Continuous quality assurance (QA) in health care has necessitated the adoption of statistical methods developed as industrial process monitoring techniques. One such statistical technique is the cumulative summation (Cusum) methodology, which can monitor continuously a production process and detect subtle deviations from a preset defined level of achievement. The method is practical, simple to apply, easy to introduce and has proved popular with trainees in some specialities. This article introduces the concepts of a sequential analysis, deals with the practical steps of setting up a data collection and monitoring performance for procedures in health care.

There has been increasing awareness of the need for monitoring the quality of health care, particularly in the area of surgery. The Cumulative Summation (Cusum) techniques have emerged as a popular tool for performance monitoring in surgery. They allow one to judge whether a given variation in performance is probably due to chance or greater than could be expected from random variation and thus a cause for concern. The Cusum techniques are simple to carry out and can be applied to any surgical process with a binary outcome. Four parameters need to be set in advance: acceptable outcome rate, unacceptable outcome rate, Type I and Type II error rates. In this article, we review the history, statistical methods and potential applications for the Cusum techniques in the field of surgery and illustrate the two common forms of charting (cumulative failure and Cusum charting) by using unadjusted outcome data from the Geelong Hospital and St Vincent's Hospital cardiac surgery databases.

Objective: To assess the practicality of using personal digital assistants (PDAs) for the collection of logbook data, procedural performance data and critical incident reports in anaesthetic trainees. Design: Pilot study. Setting: Two tertiary referral centres (in Victoria and New Zealand) and a large district hospital in Queensland. Participants: Six accredited Australian and New Zealand College of Anaesthetists (ANZCA) registrars and their ANZCA training supervisors. Interventions: Registrars and supervisors underwent initial training for one hour, and supervisors were provided with ongoing support. Main outcome measures: Reliable use of the program, average time for data entry and number of procedures logged. Results: ANZCA trainees reliably enter data into PDAs. The data can be transferred to a central database, where they can be remotely analysed before results are fed back to trainees. Conclusions: This technology can be used to monitor professional performance in ANZCA trainees.

Multivariate analysis and model generation techniques can be applied to CPET data to predict 5 yr survival after major surgery more accurately than is possible with single variable analysis.