Article of Choice #1: āArtificial intelligence in healthcareā
The article āArtificial intelligence in healthcareā is a peer-reviewed review that was published by Nature Biomedical Engineering, an academic journal with an intended audience consisting of bench scientists, engineers, and clinicians. Nature Biomedical Engineering āprovides fair and rigorous peer review” (Nature Biomedical Engineering). The article itself was published in October 2018. The authors who worked in the review are Kun-Hsing Yu, Andrew L. Beam and Isaac S. Kohane. All three are cited to be a part of the Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. Isaac S. Kohane is additionally cited to be a representative of Boston Childrenās Hospital, Boston, MA, USA. The background of the authors contributes to their credibility based on the high standards of the institutions where they operate.
Evaluating the Source
According to Resurchify (2021), Nature Biomedical Engineering is a journal covering technologies, fields, and categories related to bioengineering. Multiple topics are being mentioned and explored throughout the articles, including biomedical engineering, biotechnology, computer science applications, and medicine (miscellaneous) (Q1). The source is published by Nature Publishing Group, a publisher centered around academic data related to different research fields, including the ones mentioned prior. Moreover, Nature Biomedical Engineering is cited by a total of 5173 articles during the last three years (Researchify, 2021). This is a significant number that illustrates that multiple researchers view the data and information published in the journal as reliable and unbiased.
Evaluating the Tone
The authors’ tone is balanced and objective. That is, the narration in the article is free of ill-founded value judgments, and the language corresponds to the article’s subject matter, which is artificial intelligence in healthcare. Moreover, all the arguments are well developed and well supported. This is exemplified through the fact that the bibliography includes more than 150 sources, and no claim in the article is stated without a reference. Such methodology implied the evidence-based content presented in the review. Furthermore, while attempting to evaluate the positive effect that the implementation of AI might have on the field of healthcare, the authors put forward counter-arguments. The authors chose to explore why some of these effects might be negative and how it might be extremely expensive and complicated to imbed AI into the hospitals.
Evaluating the Content
The abstract in Artificial intelligence in healthcare consists of a few relatively concise sentences where the authors focus on highlighting the study’s purpose. Since it is a review article, methods include the gathering of information and its systematization in order to have a proper outlook on the situation. The findings address the purpose, and the paper, in general, presents a coherent piece of writing. The introduction, on the other hand, goes more in-depth in regards to the paper’s purpose and speaks about the importance of study in the modern day and age. Moreover, it provides the necessary context by starting with an example of a recently invented AI system and its significance to set the scene before proceeding to speak about AI in medicine in general. The introduction also highlights a clear roadmap of the rest of the paper.
There is no literature review in the paper per se, but 151 sources are employed. References to various studies are constantly cited, alongside visual cues to help demonstrate different models and systems. All the reviewed articles are credible, relevant and current.
The methodology in this article has the form of turning to different sources and evaluating in one paper all the possible ways of AI applications to enhance clinical practice and branches of medicines where they could be introduced. Furthermore, the ethical issues have been properly addressed since healthcare deals with people in vulnerable positions and their personal data. Research outcomes refer to the introduction and address the problem, which is the necessity to work on the gradual implementation of AI applications into medical practice despite its various advantages. The reader may check all the results independently if one wishes to since there are references supporting every claim. The discussion illustrates that the research objectives have been clearly addressed and have been related to the sources used. Furthermore, the results of the study can be generalized and theoretical and practical implications have been clearly developed. The article is coherent since there is a correspondence between the questions, their overview and outcomes.
Article of Choice #2: āFrom spreading to embedding innovation in healthcare: Implications for theory and practiceā
From spreading to embedding innovation in healthcare: Implications for theory and practice is an original article published by Health Care Management Review ā a peer-reviewed academic journal. Articles in this journal ā translate findings into implications and recommendations for health care administrators, researchers, and facultyā (Journals). The article is dated 2021, and the authors working on it are Harry Scarbrough and Yiannis Kyratsis. Harry Scarbrough is a PhD, Professor of Information Systems and Management and Co-Director with education and experience from the Centre for Healthcare Innovation Research, Bayes Business School, City, University of London, United Kingdom. Yiannis Kyratsis is a PhD Associate Professor in Organization Theory and Head of the Organization Theory Group, VU Amsterdam, the Netherlands. It is stated that both authors contributed equally to this article.
Evaluating the Source
According to Resurchify (2021) Health Care Management Review is a journal covering the technologies/fields/categories related to health policy leadership and management and strategy and management. It is published by Lippincott Williams and Wilkins Ltd. Health Care Management Review is cited by a total of 266 articles during the last 3 years (Resurchify, 2020).
Evaluating the Tone: From spreading to embedding innovation in healthcare
The authors’ tone is balanced and objective since no value judgments are provided, and the language is scientific and precise. All the arguments are well developed and well supported, with more than 40 sources included in the bibliography and all used to support claims made by the authors. The authors put forward the idea that, rather than studying the spread of innovations through diffusion across organizations or implementation within organizations, it makes sense to refocus it toward embedding innovation ā that is, the issue of the implementation of innovations at scale.
Evaluating the Content
The abstract states the issue of the study, then proposes a critical theoretical analysis, then gives insight and suggests practical implications after that. The purpose of the study is clear, and the methods are appropriate for the purpose. The article is coherent in that there is a connectedness in the way it is structured. Furthermore, the introduction clearly states the purpose of the paper and explains why previous studies of the spread of innovation in healthcare have not taken into consideration the factors that are necessary to consider. An overview of the other studies’ conclusions that support the authors’ claims is given, and a clear roadmap of the rest of the paper is provided. While there is no literature review in the article, it is to be noted that 44 sources are employed. There are numerous references to previous studies and the results they have come to in terms of their applicability to this paper. All the reviewed articles are credible, relevant and current.
The methodology includes focusing on the theory first: three mechanisms connecting the experience of the local implementation of innovations to their global diffusion within a healthcare system are outlined. Those are learning, adapting and institutionalizing, and each is expanded upon. The practical approach includes identifying self-limiting tensions within existing approaches and outlining new ones. The research outcomes clearly address the problem and propose an approach titled embedding innovation. That is to enable the experience of implementation deeply and extensively. Enough detail is provided for the reader to independently check the results. In the discussion, the research question and objectives have been clearly addressed and have been related to the sources used. Furthermore, The limitations of the study have been considered while developing the original approach to the issue, and the theoretical and practical implications have been clearly developed. The article is coherent in its structure and consistency.
References
Health Care Management Review: About the journal. Journals. (n.d.).
Health Care Management review- impact factor, overall ranking, h-index, SJR, rating, publisher, ISSN, and other important metrics. Resurchify. (2021).
Journal Information. Nature Publishing Group. (n.d.).
Nature biomedical engineering- impact factor, overall ranking, h-index, SJR, rating, publisher, ISSN, and other important metrics. Resurchify. (2021).
Yu, K. H., Beam, A. L., & Kohane, I. S. (2018). Artificial intelligence in healthcare. Nature biomedical engineering, 2(10), 719-731.
Scarbrough, H., & Kyratsis, Y. (2021). From spreading to embedding innovation in healthcare: implications for theory and practice. Health Care Management Review.