In the United States, ICUs cost the health system US$108 billion per year 21 and account for up to 13% of all hospital costs 22. Intensive care units (ICUs) are specialized hospital departments in which patients with life-threatening illnesses or critical organ failures are treated. Already enabling better manufacturing, safer autonomous vehicles and smarter sports entertainment 15, clinical physical-action support can more reliably translate the rapid flow of biomedical discoveries into error-free healthcare delivery and worldwide human benefits.
Similar to modern driver-assistance systems, this form of ambient intelligence can help clinicians and in-home caregivers to perfect the physical motions that comprise the final steps of modern healthcare. 1) embedded in the environment can form an ambient intelligence that is aware of people’s movements and adapt to their continuing health needs 11, 12, 13, 14. To avoid overwhelming the cognitive capabilities of clinicians, advances in artificial intelligence hold the promise of assisting clinicians, not only with clinical decisions but also with the physical steps of clinical decisions 6.Īdvances in machine learning and low-cost sensors can complement existing clinical decision-support systems by providing a computer-assisted understanding of the physical activities of healthcare. Similar preventable suffering occurs in other countries, as well-motivated clinicians struggle with the rapidly growing complexity of modern healthcare 9, 10.
To gain the full dividends of medical advancements requires-in part-that affordable, human-centred approaches are continuously highlighted to assist clinicians in these metaphorically dark spaces.ĭespite numerous improvement initiatives, such as surgical safety checklists 7, by the National Institutes of Health (NIH), Centres for Disease Control and Prevention (CDC), World Health Organization (WHO) and private organizations, as many as 400,000 people die every year in the United States owing to lapses and defects in clinical decision-making and physical actions 8. Health-critical activities that occur in physical spaces, including hospitals and private homes, remain obscure. By contrast, the translation of better decisions into the physical actions performed by clinicians, patients and families remains largely unassisted 6. Thoughtful use of this technology would enable us to understand the complex interplay between the physical environment and health-critical human behaviours.īoosted by innovations in data science and artificial intelligence 1, 2, decision-support systems are beginning to help clinicians to correct suboptimal and, in some cases, dangerous diagnostic and treatment decisions 3, 4, 5. Similar to other technologies, transformation into clinical applications at scale must overcome challenges such as rigorous clinical validation, appropriate data privacy and model transparency.
In daily living spaces, ambient intelligence could prolong the independence of older individuals and improve the management of individuals with a chronic disease by understanding everyday behaviour. In hospital spaces, early applications could soon enable more efficient clinical workflows and improved patient safety in intensive care units and operating rooms. Here we review how this technology could improve our understanding of the metaphorically dark, unobserved spaces of healthcare. Advances in machine learning and contactless sensors have given rise to ambient intelligence-physical spaces that are sensitive and responsive to the presence of humans.