness diagnostic guideded by optical coherence tomography using artificial intelligence methods: a scoping review protocol

2020 ◽  
Author(s):  
Fabiana Brasileiro ◽  
Frederico M. Bublitz
Keyword(s):  
Artificial Intelligence ◽  
Diabetic Retinopathy ◽  
Scoping Review ◽  
Research Question ◽  
Macular Thickness ◽  
Assessment Model ◽  
Primary Data ◽  
List Type ◽  
Data Governance ◽  
Review Protocol

ABSTRACTIntroductiondiabetic retinopathy remains the mainly cause of blindness worldwide and its diagnoses and assessment is poorer as the countryside the patient is. To minimize de boundaries of health care and improve the accuracy how the treatment is stablished, the artificial intelligence (AI) techniques have been studied years ago using fundus phothographies for instance. This scoping review aims to study these AI techniques applied in Opctical Coherence Tomography (OCT) images.MethodsThis scoping review will follow the methodology framework defined in “Scoping studies: advancing the methodology”. In this methodological framework, six stages are proposed for scoping reviewStudiesidentifying the research question; identifying relevant studies; study selection; charting the data; collating, summarizing, and reporting the results; and consultation. The research questions aim to investigate what are the methods, and techniques used in artificial intelligence due to perfmorm a clinical diagnostic using OCT scanners. The team will focus on the Scopus Document Search e PubMed (Medline). The search query is a combination of terms related to Diabetic Retinopathy AND Artificial Intelligence.Ethics and DisseminationThis is a scoping review study and there is no requirement for ethical approval, as primary data will not be collected. The results from this scoping review will be published in a peer reviewed journal and reported at scientific meetings. We intend to share the results with the Ophthalmologists.ARTICLE SUMMARYStrengths and Limitations of this studyThis protocol uses a comprehensive approach, relating one research question, that will enable the identification of the main gaps and opportunities in the area; Due to the comprehensiveness of this review protocol, it can serve as the basis for future works, with more specific scope, and the proposal of standards;The investigation of who are the mainly artificial intelligence skills will allow other researchs in this area;The inclusion of aspects of data governance in the protocol will serve as the basis for establishing an assessment model of artificial intelligence and diabetic macular thickness;One weakness of this review protocol was the number of the studies including glaucoma in their criterias.

Dashboards in Healthcare Settings: A Scoping Review Protocol (Preprint)

2021 ◽  
Author(s):  
Danielle Helminski ◽  
Jacob E. Kurlander ◽  
Anjana Deep Renji ◽  
Jeremy B. Sussman ◽  
Paul N. Pfeiffer ◽  
...  
Keyword(s):  
Organizational Performance ◽  
Scoping Review ◽  
Full Text ◽  
End Users ◽  
Healthcare Sector ◽  
Cochrane Library ◽  
Routine Practice ◽  
End User ◽  
Healthcare Organizations ◽  
Healthcare Settings

BACKGROUND Healthcare organizations increasingly depend on business intelligence tools, including “dashboards,” to capture, analyze, and present data on performance metrics. Ideally, dashboards allow users to quickly visualize actionable data to inform and optimize clinical and organizational performance. In reality, dashboards are typically embedded in complex healthcare organizations, with massive data streams, and end users with distinct needs. Thus, designing effective dashboards is a challenging task. Yet, theoretical underpinnings of healthcare dashboards are poorly characterized; even the concept of the dashboard remains ill-defined. Researchers, informaticists, clinical managers, and healthcare administrators will benefit from a clearer understanding of how dashboards have been developed, implemented, and evaluated, and how the design, end-user, and context influence their uptake and effectiveness. OBJECTIVE This scoping review first aims to survey the vast published literature of “dashboards” to describe where, why, and for whom they are used in healthcare settings, as well as how they are developed, implemented, and evaluated. Further, we will examine how dashboard design and content is informed by intended purpose and end-users. METHODS In July 2020, we searched Medline, EMBASE, Web of Science, and the Cochrane Library for peer-reviewed literature using a targeted strategy developed with a research librarian and retrieved 5,188 results. Following deduplication, 3,306 studies were screened in duplicate for title and abstract. Any abstracts mentioning a healthcare dashboard were retrieved in full-text and are undergoing duplicate review for eligibility. Articles will be included for data extraction and analysis if they describe the development, implementation, or evaluation of a dashboard that was successfully used in routine workflow. Articles will be excluded if they were published before 2015, unavailable in full-text, in a non-English language, or describe dashboards used for public health tracking, in settings where direct patient care is not provided, or in undergraduate medical education. Any discrepancies in eligibility determination will be adjudicated by a third reviewer. We chose to focus on articles published after 2015 and those that describe dashboards that were successfully used in routine practice to identify the most recent and relevant literature to support future dashboard development in the rapidly evolving field of healthcare informatics. RESULTS All articles have undergone dual review for title and abstract, with 2,019 articles mentioning use of a healthcare dashboard retrieved in full-text for further review. We are currently reviewing all full-text articles in duplicate. We aim to publish findings by summer of 2022. Findings will be reported following guidance from the PRISMA-ScR checklist. CONCLUSIONS This scoping review will provide stakeholders with an overview of existing dashboard tools, highlighting the ways in which dashboards have been developed, implemented, and evaluated in different settings and end-user groups, and identify potential research gaps. Findings will guide efforts to design and utilize dashboards in the healthcare sector more effectively.

Measuring integrated care at the interface between primary care and secondary care: a scoping review

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Aimee O'Farrell ◽  
Geoff McCombe ◽  
John Broughan ◽  
Áine Carroll ◽  
Mary Casey ◽  
...  
Keyword(s):  
Primary Care ◽  
Scoping Review ◽  
Full Text ◽  
Secondary Care ◽  
Research Question ◽  
Grey Literature ◽  
Problem Area ◽  
Cochrane Library ◽  
Comprehensive Overview ◽  
Content Type

PurposeIn many healthcare systems, health policy has committed to delivering an integrated model of care to address the increasing burden of disease. The interface between primary and secondary care has been identified as a problem area. This paper aims to undertake a scoping review to gain a deeper understanding of the markers of integration across the primary–secondary interface.Design/methodology/approachA search was conducted of PubMed, SCOPUS, Cochrane Library and the grey literature for papers published in English using the framework described by Arksey and O'Malley. The search process was guided by the “Preferred Reporting Items for Systematic Reviews and Meta-Analyses” (PRISMA).FindingsThe initial database search identified 112 articles, which were screened by title and abstract. A total of 26 articles were selected for full-text review, after which nine articles were excluded as they were not relevant to the research question or the full text was not available. In total, 17 studies were included in the review. A range of study designs were identified including a systematic review (n = 3), mixed methods study (n = 5), qualitative (n = 6) and quantitative (n = 3). The included studies documented integration across the primary–secondary interface; integration measurement and factors affecting care coordination.Originality/valueMany studies examine individual aspects of integration. However, this study is unique as it provides a comprehensive overview of the many perspectives and methodological approaches involved with evaluating integration within the primary–secondary care interface and primary care itself. Further research is required to establish valid reliable tools for measurement and implementation.

Health Equity in Artificial Intelligence and Primary Care Research: Protocol for a Scoping Review (Preprint)

2021 ◽  
Author(s):  
Jonathan Xin Wang ◽  
Sulaiman Somani ◽  
Jonathan H Chen ◽  
Sara Murray ◽  
Urmimala Sarkar
Keyword(s):  
Artificial Intelligence ◽  
Primary Care ◽  
Health Equity ◽  
Scoping Review ◽  
Underserved Populations ◽  
Care Delivery ◽  
Data Extraction ◽  
Cochrane Library ◽  
Preventative Care ◽  
The Impact

BACKGROUND Though artificial intelligence (AI) has potential to augment the patient-physician relationship in primary care, bias in intelligent healthcare systems has the potential to differentially impact vulnerable patient populations. OBJECTIVE The purpose of this scoping review is to summarize the extent to which AI systems in primary care examine the inherent bias towards or against vulnerable populations and appraise how these systems have mitigated the impact of such biases during their development. METHODS We will conduct a search update from an existing scoping review to identify AI and primary care articles in the following databases: Medline-OVID,Embase,CINAHL, Cochrane Library, Web of Science, Scopus, IEEE Xplore, ACM Digital Library, MathSciNet, AAAI, and arXiv. Two screeners will independently review all abstracts, titles and full-texts. The team will extract data using structured data extraction form and synthesize the results according to PRISMA-Scr guidelines. RESULTS This review will provide an assessment of the current state of healthcare equity within AI for primary care. Specifically, we will identify the degree to which vulnerable patients have been included, assess how bias is interpreted and documented, and understand the extent harmful biases are addressed. As of October 2020, the scoping review is in the title and abstract screening stage. The results are expected to be submitted for publication in fall of 2021. CONCLUSIONS AI applications in primary care are becoming an increasingly common tool in health care delivery, including in preventative care efforts for underserved populations. This scoping review aims to understand to what extent AI-primary care studies employ a health equity lens and take steps to mitigate bias.

scholarly journals Artificial Intelligence and Primary Care Research: A Scoping Review

2020 ◽  
Vol 18 (3) ◽  
pp. 250-258 ◽  
Author(s):  
Jacqueline K. Kueper ◽  
Amanda L. Terry ◽  
Merrick Zwarenstein ◽  
Daniel J. Lizotte
Keyword(s):  
Artificial Intelligence ◽  
Primary Care ◽  
Scoping Review ◽  
Primary Care Research ◽  
Care Research

scholarly journals A Systematic Scoping Review on Pedagogical Strategies of Interprofessional Communication for Physicians in Emergency Medicine

2021 ◽  
Vol 8 ◽  
pp. 238212052110417
Author(s):  
Zhi H. Ong ◽  
Lorraine H. E. Tan ◽  
Haziratul Z. B. Ghazali ◽  
Yun T. Ong ◽  
Jeffrey W. H. Koh ◽  
...  
Keyword(s):  
Emergency Medicine ◽  
Scoping Review ◽  
Full Text ◽  
Assessment Tools ◽  
Interprofessional Communication ◽  
Pedagogical Strategies ◽  
Research Teams ◽  
Competency Based ◽  
Longitudinal Support ◽  
Host Organization

Background Interprofessional communication (IPC) is integral to interprofessional teams working in the emergency medicine (EM) setting. Yet, the coronavirus disease 2019 pandemic has laid bare gaps in IPC knowledge, skills and attitudes. These experiences underscore the need to review how IPC is taught in EM. Purpose A systematic scoping review is proposed to scrutinize accounts of IPC programs in EM. Methods Krishna's Systematic Evidence-Based Approach (SEBA) is adopted to guide this systematic scoping review. Independent searches of ninedatabases (PubMed, Embase, CINAHL, Scopus, PsycINFO, ERIC, JSTOR, Google Scholar and OpenGrey) and “negotiated consensual validation” were used to identify articles published between January 1, 2000 and December 31, 2020. Three research teams reviewed the data using concurrent content and thematic analysis and independently summarized the included articles. The findings were scrutinized using SEBA's jigsaw perspective and funneling approach to provide a more holistic picture of the data. Results In total 18,809 titles and abstracts were identified after removal of duplicates, 76 full-text articles reviewed, and 19 full-text articles were analyzed. In total, four themes and categories were identified, namely: (a) indications and outcomes, (2) curriculum and assessment methods, (3) barriers, and (4) enablers. Conclusion IPC training in EM should be longitudinal, competency- and stage-based, underlining the need for effective oversight by the host organization. It also suggests a role for portfolios and the importance of continuing support for physicians in EM as they hone their IPC skills. Highlights • IPC training in EM is competency-based and organized around stages. • IPC competencies build on prevailing knowledge and skills. • Longitudinal support and holistic oversight necessitates a central role for the host organization. • Longitudinal, robust, and adaptable assessment tools in the EM setting are necessary and may be supplemented by portfolio use.

scholarly journals Artificial intelligence in orthopaedics: A scoping review

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0260471
Author(s):  
Simon J. Federer ◽  
Gareth G. Jones
Keyword(s):  
Artificial Intelligence ◽  
Scoping Review ◽  
Evidence Synthesis ◽  
Image Interpretation ◽  
Clinical Decision ◽  
Clinical Applications ◽  
The Body ◽  
Decision Tool ◽  
Future Studies ◽  
The Usa

There is a growing interest in the application of artificial intelligence (AI) to orthopaedic surgery. This review aims to identify and characterise research in this field, in order to understand the extent, range and nature of this work, and act as springboard to stimulate future studies. A scoping review, a form of structured evidence synthesis, was conducted to summarise the use of AI in orthopaedics. A literature search (1946–2019) identified 222 studies eligible for inclusion. These studies were predominantly small and retrospective. There has been significant growth in the number of papers published in the last three years, mainly from the USA (37%). The majority of research used AI for image interpretation (45%) or as a clinical decision tool (25%). Spine (43%), knee (23%) and hip (14%) were the regions of the body most commonly studied. The application of artificial intelligence to orthopaedics is growing. However, the scope of its use so far remains limited, both in terms of its possible clinical applications, and the sub-specialty areas of the body which have been studied. A standardized method of reporting AI studies would allow direct assessment and comparison. Prospective studies are required to validate AI tools for clinical use.

602-P: Explaining an Artificial Intelligence (AI) System for Diabetic Retinopathy (DR) Screening in Primary Care

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 602-P
Author(s):  
NISHIT UMESH PAREKH ◽  
MALAVIKA BHASKARANAND ◽  
CHAITHANYA RAMACHANDRA ◽  
SANDEEP BHAT ◽  
KAUSHAL SOLANKI
Keyword(s):  
Artificial Intelligence ◽  
Primary Care ◽  
Diabetic Retinopathy
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