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1.
Infection ; 2024 Jul 09.
Artículo en Inglés | MEDLINE | ID: mdl-38980541

RESUMEN

PURPOSE: Many curricula promote frontal teaching approaches, potentially decreasing interaction and motivation - also within infection prevention & control and infectious diseases (IPC/ID). We aimed to investigate the implementation of three innovative teaching methods (ITM) within IPC/ID education: game-based learning (GBL), peer-teaching (PT) and misinformation detection (MID). METHODS: Multi-phase study involving third-year medical students was conducted. Phase-1 included a cross-sectional survey, assessing previous ITM-experience and interest to participate in phase-2, where the students were divided into teams. Each team prepared a video covering an IPC/ID-topic with deliberately placed misinformation, which had to be identified and corrected by the opposing team, followed by qualitative evaluation (phase-3). Finally, the MID-concept was incorporated into regular curricula in a non-competitive environment (phase-4) and evaluated within a cohort not involved in phases 1-3. RESULTS: 276 students responded to phase-1. 58% expressed interest in participating in phase-2. Roughly 59% [47-71%] CI-95% of respondents without previous PT-experience stated interest in PT, while the interest in GBL and MID was even higher. 19 students participated in phase-2. All topic MID-scores ranged between 6 and 8/10 points, except for emporiatrics (3/10). Post-hoc analysis revealed a positive student-perception of ITM, particularly GBL. Phase-4 received 103 responses with general positive evaluation. Major agreements existed on the usefulness of critical information evaluation for medical practice (82% [75-91%] CI-95%) and of MID during studies (69% [59-79%] CI-95%). CONCLUSION: our results hint at a relatively high interest in ITM and show MID applicability in regular IPC/ID curricula, which could be of advantage for the learning environment.

2.
Artículo en Alemán | MEDLINE | ID: mdl-38753020

RESUMEN

Healthcare-associated infections (HCAIs) represent an enormous burden for patients, healthcare workers, relatives and society worldwide, including Germany. The central tasks of infection prevention are recording and evaluating infections with the aim of identifying prevention potential and risk factors, taking appropriate measures and finally evaluating them. From an infection prevention perspective, it would be of great value if (i) the recording of infection cases was automated and (ii) if it were possible to identify particularly vulnerable patients and patient groups in advance, who would benefit from specific and/or additional interventions.To achieve this risk-adapted, individualized infection prevention, the RISK PRINCIPE research project develops algorithms and computer-based applications based on standardised, large datasets and incorporates expertise in the field of infection prevention.The project has two objectives: a) to develop and validate a semi-automated surveillance system for hospital-acquired bloodstream infections, prototypically for HCAI, and b) to use comprehensive patient data from different sources to create an individual or group-specific infection risk profile.RISK PRINCIPE is based on bringing together the expertise of medical informatics and infection medicine with a focus on hygiene and draws on information and experience from two consortia (HiGHmed and SMITH) of the German Medical Informatics Initiative (MII), which have been working on use cases in infection medicine for more than five years.


Asunto(s)
Infección Hospitalaria , Humanos , Algoritmos , Infección Hospitalaria/prevención & control , Infección Hospitalaria/epidemiología , Alemania/epidemiología , Control de Infecciones/métodos , Control de Infecciones/normas , Vigilancia de la Población/métodos , Medición de Riesgo/métodos , Factores de Riesgo
3.
Infect Control Hosp Epidemiol ; 45(6): 746-753, 2024 Jun.
Artículo en Inglés | MEDLINE | ID: mdl-38351873

RESUMEN

OBJECTIVE: The number of hospitalized patients with severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) does not differentiate between patients admitted due to coronavirus disease 2019 (COVID-19) (ie, primary cases) and incidental SARS-CoV-2 infection (ie, incidental cases). We developed an adaptable method to distinguish primary cases from incidental cases upon hospital admission. DESIGN: Retrospective cohort study. SETTING: Data were obtained from 3 German tertiary-care hospitals. PATIENTS: The study included patients of all ages who tested positive for SARS-CoV-2 by a standard quantitative reverse-transcription polymerase chain reaction (RT-PCR) assay upon admission between January and June 2022. METHODS: We present 2 distinct models: (1) a point-of-care model that can be used shortly after admission based on a limited range of parameters and (2) a more extended point-of-care model based on parameters that are available within the first 24-48 hours after admission. We used regression and tree-based classification models with internal and external validation. RESULTS: In total, 1,150 patients were included (mean age, 49.5±28.5 years; 46% female; 40% primary cases). Both point-of-care models showed good discrimination with area under the curve (AUC) values of 0.80 and 0.87, respectively. As main predictors, we used admission diagnosis codes (ICD-10-GM), ward of admission, and for the extended model, we included viral load, need for oxygen, leucocyte count, and C-reactive protein. CONCLUSIONS: We propose 2 predictive algorithms based on routine clinical data that differentiate primary COVID-19 from incidental SARS-CoV-2 infection. These algorithms can provide a precise surveillance tool that can contribute to pandemic preparedness. They can easily be modified to be used in future pandemic, epidemic, and endemic situations all over the world.


Asunto(s)
COVID-19 , SARS-CoV-2 , Humanos , COVID-19/diagnóstico , COVID-19/epidemiología , Alemania/epidemiología , Estudios Retrospectivos , Masculino , Femenino , Persona de Mediana Edad , Adulto , Anciano , Hospitalización/estadística & datos numéricos , Hallazgos Incidentales , Anciano de 80 o más Años
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