Sensitivity analysis in a scoping review on police accountability : assessing the feasibility of reporting criteria in mixed studies reviews

In this paper, we report on the findings of a sensitivity analysis that was carried out within a previously conducted scoping review, hoping to contribute to the ongoing debate about how to assess the quality of research in mixed methods reviews. Previous sensitivity analyses mainly concluded that the exclusion of inadequately reported or lower quality studies did not have a significant effect on the results of the synthesis. In this study, we conducted a sensitivity analysis on the basis of reporting criteria with the aims of analysing its impact on the synthesis results and assessing its feasibility. Contrary to some previous studies, our analysis showed that the exclusion of inadequately reported studies had an impact on the results of the thematic synthesis. Initially, we also sought to propose a refinement of reporting criteria based on the literature and our own experiences. In this way, we aimed to facilitate the assessment of reporting criteria and enhance its consistency. However, based on the results of our sensitivity analysis, we opted not to make such a refinement since many publications included in this analysis did not sufficiently report on the methodology. As such, a refinement would not be useful considering that researchers would be unable to assess these (sub-)criteria

Case-based learning. A formal approach to generate health case studies from electronic healthcare records

There is an increasing social pressure to train medical students with a level of competency sufficient to face clinical practice already at the end of their curriculum. The case-based learning (CBL) is an efficient teaching method to prepare students for clinical practice through the use of real or realistic clinical cases. In this regard, the Electronic Healthcare Record (EHR) could be a good source of real patient stories that can be transformed into educative cases. In this paper a formal approach to generate Health Case Studies from EHR is defined

Development of a Knowledge Graph Embeddings Model for Pain

Pain is a complex concept that can interconnect with other concepts such as a disorder that might cause pain, a medication that might relieve pain, and so on. To fully understand the context of pain experienced by either an individual or across a population, we may need to examine all concepts related to pain and the relationships between them. This is especially useful when modeling pain that has been recorded in electronic health records. Knowledge graphs represent concepts and their relations by an interlinked network, enabling semantic and context-based reasoning in a computationally tractable form. These graphs can, however, be too large for efficient computation. Knowledge graph embeddings help to resolve this by representing the graphs in a low-dimensional vector space. These embeddings can then be used in various downstream tasks such as classification and link prediction. The various relations associated with pain which are required to construct such a knowledge graph can be obtained from external medical knowledge bases such as SNOMED CT, a hierarchical systematic nomenclature of medical terms. A knowledge graph built in this way could be further enriched with real-world examples of pain and its relations extracted from electronic health records. This paper describes the construction of such knowledge graph embedding models of pain concepts, extracted from the unstructured text of mental health electronic health records, combined with external knowledge created from relations described in SNOMED CT, and their evaluation on a subject-object link prediction task. The performance of the models was compared with other baseline models.Comment: Accepted at AMIA 2023, New Orlean

Building Data-Driven Pathways From Routinely Collected Hospital Data:A Case Study on Prostate Cancer

Background: Routinely collected data in hospitals is complex, typically heterogeneous, and scattered across multiple Hospital Information Systems (HIS). This big data, created as a byproduct of health care activities, has the potential to provide a better understanding of diseases, unearth hidden patterns, and improve services and cost. The extent and uses of such data rely on its quality, which is not consistently checked, nor fully understood. Nevertheless, using routine data for the construction of data-driven clinical pathways, describing processes and trends, is a key topic receiving increasing attention in the literature. Traditional algorithms do not cope well with unstructured processes or data, and do not produce clinically meaningful visualizations. Supporting systems that provide additional information, context, and quality assurance inspection are needed. Objective: The objective of the study is to explore how routine hospital data can be used to develop data-driven pathways that describe the journeys that patients take through care, and their potential uses in biomedical research; it proposes a framework for the construction, quality assessment, and visualization of patient pathways for clinical studies and decision support using a case study on prostate cancer. Methods: Data pertaining to prostate cancer patients were extracted from a large UK hospital from eight different HIS, validated, and complemented with information from the local cancer registry. Data-driven pathways were built for each of the 1904 patients and an expert knowledge base, containing rules on the prostate cancer biomarker, was used to assess the completeness and utility of the pathways for a specific clinical study. Software components were built to provide meaningful visualizations for the constructed pathways. Results: The proposed framework and pathway formalism enable the summarization, visualization, and querying of complex patient-centric clinical information, as well as the computation of quality indicators and dimensions. A novel graphical representation of the pathways allows the synthesis of such information. Conclusions: Clinical pathways built from routinely collected hospital data can unearth information about patients and diseases that may otherwise be unavailable or overlooked in hospitals. Data-driven clinical pathways allow for heterogeneous data (ie, semistructured and unstructured data) to be collated over a unified data model and for data quality dimensions to be assessed. This work has enabled further research on prostate cancer and its biomarkers, and on the development and application of methods to mine, compare, analyze, and visualize pathways constructed from routine data. This is an important development for the reuse of big data in hospitals

The Atlantic divide: methodological and epistemological differences in economic history

In the paper the development of economic history will be placed within the evolution of Western thought and culture. Therefore an analysis of the connections between economic history and contemporary epistemology will be carried out. In this perspective an analogy with the traditional division between analytic philosophy and continental philosophy would appear to be useful for economic history too: the first had long prevailed in Anglo-Saxon, the second in continental, culture. This partition evokes and embraces the antithesis between scientific and humanist culture, between logic and rhetoric, analysis and interpretation, conceptual clarification and visions of the world. The paper suggest that the opposition that loomed large over the post W.W.II decades between Anglo-American and European economic histories can also be conceived as a specific form of the wider opposition between ‘analytic style’ and ‘continental style’.economic history, methodology, epistemology, cliometrics, business history, economic thought

Linking Research and Policy: Assessing a Framework for Organic Agricultural Support in Ireland

This paper links social science research and agricultural policy through an analysis of support for organic agriculture and food. Globally, sales of organic food have experienced 20% annual increases for the past two decades, and represent the fastest growing segment of the grocery market. Although consumer interest has increased, farmers are not keeping up with demand. This is partly due to a lack of political support provided to farmers in their transition from conventional to organic production. Support policies vary by country and in some nations, such as the US, vary by state/province. There have been few attempts to document the types of support currently in place. This research draws on an existing Framework tool to investigate regionally specific and relevant policy support available to organic farmers in Ireland. This exploratory study develops a case study of Ireland within the framework of ten key categories of organic agricultural support: leadership, policy, research, technical support, financial support, marketing and promotion, education and information, consumer issues, inter-agency activities, and future developments. Data from the Irish Department of Agriculture, Fisheries and Food, the Irish Agriculture and Food Development Authority (Teagasc), and other governmental and semi-governmental agencies provide the basis for an assessment of support in each category. Assessments are based on the number of activities, availability of information to farmers, and attention from governmental personnel for each of the ten categories. This policy framework is a valuable tool for farmers, researchers, state agencies, and citizen groups seeking to document existing types of organic agricultural support and discover policy areas which deserve more attention

Unmasking Bias and Inequities: A Systematic Review of Bias Detection and Mitigation in Healthcare Artificial Intelligence Using Electronic Health Records

Objectives: Artificial intelligence (AI) applications utilizing electronic health records (EHRs) have gained popularity, but they also introduce various types of bias. This study aims to systematically review the literature that address bias in AI research utilizing EHR data. Methods: A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guideline. We retrieved articles published between January 1, 2010, and October 31, 2022, from PubMed, Web of Science, and the Institute of Electrical and Electronics Engineers. We defined six major types of bias and summarized the existing approaches in bias handling. Results: Out of the 252 retrieved articles, 20 met the inclusion criteria for the final review. Five out of six bias were covered in this review: eight studies analyzed selection bias; six on implicit bias; five on confounding bias; four on measurement bias; two on algorithmic bias. For bias handling approaches, ten studies identified bias during model development, while seventeen presented methods to mitigate the bias. Discussion: Bias may infiltrate the AI application development process at various stages. Although this review discusses methods for addressing bias at different development stages, there is room for implementing additional effective approaches. Conclusion: Despite growing attention to bias in healthcare AI, research using EHR data on this topic is still limited. Detecting and mitigating AI bias with EHR data continues to pose challenges. Further research is needed to raise a standardized method that is generalizable and interpretable to detect, mitigate and evaluate bias in medical AI.Comment: 29 pages, 2 figures, 2 tables, 2 supplementary files, 66 reference

Nursing diagnoses focused on universal self-care requisites

Aims: (1) To identify and analyse diagnoses documented by nurses in Portugal within the scope of universal self-care requisites; (2) to determine the main problems with nursing diagnoses syntaxes for semantic interoperability purposes; and (3) to suggest unified nursing diagnoses syntaxes within the scope of universal self-care requisites. Background/Introduction: Ageing societies and the increase in chronic diseases have led to significant concern regarding individuals’ dependence to ensure self-care. ICNP is widely used by Portuguese nurses in electronic health records for documentation of nursing diagnoses and interventions. Methods: A qualitative study using inductive content analysis and focus group: 1. nursing e-documentation content analysis and 2. focus group to explore implicit criteria or insights from content analysis results. Results: From a corpus of analysis with 1793 nursing diagnoses, 432 nursing diagnoses centred on universal self-care requisites emerged from the content analysis. One hundred ten nursing diagnoses resulted from the application of new encoding criteria that emerged after a focus group meeting. Conclusion: Results reveal that nursing diagnoses related to universal self-care requisites can emphasize the impairment or potentialities of the individuals performing self-care. It also shows a lack of consensus on nominating the nursing diagnoses of people with a deficit in universal self-care requisites, resulting in different diagnoses to express the same needs. Implications for nursing practice: Representation of most relevant nursing diagnoses within the scope of universal self-care requisites. Implications for health policy: Incorporating standardized language into electronic health records is not enough for improving quality and continuity of care and semantic interoperability achievement. Electronic health records need to work with a nursing ontology in the backend to meet these requirements.info:eu-repo/semantics/publishedVersio

Non-acute Care Clinical for BSN Students Chronic Illness Care and Diabetes Self-management Support

Baccalaureate nursing (BSN) programs work toward ensuring that curricula are current and relevant for the existing and evolving health care environment, health and illness trends, and care delivery systems. To this end this Systems Change Project (SCP) addresses an identified curricular gap between the traditional clinical experience of BSN students related to care of individuals with chronic illness and the predominant environment in which chronic illness care occurs. A non-acute care clinical experience was integrated into the junior year adult and chronicity clinical course of the Bethel University nursing program. The clinical experience was delivered through virtual simulation and focused on the registered nurse (RN) role in chronic illness self-management support with an emphasis on diabetes. Confirmation of the need for this and motivation to implement this SCP was provided through The future of nursing: Leading change, advancing health (IOM, 2011) coupled with the lack of documentation in the literature regarding of this type of learning experience in BSN programs. The SCP was supported through a dual theoretical framework of adult learning theory and adaption theory while being further bolstered by standards of care in self-management support and simulation development. Project evaluation data reveal the effectiveness of the simulation and provide recommendations for future practice and scholarship