A stimulus to define informatics and health information technology

<p>Abstract</p> <p>Background</p> <p>Despite the growing interest by leaders, policy makers, and others, the terminology of health information technology as well as biomedical and health informatics is poorly understood and not even agreed upon by academics and professionals in the field.</p> <p>Discussion</p> <p>The paper, presented as a Debate to encourage further discussion and disagreement, provides definitions of the major terminology used in biomedical and health informatics and health information technology. For informatics, it focuses on the words that modify the term as well as individuals who practice the discipline. Other categories of related terms are covered as well, from the associated disciplines of computer science, information technolog and health information management to the major application categories of applications used. The discussion closes with a classification of individuals who work in the largest segment of the field, namely clinical informatics.</p> <p>Summary</p> <p>The goal of presenting in Debate format is to provide a starting point for discussion to reach a documented consensus on the definition and use of these terms.</p

Lay perceptions of evidence-based information – a qualitative evaluation of a website for back pain sufferers

BACKGROUND: In an evidence-informed patient choice the patient has access to research-based information about the effectiveness of health care options and is encouraged to use this information in treatment decisions. This concept has seen growing popularity in recent years. However, we still know relatively little about users' attitudes to the use of research-based information, possibly because people have been unexposed to this type of information. After developing the BackInfo website where the results of Cochrane systematic reviews on the effects of low back pain were adapted and presented to lay users we evaluated how users responded to this information. METHODS: Focus group meetings were held with 18 chronic back pain sufferers, after they had been sent a link to the website before the meetings. RESULTS: The focus groups suggest that the most important challenges to the use of BackInfo's research-based information are not primarily tied to the comprehension or presentation of the information, but are mainly associated with participants' attitudes towards the credibility of research and researchers, and the applicability of research results to themselves as individuals. Possible explanations for participants' lack of trust in research and their apparent difficulties in applying this research to their own situations include aspects that may be typical for the general public including the media's presentation of research, and a lack of familiarity with and feelings of distance to research evidence. Other aspects may be typical for patient groups with chronic and unclear medical conditions, such as a lack of trust in the health care establishment in general. CONCLUSION: In order to enhance the credibility and applicability of research evidence, providers of research-based information could explore a number of possibilities including the use of including personal stories to illustrate the research outcomes

Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): Explanation and Elaboration

Much medical research is observational. The reporting of observational studies is often of insufficient quality. Poor reporting hampers the assessment of the strengths and weaknesses of a study and the generalisability of its results. Taking into account empirical evidence and theoretical considerations, a group of methodologists, researchers, and editors developed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) recommendations to improve the quality of reporting of observational studies. The STROBE Statement consists of a checklist of 22 items, which relate to the title, abstract, introduction, methods, results and discussion sections of articles. Eighteen items are common to cohort studies, case-control studies and cross-sectional studies and four are specific to each of the three study designs. The STROBE Statement provides guidance to authors about how to improve the reporting of observational studies and facilitates critical appraisal and interpretation of studies by reviewers, journal editors and readers. This explanatory and elaboration document is intended to enhance the use, understanding, and dissemination of the STROBE Statement. The meaning and rationale for each checklist item are presented. For each item, one or several published examples and, where possible, references to relevant empirical studies and methodological literature are provided. Examples of useful flow diagrams are also included. The STROBE Statement, this document, and the associated Web site (http://www.strobe-statement.org/) should be helpful resources to improve reporting of observational research

Mejorar la comunicación de estudios observacionales en epidemiología (STROBE): explicación y elaboración

Gran parte de la investigación biomédica es de tipo observacional. Los informes de los estudios observacionales a menudo poseen una calidad insuficiente, lo que dificulta la evaluación de sus fortalezas y debilidades para generalizar los resultados. Teniendo en cuenta la evidencia empírica y consideraciones teóricas, un grupo de expertos en metodología, investigadores y editores de revistas científicas, desarrollaron una lista de recomendaciones para aumentar la calidad de las publicaciones de los estudios observacionales: (STROBE). La Declaración STROBE consiste en una lista de verificación de 22 puntos que guardan relación con las diferentes secciones de un artículo: título, resumen, introducción, metodología, resultados y discusión. De ellos, 18 puntos son comunes a los tres diseños de estudio: cohorte, casos y controles, y transversales; los otros cuatro son específicos para cada una de estas tres modalidades. La Declaración STROBE proporciona a los autores información sobre cómo mejorar la calidad de los artículos sobre estudios observacionales y facilita a los revisores, editores de revistas y lectores su apreciación crítica y su interpretación. Este documento explicativo tiene el propósito de impulsar el uso, la comprensión y la difusión de la Declaración STROBE. Se presentan el significado y el análisis razonado para cada punto de la lista de verificación, proporcionando uno o varios ejemplos publicados en la literatura y, en lo posible, referencias de estudios empíricos relevantes y literatura metodológica. También se incluyen ejemplos de diagramas de flujo. La Declaración STROBE, el presente documento y la página Web asociada () son recursos útiles para mejorar la divulgación de la investigación observacional.Much medical research is observational. The reporting of observational studies is often of insufficient quality. Poor reporting hampers the assessment of the strengths and weaknesses of a study and the generalisability of its results. Taking into account empirical evidence and theoretical considerations, a group of methodologists, researchers, and editors developed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) recommendations to improve the quality of reporting of observational studies. The STROBE Statement consists of a checklist of 22 items, which relate to the title, abstract, introduction, methods, results and discussion sections of articles. Eighteen items are common to cohort studies, case-control studies and cross-sectional studies and four are specific to each of the three study designs. The STROBE Statement provides guidance to authors about how to improve the reporting of observational studies and facilitates critical appraisal and interpretation of studies by reviewers, journal editors and readers. This explanatory and elaboration document is intended to enhance the use, understanding, and dissemination of the STROBE Statement. The meaning and rationale for each checklist item are presented. For each item, one or several published examples and, where possible, references to relevant empirical studies and methodological literature are provided. Examples of useful flow diagrams are also included. The STROBE Statement, this document, and the associated Web site () should be helpful resources to improve reporting of observational research

Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): Explanation and Elaboration.

Much medical research is observational. The reporting of observational studies is often of insufficient quality. Poor reporting hampers the assessment of the strengths and weaknesses of a study and the generalisability of its results. Taking into account empirical evidence and theoretical considerations, a group of methodologists, researchers, and editors developed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) recommendations to improve the quality of reporting of observational studies. The STROBE Statement consists of a checklist of 22 items, which relate to the title, abstract, introduction, methods, results and discussion sections of articles. Eighteen items are common to cohort studies, case-control studies and cross-sectional studies and four are specific to each of the three study designs. The STROBE Statement provides guidance to authors about how to improve the reporting of observational studies and facilitates critical appraisal and interpretation of studies by reviewers, journal editors and readers. This explanatory and elaboration document is intended to enhance the use, understanding, and dissemination of the STROBE Statement. The meaning and rationale for each checklist item are presented. For each item, one or several published examples and, where possible, references to relevant empirical studies and methodological literature are provided. Examples of useful flow diagrams are also included. The STROBE Statement, this document, and the associated Web site (http://www. strobe-statement.org/) should be helpful resources to improve reporting of observational research. Present article is Russian-language translation of the original manuscript edited by Doctor of Medicine R.T. Saygitov.Present translation was first published in Digital Diagnostics. doi: 10.17816/DD70821. It is published with minor changes related to the literary editing of the translation itself

Повышение качества отчётов о наблюдательных исследованиях в эпидемиологии (STROBE): разъяснения и уточнения

Большинство медицинских исследований являются наблюдательными (observational). Сообщения о таких ис-следованиях часто невысокого качества, что затрудняет оценку сильных и слабых сторон работы, а также обоб-щаемости (generalizability) её результатов. Принимая во внимание эмпирические свидетельства и теоретические соображения, группа методологов, исследователей и научных редакторов разработала рекомендации «Повышение качества отчётов о наблюдательных исследованиях в эпидемиологии (STROBE): разъяснения и уточнения». Реко-мендации STROBE содержат 22 пункта, связанных с оформлением следующих разделов научных статей: название, аннотация, введение, методы, результаты и их обсуждение, при этом 18 пунктов являются общими для когортных исследований (cohort studies), исследований «случай–контроль» (case-control studies) и одномоментных исследова-ний (cross-sectional studies); 4 пункта специфичны для каждого из указанных дизайнов исследований (study designs). STROBE ― руководство для авторов, необходимое для повышения качества отчётов о наблюдательных исследова-ниях, облегчающее критическую оценку исследования и его интерпретацию рецензентами, редакторами журналов и читателями. Цель этой разъясняющей и уточняющей статьи ― способствовать более широкому применению, пониманию и распространению стандартов STROBE. В ней даётся разъяснение смысла и обоснование применения каждого пункта руководства (checklist). По каждому пункту приводятся один или несколько опубликованных при-меров правильного представления исследований и, при возможности, библиографические ссылки на подходящие эмпирические исследования и методологическую литературу. Представлены примеры потоковых диаграмм (flow diagrams) для описания последовательности исследования. Рекомендации STROBE, настоящая статья и соответ-ствующий веб-сайт (http://www.strobe-statement.org/) должны стать полезным источником для повышения качества отчётов о результатах наблюдательных исследований. This article is the reprint with Russian translation of the original that can be observed here: Vandenbroucke JP, von Elm E, Altman DG, Gotzsche PC, Mulrow CD, et al. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): Explanation and Elaboration. PLoS Med. 2007;4(10):e297. doi: 10.1371/journal.pmed.0040297

Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI Extension

The SPIRIT 2013 (The Standard Protocol Items: Recommendations for Interventional Trials) statement aims to improve the completeness of clinical trial protocol reporting, by providing evidence-based recommendations for the minimum set of items to be addressed. This guidance has been instrumental in promoting transparent evaluation of new interventions. More recently, there is a growing recognition that interventions involving artificial intelligence need to undergo rigorous, prospective evaluation to demonstrate their impact on health outcomes. The SPIRIT-AI extension is a new reporting guideline for clinical trials protocols evaluating interventions with an AI component. It was developed in parallel with its companion statement for trial reports: CONSORT-AI. Both guidelines were developed using a staged consensus process, involving a literature review and expert consultation to generate 26 candidate items, which were consulted on by an international multi-stakeholder group in a 2-stage Delphi survey (103 stakeholders), agreed on in a consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The SPIRIT-AI extension includes 15 new items, which were considered sufficiently important for clinical trial protocols of AI interventions. These new items should be routinely reported in addition to the core SPIRIT 2013 items. SPIRIT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention will be integrated, considerations around the handling of input and output data, the human-AI interaction and analysis of error cases. SPIRIT-AI will help promote transparency and completeness for clinical trial protocols for AI interventions. Its use will assist editors and peer-reviewers, as well as the general readership, to understand, interpret and critically appraise the design and risk of bias for a planned clinical trial

Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement

David Moher and colleagues introduce PRISMA, an update of the QUOROM guidelines for reporting systematic reviews and meta-analyse

Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension

The SPIRIT 2013 statement aims to improve the completeness of clinical trial protocol reporting by providing evidence-based recommendations for the minimum set of items to be addressed. This guidance has been instrumental in promoting transparent evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate their impact on health outcomes. The SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence) extension is a new reporting guideline for clinical trial protocols evaluating interventions with an AI component. It was developed in parallel with its companion statement for trial reports: CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 26 candidate items, which were consulted upon by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The SPIRIT-AI extension includes 15 new items that were considered sufficiently important for clinical trial protocols of AI interventions. These new items should be routinely reported in addition to the core SPIRIT 2013 items. SPIRIT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention will be integrated, considerations for the handling of input and output data, the human–AI interaction and analysis of error cases. SPIRIT-AI will help promote transparency and completeness for clinical trial protocols for AI interventions. Its use will assist editors and peer reviewers, as well as the general readership, to understand, interpret, and critically appraise the design and risk of bias for a planned clinical trial

Does Cultural Competency Training of Health Professionals Improve Patient Outcomes? A Systematic Review and Proposed Algorithm for Future Research

BACKGROUND: Cultural competency training has been proposed as a way to improve patient outcomes. There is a need for evidence showing that these interventions reduce health disparities. OBJECTIVE: The objective was to conduct a systematic review addressing the effects of cultural competency training on patient-centered outcomes; assess quality of studies and strength of effect; and propose a framework for future research. DESIGN: The authors performed electronic searches in the MEDLINE/PubMed, ERIC, PsycINFO, CINAHL and Web of Science databases for original articles published in English between 1990 and 2010, and a bibliographic hand search. Studies that reported cultural competence educational interventions for health professionals and measured impact on patients and/or health care utilization as primary or secondary outcomes were included. MEASUREMENTS: Four authors independently rated studies for quality using validated criteria and assessed the training effect on patient outcomes. Due to study heterogeneity, data were not pooled; instead, qualitative synthesis and analysis were conducted. RESULTS: Seven studies met inclusion criteria. Three involved physicians, two involved mental health professionals and two involved multiple health professionals and students. Two were quasi-randomized, two were cluster randomized, and three were pre/post field studies. Study quality was low to moderate with none of high quality; most studies did not adequately control for potentially confounding variables. Effect size ranged from no effect to moderately beneficial (unable to assess in two studies). Three studies reported positive (beneficial) effects; none demonstrated a negative (harmful) effect. CONCLUSION: There is limited research showing a positive relationship between cultural competency training and improved patient outcomes, but there remains a paucity of high quality research. Future work should address challenges limiting quality. We propose an algorithm to guide educators in designing and evaluating curricula, to rigorously demonstrate the impact on patient outcomes and health disparities