ABSTRACT
BACKGROUND: Current dialysis devices are not able to react when unexpected changes occur during dialysis treatment or to learn about experience for therapy personalization. Furthermore, great efforts are dedicated to develop miniaturized artificial kidneys to achieve a continuous and personalized dialysis therapy, in order to improve the patient's quality of life. These innovative dialysis devices will require a real-time monitoring of equipment alarms, dialysis parameters, and patient-related data to ensure patient safety and to allow instantaneous changes of the dialysis prescription for the assessment of their adequacy. The analysis and evaluation of the resulting large-scale data sets enters the realm of "big data" and will require real-time predictive models. These may come from the fields of machine learning and computational intelligence, both included in artificial intelligence, a branch of engineering involved with the creation of devices that simulate intelligent behavior. The incorporation of artificial intelligence should provide a fully new approach to data analysis, enabling future advances in personalized dialysis therapies. With the purpose to learn about the present and potential future impact on medicine from experts in artificial intelligence and machine learning, a scientific meeting was organized in the Hospital Universitari Bellvitge (L'Hospitalet, Barcelona). As an outcome of that meeting, the aim of this review is to investigate artificial intel ligence experiences on dialysis, with a focus on potential barriers, challenges, and prospects for future applications of these technologies. SUMMARY AND KEY MESSAGES: Artificial intelligence research on dialysis is still in an early stage, and the main challenge relies on interpretability and/or comprehensibility of data models when applied to decision making. Artificial neural networks and medical decision support systems have been used to make predictions about anemia, total body water, or intradialysis hypotension and are promising approaches for the prescription and monitoring of hemodialysis therapy. Current dialysis machines are continuously improving due to innovative technological developments, but patient safety is still a key challenge. Real-time monitoring systems, coupled with automatic instantaneous biofeedback, will allow changing dialysis prescriptions continuously. The integration of vital sign monitoring with dialysis parameters will produce large data sets that will require the use of data analysis techniques, possibly from the area of machine learning, in order to make better decisions and increase the safety of patients.
ABSTRACT
BACKGROUND: This study intended to investigate the degree of compliance with hand hygiene and use of gloves by health workers in haemodialysis (HD) units, and the factors that influenced adherence to hand hygiene protocols. METHODS: During the month of November 2003, one person observed the health care staff in each of nine different dialysis units, during 495 randomly distributed 30 min observation periods that covered all steps of a haemodialysis session (connection, dialysis and disconnection). The observers noted the number of potential opportunities to implement standard precautions and the number of occasions on which the precautions were actually taken. Adherence to standard precautions was evaluated, analysing the influence of the following variables: the patient-to-nurse ratio, the number of HD shifts scheduled per day, acute HD units vs chronic, whether or not infectious patients were isolated and in-house vs contract cleaning personnel. RESULTS: There were a total of 977 opportunities to wear gloves for, and to wash the hands following, a patient-oriented activity, and 1902 opportunities to wash hands before such an activity. Gloves were actually used on 92.9% of these occasions. Hands were washed only 35.6% of the time after patient contact, and only 13.8% of the time before patient contact. Poor adherence to hand washing was associated with the number of shifts per HD unit per day and with higher patient-to-nurse ratios. In the acute HD units, there was greater adherence to standard precautions than in the chronic units, although there too it was substandard. The personnel's knowledge of patients' infectious status did not modify their adherence to hand hygiene practices. A higher patient-to-nurse ratio independently influenced hand washing both before and after patient contact. CONCLUSIONS: The overall adherence of health care workers to recommended hand washing practices is low. Whether or not programmes promoting higher hand hygiene standards and the potential use of alcohol-based hand cleansers will improve hand hygiene practices in HD units requires further investigation.
