Explore the vast range of titles available.

Background Health services research often relies on secondary data, necessitating quality checks for completeness, validity, and potential errors before use. Various methods address implausible data, including data elimination, statistical estimation, or value substitution from the same or another dataset. This study presents an internal validation process of a secondary dataset used to investigate hospital compliance with minimum caseload requirements (MCR) in Germany. The secondary data source validated is the German Hospital Quality Reports (GHQR), an official dataset containing structured self-reported data from all hospitals in Germany. Methods This study conducted an internal cross-field validation of MCR-related data in GHQR from 2016 to 2021. The validation process checked the validity of reported MCR caseloads, including data availability and consistency, by comparing the stated MCR caseload with further variables in the GHQR. Subsequently, implausible MCR caseload values were corrected using the most plausible values given in the same GHQR. The study also analysed the error sources and used reimbursement-related Diagnosis Related Groups Statistic data to assess the validation outcomes. Results The analysis focused on four MCR procedures. 11.8–27.7% of the total MCR caseload values in the GHQR appeared ambiguous, and 7.9–23.7% were corrected. The correction added 0.7–3.7% of cases not previously stated as MCR caseloads and added 1.5–26.1% of hospital sites as MCR performing hospitals not previously stated in the GHQR. The main error source was this non-reporting of MCR caseloads, especially by hospitals with low case numbers. The basic plausibility control implemented by the Federal Joint Committee since 2018 has improved the MCR-related data quality over time. Conclusions This study employed a comprehensive approach to dataset internal validation that encompassed: (1) hospital association level data, (2) hospital site level data and (3) medical department level data, (4) report data spanning six years, and (5) logical plausibility checks. To ensure data completeness, we selected the most plausible values without eliminating incomplete or implausible data. For future practice, we recommend a validation process when using GHQR as a data source for MCR-related research. Additionally, an adapted plausibility control could help to improve the quality of MCR documentation.

Background India, with the highest global burden of tuberculosis (TB) and drug-resistant TB, aims to eliminate TB by 2025. Yet, limited evidence exists on drug resistance patterns and retreatment among patients with silico-tuberculosis. This study explores these patterns and assesses the impact of silicosis on TB retreatment in India. Methods This secondary data analysis stems from a larger retrospective cohort study conducted in Khambhat, Gujarat, between January 2006 and February 2022. It included 138 patients with silico-tuberculosis and 2,610 TB patients without silicosis. Data from the Nikshay TB information portal were linked with silicosis diagnosis reports from the Pneumoconiosis Board using the unique Nikshay ID as the linking variable. Drug-resistant TB was defined as resistance to any anti-TB drug recorded in Nikshay. Retreatment refers to TB patients who have previously undergone anti-TB treatment for one month or more and need further treatment. Recurrent TB denotes patients who were previously declared cured or had completed treatment but later tested positive for microbiologically confirmed TB. Multivariable logistic regression was used to determine the impact of co-prevalent silicosis on drug resistance and retreatment. Results Patients with silico-tuberculosis showed a higher proportion of retreatment compared to those without silicosis (55% vs. 23%, p  < 0.001). Notably, 28% of patients with silico-tuberculosis were recurrent TB cases, compared to 11% among those without silicosis. Regarding drug resistance, the silico-tuberculosis group exhibited a higher rate (6% vs. 3%), largely due to rifampicin resistance (5% vs. 2%, p  = 0.022). Co-prevalent silicosis was associated with a 2.5 times greater risk of drug-resistant TB (adjusted OR 2.5, 95% CI, 1.1–5.3; p  = 0.021). Additionally, patients with silico-tuberculosis had a fourfold increased risk of retreatment for TB (adjusted OR 4, 95% CI, 3–6; p  < 0.001). Conclusions Co-prevalent silicosis significantly elevates the risk of drug resistance, recurrence, and retreatment among TB patients in India. This study indicates a need for improved treatment protocols and suggests that future research should focus on randomized controlled trials to evaluate appropriate anti-TB regimen and duration of therapy for this high-risk group. Given India’s goal to eliminate TB by 2025, addressing the challenges posed by silico-tuberculosis is critical.

BackgroundChanges in well-being of patients with multiple myeloma (MM) before and after diagnosis have not been quantified.AimsExplore the use of secondary data to examine the changes in the well-being of older patients with MM.MethodsWe used the Health and Retirement Study (HRS), linked to Medicare claims to identify older MM patients. We compared patient-reported measures (PRM), including physical impairment, sensory impairment, and patient experience (significant pain, self-rated health, depression) in the interviews before and after MM diagnosis using McNemar’s test. We propensity-matched each MM patient to five HRS participants without MM diagnosis based on baseline characteristics. We compared the change in PRM between the MM patients and their matches.ResultsWe identified 92 HRS patients with MM diagnosis (mean age = 74.6, SD = 8.4). Among the surviving patients, there was a decline in well-being across most measures, including ADL difficulty (23% to 40%, p value = 0.016), poor or fair self-rated health (38% to 61%, p value = 0.004), and depression (15% to 30%, p value = 0.021). Surviving patients reported worse health than participants without MM across most measures, including ADL difficulty (40% vs. 27%, p value = 0.04), significant pain (38% vs. 22%, p value = 0.01), and depression (29% vs. 11%, p value = 0.003).DiscussionSecondary data were used to identify patients with MM diagnosis, and examine changes across multiple measures of well-being. MM diagnosis negatively affects several aspects of patients’ well-being, and these declines are larger than those experienced by similar participants without MM.ConclusionThe results of this study are valuable addition to understanding the experience of patients with MM, despite several data limitations.

PurposeSmart manufacturing (SM) lies at the core of Industry 4.0. Uniform adoption of SM across business partners is crucial to exploit its value creation potential. However, firms' willingness to invest in SM is limited by insufficient or inconclusive evidence on its performance-related benefits. To close this gap, this paper develops and tests a model linking SM adoption to firms' financial performance. Improvements along the four dimensions of operational performance (i.e. cost quality, delivery and flexibility) mediate this relation.Design/methodology/approachThis study follows an empirical research approach. In particular, survey data from 234 automotive component suppliers are analyzed via covariance-based structural equation modeling to explore the link between SM adoption and operational performance. Survey data are then matched with secondary data from balance sheets of 81 firms to investigate the impact of SM on financial performance via partial least square structural equation modeling.FindingsFindings highlight that adoption of SM results in improvements in cost, quality, delivery performance, thus suggesting that SM is a mean to overcome performance trade-offs. Improvements in operational performance enabled by SM do not give rise to superior financial performance, thus implying that SM might support firms in maintaining the competitive position in the market, but could be insufficient to generate higher margin.Originality/valueResults have implications for SM research and for manufacturing executives engaged in the adoption of SM, as they provide a detailed analysis of the impact of SM on operational performance and clarify the effect that SM adoption has on financial performance.

This article outlines a five-phase process of qualitative analysis that draws on deductive (codes developed a priori) and inductive (codes developed in the course of the analysis) coding strategies, as well as guided memoing and analytic questioning, to support trustworthy qualitative studies. The five-phase process presented here can be used as a whole or in part to support researchers in planning, articulating, and executing systematic and transparent qualitative data analysis; developing an audit trail to ensure study dependability and trustworthiness; and/or fleshing out aspects of analysis processes associated with specific methodologies.

Purpose The purpose of this study is to examine how organisations report on the detection and remediation of modern slavery in their operations and supply chains and to understand their approaches to disclosing information in response to modern slavery legislation. Design/methodology/approach An analysis of secondary data based on the statements is released in response to the 2015 UK Modern Slavery Act by 101 firms in the clothing and textiles sector. Findings Many firms use the same practices to detect and remediate modern slavery as for other social issues. But the hidden, criminal nature of modern slavery and the involvement of third party labour agencies mean practices need to either be tailored or other more innovative approaches developed, including in collaboration with traditional and non-traditional actors. Although five broad types of disclosure are identified, there is substantial heterogeneity in the statements. It is posited however that firms will converge on a more homogenous set of responses over time. Research limitations/implications The study is limited to one industry, responses to UK legislation and the information disclosed by focal firms only. Future research could expand the focus to include other industries, country contexts and stakeholders. Practical implications Managers must consider how their own firm’s behaviour contributes to the modern slavery threat, regulates both their stock and non-stock supply chains and ensures modern slavery is elevated from the procurement function to the boardroom. In making disclosures, managers may trade-off the potential competitive gains of transparency against the threat of information leakage and reputational risk should their statements be falsified. The managers should also consider what signals their statements send back up the chain to (sub-)suppliers. Findings also have potential policy implications. Originality/value The study expands the authors’ understanding of: modern slavery from a supply chain perspective, e.g. identifying the importance of standard setting and risk avoidance; and, supply chain information disclosure in response to legislative demands. This is the first academic paper to examine the statements produced by organisations in response to the UK Modern Slavery Act.

Background/Introduction Artificial intelligence (AI) in the healthcare sector is receiving attention from researchers and health professionals. Few previous studies have investigated this topic from a multi-disciplinary perspective, including accounting, business and management, decision sciences and health professions. Methods The structured literature review with its reliable and replicable research protocol allowed the researchers to extract 288 peer-reviewed papers from Scopus. The authors used qualitative and quantitative variables to analyse authors, journals, keywords, and collaboration networks among researchers. Additionally, the paper benefited from the Bibliometrix R software package. Results The investigation showed that the literature in this field is emerging. It focuses on health services management, predictive medicine, patient data and diagnostics, and clinical decision-making. The United States, China, and the United Kingdom contributed the highest number of studies. Keyword analysis revealed that AI can support physicians in making a diagnosis, predicting the spread of diseases and customising treatment paths. Conclusions The literature reveals several AI applications for health services and a stream of research that has not fully been covered. For instance, AI projects require skills and data quality awareness for data-intensive analysis and knowledge-based management. Insights can help researchers and health professionals understand and address future research on AI in the healthcare field.

This paper illustrates the use of secondary data for operations and supply chain management research by investigating the association between efficient supply chain management and innovation of firms. An empirical inquiry is conducted using archival financial statement information and patent citation data for firms in the manufacturing sector, over a 10‐year period from 1987 to 1996. Longitudinal analysis, focusing on the influence of efficient supply chain management on a firm's innovation over time, is conducted. Results and limitations are discussed along with a summary of steps, which may be followed when using secondary data for operations and supply chain management research.

Fund and use ‘living’ reviews of the latest data to steer research, practice and policy. Fund and use ‘living’ reviews of the latest data to steer research, practice and policy.

Randomized clinical trials (RCT) are accepted as the gold-standard approaches to measure effects of intervention or treatment on outcomes. They are also the designs of choice for health technology assessment (HTA). Randomization ensures comparability, in both measured and unmeasured pretreatment characteristics, of individuals assigned to treatment and control or comparator. However, even adequately powered RCTs are not always feasible for several reasons such as cost, time, practical and ethical constraints, and limited generalizability. RCTs rely on data collected on selected, homogeneous population under highly controlled conditions; hence, they provide evidence on efficacy of interventions rather than on effectiveness. Alternatively, observational studies can provide evidence on the relative effectiveness or safety of a health technology compared to one or more alternatives when provided under the setting of routine health care practice. In observational studies, however, treatment assignment is a non-random process based on an individual's baseline characteristics; hence, treatment groups may not be comparable in their pretreatment characteristics. As a result, direct comparison of outcomes between treatment groups might lead to biased estimate of the treatment effect. Propensity score approaches have been used to achieve balance or comparability of treatment groups in terms of their measured pretreatment covariates thereby controlling for confounding bias in estimating treatment effects. Despite the popularity of propensity scores methods and recent important methodological advances, misunderstandings on their applications and limitations are all too common. In this article, we present a review of the propensity scores methods, extended applications, recent advances, and their strengths and limitations.