Patient Safety and Legal Regulations: A Total-Scale Analysis of the Scientific Literature.

OBJECTIVE: The aim of the study was to quantitatively analyze the scientific literature landscape covering legal regulations of patient safety. METHODS: This retrospective bibliometric analysis queried Web of Science database to identify relevant publications. The identified scientific literature was quantitatively evaluated to reveal prevailing study themes, contributing journals, countries, institutions, and authors, as well as citation patterns. RESULTS: The identified 1295 publications had a mean of 13.8 citations per publication and an h-index of 57. Approximately 78.8% of them were published since 2010, with the United States being the top contributor and having the greatest publication growth. A total of 79.2% (n = 1025) of the publications were original articles, and 12.5% (n = 162) were reviews. The top authors (by number of publications published on the topic) were based in the United States and Spain and formed 3 collaboration clusters. The top institutions by number of published articles were mainly based in the United States and United Kingdom, with Harvard University being on top. Internal medicine, surgery, and nursing were the most recurring clinical disciplines. Among 4 distinct approaches to improve patient safety, reforms of the liability system (n = 91) were most frequently covered, followed by new forms of regulation (n = 73), increasing transparency (n = 67), and financial incentives (n = 38). CONCLUSIONS: Approximately 78.8% of the publications on patient safety and its legal implications were published since 2010, and the United States was the top contributor. Approximately 79.2% of the publications were original articles, whereas 12.5% were reviews. Healthcare sciences services was the most recurring journal category, with internal medicine, surgery, and nursing being the most recurring clinical disciplines. Key relevant laws around the globe were identified from the literature set, with some examples highlighted from the United States, Germany, Italy, France, Sweden, Poland, and Indonesia. Our findings highlight the evolving nature and the diversity of legislative regulations at international scale and underline the importance of healthcare workers to be aware of the development and latest advancement in this field and to understand that different requirements are established in different jurisdictions so as to safeguard the necessary standards of patient safety.

Evidence-based Clinical Decision Support Systems for the prediction and detection of three disease states in critical care: A systematic literature review.

Background: Clinical decision support (CDS) systems have emerged as tools providing intelligent decision making to address challenges of critical care. CDS systems can be based on existing guidelines or best practices; and can also utilize machine learning to provide a diagnosis, recommendation, or therapy course. Methods: This research aimed to identify evidence-based study designs and outcome measures to determine the clinical effectiveness of clinical decision support systems in the detection and prediction of hemodynamic instability, respiratory distress, and infection within critical care settings. PubMed, ClinicalTrials.gov and Cochrane Database of Systematic Reviews were systematically searched to identify primary research published in English between 2013 and 2018. Studies conducted in the USA, Canada, UK, Germany and France with more than 10 participants per arm were included. Results: In studies on hemodynamic instability, the prediction and management of septic shock were the most researched topics followed by the early prediction of heart failure. For respiratory distress, the most popular topics were pneumonia detection and prediction followed by pulmonary embolisms. Given the importance of imaging and clinical notes, this area combined Machine Learning with image analysis and natural language processing. In studies on infection, the most researched areas were the detection, prediction, and management of sepsis, surgical site infections, as well as acute kidney injury. Overall, a variety of Machine Learning algorithms were utilized frequently, particularly support vector machines, boosting techniques, random forest classifiers and neural networks. Sensitivity, specificity, and ROC AUC were the most frequently reported performance measures. Conclusion: This review showed an increasing use of Machine Learning for CDS in all three areas. Large datasets are required for training these algorithms; making it imperative to appropriately address, challenges such as class imbalance, correct labelling of data and missing data. Recommendations are formulated for the development and successful adoption of CDS systems.

Autologous blood donation in total knee arthroplasties is not necessary.

BACKGROUND: This study intends to prove the hypothesis that preoperative autologous blood donation in total knee arthroplasties (TKA) is dispensable. PATIENTS AND METHODS: The study comprises a prospective analysis of 81 consecutive TKA without preoperative autologous blood donation (AB-donation). Guidelines for blood retransfusion were used. Surgery, as well as the pre- and postoperative procedures were identical for each patient. In the analysis of the data, the consecutive TKAs were divided into patients who were eligible for preoperative autologous blood donation (group 1, n = 46) and those with relevant risk factors not permitting preoperative autologous blood donation (group 2, n = 35). RESULTS: None of the patients in group 1 needed a blood transfusion. 14 of 35 patients in group 2 needed an allogenic blood transfusion. INTERPRETATION: Total knee arthroplasty can be managed without preoperative AB-donation if it is performed using a tourniquet, if a postoperative collection and direct retransfusion system is used for the wound blood, and if the transfusion algorithm is defined according to compulsory and practical guidelines.