Computers in critical care: opportunities and challenges

The impact of a Critical Care Information System (CCIS) on time spent charting and in direct patient care by staff in the ICU: a review of the literature

PURPOSE

The introduction of a Critical Care Information System (CCIS) into an intensive care unit (ICU) is purported to reduce the time health care providers (HCP) spend on documentation and increase the time available for direct patient care. However, there is a paucity of rigorous empirical research that has investigated these assertions. Moreover, those studies that have sought to elucidate the relationship between the introduction of a CCIS and the time spent by staff on in/direct patient care activities have published contradictory findings. The objective of this literature review is to establish the impact of a CCIS on time spent documenting and in direct patient care by staff in the ICU.

METHODS

Five electronic databases were searched including PubMed Central, EMBASE, CINAHL, IEEE Xplore, and the Cochrane Database of Systematic Reviews. Reference lists of all published papers were hand searched, and citations reviewed to identify extra papers. We included studies that were empirical articles, published in English, and provided original data on the impact of a CCIS on time spent documenting and in direct patient care by staff in the ICU.

RESULTS

In total, 12 articles met the inclusion criteria. Workflow analysis (66%) and time-and-motion analysis (25%) were the most common forms of data collection. Three (25%) studies found an increase in time spent charting, five (42%) found no difference, and four (33%) studies reported a decrease. Results on the impact of a CCIS on direct patient care were similarly inconclusive.

CONCLUSIONS

Due to the discrepant findings and several key methodological issues, the impact of a CCIS on time spent charting and in direct patient care remains unclear. This review highlights the need for an increase in rigorous empirical research in this area and provides recommendations for the design and implementation of future studies.

Prognosis in critical care

Prognostic risk prediction models have been employed in the intensive care unit (ICU) setting since the 1980s and provide health care providers with important information to help inform decisions related to treatment and prognosis, as well as to compare outcomes across institutions. Prognostic models for critical care are among the most widely utilized and tested predictive models in healthcare. In this article, we review and compare mortality prediction models, including the APACHE (1981), SAPS (1984), APACHE-II (1985), MPM (1987), APACHE-III (1991), SAPS-II (1993), and MPM-II (1993). We emphasize the importance of model calibration in this domain. In addition, we present a brief review of the statistical methodology, multiple logistic regression, which underlies most of the models currently used in critical care.