A work domain analysis framework for modelling intensive care unit patients

Work domain analysis (WDA) has been applied to a range of complex work domains, but few WDAs have been undertaken in medical contexts. One pioneering effort suggested that clinical abstraction is not based on means-ends relations, whereas another effort downplayed the role of bio-regulatory mechanisms. In this paper it is argued that bio-regulatory mechanisms that govern physiological behaviour must be part of WDA models of patients as the systems at the core of intensive care units. Furthermore it is argued that because the inner functioning of patients is not completely known, clinical abstraction is based on hypothetico-deductive abstract reasoning. This paper presents an alternative modelling framework that conforms to the broader aspirations of WDA. A modified version of the viable systems model is used to represent the patient system as a nested dissipative structure while aspects of the recognition primed decision model are used to represent the information resources available to clinicians in ways that support lsquoif...thenrsquo conceptual relations. These two frameworks come together to form the recursive diagnostic framework, which may provide a more appropriate foundation for information display design in the intensive care unit.

Australian and New Zealand practice in intensive care (ANZPIC II): Predictors of physician confidence to diagnose pneumonia and determine illness severity in ventilated patients

The manner in which elements of clinical history, physical examination and investigations influence subjectively assessed illness severity and outcome prediction is poorly understood. This study investigates the relationship between clinician and objectively assessed illness severity and the factors influencing clinician's diagnostic confidence and illness severity rating for ventilated patients with suspected pneumonia in the intensive care unit (ICU). A prospective study of fourteen ICUs included all ventilated admissions with a clinical diagnosis of pneumonia. Data collection included pneumonia type - community-acquired (CAP), hospital-acquired (HAP) and ventilator-associated (VAP), clinician determined illness severity (CDIS), diagnostic methods, clinical diagnostic confidence (CDC), microbiological isolates and antibiotic use. For 476 episodes of pneumonia (48% CAP, 24% HAP, 28% VAP), CDC was greatest for CAP (64% CAP, 50% HAP and 49% VAP, P < 0.01) or when pneumonia was considered life-threatening (84% high CDC, 13% medium CDC and 3% low CDC, P < 0.001). Life-threatening pneumonia was predicted by worsening gas exchange (OR 4.8, CI 95% 2.3-10.2, P < 0.001), clinical signs of consolidation (OR 2.0, CI 95% 1.2-3.2, P < 0.01) and the Sepsis-Related Organ Failure Assessment (SOFA) Score (OR 1.1, CI 95% 1.1-1.2, P < 0.001). Diagnostic confidence increased with CDIS (OR 163, CI 95% 8.4-31.4, P < 0.001), definite pathogen isolation (OR 3.3, CI 95% 2.0-5.6) and clinical signs of consolidation (OR 2.1, CI 95% 1.3-3.3, P = 0.001). Although the CDIS, SOFA Score and the Simplified Acute Physiologic Score (SAPS II) were all associated with mortality, the SAPS II Score was the best predictor of mortality (P = 0.02). Diagnostic confidence for pneumonia is moderate but increases with more classical presentations. A small set of clinical parameters influence subjective assessment. Objective assessment using SAPS II Scoring is a better predictor of mortality.

Re-admission to intensive care: Identification of risk factors

Background and Purpose. The re-admission of patients to intensive care is associated with increased morbidity, mortality, loss of morale for patients and family, and increased health costs. The aim of the present study was to identify factors which place patients at a higher risk of re-admission to intensive care. Method. A prospective study of patients who were re-admitted to a 22-bed tertiary level intensive care facility within a 12-month period. Data were kept on every patient re-admitted to intensive care, including standard demographic data, initial admission diagnosis, co-morbidities, re-admission diagnosis, mobility on discharge, secretions, airway, chest X-ray, PaCO2, PaO2, PaO2/FiO2and time of discharge. Subjects included 74 patients who had been re-admitted to intensive care in a 12-month period and a comparison group of patients who were not re-admitted to intensive care. A cross-tabs procedure was initially used to estimate maximum likelihood. Significant factors with an value of 65 years (p

The costs of caring for patients in a tertiary referral Australian intensive care unit

We determined the direct cost of an Intensive Care Unit (ICU) bed in a tertiary referral Australian ICU and the cost drivers thereof, by retrospectively analysing a number of prospectively designed Hospital- and Unit-specific electronic databases. The study period was a financial year, from 1 July 2002 to 30 June 2003. There were 1615 patients occupying 5692 fractional occupied bed days at a total cost of A$15,915,964, with an average length of stay of 3.69 days (range 0.5-77, median 1.06, interquartile range 2.33). The main cost driver not incorporated into this analysis was blood products (paid for centrally). The average costs of an ICU day and total stay per patient were A$2670 and A$9852 respectively. Staff-related charges were 68.76%, with consumables related expenditure making up 19.65%, clinical support services 9.55% and capital equipment 2.04%. Overtime charges and nursing agency staff were 19.4% of staff-related charges (2.9% for agency staff), 3.9% lower than expenditure associated with full-time employment charges, such as pension and leave. The emergency nature of ICU means it is difficult to accurately set a nursing establishment to cater for all admissions and therefore it is hard to decide what is an acceptable percentage difference between agency/overtime costs compared with the costs associated with full-time staff appointments. Consumable expenditure is likely to increase the most with new innovation and therapies. Using protocol driven practices may tighten and control costs incurred in ICU.

Antibiotic resistance in the intensive care unit: A primer in bacteriology

The clinical use of potent, well-tolerated, broad-spectrum antibiotics has been paralleled by the development of resistance in bacteria, and the prevalence of highly resistant bacteria in some intensive care units is despairingly commonplace. The intensive care community faces the realistic prospect of untreatable nosocomial infections and should be searching for new approaches to diagnose and manage resistant bacteria. In this review, we discuss some of the relevant underlying biology, with a particular focus on genetic transfer vehicles and the relationship of selection pressure to their movements. It is an attempt to demystify the relevant language and concepts for the anaesthetist and intensivist, to explain some of the reasons for the emergence of resistance in bacteria, and to provide a contextual basis for discussion of management approaches such as selective decontamination and antibiotic cycling.

Performance measurement of intensive care services in hospitals:The case of Barbados

The Intensive Care Unit (ICU) being one of those vital areas of a hospital providing clinical care, the quality of service rendered must be monitored and measured quantitatively. It is, therefore, essential to know the performance of an ICU, in order to identify any deficits and enable the service providers to improve the quality of service. Although there have been many attempts to do this with the help of illness severity scoring systems, the relative lack of success using these methods has led to the search for a form of measurement, which would encompass all the different aspects of an ICU in a holistic manner. The Analytic Hierarchy Process (AHP), a multiple-attribute, decision-making technique is utilised in this study to evolve a system to measure the performance of ICU services reliably. This tool has been applied to a surgical ICU in Barbados; we recommend AHP as a valuable tool to quantify the performance of an ICU. Copyright © 2004 Inderscience Enterprises Ltd.

Application of analytic hierarchy process for measuring and comparing the global performance of intensive care units

Purpose: To develop a model for the global performance measurement of intensive care units (ICUs) and to apply that model to compare the services for quality improvement. Materials and Methods: Analytic hierarchy process, a multiple-attribute decision-making technique, is used in this study to evolve such a model. The steps consisted of identifying the critical success factors for the best performance of an ICU, identifying subfactors that influence the critical factors, comparing them pairwise, deriving their relative importance and ratings, and calculating the cumulative performance according to the attributes of a given ICU. Every step in the model was derived by group discussions, brainstorming, and consensus among intensivists. Results: The model was applied to 3 ICUs, 1 each in Barbados, Trinidad, and India in tertiary care teaching hospitals of similar setting. The cumulative performance rating of the Barbados ICU was 1.17 when compared with that of Trinidad and Indian ICU, which were 0.82 and 0.75, respectively, showing that the Trinidad and Indian ICUs performed 70% and 64% with respect to Barbados ICU. The model also enabled identifying specific areas where the ICUs did not perform well, which helped to improvise those areas. Conclusions: Analytic hierarchy process is a very useful model to measure the global performance of an ICU. © 2005 Elsevier Inc. All rights reserved.