Clinical Application of Patient Data Management Systems (PDMS): Computer-Assisted Weaning from Artificial Ventilation (KBWEAN). In: Lenz K, Metnitz PGH, editors. Patient Data Management in Intensive Care. Vienna: Springer; 1993. pp. 129-38. [DOI: 10.1007/978-3-7091-9320-4_12]

The Section on Medical Expert and Knowledge-Based Systems at the Department of Medical Computer Sciences of the University of Vienna Medical School

The Section on Medical Expert and Knowledge-Based Systems at the Department of Medical Computer Sciences pursues methodological research in and practical development of knowledge-based computer systems to assist in the decision-making processes for all areas of medical application. Vagueness of medical terms, uncertainty in the co-occurrence of medical entities, and incompleteness in medical theories are well-known characteristics of medical knowledge and ought to be considered in practically-used medical knowledge-based systems. We found that fuzzy set theory and fuzzy logic are powerful theories that model the above-mentioned characteristics. Fuzzy set theory and fuzzy logic were applied in the following systems: CADIAG-II and MedFrame/CADIAG-IV, FuzzyARDS, and FuzzyKBWean. CADIAG-II and MedFrame/CADIAG-IV are framework programs for consultation systems to aid in the differential diagnostic process in internal medicine. FuzzyARDS is an intelligent on-line monitoring program of data from patients with acute respiratory distress syndrome (ARDS) at an intensive care unit (ICU). It employs fuzzy trend detection and fuzzy automata. FuzzyKBWean is an open-loop fuzzy control program for optimization and quality control of the ventilation and weaning process of patients after cardiac surgery at the ICU. The above-mentioned computer systems have reached the state of extensive clinical integration and testing at the Vienna General Hospital. The obtained results show the applicability and usefulness of these systems.

Computer assisted data analysis in intensive care: the ICDEV project--development of a scientific database system for intensive care (Intensive Care Data Evaluation Project)

INTRODUCTION

Patient Data Management Systems (PDMS) for ICUs collect, present and store clinical data. Various intentions make analysis of those digitally stored data desirable, such as quality control or scientific purposes. The aim of the Intensive Care Data Evaluation project (ICDEV), was to provide a database tool for the analysis of data recorded at various ICUs at the University Clinics of Vienna.

SETTINGS

General Hospital of Vienna, with two different PDMSs used: CareVue 9000 (Hewlett Packard, Andover, USA) at two ICUs (one medical ICU and one neonatal ICU) and PICIS Chart+ (PICIS, Paris, France) at one Cardiothoracic ICU. CONCEPT AND METHODS: Clinically oriented analysis of the data collected in a PDMS at an ICU was the beginning of the development. After defining the database structure we established a client-server based database system under Microsoft Windows NI and developed a user friendly data quering application using Microsoft Visual C++ and Visual Basic;

RESULTS

ICDEV was successfully installed at three different ICUs, adjustment to the different PDMS configurations were done within a few days. The database structure developed by us enables a powerful query concept representing an 'EXPERT QUESTION COMPILER' which may help to answer almost any clinical questions. Several program modules facilitate queries at the patient, group and unit level. Results from ICDEV-queries are automatically transferred to Microsoft Excel for display (in form of configurable tables and graphs) and further processing.

CONCLUSIONS

The ICDEV concept is configurable for adjustment to different intensive care information systems and can be used to support computerized quality control. However, as long as there exists no sufficient artifact recognition or data validation software for automatically recorded patient data, the reliability of these data and their usage for computer assisted quality control remain unclear and should be further studied.