Clusters of nosocomial infection often occur undetected, at substantial cost to the medical system and individual patients. We evaluated binary cumulative sum (CUSUM) and moving average (MA) control charts for automated detection of nosocomial clusters. We selected two outbreaks with genotyped strains and used resistance as inputs to the control charts. We identified design parameters for the CUSUM and MA (window size, k, α, β, p0, p1) that detected both outbreaks, then calculated an associated positive predictive value (PPV) and time until detection (TUD) for sensitive charts. For CUSUM, optimal performance (high PPV, low TUD, fully sensitive) was for 0.1 <α ≤0.25 and 0.2 <β <0.25, with p0 = 0.05, with a mean TUD of 20 (range 8–43) isolates. Mean PPV was 96.5% (relaxed criteria) to 82.6% (strict criteria). MAs had a mean PPV of 88.5% (relaxed criteria) to 46.1% (strict criteria). CUSUM and MA may be useful techniques for automated surveillance of resistant infections.

An explicit focus on safety first can help accelerate the adoption of robotics and intelligent automation technology across many sectors of the economy. The safety-to-autonomy approach to introducing automation in the workplace provides a glide path that allows businesses to leverage the benefits of new technology at the pace they are comfortable with and that they can sustain, while realizing return on investment from day one. The transition to large-scale adoption of intelligent automation starts with sensorized industrial equipment that provides active safety features to immediately curb accident rates. They continuously collect data and model how humans use them in existing processes. As businesses become comfortable with these augmented machines, additional autonomous functionality can be enabled incrementally, supported by the information learned from observing human operators over time. Focusing on safety can provide the glide path for businesses to embrace the robot revolution across all sectors.

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