Crit Care Med 2021 Feb 24. Epub 2021 Feb 24.
Department of Intensive Care Medicine, Amsterdam Medical Data Science (AMDS), Amsterdam Cardiovascular Sciences (ACS), Amsterdam Infection and Immunity Institute (AI&II), Amsterdam UMC, Vrije Universiteit, Universiteit van Amsterdam, Amsterdam, The Netherlands. Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands. Department of Intensive Care Medicine, Erasmus MC, Rotterdam, The Netherlands. Division of Trauma, Surgical Critical Care and Emergency Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA. Department of Emergency Medicine, Durham VA Medical Center, Durham, NC. Executive Committee, Society of Critical Care Medicine, Mount Prospect, IL. Department of Intensive Care Medicine, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands. Executive Committee, European Society of Intensive Care Medicine, Brussels, Belgium. Department of Anaesthesia and Intensive Care, Humanitas Research Hospital, Humanitas University, Milan, Italy. Department of Medicine, University of Wisconsin, Madison, WI. Department of Critical Care Medicine, CRISMA Laboratory, University of Pittsburgh, Pittsburgh, PA. University of Cambridge, Cambridge, United Kingdom. Alan Turing Institute, London, United Kingdom. Division of Anaesthesia, University of Cambridge, Cambridge, United Kingdom. Data Science Section, European Society of Intensive Care Medicine, Brussels, Belgium.
Objectives: Critical care medicine is a natural environment for machine learning approaches to improve outcomes for critically ill patients as admissions to ICUs generate vast amounts of data. However, technical, legal, ethical, and privacy concerns have so far limited the critical care medicine community from making these data readily available. The Society of Critical Care Medicine and the European Society of Intensive Care Medicine have identified ICU patient data sharing as one of the priorities under their Joint Data Science Collaboration. To encourage ICUs worldwide to share their patient data responsibly, we now describe the development and release of Amsterdam University Medical Centers Database (AmsterdamUMCdb), the first freely available critical care database in full compliance with privacy laws from both the United States and Europe, as an example of the feasibility of sharing complex critical care data.
Setting: University hospital ICU.
Subjects: Data from ICU patients admitted between 2003 and 2016.
Interventions: We used a risk-based deidentification strategy to maintain data utility while preserving privacy. In addition, we implemented contractual and governance processes, and a communication strategy. Patient organizations, supporting hospitals, and experts on ethics and privacy audited these processes and the database.
Measurements And Main Results: AmsterdamUMCdb contains approximately 1 billion clinical data points from 23,106 admissions of 20,109 patients. The privacy audit concluded that reidentification is not reasonably likely, and AmsterdamUMCdb can therefore be considered as anonymous information, both in the context of the U.S. Health Insurance Portability and Accountability Act and the European General Data Protection Regulation. The ethics audit concluded that responsible data sharing imposes minimal burden, whereas the potential benefit is tremendous.
Conclusions: Technical, legal, ethical, and privacy challenges related to responsible data sharing can be addressed using a multidisciplinary approach. A risk-based deidentification strategy, that complies with both U.S. and European privacy regulations, should be the preferred approach to releasing ICU patient data. This supports the shared Society of Critical Care Medicine and European Society of Intensive Care Medicine vision to improve critical care outcomes through scientific inquiry of vast and combined ICU datasets.