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An overview of the design, implementation, and analyses of longitudinal studies on aging.

ABSTRACT: Longitudinal studies have contributed substantially to understanding of aging and geriatric syndromes. These efforts have provided a base of knowledge of the critical factors to consider in designing and implementing new longitudinal studies in older adults. This review highlights some of the major considerations in planning and implementing this type of study. Longitudinal studies can assess change over time and specific disease endpoints. Such projects require multidisciplinary teams with expertise in the many health and contextual factors that must be considered. Recent advances in study design include the use of imaging and biomarkers to assess mechanisms and approaches that raise the ceiling on measurement and integrate assessment of exposures over time. Study implementation requires careful planning and monitoring to maintain fidelity to the scientific goals. Analysis of longitudinal data requires approaches that account for inevitable missing data. New studies should take advantage of the experience obtained from longitudinal studies on aging already conducted.

SUBMITTER: Newman AB

PROVIDER: S-EPMC3008590 | biostudies-literature | 2010 Oct

REPOSITORIES: biostudies-literature

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An overview of the design, implementation, and analyses of longitudinal studies on aging.

Newman Anne B AB

Journal of the American Geriatrics Society 20101001

Longitudinal studies have contributed substantially to understanding of aging and geriatric syndromes. These efforts have provided a base of knowledge of the critical factors to consider in designing and implementing new longitudinal studies in older adults. This review highlights some of the major considerations in planning and implementing this type of study. Longitudinal studies can assess change over time and specific disease endpoints. Such projects require multidisciplinary teams with expe ...[more]

PMID: 21029055

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Project description:Implementation of artificial intelligence systems for healthcare is challenging. Understanding the barriers and implementation strategies can impact their adoption and allows for better anticipation and planning. This study's objective was to create a detailed inventory of barriers to and strategies for AI implementation in healthcare to support advancements in methods and implementation processes in healthcare. A sequential explanatory mixed method design was used. Firstly, scoping reviews and systematic literature reviews were identified using PubMed. Selected studies included empirical cases of AI implementation and use in clinical practice. As the reviews were deemed insufficient to fulfil the aim of the study, data collection shifted to the primary studies included in those reviews. The primary studies were screened by title and abstract, and thereafter read in full text. Then, data on barriers to and strategies for AI implementation were extracted from the included articles, thematically coded by inductive analysis, and summarized. Subsequently, a direct qualitative content analysis of 69 interviews with healthcare leaders and healthcare professionals confirmed and added results from the literature review. Thirty-eight empirical cases from the six identified scoping and literature reviews met the inclusion and exclusion criteria. Barriers to and strategies for AI implementation were grouped under three phases of implementation (planning, implementing, and sustaining the use) and were categorized into eleven concepts; Leadership, Buy-in, Change management, Engagement, Workflow, Finance and human resources, Legal, Training, Data, Evaluation and monitoring, Maintenance. Ethics emerged as a twelfth concept through qualitative analysis of the interviews. This study illustrates the inherent challenges and useful strategies in implementing AI in healthcare practice. Future research should explore various aspects of leadership, collaboration and contracts among key stakeholders, legal strategies surrounding clinicians' liability, solutions to ethical dilemmas, infrastructure for efficient integration of AI in workflows, and define decision points in the implementation process.

Dataset Information

An overview of the design, implementation, and analyses of longitudinal studies on aging.

Publications

An overview of the design, implementation, and analyses of longitudinal studies on aging.

Similar Datasets

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