There is no clear evidence as to whether the co-location of primary care professionals in the same facility positively influences their way of working and the quality of healthcare as perceived by patients. The aim of this study was to identify the relationships between general practitioner (GP) co-location with other GPs and/or other professionals and the GP outcomes and patients’ experiences. We wanted to test whether GP co-location is related to a broader range of services provided, the use of clinical governance tools and inter-professional collaboration, and whether the patients of co-located GPs perceive a better quality of care in terms of accessibility, comprehensiveness and continuity of care with their GPs. The source of data was the QUALICOPC study (Quality and Costs of Primary Care in Europe), which involved surveys of GPs and their patients in 34 countries, mostly in Europe. In order to study the relationships between GP co-location and both GPs’ outcomes and patients’ experience, multilevel linear regression analysis was carried out. The GP questionnaire was filled in by 7183 GPs and the patient experience questionnaire by 61,931 patients. Being co-located with at least one other professional is the most common situation of the GPs involved in the study. Compared with single-handed GP practices, GP co-location are positively associated with the GP outcomes. Considering the patients’ perspective, comprehensiveness of care has the strongest negative relationship of GP co-location of all the dimensions of patient experiences analysed. The paper highlights that GP mono- and multi-disciplinary co-location is related to positive outcomes at a GP level, such as a broader provision of technical procedures, increased collaboration among different providers and wider coordination with secondary care. However, GP co-location, particularly in a multidisciplinary setting, is related to less positive patient experiences, especially in countries with health systems characterised by a weak primary care structure.

Background: Many research projects in general practice face problems when recruiting patients, often resulting in low recruitment rates and an unknown selection bias, thus limiting their value for health services research. The objective of the study is to evaluate the recruitment performance of the practice staff in 25 participating general practices when using a clinical trial alert (CTA) tool. Methods: The CTA tool was developed for an osteoporosis survey of patients at risk for osteoporosis and fractures. The tool used data from electronic patient records (EPRs) to automatically identify the population at risk (net sample), to apply eligibility criteria, to contact eligible patients, to enrol and survey at least 200 patients per practice. The effects of the CTA intervention were evaluated on the basis of recruitment efficiency and selection bias. Results: The CTA tool identified a net sample of 16,067 patients (range 162 to 1,316 per practice), of which the practice staff reviewed 5,161 (32%) cases for eligibility. They excluded 3,248 patients and contacted 1,913 patients. Of these, 1,526 patients (range 4 to 202 per practice) were successfully enrolled and surveyed. This made up 9% of the net sample and 80% of the patients contacted. Men and older patients were underrepresented in the study population. Conclusion: Although the recruitment target was unreachable for most practices, the practice staff in the participating practices used the CTA tool successfully to identify, document and survey a large patient sample. The tool also helped the research team to precisely determine a slight selection bias.

Background: Postoperative delirium (POD) is a severe complication following cardiac surgery and is associated with increased morbidity and mortality. The impact of intra- and early postoperative factors on the occurrence of POD following cardiac surgery remains controversial. To close this gap, we investigated intra- and early postoperative factors and their predictive values for POD. Methods: We performed a prospective observational study that aimed to FIND DElirium RIsk factors (FINDERI) for patients undergoing elective cardiac surgery. POD was assessed using the Confusion Assessment Method algorithm. Intra- and early postoperative factors were extracted from electronic medical records and reviewed by cardiac surgeons. To identify potential predictors of POD, we used univariate and multivariate logistic regression along with machine learning (ML) with ten-fold cross-validation. Results: In our study cohort of 490 patients, 106 screened positive for POD (21.6%). In the multivariate analysis, we found a positive association between POD occurrence and age (P < 0.001), duration of surgery (P = 0.027), combined (versus isolated) surgical procedures (P = 0.024), opening of the cardiac chambers (P = 0.046), and ventilation time (P < 0.001). The ML-based decision tree identified a two level-algorithm including ventilation time and aortic cross-clamping time, with an AUC of 0.7116 (P = 0.0002) in the validation set. In the ML-based LASSO regression analysis, we identified ventilation time, administration of erythrocyte concentrates (EC), and usage of cardiopulmonary bypass (CPB) as predictors of POD, with an AUC of 0.7407 (P < 0.0001) in the validation set. Conclusion: The results of this analysis highlight the associations between ventilation time, aortic cross-clamping time, administration of EC, and usage of CPB and POD. Additionally, they suggest that the optimization of surgical protocols has the potential to reduce POD risk in individuals undergoing cardiac surgery.

Background: With population-wide vaccination availability, the global COVID-19 pandemic entered a new phase. Despite vaccination status, some people who were infected with SARS-CoV-2 experience long-term symptoms. Objective: In this study, we aim to characterize the long-term effects of SARS-CoV-2 infection and the pandemic. We also aim to build symptom clusters and determine risk factors for developing long COVID symptoms. Furthermore, we assess social participation and health-related quality of life in patients with long COVID and in the general population during a global pandemic. Methods: With a mixed-methods, web-based approach, we aim to recruit 2000 people in Germany who are older than 18 years and can provide informed consent. In the quantitative arm of the study, we identify symptoms of and predictive factors for long COVID manifestations with cluster analysis and assess social participation during the pandemic with standardized questionnaires. The qualitative arm of the study uses individual interviews and focus group discussions to better understand the illness experience of persons who experience long COVID. Results: Recruitment started in September 2021. Up until July 2022, we recruited approximately 4500 participants via our web-based database. Conclusions: This study aims to build an innovative, patient-centered, web-based research platform appropriate for the pandemic by minimizing physical contact between study personnel and participants. All study activities are designed to better understand the long COVID syndrome, social participation during the pandemic, and the illness experiences of persons affected by long COVID. Trial Registration: German Clinical Trial Registry DRKS00026007; https://tinyurl.com/yh282fkt International Registered Report Identifier (IRRID): DERR1-10.2196/38718

Background: Medical data from family doctors are of great importance to health care researchers but seem to be locked in German practices and, thus, are underused in research. The RADAR project (Routine Anonymized Data for Advanced Health Services Research) aims at designing, implementing and piloting a generic research architecture, technical software solutions as well as procedures and workflows to unlock data from family doctor’s practices. A long-term medical data repository for research taking legal requirements into account is established. Thereby, RADAR helps closing the gap between the European countries and to contribute data from primary care in Germany. Methods: The RADAR project comprises three phases: (1) analysis phase, (2) design phase, and (3) pilot. First, interdisciplinary workshops were held to list prerequisites and requirements. Second, an architecture diagram with building blocks and functions, and an ordered list of process steps (workflow) for data capture and storage were designed. Third, technical components and workflows were piloted. The pilot was extended by a data integration workflow using patient-reported outcomes (paper-based questionnaires). Results: The analysis phase resulted in listing 17 essential prerequisites and guiding requirements for data management compliant with the General Data Protection Regulation (GDPR). Based on this list existing approaches to fulfil the RADAR tasks were evaluated—for example, re-using BDT interface for data exchange and Trusted Third Party-approach for consent management and record linkage. Consented data sets of 100 patients were successfully exported, separated into person-identifying and medical data, pseudonymised and saved. Record linkage and data integration workflows for patient-reported outcomes in the RADAR research database were successfully piloted for 63 responders. Conclusion: The RADAR project successfully developed a generic architecture together with a technical framework of tools, interfaces, and workflows for a complete infrastructure for practicable and secure processing of patient data from family doctors. All technical components and workflows can be reused for further research projects. Additionally, a Trusted Third Party-approach can be used as core element to implement data privacy protection in such heterogeneous family doctor’s settings. Optimisations identified comprise a fully-electronic consent recording using tablet computers, which is part of the project’s extension phase.

Background: Many patients receive Z-drugs for hospital-associated sleep problems, in spite of well-known risks. The aim of this study was to learn more about the attractiveness of Z-drugs, seen from the doctors’ and nurses’ perspective. Methods: Using a standardized questionnaire, doctors (63/116) and nurses (73/243) in a German general hospital were surveyed about the risks and benefits of Z-drugs, compared with benzodiazepines. Result: “Reduced time to get to sleep” was perceived by doctors (51%) and nurses (53%) to be a strong benefit of Z-drugs; “confusion” and “falls” were perceived by ca. 10% of doctors and ca. 15% of nurses to be a frequent problem. Compared with benzodiazepines, respondents more often answered “unable to judge” for Z-drugs; e.g. for doctors, 18% (benzodiazepines) vs. 45% (Z-drugs) were unable to judge “improved daytime functioning” and 12% (benzodiazepines) vs. 37% (Z-drugs) were unable to judge “falls.” Conclusion: Z-drugs seem to be attractive because experiential knowledge overemphasizes their benefits and fails to take risks such as drug-related falls and confusion into account. Difficulties to judge a drug’s risk-benefit ratio do not prevent doctors and nurses from using them. Interventions for reducing Z-drug usage should incorporate local quality assurance data about relevant patient risks.