A study into the knowledge, skills, abilities and behaviours needed in an ‘always on’ workplace

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How much is left in your “sleep tank”? Proof of concept for a simple model for sleep history feedback

Technology-supported methods for sleep recording are becoming increasingly affordable. Sleep history feedback may help with fatigue-related decision making – Should I drive? Am I fit for work? This study examines a “sleep tank” model (SleepTank™), which is analogous to the fuel tank in a car, refilled by sleep, and depleted during wake. Required inputs are sleep period time and sleep efficiency (provided by many consumer-grade actigraphs). Outputs include suggested hours remaining to “get sleep” and percentage remaining in tank (Tank%). Initial proof of concept analyses were conducted using data from a laboratory-based simulated nightshift study. Ten, healthy males (18–35y) undertook an 8h baseline sleep opportunity and daytime performance testing (BL), followed by four simulated nightshifts (2000 h–0600 h), with daytime sleep opportunities (1000 h–1600 h), then an 8 h night-time sleep opportunity to return to daytime schedule (RTDS), followed by daytime performance testing. Psychomotor Vigilance Task (PVT) and Karolinska Sleepiness Scale were performed at 1200 h on BL and RTDS, and at 1830 h, 2130 h 0000 h and 0400 h each nightshift. A 40-minute York Driving Simulation was performed at 1730 h, 2030 h and 0300 h on each nightshift. Model outputs were calculated using sleep period timing and sleep efficiency (from polysomnography) for each participant. Tank% was a significant predictor of PVT lapses (p < 0.001), and KSS (p < 0.001), such that every 5% reduction resulted in an increase of two lapses, or one point on the KSS. Tank% was also a significant predictor of %time in the Safe Zone from the driving simulator (p = 0.001), such that every 1% increase in the tank resulted in a 0.75% increase in time spent in the Safe Zone. Initial examination of the correspondence between model predictions and performance and sleepiness measures indicated relatively good predictive value. Results provide tentative evidence that this “sleep tank” model may be an informative tool to aid in individual decision-making based on sleep history

How to specify healthcare process improvements collaboratively using rapid, remote consensus-building: a framework and a case study of its application

Background: Practical methods for facilitating process improvement are needed to support high quality, safe care. How best to specify (identify and define) process improvements – the changes that need to be made in a healthcare process – remains a key question. Methods for doing so collaboratively, rapidly and remotely offer much potential, but are under-developed. We propose an approach for engaging diverse stakeholders remotely in a consensus-building exercise to help specify improvements in a healthcare process, and we illustrate the approach in a case study. Methods: Organised in a five-step framework, our proposed approach is informed by a participatory ethos, crowdsourcing and consensus-building methods: (1) define scope and objective of the process improvement; (2) produce a draft or prototype of the proposed process improvement specification; (3) identify participant recruitment strategy; (4) design and conduct a remote consensus-building exercise; (5) produce a final specification of the process improvement in light of learning from the exercise. We tested the approach in a case study that sought to specify process improvements for the management of obstetric emergencies during the COVID-19 pandemic. We used a brief video showing a process for managing a post-partum haemorrhage in women with COVID-19 to elicit recommendations on how the process could be improved. Two Delphi rounds were then conducted to reach consensus. Results: We gathered views from 105 participants, with a background in maternity care (n = 36), infection prevention and control (n = 17), or human factors (n = 52). The participants initially generated 818 recommendations for how to improve the process illustrated in the video, which we synthesised into a set of 22 recommendations. The consensus-building exercise yielded a final set of 16 recommendations. These were used to inform the specification of process improvements for managing the obstetric emergency and develop supporting resources, including an updated video. Conclusions: The proposed methodological approach enabled the expertise and ingenuity of diverse stakeholders to be captured and mobilised to specify process improvements in an area of pressing service need. This approach has the potential to address current challenges in process improvement, but will require further evaluation