Rapidly detecting problems in the quality of care is of utmost importance for the well-being of patients. Without proper inspection schemes, such problems can go undetected for years. Cumulative... Show moreRapidly detecting problems in the quality of care is of utmost importance for the well-being of patients. Without proper inspection schemes, such problems can go undetected for years. Cumulative sum (CUSUM) charts have proven to be useful for quality control, yet available methodology for survival outcomes is limited. The few available continuous time inspection charts usually require the researcher to specify an expected increase in the failure rate in advance, thereby requiring prior knowledge about the problem at hand. Misspecifying parameters can lead to false positive alerts and large detection delays. To solve this problem, we take a more general approach to derive the new Continuous time Generalized Rapid response CUSUM (CGR-CUSUM) chart. We find an expression for the approximate average run length (average time to detection) and illustrate the possible gain in detection speed by using the CGR-CUSUM over other commonly used monitoring schemes on a real-life data set from the Dutch Arthroplasty Register as well as in simulation studies. Besides the inspection of medical procedures, the CGR-CUSUM can also be used for other real-time inspection schemes such as industrial production lines and quality control of services. Show less
Lovato, A.; Kraak, J.; Hensen, E.F.; Smit, C.F.; Giacomelli, L.; Filippis, C. de; Merkus, P. 2019
Purpose: To evaluate stapedotomy learning curve with cumulative summation methodology using different success criteria (ie, air-bone gap [ABG] <= 10 dB, ABG <= 15 dB, restoration of... Show morePurpose: To evaluate stapedotomy learning curve with cumulative summation methodology using different success criteria (ie, air-bone gap [ABG] <= 10 dB, ABG <= 15 dB, restoration of interaural symmetry, or hearing threshold gain >20 dB), and to assess patient characteristics influencing or modifying the learning curve. Methods: Retrospective chart review of primary and revision stapedotomy cases performed by surgeon 1 (S1, n = 78) and surgeon 2 (S2, n = 85). Results: Using the classic criterion for a successful stapedotomy (ABG <= 10 dB), patients with preoperative ABG >34 dB were associated with unsuccessful procedures (S1P= .02; S2P= .07). Revision surgery was associated with unsuccessful outcomes (S1P= .005; S2P= .0012). Cumulative summation plots using different criteria did not show a linear trend of association between stapedotomy success and number of operations, but preoperative characteristics of the patients who underwent stapedotomy significantly influenced the plots. Cumulative summation plots showed an initial increasing tendency with improving results, but when ear surgeons got more skilled, they operated on more complex cases (ie, patients with higher preoperative ABG or revision stapedotomy) and they could not meet the success criteria. Conclusions: Cumulative summation plots do not seem useful to evaluate the stapedotomy learning curve, as they do not correctly deal with heterogeneous case series. The increasing complexity of the stapedotomy patients during the surgeons' career impacts on the outcome of stapedotomy and confounds the evaluation of the growing skills of the surgeon. Stapedotomy audiological success rates are strongly influenced by the success criteria used. Show less
