Variables determining clinical complexity in hospitalized Internal Medicine patients: a workload analysis

Submitted: 8 April 2016
Accepted: 17 October 2016
Published: 14 June 2017
Abstract Views: 1433
PDF: 769
HTML: 606
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Authors

The clinical complexity of Internal Medicine patients is a daily challenge for clinicians. Although clinical complexity cannot be directly measured, several scores describe the variability of clinical severity and comorbidity. The aim of this study was to analyze staff workload by assessing the nursing and medical complexity of patients admitted to an Internal Medicine ward. We included 40 consecutive inpatients [52.5% females, mean age 71.2 (18.2) years] classified according to the index of clinical complexity (ICC, type A: very high; type B: high; type C: moderate) and the cumulative illness rating scale (CIRS) severity and comorbidity index. Patient outcomes, hospitalization duration, tests performed, number of daily medications and time to perform standard nursing tasks were analysed across groups. Mean duration of hospitalization was 15.6 (10.1) days; in-hospital mortality was 15%. Mean CIRS severity index (SI) was 1.03 (0.31) and median CIRS comorbidity index (CI) was 2 (range 1-5). Significant differences were observed among ICC groups in time spent performing specific tasks [univariate analysis of variance F(2.37)=17.26, P<0.001]. No significant differences were found between the three groups for mean CIRS-SI [F(2.37)=3.033, P=0.060] and median CIRS-CI [Kruskal Wallis test: c2(2)= 1.672, P=0.433]. Clinical complexity and caring complexity were not correlated in our sample of Internal Medicine inpatients. Optimal care of Internal Medicine patients must take into account their complexity in both the medical and nursing aspects.

Dimensions

Altmetric

PlumX Metrics

Downloads

Download data is not yet available.

PlumX Metrics

PlumX Metrics  provide insights into the ways people interact with individual pieces of research output (articles, conference proceedings, book chapters, and many more) in the online environment. Examples include, when research is mentioned in the news or is tweeted about. Collectively known as PlumX Metrics, these metrics are divided into five categories to help make sense of the huge amounts of data involved and to enable analysis by comparing like with like.

Citations

How to Cite

Tommasi, V., Campolongo, A., Caridi, I., Gatti, S., Lagana, L., Simonelli, I., Piccolo, P., & Manfellotto, D. (2017). Variables determining clinical complexity in hospitalized Internal Medicine patients: a workload analysis. Italian Journal of Medicine, 11(2), 202–206. https://doi.org/10.4081/itjm.2017.725