patient safety

We all know that working in stressful environments makes health workers more prone to medical errors which can lead to patient harm. The COVID-19 pandemic has revealed the risks health workers are facing including emotional disturbances, healthcare-associated infections, illness and even death. As we mark World Patient Safety Day 2020 , it is crucial to address health worker safety as well. Recently, guidance and checklists have been published to support health workers by identifying key domains to be covered in a pandemic preparedness plan. In the ‘new normal’, we see hospital teams constantly asked to adopt and replicate existing and new safety protocols and to develop dedicated COVID-19 operating theaters, which help contain the spread of disease . The duration of operating room (OR) services should be shortened, staff exposure to the nosocomial spread of COVID-19 decreased, and greater emphasis should be put on reducing medical errors by implementing safety checklists to enhance teamwork, communication and culture of safety. Safer surgery There is robust scientific literature on checklists for safer surgery including in the reduction of hospital-acquired infections – for which COVID-19 is our latest test. But we know that impressive clinical outcomes are only seen when compliance with existing safety protocols is high. This is where we are seeing the synergistic benefits of digital. Many hospital teams have turned traditional paper checklists into digitally synchronized OR workflows, guiding teams step-by-step throughout surgery. Using digital technology coupled with mandatory staff confirmation of each procedural step, surgical teams are now experiencing how real-time digital workflows displayed on dedicated OR screens can improve efficiency by reducing surgery time and risk of healthcare-acquired infection for patients and health workers. Addressing teamwork failures Evidence suggests that digital workflows decrease variability in surgery and enhance compliance to previously aligned protocols. Moreover, the usage of digital...
Deep learning is a form of machine learning with the potential to extract meaningful information from the mountains of data generated by healthcare companies. One of the major obstacles to embracing deep learning in the world of health arises from challenges around data sharing. As we all know, while there is no shortage of patient data, it may be dispersed across several sites. For example, data on a single patient could be found in their doctor's office, on hospital systems, in a clinical trial database, a patient registry, company files and held by an insurance company. Given privacy and data protection issues, working with all this data can be very challenging. However, it is not impossible. This is a problem that I believe can be solved through what is known as federated distributed learning procedure. That's quite a mouthful but let me explain. Federated distributed learning is defined as learning from data, without removing the data from the location where it is stored. The beauty of this, in my opinion, is that it offers a chance to unlock the potential of the data without moving it or jeopardising data protection. Collaborative data mining It also opens to door to great collaboration. Data collaborative innovation is when a group of actors from different data domains work together toward common goals. Preservation of privacy in data mining has emerged as an absolute prerequisite for exchanging confidential information in terms of data analysis, validation, and publishing. Our approach to advancing federated deep learning has been fueled by growth in the number of sources of data, including increased access to published research, electronic health records, the various "omics" fields and even social media. I am building federating deep learning models and algorithms that analyze molecular and medical imaging libraries, as well as patient profiles...