The use of patient mix-adjusted control charts to compare in-hospital costs of care

The Contribution of Variable Control Charts to Quality Improvement in Healthcare: A Literature Review

Objective

To conduct a literature review to determine where and how variable control charts have contributed to quality improvement in healthcare.

Methods

A targeted literature search of the ABI/INFORM Global, Science Direct, Medline and Google Scholar databases was conducted, which yielded 6875 papers. Screening articles on the basis of title and abstract resulted in references to 163 articles, leading to the identification of 29 articles published between 1992 and 2019 that met the inclusion criteria. Common themes, variables and units of analysis were then analyzed.

Results

Variable control charts have been applied in 11 different healthcare contexts, using 17 different variables, at various levels within healthcare organizations. The main reason for applying variable control charts is to demonstrate a process change, usually following a specific change or quality intervention. The study identified various limitations and benefits of applying variable control charts. The charts are visually easy to understand for both management and employees, but they are limited by their requirement for potentially complex and resource-intensive data collection.

Conclusion

Variable control charts contribute to quality improvement in healthcare by enabling visualization and monitoring of variations and changes in healthcare processes. The methodology has been most frequently used to demonstrate process shifts after quality interventions. There still is a great potential for more studies applying variable control charts.

Risk-adjusted number-between failures charting procedures for monitoring a patient care process for acute myocardial infarctions

Risk-adjusted charts for monitoring a surgical or patient care process have recently gained prominence in the literature. To monitor a patient care process, especially to detect deterioration is crucial in saving patients' lives. In this paper, a new charting procedure is developed for monitoring a patient care process for patients admitted to a hospital with acute myocardial infarctions. This procedure is based on the number-between failures by taking the patients' risks into account. When there is a change in the risk distribution of incoming patients, it is demonstrated that this procedure will behave properly while the non-risk-adjusted counterpart will signal incorrectly that the patient care process has changed. The setting up of this chart to monitor a patient care process for patients with acute myocardial infarctions is described in detail.

The cost of a hospital ward in Europe: is there a methodology available to accurately measure the costs?

Costing health care services has become a major requirement due to an increase in demand for health care and technological advances. Several studies have been published describing the computation of the costs of hospital wards. The objective of this article is to examine the methodologies utilised to try to describe the basic components of a standardised method, which could be applied throughout Europe. Cost measurement however is a complex matter and a lack of clarity exists in the terminology and the cost concepts utilised. The methods discussed in this review make it evident that there is a lack of standardized methodologies for the determination of accurate costs of hospital wards. A standardized costing methodology would facilitate comparisons, encourage economic evaluation within the ward and hence assist in the decision-making process with regard to the efficient allocation of resources.

Application of variables control charts to risk-adjusted time-ordered healthcare data

In a previous article (M. K. Hart, Qual Manag Health Care. 2003;12(1):5-19), the authors presented risk-adjusted control charts applicable for attributes data. The present article discusses a similar class of control charts applicable for variables data that are often skewed. The key feature of these charts is their application of risk-adjusted data in addition to actual performance data. The resulting charts should decrease the occurrence of both type I and type II errors as compared to the unadjusted control charts. This article presents several control charts that vary in the data transformation and combination approaches. Data depicting hospital length of stay following coronary artery bypass graft procedures were used to illustrate the use of transformed and risk-adjusted control charts.

Continuous improvement evaluation: a framework for multisite evaluation studies

This article proposes the use of the continuous improvement evaluation (CIE), a framework for multisite demonstration or evaluation studies. This framework is designed for studying intervention programs that change during the evaluation. The development of family drug courts is provided as an example. CIE relies on outcome data collected over time and benchmarked across similar cases in comparison sites; thus, this study was designed to collect data on effectiveness of intervention programs at multiple sites and over time. A weight is calculated for similarity of any two cases based on features they share. In statistical process control charts, these weights are used to compare outcomes at the site against the average of similar cases in comparison groups. Once data are benchmarked, program staff meet to discuss process changes that have led to improvements in outcomes. To ensure that intervention programs have access to evaluation reports on demand, information technology is used to collect, clean, and pool data. Computers generate study reports, and evaluators review reports after release to clients. Statistical tools can be used to evaluate changing programs. Traditional evaluators may be concerned about some threats to validity associated with CIE. The article concludes with a discussion of typical threats to validity and how these threats are addressed in the CIE framework.