Departures from the protocol during conduct of a clinical trial: a pattern from the data record consistent with a learning curve.

OBJECTIVE: Recognition of learning curves in medical skill acquisition has enhanced patient safety through improved training techniques. Clinical trials research has not been similarly scrutinised. The VALsartan In Acute myocardial iNfarcTion, a large multinational, pragmatic, randomised, double-blind, multicentre trial, was retrospectively evaluated for evidence of research conduct consistent with a performance "learning curve". DESIGN: Records provided protocol departure (deviations/violations) and documentation query data. For each site, analysis included patient order (eg, first, second), recruitment rate and first enrollment relative to study start date. SETTING: Computerised data from a trial coordinated by an academic research organisation collaborating with 10 academic and 2 commercial research organisations and an industry sponsor. Interventions 931 sites enrolled 14,703 patients. Departures were restricted to the first year. Exclusions included patient's death or loss to follow-up within 12 months and subjects enrolled 80th or higher at a site. Departures were assessed for variance with higher patient rank, more frequent recruitment and later start date. METHODS AND RESULTS: 12,367 patients at 931 sites were analysed. Departures were more common for patients enrolled earlier at a site (p<0.0001). For example, compared with the 30th patient, the first had 47% more departures. Departures were also more common with slower enrollment and site start closer to the trial start date (p<0.0001). Similar patterns existed for queries. CONCLUSIONS: Research performance improved during the VALsartan In Acute myocardial iNfarcTion consistent with a "learning curve". Although effects were not related to a change in outcome (mortality), learning curves in clinical research may have important safety, ethical, research quality and economic implications for trial conduct.

Time of day effects on the incidence of anesthetic adverse events.

BACKGROUND: We hypothesized that time of day of surgery would influence the incidence of anesthetic adverse events (AEs). METHODS: Clinical observations reported in a quality improvement database were categorized into different AEs that reflected (1) error, (2) harm, and (3) other AEs (error or harm could not be determined) and were analyzed for effects related to start hour of care. RESULTS: As expected, there were differences in the rate of AEs depending on start hour of care. Compared with a reference start hour of 7 am, other AEs were more frequent for cases starting during the 3 pm and 4 pm hours (p < 0.0001). Post hoc inspection of data revealed that the predicted probability increased from a low of 1.0% at 9 am to a high of 4.2% at 4 pm. The two most common event types (pain management and postoperative nausea and vomiting) may be primary determinants of these effects. CONCLUSIONS: Our results indicate that clinical outcomes may be different for patients anesthetized at the end of the work day compared with the beginning of the day. Although this may result from patient related factors, medical care delivery factors such as case load, fatigue, and care transitions may also be influencing the rate of anesthetic AEs for cases that start in the late afternoon.

Objective measures of situation awareness in a simulated medical environment.

One major limitation in the use of human patient simulators is a lack of objective, validated measures of human performance. Objective measures are necessary if simulators are to be used to evaluate the skills and training of medical practitioners and teams or to evaluate the impact of new processes or equipment design on overall system performance. Situation awareness (SA) refers to a person's perception and understanding of their dynamic environment. This awareness and comprehension is critical in making correct decisions that ultimately lead to correct actions in medical care settings. An objective measure of SA may be more sensitive and diagnostic than traditional performance measures. This paper reviews a theory of SA and discusses the methods required for developing an objective measure of SA within the context of a simulated medical environment. Analysis and interpretation of SA data for both individual and team performance in health care are also presented.