Digital solutions for decision support in general practice – a rapid review focused on systems developed for the universal healthcare setting in Denmark

Clausen, Anne; Christensen, Emilie Rosenfeldt; Jakobsen, Pernille Ravn; Søndergaard, Jens; Abrahamsen, Bo; Rubin, Katrine Hass

doi:10.1186/s12875-023-02234-y

Cited by 3 publications

(1 citation statement)

References 27 publications

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“…Outside of these, there has been little attempt to catalogue digital solution areas for manufacturing. However, there have been catalogues of existing digital solutions focused on medicine [38] and solutions to support the reuse or recycling of clothing [39]. However, these have not focused on low-cost solutions.…”

Section: Process Of Creating Catalogue For Low-cost Digital Solution ...mentioning

confidence: 99%

The Role of Low-Cost Digital Solutions in Supporting Industrial Sustainability

Abdel-Aty,

McFarlane,

Brooks

et al. 2024

Sustainability

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Small and medium enterprise (SME) manufacturers are impeded from participating in sustainability initiatives using new technologies due to the high cost and the lack of clarity on where to start. The integration of low-cost digital solutions has enabled SME manufacturers to adopt Industry 4.0 technologies to support operations. However, using low-cost technologies to address sustainability challenges is underexplored. This article addresses three key research questions: What digital solutions do SMEs need to address industrial sustainability challenges? To what extent can existing low-cost digital solutions be used to address industrial sustainability challenges? How should new digital solutions for developing greater sustainability be prioritised? Three main tasks were conducted. Initially, a new sustainability-focused sub-catalogue was created using an existing catalogue of low-cost solution areas for manufacturing. Secondly, a workshop with 17 participants was used to identify the top ten priority solution areas, with process monitoring, energy monitoring, and quality inspection at the top. Lastly, existing low-cost digital solutions within the top ten priority areas were evaluated to identify how they could contribute to lean manufacturing. Predominantly existing solutions could contribute to waste or use reduction in lean manufacturing. This study provides a foundation for the future development of low-cost solutions for sustainability by indicating manufacturers’ key priority areas and outlining how existing solutions could be adapted to support waste reduction.

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Section: Process Of Creating Catalogue For Low-cost Digital Solution ...mentioning

confidence: 99%

The Role of Low-Cost Digital Solutions in Supporting Industrial Sustainability

Abdel-Aty,

McFarlane,

Brooks

et al. 2024

Sustainability

View full text Add to dashboard Cite

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Obtaining the Most Accurate, Explainable Model for Predicting Chronic Obstructive Pulmonary Disease: Triangulation of Multiple Linear Regression and Machine Learning Methods

Kamis,

Gadia,

Luo

et al. 2024

JMIR AI

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Background: Lung disease is a severe problem in the United States. Despite the decreasing rates of cigarette smoking, COPD continues to be health burden in the United States. In this paper, we focus on Chronic Obstructive Pulmonary Disease in the United States from 2016 to 2019.Objective: We gather a diverse set of data sources to better understand and predict COPD rates at the level of Core-Based Statistical Area in the United States. The objective is to compare linear models with machine learning models to obtain the most accurate and interpretable model of COPD. Methods:We integrate data from multiple Centers for Disease Control sources and use them to analyze Chronic Obstructive Pulmonary Disease by using different types of methods. We include cigarette smoking, a well-known contributing factor, and race / ethnicity variables because health disparities among different races and ethnicities in the United States are also wellknown. The models also include air quality index, education, employment, and economic variables. We fit models with both multiple linear regression and machine learning methods. Results:The most accurate multiple linear regression model has variance explained = 81.1% and Root Mean Squared Error = 0.73. The most accurate machine learning model has variance explained = 87.1% and Root Mean Squared Error = 0.53. Overall, cigarette smoking and household income are the strongest predictor variables. Hispanic percentage of CBSA, Education, and American Indian / Alaska Native percentage of CBSA are moderately strong predictors.Conclusions: This research highlights the importance of using diverse data sources as well as multiple methods to understand and predict COPD. The most accurate model is a Support Vector Machine, which captured non-linearities in a model whose accuracy is superior to the best multiple linear regression. Our interpretable models suggest ways that individual predictor variables can be used in interventions aimed at decreasing COPD rates. Gaps in understanding the health impacts of air pollution, particularly in relation to climate change, suggest a need for further research to design interventions and improve public health.

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The prerequisites for artificial intelligence in Danish general practice: A qualitative vignette study among general practitioners (Preprint)

Jørgensen,

Merrild,

Jensen

et al. 2024

Preprint

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BACKGROUND Artificial intelligence has been deemed revolutionary in medicine, but very few artificial intelligence solutions have been observed in Danish general practice. General practice in Denmark has an excellent system of digitization to develop and utilize artificial intelligence. However, a lack of involvement of general practitioners in the development of artificial intelligence exists. The perspectives of general practitioners as end users are essential to facilitate the development and implementation of artificial intelligence in general practice. OBJECTIVE This study aimed to characterize the prerequisites that must be met to enable the development and implementation of artificial intelligence in Danish general practice. METHODS This study applied semi-structured interviews and vignettes to gain perspectives on the potential for developing and implementing artificial intelligence among general practitioners. Twelve general practitioners were interviewed, resulting in an exhaustive dataset. The interviews were transcribed, and thematic analysis was conducted to identify the dominant themes throughout the data. RESULTS Four main themes were identified in the data analysis as prerequisites that general practitioners found important to consider when developing and implementing AI in general practice: ‘AI must begin with the low-hanging fruit’, ‘AI must be meaningful in the GP’s work’, ‘The GP-patient relationship must be maintained despite AI’, and ‘AI must be a free, active, and integrated option in the EHR’. CONCLUSIONS The four themes contributing to defining prerequisites for artificial intelligence can potentially lead the first steps of future development and implementation of artificial intelligence in Danish general practice. The participating general practitioners were positive towards developing and implementing artificial intelligence in their clinics, and it would be valuable to consider the defined prerequisites when considering new artificial intelligence tools for general practice.

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Digital solutions for decision support in general practice – a rapid review focused on systems developed for the universal healthcare setting in Denmark

Cited by 3 publications

References 27 publications

The Role of Low-Cost Digital Solutions in Supporting Industrial Sustainability

The Role of Low-Cost Digital Solutions in Supporting Industrial Sustainability

Obtaining the Most Accurate, Explainable Model for Predicting Chronic Obstructive Pulmonary Disease: Triangulation of Multiple Linear Regression and Machine Learning Methods

The prerequisites for artificial intelligence in Danish general practice: A qualitative vignette study among general practitioners (Preprint)

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