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How defensive medicine is defined and understood in European medical literature: protocol for a systematic review.

ABSTRACT: INTRODUCTION:The term defensive medicine, referring to actions motivated primarily by litigious concerns, originates from the USA and has been used in medical research literature since the late 1960s. Differences in medical legal systems between the US and most European countries with no tort legislation raise the question whether the US definition of defensive medicine holds true in Europe. AIM:To present the protocol of a systematic review investigating variations in definitions and understandings of the term 'defensive medicine' in European research articles. METHODS AND ANALYSIS:In concordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a systematic review of all medical research literature that investigate defensive medicine will be performed by two independent reviewers. The databases PubMed, Embase and Cochrane will be systematically searched on the basis of predetermined criteria. Data from all included European studies will systematically be extracted including the studies' definitions and understandings of defensive medicine, especially the motives for doing medical actions that the study regards as 'defensive'. ETHICS AND DISSEMINATION:No ethics clearance is required as no primary data will be collected. The results of the systematic review will be published in a peer-reviewed, international journal. PROSPERO REGISTRATION NUMBER:This review has been submitted to International Prospective Register of Systematic Reviews (PROSPERO) and is awaiting registration.

SUBMITTER: Baungaard N

PROVIDER: S-EPMC7050374 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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How defensive medicine is defined and understood in European medical literature: protocol for a systematic review.

Baungaard Nathalie N Skovvang Pia P Assing Hvidt Elisabeth E Gerbild Helle H Kirstine Andersen Merethe M Lykkegaard Jesper J

BMJ open 20200228 2

<h4>Introduction</h4>The term defensive medicine, referring to actions motivated primarily by litigious concerns, originates from the USA and has been used in medical research literature since the late 1960s. Differences in medical legal systems between the US and most European countries with no tort legislation raise the question whether the US definition of defensive medicine holds true in Europe.<h4>Aim</h4>To present the protocol of a systematic review investigating variations in definitions ...[more]

PMID: 32114473

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Project description:BackgroundApplications of artificial intelligence (AI) are pervasive in modern biomedical science. In fact, research results suggesting algorithms and AI models for different target diseases and conditions are continuously increasing. While this situation undoubtedly improves the outcome of AI models, health care providers are increasingly unsure which AI model to use due to multiple alternatives for a specific target and the "black box" nature of AI. Moreover, the fact that studies rarely use guidelines in developing and reporting AI models poses additional challenges in trusting and adapting models for practical implementation.ObjectiveThis review protocol describes the planned steps and methods for a review of the synthesized evidence regarding the quality of available guidelines and frameworks to facilitate AI applications in medicine.MethodsWe will commence a systematic literature search using medical subject headings terms for medicine, guidelines, and machine learning (ML). All available guidelines, standard frameworks, best practices, checklists, and recommendations will be included, irrespective of the study design. The search will be conducted on web-based repositories such as PubMed, Web of Science, and the EQUATOR (Enhancing the Quality and Transparency of Health Research) network. After removing duplicate results, a preliminary scan for titles will be done by 2 reviewers. After the first scan, the reviewers will rescan the selected literature for abstract review, and any incongruities about whether to include the article for full-text review or not will be resolved by the third and fourth reviewer based on the predefined criteria. A Google Scholar (Google LLC) search will also be performed to identify gray literature. The quality of identified guidelines will be evaluated using the Appraisal of Guidelines, Research, and Evaluation (AGREE II) tool. A descriptive summary and narrative synthesis will be carried out, and the details of critical appraisal and subgroup synthesis findings will be presented.ResultsThe results will be reported using the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analyses) reporting guidelines. Data analysis is currently underway, and we anticipate finalizing the review by November 2023.ConclusionsGuidelines and recommended frameworks for developing, reporting, and implementing AI studies have been developed by different experts to facilitate the reliable assessment of validity and consistent interpretation of ML models for medical applications. We postulate that a guideline supports the assessment of an ML model only if the quality and reliability of the guideline are high. Assessing the quality and aspects of available guidelines, recommendations, checklists, and frameworks-as will be done in the proposed review-will provide comprehensive insights into current gaps and help to formulate future research directions.International registered report identifier (irrid)DERR1-10.2196/47105.

Project description:BackgroundSystematic review is an indispensable tool for optimal evidence collection and evaluation in evidence-based medicine. However, the explosive increase of the original literatures makes it difficult to accomplish critical appraisal and regular update. Artificial intelligence (AI) algorithms have been applied to automate the literature screening procedure in medical systematic reviews. In these studies, different algorithms were used and results with great variance were reported. It is therefore imperative to systematically review and analyse the developed automatic methods for literature screening and their effectiveness reported in current studies.MethodsAn electronic search will be conducted using PubMed, Embase, ACM Digital Library, and IEEE Xplore Digital Library databases, as well as literatures found through supplementary search in Google scholar, on automatic methods for literature screening in systematic reviews. Two reviewers will independently conduct the primary screening of the articles and data extraction, in which nonconformities will be solved by discussion with a methodologist. Data will be extracted from eligible studies, including the basic characteristics of study, the information of training set and validation set, and the function and performance of AI algorithms, and summarised in a table. The risk of bias and applicability of the eligible studies will be assessed by the two reviewers independently based on Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). Quantitative analyses, if appropriate, will also be performed.DiscussionAutomating systematic review process is of great help in reducing workload in evidence-based practice. Results from this systematic review will provide essential summary of the current development of AI algorithms for automatic literature screening in medical evidence synthesis and help to inspire further studies in this field.Systematic review registrationPROSPERO CRD42020170815 (28 April 2020).

Project description:OBJECTIVE:Synthesise evidence about the impact of family medicine/general practice (FM) clerkships on undergraduate medical students, teaching general/family practitioners (FPs) and/or their patients. DATA SOURCES:Medline, ERIC, PsycINFO, EMBASE and Web of Knowledge searched from 21 November to 17 December 2013. Primary, empirical, quantitative or qualitative studies, since 1990, with abstracts included. No country restrictions. Full text languages: English, French, Spanish, German, Dutch or Italian. REVIEW METHODS:Independent selection and data extraction by two authors using predefined data extraction fields, including Kirkpatrick's levels for educational intervention outcomes, study quality indicators and Best Evidence Medical Education (BEME) strength of findings' grades. Descriptive narrative synthesis applied. RESULTS:Sixty-four included articles: impact on students (48), teaching FPs (12) and patients (8). Sample sizes: 16-1095 students, 3-146 FPs and 94-2550 patients. Twenty-six studies evaluated at Kirkpatrick level 1, 26 at level 2 and 6 at level 3. Only one study achieved BEME's grade 5. The majority was assessed as grade 4 (27) and 3 (33). Students reported satisfaction with content and process of teaching as well as learning in FM clerkships. They enhanced previous learning, and provided unique learning on dealing with common acute and chronic conditions, health maintenance, disease prevention, communication and problem-solving skills. Students' attitudes towards FM were improved, but new or enhanced interest in FM careers did not persist without change after graduation. Teaching FPs reported increased job satisfaction and stimulation for professional development, but also increased workload and less productivity, depending on the setting. Overall, student's presence and participation did not have a negative impact on patients. CONCLUSIONS:Research quality on the impact of FM clerkships is still limited, yet across different settings and countries, positive impact is reported on students, FPs and patients. Future studies should involve different stakeholders, medical schools and countries, and use standardised and validated evaluation tools.

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How defensive medicine is defined and understood in European medical literature: protocol for a systematic review.

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How defensive medicine is defined and understood in European medical literature: protocol for a systematic review.

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