Perioperative intelligence: applications of artificial intelligence in perioperative medicine

Over the past decades, we have made tremendous strides in reducing intraoperative mortality but postoperative morbidity is still high and overall surgical care is costly [1, 2]. Novel technologies like machine learning [3], artificial intelligence [4], and big data [5] may help deliver appropriate and safe perioperative care. But there is lot of hype and it is not clear how. Perioperative intelligence provides a framework for collaborative work to deliver safe, timely and affordable perioperative care using artificial intelligence; it focuses on three key domains—identification of at-risk patients, early detection of complications, and timely and effective treatment. In other words perioperative intelligence is an application of artificial intelligence in perioperative medicine. First, we need to understand big data, machine learning and artificial intelligence. Big data is a concept to describe gathering and analyzing data which is high in volume, velocity, variety, variability, and complexity [6]. The healthcare data from electronic health records, genomics, physiologic monitoring, local, and national databases is accumulating to the level of big data. Whereas machine learning is a method of data analysis not relying on specific instructions but learning independently from the data. Machine learning algorithms [7] of varying complexity are used in the analysis of big data. For example, convolutional neural networks are used for deep learning and principal component analysis for dimensionality reduction. Artificial intelligence is more of a goal and is a field of science dedicated to development of systems or machines which can reproduce human intelligence; in other words, artificial intelligence is dedicated to development of technology which can help in clinical decision-making. The vast majority of current work is focused on identification of at-risk patients. Numerous models—for example POSSUM, NSQIP, or Surgical APGAR—are developed to predict postoperative complications, but the broader applicability is lacking. Even the best predictive models, developed by Google, for length of stay and readmission risk are imperfect and lack generalizability [8]. Any predictive/risk score is dependent on the data it is derived from and the technology used to process the data. The POSSUM, NSQIP, Surgical APGAR scores are limited by both the data and technology. Lee et al. used advanced machine learning technology but are limited by the data it is derived from [9]. Even in the best settings, the healthcare data are not complete. Mostly our information is limited to what is documented in electronic health records. We need high quality continuous data from multiple domains to make better predictions. In other words we need to know everything about the patient’s present state before the future state is described. Advancements in health data processing, biosensors, genomics, and proteomics all will help provide a complete picture of a patient which will enable perioperative intelligence. The healthcare systems, payers, and technology companies need to collaborate and fund advance technologies. Early detection of complications is the next logical area where artificial intelligence can help. Currently, in a reactive management system harmful processes are managed once they have already started or the injury is established. We need to move away from this reactive management system, * Kamal Maheshwari maheshk@ccf.org http://www.or.org