Healthcare-associated infection surveillance and bedside alerts.

Expectations and requirements concerning the identification and surveillance of healthcare-associated infections (HAIs) are increasing, calling for differentiated automated approaches. In an attempt to bridge the "definition swamp" of these infections and serve the needs of different users, we improved the monitoring of nosocomial infections (MONI) software to create better surveillance reports according to consented national and international definitions, as well as produce infection overviews on complex clinical matters including alerts for the clinician's ward and bedside work. MONI contains and processes surveillance definitions for intensive-care-unit-acquired infections from the European Centre for Disease Prevention and Control, Sweden, as well as the Centers for Disease Control and Prevention, USA. The latest release of MONI also includes KISS criteria of the German National Reference Center for Surveillance of Nosocomial Infections. In addition to these "classic" surveillance criteria, clinical alert criteria--which are similar but not identical to the surveillance criteria--were established together with intensivists. This is an important step to support both infection control and clinical personnel; and--last but not least--to foster co-evolution of the two groups of definitions: surveillance and alerts.

Can we bridge the definition diversity in healthcare-associated infection surveillance? From IT-supported surveillance to IT-supported infection prevention and control.

Expectations and requirements of the surveillance of healthcare-associated infections (HAIs) trigger a growing differentiation of HAI surveillance approaches. In an attempt to bridge this diversity of definitions and to serve the needs of different user groups, we have enhanced MONI (identification, monitoring, and reporting of nosocomial infections) not only to create better reports, but also to output overviews on complex clinical matters, as well as to generate alerts and reminders for the clinicians' bedside work.

Effectiveness of an automated surveillance system for intensive care unit-acquired infections.

This study assessed the effectiveness of a fully automated surveillance system for the detection of healthcare-associated infections (HCAIs) in intensive care units. Manual ward surveillance (MS) and electronic surveillance (ES) were performed for two intensive care units of the Vienna General Hospital. All patients admitted for a period longer than 48 h between 13 November 2006 and 7 February 2007 were evaluated according to HELICS-defined rules for HCAI. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and personnel time spent per surveillance type were calculated. Ninety-three patient admissions were observed, whereby 30 HCAI episodes were taken as a reference standard. Results with MS versus ES were: sensitivity 40% versus 87%, specificity 94% versus 99%, PPV 71% versus 96%, NPV 80% versus 95%, and time spent per surveillance type 82.5 h versus 12.5 h. In conclusion, ES was found to be more effective than MS while consuming fewer personnel resources.

Concise healthcare-associated infection reporting and benchmarking with minimal staff resources.

We report on intelligent information technology tools that produce fully-automated surveillance reports of high precision for 12 intensive care units (ICUs) without relevant time expenditure of infection control or ICU staff. This is accomplished by MONI-ICU, a computerized system for automated identification and continuous monitoring of ICU-associated infections, which makes surveillance data readily accessible and presents them in easily perceptible reporting format.

Electronic surveillance of healthcare-associated infections with MONI-ICU--a clinical breakthrough compared to conventional surveillance systems.

Surveillance of clinical entities such as healthcare-associated infections (HCAI) by conventional techniques is a time-consuming task for highly trained experts. Such are neither available nor affordable in sufficient numbers on a permanent basis. Nevertheless, expert surveillance is a key parameter for good clinical practice, especially in intensive care medicine. MONI-ICU (monitoring of nosocomial infections in intensive care units) has been developed methodically and practically in a stepwise manner over the last 20 years and is now a reliable tool for clinical experts. It provides an almost real-time view of clinical indicators for HCAI--at the cost of almost no additional time on the part of surveillance staff or clinicians. We describe the use of this system in clinical routine and compare the results generated automatically by MONI-ICU with those generated in parallel by trained surveillance staff using patient chart reviews and other available information ("gold standard"). A total of 99 ICU patient admissions representing 1007 patient days were analyzed. MONI-ICU identified correctly the presence of an HCAI condition in 28/31 cases (sensitivity, 90.3%) and their absence in 68/68 of the non-HCAI cases (specificity, 100%), the latter meaning that MONI-ICU produced no "false alarms". The time taken for conventional surveillance at the 52 ward visits was 82.5 hours. MONI-ICU analysis of the same patient cases, including careful review of the generated results required only 12.5 hours (15.2%).

Processing gradual information with Fuzzy Arden syntax.

The programming language Arden Syntax is especially adapted to the needs of computer-based clinical decision support. Recently, an extension of Arden Syntax, named Fuzzy Arden Syntax, was proposed by the authors. Fuzzy Arden Syntax is a conservative extension of Arden Syntax and offers special functionality to process gradual information. The background is the observation that in medicine we frequently deal with statements which are neither clearly false nor clearly true but hold to some intermediate degree. In this paper, we demonstrate under which circumstances a Medical Logic Module (a program unit written in Arden Syntax) may show unintended behavior and how the situation can easily be improved by means of the possibilities offered by Fuzzy Arden Syntax. To this end, an example from the domain of nosocomial infection control is discussed in detail.

Fuzzy Arden Syntax: A fuzzy programming language for medicine.

OBJECTIVE: The programming language Arden Syntax has been optimised for use in clinical decision support systems. We describe an extension of this language named Fuzzy Arden Syntax, whose original version was introduced in S. Tiffe's dissertation on "Fuzzy Arden Syntax: Representation and Interpretation of Vague Medical Knowledge by Fuzzified Arden Syntax" (Vienna University of Technology, 2003). The primary aim is to provide an easy means of processing vague or uncertain data, which frequently appears in medicine. METHODS: For both propositional and number data types, fuzzy equivalents have been added to Arden Syntax. The Boolean data type was generalised to represent any truth degree between the two extremes 0 (falsity) and 1 (truth); fuzzy data types were introduced to represent fuzzy sets. The operations on truth values and real numbers were generalised accordingly. As the conditions to decide whether a certain programme unit is executed or not may be indeterminate, a Fuzzy Arden Syntax programme may split. The data in the different branches may be optionally aggregated subsequently. RESULTS: Fuzzy Arden Syntax offers the possibility to formulate conveniently Medical Logic Modules (MLMs) based on the principle of a continuously graded applicability of statements. Furthermore, ad hoc decisions about sharp value boundaries can be avoided. As an illustrative example shows, an MLM making use of the features of Fuzzy Arden Syntax is not significantly more complex than its Arden Syntax equivalent; in the ideal case, a programme handling crisp data remains practically unchanged when compared to its fuzzified version. In the latter case, the output data, which can be a set of weighted alternatives, typically depends continuously from the input data. CONCLUSION: In typical applications an Arden Syntax MLM can produce a different output after only slight changes of the input; discontinuities are in fact unavoidable when the input varies continuously but the output is taken from a discrete set of possibilities. This inconvenience can, however, be attenuated by means of certain mechanisms on which the programme flow under Fuzzy Arden Syntax is based. To write a programme making use of these possibilities is not significantly more difficult than to write a programme according to the usual practice.