Evaluating the safety and patient impacts of an artificial intelligence command centre in acute hospital care: a mixed-methods protocol

Introduction This paper presents a mixed-methods study protocol that will be used to evaluate a recent implementation of a real-time, centralised hospital command centre in the UK. The command centre represents a complex intervention within a complex adaptive system. It could support better operational decision-making and facilitate identification and mitigation of threats to patient safety. There is, however, limited research on the impact of such complex health information technology on patient safety, reliability and operational efficiency of healthcare delivery and this study aims to help address that gap. Methods and analysis We will conduct a longitudinal mixed-method evaluation that will be informed by public-and-patient involvement and engagement. Interviews and ethnographic observations will inform iterations with quantitative analysis that will sensitise further qualitative work. Quantitative work will take an iterative approach to identify relevant outcome measures from both the literature and pragmatically from datasets of routinely collected electronic health records. Ethics and dissemination This protocol has been approved by the University of Leeds Engineering and Physical Sciences Research Ethics Committee (#MEEC 20-016) and the National Health Service Health Research Authority (IRAS No.: 285933). Our results will be communicated through peer-reviewed publications in international journals and conferences. We will provide ongoing feedback as part of our engagement work with local trust stakeholders.

Automated summary

This paper presents a study protocol for evaluating the implementation of a real-time, centralized hospital command center in the UK. The command center is a complex intervention that could improve operational decision-making and mitigate threats to patient safety. The study aims to address the limited research on the impact of complex health information technology on patient safety, reliability, and operational efficiency in healthcare delivery.