Contextual Operating Room Monitoring

Abstract

Modern healthcare struggles as the population ages and personnel becomes more scarce. Operating rooms (ORs) need to adhere to strict standards and present a major cost for hospitals. Safety of the patient should always be the main concern, and wellbeing of the staff must not be forgotten. Patients have to deal with emotional hardship, delays, and sometimes rescheduling of their treatment. Healthcare professionals experience high workloads due to personnel shortages, culture and new technologies changing the working environment. Hospital management is confronted with the resulting high turnover rates, whilst serving society with their limited available resources.
Efforts are made to improve this situation by assisting hospital employees in their work. New tools can ease tasks, and make them more efficient or safer. Alternatively, finding and teaching optimal ways of working improves workflow efficiency. The desired outcome is to compensate personnel shortages by decreasing the load on employees, whilst maintaining availability and quality of care.
Supportive healthcare tools are often implemented using technology. For example, artificial intelligence (AI) helps find disease in medical imaging, and may assist procedure planning. Devices enable e.g. clear X-Ray imaging with minimal radiation, and steady robotic surgery. However, technological changes to the perioperative setting can burden healthcare professionals in some situations. Beside the patient, they now need to pay attention to these devices. Malfunctions can delay procedures, cause risks for the patient, and induce stress in the staff.
Education through workflow analysisand optimisation presents an unintrusive path to efficiency improvements. Where process optimisation is already mature in industry, these techniques do not translate directly to hospitals. Healthcare tasks cannot be divided and simplified as in some industries, and every patient has unique needs. However, knowledge-based feedback and support can still be beneficial. Analysing on scale how specialists work allows to extract best practices for safety, efficiency and wellbeing. For example, workflow insights can reveal optimal procedure scheduling, approaches to a workflow phase, adaptation to different patients, and use of new technologies. A current challenge therefore is to formulate scalable workflow analysis methods in hospitals.
Automation through algorithms is a promising tool for scalable workflow analysis, where recorded data from the OR can be translated to workflow metrics. Available datastreams include (monitoring) videos, device logs, and diagnostic measurements. Differences in protocol and workflow preference between hospitals and medical teams present difficulties. Perioperative workflow analysis must be robust against such variability, recognising relevant patterns regardless of the team or OR. One technology that excels at such robust pattern recognition is deep learning.
Datastreams from the OR present a tradeoff between scope and generalisability. For example, devices may keep logs, which generalise well between ORs, but have a small scope as only device usage is recorded. Although monitoring videos generalise poorly due to visually unique ORs and viewpoints, their view of the whole room yields a large scope. This dissertation investigates the use of monitoring videos for generalisable workflow analysis. Cameras were mounted on the ceiling in a cardiac catheterisation laboratory (Cath Lab) and several ORs. The Cath Lab is a special OR for minimally invasive cardiac interventions, which high level of standardisation presents opportunities for explorative workflow study. Videos of several hundreds of real interventional procedures were recorded. Computer vision (CV) algorithms were used to extract visualand workflow features.