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Objective: Expert surveillance of healthcare-associated infections (HCAIs) is a key parameter for good clinical practice, especially in intensive care medicine. Assessment of clinical entities such as HCAIs is a time-consuming task for highly trained experts. Such are neither available nor affordable in sufficient numbers for continuous surveillance services. Intelligent information technology (IT) tools are in urgent demand., Methods: MONI-ICU (monitoring of nosocomial infections in intensive care units (ICUs)) has been developed methodologically and practically in a stepwise manner and is a reliable surveillance IT tool for clinical experts. It uses information from the patient data management systems in the ICUs, the laboratory information system, and the administrative hospital information system of the Vienna General Hospital as well as medical expert knowledge on infection criteria applied in a multilevel approach which includes fuzzy logic rules., Results: 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 3 missed cases were due to correctable technical errors. 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%)., Conclusion: Provided structured and sufficient information on clinical findings is online available, MONI-ICU 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.

To evaluate the performance of CADIAG-II/RHEUMA as consultant in the primary evaluation of patients visiting a rheumatological outpatient clinic, a CADIAG-II/RHEUMA consultation was done for 54 patients and the list of generated diagnostic hypotheses was compared to each clinical discharge diagnosis. For 26 of a total of 126 rheumatological discharge diagnoses, no matching CADIAG-II/RHEUMA diagnosis was available. 94% of all other discharge diagnoses were found in the list of CADIAG-II/RHEUMA hypotheses, 82% among the first third of the list of hypotheses and 48% among the first five hypotheses. We identified the following factors limiting the ability of CADIAG-II/RHEUMA to generate a comprehensive and correctly ranked list of diagnostic hypotheses: (1) a large percentage of patients with early stages of not clearly identified rheumatological conditions; (2) the limited number of CADIAG-II/RHEUMA diagnoses compared to the large number of known rheumatological conditions; (3) the fact that rheumatological diseases are rarely characterized by a single pathognomonic feature but are usually diagnosed by combinations of rather unspecific findings.

Objective: Since the treatment of patients with severe ARDS using the extracorporal lung assist (ECLA) methods remains a cost intensive and speculative procedure, a knowledge based computer system should be created and evaluated in order to support clinical decisions., Methods: The model was based on the fuzzy set theory and therefore able to give decisions between yes and no, that means that a criterion could also be fulfilled to 35% or 80% for example. The development of this computer program consists of two steps: first, the entry criteria for the ECLA therapy were established within a framework of an international evaluation of clinical data from 3 centres (Berlin, Marburg, Vienna). Here, inherent vagueness, uncertainty of the occurrence and limited availability of medical data are to be considered to establish a useful tool. Secondly, this was done by grouping and weighting of parameters by the system and the status of each patient or patient group was assigned by the percentage of fulfillment of the criterion., Results: By using a mixed sample of patients from these three centres, the fulfillment of entry criteria according either to definitions of Berlin or to definition of Marburg was different (68% versus 36%). Other differences (36% vs. 22% and 68% vs. 60%) were found between the fuzzy based score and the crisp score which represents the usually performed method., Conclusions: This now preevaluated minimal data set to describe severe ARDS patients based on the fuzzy set theory may be useful to evaluate patients for ECLA therapy or for another controlled ARDS-therapy.

In 1987, the American Rheumatism Association issued a set of criteria for the classification of rheumatoid arthritis (RA) to provide a uniform definition of RA patients. Fuzzy set theory and fuzzy logic were used to transform this set of criteria into a diagnostic tool that offers diagnoses at different levels of confidence: a definite level, which was consistent with the original criteria definition, as well as several possible and superdefinite levels. Two fuzzy models and a reference model which provided results at a definite level only were applied to 292 clinical cases from a hospital for rheumatic diseases. At the definite level, all models yielded a sensitivity rate of 72.6% and a specificity rate of 87.0%. Sensitivity and specificity rates at the possible levels ranged from 73.3% to 85.6% and from 83.6% to 87.0%. At the superdefinite levels, sensitivity rates ranged from 39.0% to 63.7% and specificity rates from 90.4% to 95.2%. Fuzzy techniques were helpful to add flexibility to preexisting diagnostic criteria in order to obtain diagnoses at the desired level of confidence.