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Abstract Background The implementation of computerised clinical decision support systems has the potential to enhance healthcare by improving patient safety, practitioner performance, and patient outcomes. Notwithstanding the numerous advantages, the uptake of clinical decision support systems remains constrained, thereby impeding the full realisation of their potential. To ensure the effective and successful implementation of these systems, it is essential to identify and analyse the reasons for their low uptake and adoption. This protocol outlines an umbrella review, which will synthesise the findings of existing literature reviews to generate a comprehensive overview of the barriers and facilitators to the implementation and adoption of decision support systems across healthcare settings. Methods This umbrella review protocol was developed in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) guidelines. Searches for eligible articles will be conducted in four electronic bibliographic databases, including PubMed/MEDLINE, IEEE Xplore, Scopus, and Web of Science. Obtained results will be independently screened by four reviewers based on pre-defined eligibility criteria. The risk of bias will be assessed for all eligible articles. Data on barriers and facilitators to the implementation and adoption of clinical decision support systems will be extracted, summarised, and further categorised into themes that aim to describe the originating environment or concept of the respective factor. The frequency of all identified barriers and facilitators within the group of included reviews will be determined in order to establish a prioritisation of the factors. Discussion This umbrella review protocol presents a methodology for the systematic synthesis of barriers and facilitators to the implementation and adoption of clinical decision support systems across healthcare settings. The umbrella review will enable the development of novel implementation and adoption strategies that reinforce the identified facilitators and circumvent barriers, thereby promoting the use-oriented evaluation and effective utilisation of clinical decision support systems. Systematic review registration PROSPERO CRD42024507614

Bartonella spp. are fastidious, gram-negative intracellular bacteria that are transmitted to humans by insect vectors. The genus includes 12 species associated with human infection: B. henselae, B. quintana, B. bacilliformis, [...]

Molecular methods can enable rapid identification of Bartonella spp. infections, which are difficult to diagnose by using culture or serology. We analyzed clinical test results of PCR that targeted bacterial 16S rRNA hypervariable V1–V2 regions only or in parallel with PCR of Bartonella-specific ribC gene. We identified 430 clinical specimens infected with Bartonella spp. from 420 patients in the United States. Median patient age was 37 (range 1–79) years; 62% were male. We identified B. henselae in 77%, B. quintana in 13%, B. clarridgeiae in 1%, B. vinsonii in 1%, and B. washoensis in 1% of specimens. B. quintana was detected in 83% of cardiac specimens; B. henselae was detected in 34% of lymph node specimens. We detected novel or uncommon Bartonella spp. in 9 patients. Molecular diagnostic testing can identify Bartonella spp. infections, including uncommon and undescribed species, and might be particularly useful for patients who have culture-negative endocarditis or lymphadenitis.

Background: The implementation of computerised clinical decision support systems has the potential to enhance healthcare by improving patient safety, practitioner performance, and patient outcomes. Notwithstanding the numerous advantages, the uptake of clinical decision support systems remains constrained, thereby impeding the full realisation of their potential. To ensure the effective and successful implementation of these systems, it is essential to identify and analyse the reasons for their low uptake and adoption. This protocol outlines an umbrella review, which will synthesise the findings of existing literature reviews to generate a comprehensive overview of the barriers and facilitators to the implementation and adoption of decision support systems across healthcare settings. Methods: This umbrella review protocol was developed in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) guidelines. Searches for eligible articles will be conducted in four electronic bibliographic databases, including PubMed/MEDLINE, IEEE Xplore, Scopus, and Web of Science. Obtained results will be independently screened by four reviewers based on pre-defined eligibility criteria. The risk of bias will be assessed for all eligible articles. Data on barriers and facilitators to the implementation and adoption of clinical decision support systems will be extracted, summarised, and further categorised into themes that aim to describe the originating environment or concept of the respective factor. The frequency of all identified barriers and facilitators within the group of included reviews will be determined in order to establish a prioritisation of the factors. Discussion: This umbrella review protocol presents a methodology for the systematic synthesis of barriers and facilitators to the implementation and adoption of clinical decision support systems across healthcare settings. The umbrella review will enable the development of novel implementation and adoption strategies that reinforce the identified facilitators and circumvent barriers, thereby promoting the use-oriented evaluation and effective utilisation of clinical decision support systems. Systematic review registration: PROSPERO CRD42024507614 [ABSTRACT FROM AUTHOR]

Background: Although all hospitals aim to deliver high-quality care, there is considerable variation in their adoption of quality management (QM) practices. Organizational and environmental factors are known to drive strategic decision-making in hospitals, but their impact on the adoption of QM practices remains unclear. Purpose: Our study aims to identify multiple organizational and environmental factors that explain variation in the adoption of QM practices among hospitals and to explore mechanisms underlying these relationships. Methodology: We conducted a two-phase, sequential mixed-methods study of German acute care hospitals. The quantitative phase used between-effects regressions to identify factors explaining variation in the number of QM practices adopted by hospitals from 2015 to 2019. The qualitative phase used semistructured interviews with quality managers to gain in-depth insights. Results: The number of QM practices adopted by a hospital was significantly associated with factors like hospital size and the presence of an emergency department or QM steering committee. Our qualitative findings highlighted potential mechanisms such as the presence of an emergency department serving as a proxy for organizational complexity or urgency of case-mix. Conclusion: We provide an overview of factors driving QM adoption in hospitals, extending beyond the focus on single factors in previous research. Future studies could explore additional factors highlighted by our interviewees. Practice Implications: Our results can inform interventions to strengthen QM in hospitals and guide future research on this topic. [ABSTRACT FROM AUTHOR]

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In recent years, artificial intelligence (AI) has gained momentum in many fields of daily live. In healthcare, AI can be used for diagnosing or predicting illnesses. However, explainable AI (XAI) is needed to ensure that users understand how the algorithm arrives at a decision. In our research project, machine learning methods are used for individual risk prediction of hospital-onset bacteremia (HOB). This paper presents a vision on a step-wise process for implementation and evaluation of user-centered XAI for risk prediction of HOB. An initial requirement analysis revealed first insights on the users' needs of explainability to use and trust such risk prediction applications. The findings were then used to propose step-wise process towards a user-centered evaluation. [ABSTRACT FROM AUTHOR]

Abstract Background Invasive Mucorales infections (IMI) lead to significant morbidity and mortality in immunocompromised hosts. The role of season and climatic conditions in case clustering of IMI remain poorly understood. Methods Following detection of a cluster of sinopulmonary IMIs in patients with hematologic malignancies, we reviewed center-based medical records of all patients with IMIs and other invasive fungal infections (IFIs) between January of 2012 and August of 2015 to assess for case clustering in relation to seasonality. Results A cluster of 7 patients were identified with sinopulmonary IMIs (Rhizopus microsporus/azygosporus, 6; Rhizomucor pusillus, 1) during a 3 month period between June and August of 2014. All patients died or were discharged to hospice. The cluster was managed with institution of standardized posaconazole prophylaxis to high-risk patients and patient use of N-95 masks when outside of protected areas on the inpatient service. Review of an earlier study period identified 11 patients with IMIs of varying species over the preceding 29 months without evidence of clustering. There were 9 total IMIs in the later study period (12 month post-initial cluster) with 5 additional cases in the summer months, again suggesting seasonal clustering. Extensive environmental sampling did not reveal a source of mold. Using local climatological data abstracted from National Centers for Environmental Information the clusters appeared to be associated with high temperatures and low precipitation. Conclusions Sinopulmonary Mucorales clusters at our center had a seasonal variation which appeared to be related to temperature and precipitation. Given the significant mortality associated with IMIs, local climatic conditions may need to be considered when considering center specific fungal prevention and prophylaxis strategies for high-risk patients.

Metronidazole- and carbapenem-resistant Bacteroides fragilis are rare in the United States. We isolated a multidrug-resistant anaerobe from the bloodstream and intraabdominal abscesses of a patient who had traveled to India. Whole-genome sequencing identified the organism as a novel Bacteroides genomospecies. Physicians should be aware of the possibility for concomitant carbapenem- and metronidazole-resistant Bacteroides infections.

Invoking a dialogue between two scholars, authors Jane Hoogestraat and Hillery Glasby discuss the exigence for, construction of, and differentiation between LGBT and queer ethos. Drawing from Larry Kramer’s The Normal Heart and the construction of a gay identity, the text explores connections between queer theory, LGBT(Q) ethos, and queer futurity, ultimately arguing for a more nuanced and critical understanding of the undecidability and performativity of LGBT and queer ethos. In framing LGBT and queer ethos as being at the same time a self and socially constructed and mediated—legitimate and illegitimate—ethos can be understood not only as a site for rhetorical agency, but also as an orientation and a form of activism. Finally, the text offers a case study of Adrienne Rich’s “Yom Kippur,” which is a poem that offers a queer (and) Jewish perspective on identity—from an individual and community level—exhibiting both an LGBT and queer ethos.

Classifying individual bacterial species comprising complex, polymicrobial patient specimens remains a challenge for culture-based and molecular microbiology techniques in common clinical use. We therefore adapted practices from metagenomics research to rapidly catalog the bacterial composition of clinical specimens directly from patients, without need for prior culture. We have combined a semiconductor deep sequencing protocol that produces reads spanning 16S ribosomal RNA gene variable regions 1 and 2 (∼360 bp) with a de-noising pipeline that significantly improves the fraction of error-free sequences. The resulting sequences can be used to perform accurate genus- or species-level taxonomic assignment. We explore the microbial composition of challenging, heterogeneous clinical specimens by deep sequencing, culture-based strain typing, and Sanger sequencing of bulk PCR product. We report that deep sequencing can catalog bacterial species in mixed specimens from which usable data cannot be obtained by conventional clinical methods. Deep sequencing a collection of sputum samples from cystic fibrosis (CF) patients reveals well-described CF pathogens in specimens where they were not detected by standard clinical culture methods, especially for low-prevalence or fastidious bacteria. We also found that sputa submitted for CF diagnostic workup can be divided into a limited number of groups based on the phylogenetic composition of the airway microbiota, suggesting that metagenomic profiling may prove useful as a clinical diagnostic strategy in the future. The described method is sufficiently rapid (theoretically compatible with same-day turnaround times) and inexpensive for routine clinical use.