As the world's largest healthcare provider, the NHS has a huge amount of health data that scientists and researchers need to have access to in order to facilitate the discovery of a cure or disease prevention. Scientists and researchers have not always had access to the data of NHS patients as much as they would like. But the threat of COVID-19 prompted the NHS to make the huge data repository available to scientists and researchers as soon as possible to help them find answers to questions such as why some people are more likely to die from the virus. and whether the medications a patient is taking may have side effects, whether they have serious symptoms or not. OpenSafely, a new one platform open source that includes detailed data, has made them archives The health of tens of millions of people in the UK who have been to the NHS, available to scientists and researchers in their fight against COVID-19. Through the OpenSafely platform, scientists and researchers will be able to analyze the electronic health records of millions of people in an effort to understand COVID-19. The files contain the complete ones data 24 million people, and more will be added soon. The software analysis is open to security testing, scientific review and reuse. OpenSafely was created in just five weeks by the University of Oxford, the London School of Hygiene and Tropical Medicine, and medical companies. NHS England is responsible for data processing. While the idea of creating a data analytics platform such as OpenSafely existed before COVID, the threat of the virus and the understanding of the value of the data held by the NHS, prompted organizations to implement the project.
Issues concerning the safety and secrecy have in the past hampered projects that required NHS data to be used for research, due to the fact that it is extremely sensitive data. OpenSafely uses a series of step-by-step tables, each of which provides little information about individuals and researchers do not have access to execute a database query for a patient's data at the event level. To keep NHS patients' data as secure as possible, OpenSafely has been transformed into a model based on trust in one based on evidence.
Scientists and researchers will be able to analyze OpenSafely data only in the data center that includes the company's electronic health records. Instead of the usual data set model that researchers work locally (and thus expose it to all local security risks), all analysis is done where the files are located and only research tables can be extracted by researchers. OpenSafely is also available with open source license, with all code published in GitHub in parallel with the definition of the study for the first study performed in the data.
Projects like OpenSafely could ultimately help the scientific and research community take a more open, less proprietary approach to data and analysis. The way the team has created OpenSafely aims to encourage scientists and researchers to share what they are doing. When users create a password list - a list of people with a specific status, for example - or a detailed script, everything is shared on GitHub.
It did not take long for OpenSafely to bear its first fruits. According to a study of 17 million archives published last month, people of "black and Asian background" were at greater risk of dying from COVID-19. Also identify key risk factors for death from COVID-19, including being a man, the elderly, or having severe asthma or poorly controlled diabetes. OpenSafely was able to move from structure to first research in a matter of weeks, using a team of programmers-epidemiologists. OpenSafely data is expected to be used to help answer questions about how effective the proposed treatments are for COVID-19, the risk factors for developing severe symptoms, how the virus can spread, and how effective is the intervention in public health and with unexpected health effects caused by the virus, such as delayed referrals for cancer or vaccinations. There are also plans for a second phase of the project, which will not be limited to COVID-19.