Quality Health Care

SMART DIALYSIS - Scaling Machine Learning and Artificial Intelligence AlgoRithms to OpTimize the Performance and Delivery of Acute DIALYSIS. Hypothesis: Can the investigators develop and implement Machine Learning and Artificial Intelligence Algorithms into Clinical Information Systems to Optimize the Prescription, Delivery, and Performance of Acute Dialysis? Objective(s): 1. Identify variables surrounding identified Key Performance Indicators that may be used by Machine Learning and Artificial Intelligence algorithms to optimize the prescription and performance of acute dialysis. 2. Develop Machine Learning and Artificial Intelligence algorithms to help guide the prescription and delivery of acute dialysis in the development of Clinical Decision Support tools and Best Practice Advisories and create a ML/AI Augmented SMART DIALYSIS Digital Dashboard. 3. Implement and evaluate the performance of the developed Machine Learning and Artificial Intelligence algorithms on patient-centered and health economic outcomes. 4. Validate and benchmark the performance of the evaluated Machine Learning and Artificial Intelligence algorithms across multiple jurisdictions.

The study examines whether midwives and doctors are familiar with quality indicators in clinical obstetrics and whether these are used as a tool for continuous improvement process.

The goal of this study is to evaluate the quality and safety of labor analgesia by comparing the use of Dural puncture epidural technique to Standard epidural technique while maintaining labor analgesia by means of continuous epidural infusion in parturient. The main questions it aims to answer are * Effectiveness and quality of labor analgesia * Frequency of catheter adjustments * Need for catheter replacements * Incidence of failed regional anesthesia requiring conversion to general anesthesia