Monitoring surgical outcome quality by risk‐adjusted control charts has attracted wide attention. The hidden medical errors may cause increasing of adverse events such as infection, rehospitalization, and even death. Quickly and timely detecting abnormal changes of surgical performance helps reduce the probability of adverse events and improve health care quality. Most existing monitoring schemes focus on the binary surgical outcomes. However, continuous survival times of patients should be considered for more accurate monitoring. In this paper, a new exponentially weighted moving average (EWMA) control chart is proposed for monitoring continuous surgical outcomes. To describe surgical performance, a patient's actual survival time and predicted mortality are combined in an illustrative and interpretable way. Performance of the proposed chart is evaluated with different chart parameters under different shifts by a simulation study. We compare our chart with the risk‐adjusted survival time cumulative sum chart, and the simulation results demonstrate that the proposed monitoring scheme has better efficiency. The implementation of the proposed chart is illustrated by a real example. Besides an analysis of the entire dataset, the surgical performance of each surgeon is monitored, because each of them has patients with different risk levels.

Wave pressure on the wet surface of a V-shaped floating breakwater in random seas is investigated. Considering the diffraction effect, the unit velocity potential caused by the single regular waves around the breakwater is solved using the finite-depth Green function and boundary element method, in which the Green function is solved by integral method. The Response-Amplitude Operator (RAO) of wave pressure is acquired according to the Longuet-Higgins' wave model and the linear Bernoulli equation. Furthermore, the wave pressure's response spectrum is calculated according to the wave spectrum by discretizing the frequency domain. The wave pressure's characteristic value corresponding to certain cumulative probability is determined according to the Rayleigh distribution of wave heights. The numerical results and field test results are compared, which indicates that the wave pressure calculated in random seas agrees with that of field measurements. It is found that the bigger angle between legs will cause the bigger pressure response, while the increase in leg length does not influence the pressure significantly. The pressure at the side of head sea is larger than that of back waves. When the incident wave angle changes from 0˚ to 90˚, the pressure at the side of back waves decreases clearly, while at the side of head sea, the situation is more complicated and there seems no obvious tendency. The concentration of wave energy around low frequency (long wavelength) will induce bigger wave pressure, and more attention should be paid to this situation for the structure safety.

No abstract

No abstract