scholarly journals The Impact Of Patient Preference Studies In The German Healthcare System

2017 ◽  
Vol 20 (9) ◽  
pp. A690 ◽  
Author(s):  
K Krinke ◽  
K Borchert ◽  
S Braun ◽  
T Mittendorf
Keyword(s):  
Healthcare System ◽  
Patient Preference ◽  
German Healthcare System ◽  
The Impact

The Role of Complementary and Alternative Medicine in the Healthcare System: A German Paradox

2017 ◽  
Vol 24 (5) ◽  
pp. 290-294 ◽  
Author(s):  
Eva Jansen
Keyword(s):  
Alternative Medicine ◽  
Complementary And Alternative Medicine ◽  
Healthcare System ◽  
Conventional Medicine ◽  
Future Research ◽  
Scientific Validity ◽  
Economic Interests ◽  
German Healthcare System ◽  
The Impact ◽  
Complementary And Alternative

Background: This paper examines a paradox in the German healthcare system: Complementary and alternative medicine (CAM) practices are a major element of medical encounters in Germany. Patients seek them, physicians provide them, and public health insurances pay for them in part. Despite all this, CAM practices are not acknowledged as scientifically valid. Material and Method: I will examine this situation in detail based on 2 ethnographic studies. The first study refers to an attempt to introduce homeopathic education at a German university. The second one is a study in the context of cancer and CAM. These cases are perfect examples of the current power struggles that are impeding the expansion of CAM practices in Germany. Results: The results should be seen from the theoretical angle of the study of science. The conventional method of proving scientific validity is in contradiction to those frameworks in which the impact of CAM might be demonstrated. There are economic interests invested in preventing the integration of CAM into existing scientific structures. However, the current hybridization of CAM with conventional medicine might be a step towards an institutionalized heterogenization of medical practices in Germany. Conclusions: A broader understanding of scientific methods within the CAM community could provide a useful frame for future research. I suggest that the CAM community more actively takes part in the discourse with representatives of conventional medicine and come out of the closet.

scholarly journals Impact of the COVID-19 Vaccine on Asymptomatic Infection Among Patients Undergoing Pre-Procedural COVID-19 Molecular Screening

2021 ◽  
Author(s):  
Aaron J Tande ◽  
Benjamin D Pollock ◽  
Nilay D Shah ◽  
Gianrico Farrugia ◽  
Abinash Virk ◽  
...  
Keyword(s):  
United States ◽  
Relative Risk ◽  
Healthcare System ◽  
The United States ◽  
Asymptomatic Infection ◽  
Screening Tests ◽  
Molecular Screening ◽  
Molecular Tests ◽  
Asymptomatic Individuals ◽  
The Impact

Abstract Background Several vaccines are now clinically available under emergency use authorization in the United States and have demonstrated efficacy against symptomatic COVID-19. The impact of vaccines on asymptomatic SARS-CoV-2 infection is largely unknown. Methods We conducted a retrospective cohort study of consecutive, asymptomatic adult patients (n = 39,156) within a large United States healthcare system who underwent 48,333 pre-procedural SARS-CoV-2 molecular screening tests between December 17, 2020 and February 8, 2021. The primary exposure of interest was vaccination with at least one dose of an mRNA COVID-19 vaccine. The primary outcome was relative risk of a positive SARS-CoV-2 molecular test among those asymptomatic persons who had received at least one dose of vaccine, as compared to persons who had not received vaccine during the same time period. Relative risk was adjusted for age, sex, race/ethnicity, patient residence relative to the hospital (local vs. non-local), healthcare system regions, and repeated screenings among patients using mixed effects log-binomial regression. Results Positive molecular tests in asymptomatic individuals were reported in 42 (1.4%) of 3,006 tests performed on vaccinated patients and 1,436 (3.2%) of 45,327 tests performed on unvaccinated patients (RR=0.44 95% CI: 0.33-0.60; p<.0001). Compared to unvaccinated patients, the risk of asymptomatic SARS-CoV-2 infection was lower among those >10 days after 1 st dose (RR=0.21; 95% CI: 0.12-0.37; p<.0001) and >0 days after 2 nd dose (RR=0.20; 95% CI: 0.09-0.44; p<.0001) in the adjusted analysis. Conclusions COVID-19 vaccination with an mRNA-based vaccine showed a significant association with a reduced risk of asymptomatic SARS-CoV-2 infection as measured during pre-procedural molecular screening. The results of this study demonstrate the impact of the vaccines on reduction in asymptomatic infections supplementing the randomized trial results on symptomatic patients.

scholarly journals Implementation of an integrated care programme to avoid fragility fractures of the hip in older adults in 18 Bavarian hospitals – study protocol for the cluster-randomised controlled fracture liaison service FLS-CARE

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Isabel Geiger ◽  
◽  
Christian Kammerlander ◽  
Christine Höfer ◽  
Ruth Volland ◽  
...  
Keyword(s):  
Hip Fracture ◽  
Healthcare System ◽  
Fragility Fractures ◽  
Term Care ◽  
Care Programme ◽  
Secondary Fracture ◽  
Cluster Randomised ◽  
German Healthcare System ◽  
Randomised Controlled ◽  
Fracture Liaison

Abstract Background The economic and public health burden of fragility fractures of the hip in Germany is high. The likelihood of requiring long-term care and the risk of suffering from a secondary fracture increases substantially after sustaining an initial fracture. Neither appropriate confirmatory diagnostics of the suspected underlying osteoporosis nor therapy, which are well-recognised approaches to reduce the burden of fragility fractures, are routinely initiated in the German healthcare system. Therefore, the aim of the study FLS-CARE is to evaluate whether a coordinated care programme can close the prevention gap for patients suffering from a fragility hip fracture through the implementation of systematic diagnostics, a falls prevention programme and guideline-adherent interventions based on the Fracture Liaison Services model. Methods The study is set up as a non-blinded, cluster-randomised, controlled trial with unequal cluster sizes. Allocation to intervention group (FLS-CARE) and control group (usual care) follows an allocation ratio of 1:1 using trauma centres as the unit of allocation. Sample size calculations resulted in a total of 1216 patients (608 patients per group distributed over 9 clusters) needed for the analysis. After informed consent, all participants are assessed directly at discharge, after 3 months, 12 months and 24 months. The primary outcome measure of the study is the secondary fracture rate 24 months after initial hip fracture. Secondary outcomes include differences in the number of falls, mortality, quality-adjusted life years, activities of daily living and mobility. Discussion This study is the first to assess the effectiveness and cost-effectiveness/utility of FLS implementation in Germany. Findings of the process evaluation will also shed light on potential barriers to the implementation of FLS in the context of the German healthcare system. Challenges for the study include the successful integration of the outpatient sector as well as the future course of the coronavirus pandemic in 2020 and its influence on the intervention. Trial registration German Clinical Trial Register (DRKS) 00022237, prospectively registered 2020-07-09

Big data and artificial intelligence for health system sustainability: The case of Veneto Region

2021 ◽  
pp. 31-52
Author(s):  
Grazia Dicuonzo ◽  
Francesca Donofrio ◽  
Antonio Fusco ◽  
Vittorio Dell’Atti
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Healthcare System ◽  
Care Delivery ◽  
Research Question ◽  
Health Agency ◽  
Time Frame ◽  
Structured Interviews ◽  
Case Study Methodology ◽  
The Impact

This paper investigates the digitalization challenges facing the Italian healthcare system. The aim of the paper is to support healthcare organizations as they take advantage of the potential of big data and artificial intelligence (AI) to promote sustainable healthcare systems. Both the development of innovative processes in the management of health care activities and the introduction of healthcare forecasting systems are valuable resources for clinical and care activities and enable a more efficient use of inputs in essential-level care delivery. By examining an innovative project developed by the Regional Social Health Agency (ARSS) of Veneto, this study analyses the impact of big data and AI on the sustainability of a healthcare system. In order to answer the research question, we used a case study methodology. We conducted semi-structured interviews with key members of the organizational group involved in the case. The results show that the implementation of AI algorithms based on big data in healthcare both improves the interpretation and processing of data, and reduces the time frame necessary for clinical processes, having a positive effect on sustainability.

scholarly journals The German healthcare system

2010 ◽  
Vol 1 (4) ◽  
pp. 535-547 ◽  
Author(s):  
Andrea Döring ◽  
Friedemann Paul
Keyword(s):  
Healthcare System ◽  
German Healthcare System

scholarly journals Opportunities and Challenges for Interpreting Rare Variation in Clinically Important Genes

2020 ◽  
Author(s):  
Gregory McInnes ◽  
Andrew G. Sharo ◽  
Megan L. Koleske ◽  
Julie E. H. Brown ◽  
Matthew Norstad ◽  
...  
Keyword(s):  
Healthcare System ◽  
Protein Function ◽  
Rare Variants ◽  
Clinical Importance ◽  
Computational Prediction ◽  
Ethical Challenges ◽  
Inborn Errors ◽  
Rare Variation ◽  
Learning Healthcare System ◽  
The Impact

Genome sequencing is enabling precision medicine—tailoring treatment to the unique constellation of variants in an individual’s genome. The impact of recurrent pathogenic variants is often understood, leaving a long tail of rare genetic variants that are uncharacterized. The problem of uncharacterized rare variation is especially acute when it occurs in genes of known clinical importance with functionally consequent frequent variants and associated mechanisms. Variants of unknown significance (VUS) in these genes are discovered at a rate that outpaces current ability to classify them using databases of previous cases, experimental evaluation, and computational predictors. Clinicians are thus left without guidance about the significance of variants that may have actionable consequences. Computational prediction of the impact of rare genetic variation is increasingly becoming an important capability. In this paper, we review the technical and ethical challenges of interpreting the function of rare variants in two settings: inborn errors of metabolism in newborns, and pharmacogenomics. We propose a framework for a genomic learning healthcare system with an initial focus on early-onset treatable disease in newborns and actionable pharmacogenomics. We argue that (1) a genomic learning healthcare system must allow for continuous collection and assessment of rare variants, (2) emerging machine learning methods will enable algorithms to predict the clinical impact of rare variants on protein function, and (3) ethical considerations must inform the construction and deployment of all rare-variation triage strategies, particularly with respect to health disparities arising from unbalanced ancestry representation.

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