Evaluation of Standard Precautions Compliance Instruments: A Systematic Review Using COSMIN Methodology.

BACKGROUND: Standard precautions (SPs) are first-line strategies with a dual goal: to protect health care workers from occupational contamination while providing care to infected patients and to prevent/reduce health care-associated infections (HAIs). This study aimed at (1) identifying the instruments currently available for measuring healthcare professionals' compliance with standard precautions; (2) evaluating their measurement properties; and (3) providing sound evidence for instrument selection for use by researchers, teachers, staff trainers, and clinical tutors. METHODS: We carried out a systematic review to examine the psychometric properties of standard precautions self-assessment instruments in conformity with the COSMIN guidelines. The search was conducted on the databases PubMed, CINAHL, and APA PsycInfo. RESULTS: Thirteen instruments were identified. These were classified into four categories of tools assessing: compliance with universal precautions, adherence to standard precautions, compliance with hand hygiene, and adherence to transmission-based guidelines and precautions. The psychometric properties of instruments and methodological approaches of the included studies were often not satisfactory. Only four instruments were classified as high-quality measurements. CONCLUSIONS: The available instruments that measure healthcare professionals' compliance with standard precautions are of low-moderate quality. It is necessary that future research completes the validation processes undertaken for long-established and newly developed instruments, using higher-quality methods and estimating all psychometric properties.

Appraisal and Evaluation of the Learning Environment Instruments of the Student Nurse: A Systematic Review Using COSMIN Methodology.

BACKGROUND: Nursing education consists of theory and practice, and student nurses' perception of the learning environment, both educational and clinical, is one of the elements that determines the success or failure of their university study path. This study aimed to identify the currently available tools for measuring the clinical and educational learning environments of student nurses and to evaluate their measurement properties in order to provide solid evidence for researchers, educators, and clinical tutors to use in the selection of tools. METHODS: We conducted a systematic review to evaluate the psychometric properties of self-reported learning environment tools in accordance with the Consensus-based Standards for the Selection of Health Measurement Instruments (COSMIN) Guidelines of 2018. The research was conducted on the following databases: PubMed, CINAHL, APA PsycInfo, and ERIC. RESULTS: In the literature, 14 instruments were found that evaluate both the traditional and simulated clinical learning environments and the educational learning environments of student nurses. These tools can be ideally divided into first-generation tools developed from different learning theories and second-generation tools developed by mixing, reviewing, and integrating different already-validated tools. CONCLUSION: Not all the relevant psychometric properties of the instruments were evaluated, and the methodological approaches used were often doubtful or inadequate, thus threatening the instruments' external validity. Further research is needed to complete the validation processes undertaken for both new and already developed instruments, using higher-quality methods and evaluating all psychometric properties.

A novel, integrated approach for understanding and investigating Healthcare Associated Infections: A risk factors constellation analysis.

INTRODUCTION: Healthcare-associated infections (HAIs) and antimicrobial resistance (AMR) are major public health threats in upper- and lower-middle-income countries. Electronic health records (EHRs) are an invaluable source of data for achieving different goals, including the early detection of HAIs and AMR clusters within healthcare settings; evaluation of attributable incidence, mortality, and disability-adjusted life years (DALYs); and implementation of governance policies. In Italy, the burden of HAIs is estimated to be 702.53 DALYs per 100,000 population, which has the same magnitude as the burden of ischemic heart disease. However, data in EHRs are usually not homogeneous, not properly linked and engineered, or not easily compared with other data. Moreover, without a proper epidemiological approach, the relevant information may not be detected. In this retrospective observational study, we established and engineered a new management system on the basis of the integration of microbiology laboratory data from the university hospital "Policlinico Tor Vergata" (PTV) in Italy with hospital discharge forms (HDFs) and clinical record data. All data are currently available in separate EHRs. We propose an original approach for monitoring alert microorganisms and for consequently estimating HAIs for the entire period of 2018. METHODS: Data extraction was performed by analyzing HDFs in the databases of the Hospital Information System. Data were compiled using the AREAS-ADT information system and ICD-9-CM codes. Quantitative and qualitative variables and diagnostic-related groups were produced by processing the resulting integrated databases. The results of research requests for HAI microorganisms and AMR profiles sent by the departments of PTV from 01/01/2018 to 31/12/2018 and the date of collection were extracted from the database of the Complex Operational Unit of Microbiology and then integrated. RESULTS: We were able to provide a complete and richly detailed profile of the estimated HAIs and to correlate them with the information contained in the HDFs and those available from the microbiology laboratory. We also identified the infection profile of the investigated hospital and estimated the distribution of coinfections by two or more microorganisms of concern. Our data were consistent with those in the literature, particularly the increase in mortality, length of stay, and risk of death associated with infections with Staphylococcus spp, Pseudomonas aeruginosa, Klebsiella pneumoniae, Clostridioides difficile, Candida spp., and Acinetobacter baumannii. Even though less than 10% of the detected HAIs showed at least one infection caused by an antimicrobial resistant bacterium, the contribution of AMR to the overall risk of increased mortality was extremely high. CONCLUSIONS: The increasing availability of health data stored in EHRs represents a unique opportunity for the accurate identification of any factor that contributes to the diffusion of HAIs and AMR and for the prompt implementation of effective corrective measures. That said, artificial intelligence might be the future of health data analysis because it may allow for the early identification of patients who are more exposed to the risk of HAIs and for a more efficient monitoring of HAI sources and outbreaks. However, challenges concerning codification, integration, and standardization of health data recording and analysis still need to be addressed.