Monitoring the evolutionary process of quality: Risk-adjusted charting to track outcomes in intensive care

Abstract

Risk-adjusted process control charting procedures for continuous monitoring of intensive care unit outcomes are proposed as quality management tools. A modified Shewhart p chart and cumulative sum process control chart, using the Acute Physiology and Chronic Health Evaluation III model mortality prediction for risk adjustment, are presented. The risk-adjusted p chart summarizes performance at arbitrary intervals and plots observed against predicted mortality rate to detect large changes in risk-adjusted mortality. The risk-adjusted cumulative sum procedure is a likelihood-based scoring method that adjusts for estimated risk of death, accumulating evidence from outcomes of all previous patients. It formally tests the hypothesis of a change in the odds of death. In this application, we detected a decrease from above to predicted risk-adjusted mortality. This was temporally related to increased senior staffing levels and enhanced ongoing multidisciplinary review of practice, quality improvement, and educational activities. Formulas and analyses are provided as appendices.

A Method for Detecting Runs of Good and Bad Clinical Outcomes on Variable Life-Adjusted Display (VLAD) Charts

“…In the United Kingdom it has gained particular prominence since events at Bristol Royal Infirmary, where a suspicion of poor performance prompted the UK Department of Health to instigate a major Public Inquiry [17]. Several graphical tools have been introduced to assist with such monitoring, and have been applied to a variety of disciplines within the hospital setting including cardiac surgery [3,10,16], intensive care [2], hospital acquired infections [11] and treatment of acute myocardial infarction [9]. More recently, another major Inquiry into mortality of patients under the care of the General Practitioner, Harold Shipman, has led to the suggestion of applying similar methods to Primary Care [1].…”

Comment on ‘Modern analysis of customer satisfaction surveys: comparison of models and integrated analysis’

“…(1) Charts adjusted for categorical covariates were developed for healthcare applications (see [13], [14] or [15], for instance). Such approaches may be adapted for simultaneously monitoring surveys over a population distinguished in groups of customers by covariates such as age, income, interest and others.…”

Real-time monitoring of coronary care mortality: A comparison and combination of two monitoring tools

“…Rather than use such methods for judging performance, it is recommended that hospitals develop systems for selfmonitoring in order to deliver continual improvement in outcomes [2,3]. Several graphical tools have been introduced, which have been applied to a variety of disciplines including cardiac surgery [4][5][6][7], intensive care [8], hospital acquired infections [9] and treatment of acute myocardial infarction [10]. Because of their recommended use as selfmonitoring tools, it is important that they are readily understood by clinical staff and easy to use.…”