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Objective: Clinical practice guidelines (CPGs) are means to provide evidence-based medical knowledge. In order to make up-to-date ''best'' scientific evidence available these documents need to be updated on an ongoing basis. An effective method to accomplish this aim is offered by the so-called ''living guidelines'': Living guidelines are documents presenting up-to-date and state-of-the-art knowledge to practitioners. To have guidelines implemented by computer-support they have to be formalized in a computer-interpretable form in a first step. Due to the complexity of such formats the formalization process is burdensome and time-consuming. Automating parts of the modeling process and, consequently, modeling updates of these guideline documents are demanded. Methods and material: The LASSIE methodology supports this task by formalizing guidelines in several steps from the textual form to the guideline representation language Asbru using a document-centric approach. LASSIE uses information extraction techniques to semi-automatically accomplish these steps. We apply LASSIE to support the implementation of living guidelines. Results: Based on a living guideline published by the Scottish Intercollegiate Guidelines Network (SIGN) we show that adaptations of previously formalized guidelines can be accomplished easily and fast. Thereby, the different versions of guideline documents are compared and updates are identified. Due to the traceable formalization method of linking text parts and their corresponding formal models, we are able to inherit unchanged models from previously formalized versions. Thus, we only need to formalize updated text parts using the semi-automatic formalization method LASSIE. Conclusion: We propose a simple, time-saving, but effective method called LASSIE to formalize new guideline versions of previously formalized CPGs. Furthermore, models that have been added or modified by knowledge engineers in previous versions can also be transferred easily. This will result in a faster implementation of new guideline versions also known as living guidelines to provide up-to-date knowledge necessary for accomplishing the daily work of health care professionals.

Objective: Formalizing clinical practice guidelines (CPGs) for a subsequent computer-supported processing is a challenging, but burdensome and time-consuming task. Existing methods and tools to support this task demand detailed medical knowledge, knowledge about the formal representations, and a manual modeling. Furthermore, formalized guideline documents mostly fall far short in terms of readability and understandability for the human domain modeler. Methods and material: We propose a new multi-step approach using information extraction methods to support the human modeler by both automating parts of the modeling process and making the modeling process traceable and comprehensible. This paper addresses the first steps to obtain a representation containing processes which is independent of the final guideline representation language. Results: We have developed and evaluated several heuristics without the need to apply natural language understanding and implemented them in a framework to apply them to several guidelines from the medical subject of otolaryngology. Findings in the evaluation indicate that using semi-automatic, step-wise information extraction methods are a valuable instrument to formalize CPGs. Conclusion: Our evaluation shows that a heuristic-based approach can achieve good results, especially for guidelines with a major portion of semi-structured text. It can be applied to guidelines irrespective to the final guideline representation format.

Objective: Currently, visualization support for patient data analysis is mostly limited to the representation of directly measured data. Contextual information on performed treatment steps is an important source to find reasons and explanations for certain phenomena in the measured patient data, but is mostly spared out in the analysis process. This work aims to fill this gap via integrating classical data visualization and visualization of treatment information. Methods and material: We considered temporal as well as logical data aspects and applied a user-centered development approach that was guided by user input gathered via a user study, design reviews, and prototype evaluations. Furthermore, we investigated the novel PlanningLine glyph, that is used to represent plans in the temporal domain, via a comparative empirical user study. Results: Our interactive visualization approach CareVis provides multiple simultaneous views to cover different aspects of the complex underlying data structure of treatment plans and patient data. The tightly coupled views use visualization methods well-known to domain experts and are designed to facilitate users' tasks. The views are based on the concepts of clinical algorithm maps and LifeLines which have been extended in order to cope with the powerful and expressive plan representation language Asbru. Initial feedback of physicians was encouraging and is accompanied by empirical evidence which verifies that PlanningLines are well suited to manage temporal uncertainty. Conclusion: The interactive integration of different visualization methods forms a novel way of combining, relating, and analyzing different kinds of medical data and information that otherwise would be separated.

OBJECTIVES: During the last decade, evidence-based medicine has given rise to an increasing number of medical practice guidelines and protocols. However, the work done on developing and distributing protocols outweighs the efforts on guaranteeing their quality. Indeed, anomalies like ambiguity and incompleteness are frequent in medical protocols. Recent efforts have tried to address the problem of protocol improvement, but they are not sufficient since they rely on informal processes and notations. Our objective is to improve the quality of medical protocols.APPROACH: The solution we suggest to the problem of quality improvement of protocols consists in the utilisation of formal methods. It requires the definition of an adequate protocol representation language, the development of techniques for the formal analysis of protocols described in that language and, more importantly, the evaluation of the feasibility of the approach based on the formalisation and verification of real-life medical protocols. For the first two aspects we rely on earlier work from the fields of knowledge representation and formal methods. The third aspect, i.e. the evaluation of the use of formal methods in the quality improvement of protocols, constitutes our main objective. The steps with which we have carried out this evaluation are the following: (1) take two real-life reference protocols which cover a wide variety of protocol characteristics; (2) formalise these reference protocols; (3) check the formalisation for the verification of interesting protocol properties; and (4) determine how many errors can be uncovered in this way.RESULTS: Our main results are: a consolidated formal language to model medical practice protocols; two protocols, each both modelled and formalised; a list of properties that medical protocols should satisfy; verification proofs for these protocols and properties; and perspectives of the potentials of this approach. Our results have been evaluated by a panel of medical experts, who judged that the problems we detected in the protocols with the help of formal methods were serious and should be avoided.CONCLUSIONS: We have succeeded in demonstrating the feasibility of formal methods for improving medical protocols.

As shown in numerous studies, a significant part of published clinical guidelines is tainted with different types of semantical errors that interfere with their practical application. The adaptation of generic guidelines, necessitated by circumstances such as resource limitations within the applying organization or unexpected events arising in the course of patient care, further promotes the introduction of defects. Still, most current approaches for the automation of clinical guidelines are lacking mechanisms, which check the overall correctness of their output. In the domain of software engineering in general and in the domain of knowledge-based systems (KBS) in particular, a common strategy to examine a system for potential defects consists in its verification. The focus of this work is to present an approach, which helps to ensure the semantical correctness of clinical guidelines in a three-step process. We use a particular guideline specification language called Asbru to demonstrate our verification mechanism. A scenario-based evaluation of our method is provided based on a guideline for the artificial ventilation of newborn infants. The described approach is kept sufficiently general in order to allow its application to several other guideline representation formats.

Medical diagnosis and therapy planning at modern intensive care units (ICUs) have been refined by the technical improvement of their equipment. However, the bulk of continuous data arising from complex monitoring systems in combination with discontinuously assessed numerical and qualitative data creates a rising information management problem at neonatal ICUs (NICUs). We developed methods for data validation and therapy planning which incorporate knowledge about point and interval data, as well as expected qualitative trend descriptions to arrive at unified qualitative descriptions of parameters (temporal data abstraction). Our methods are based on schemata for data-point transformation and curve fitting which express the dynamics of and the reactions to different degrees of parameters' abnormalities as well as on smoothing and adjustment mechanisms to keep the qualitative descriptions stable. We show their applicability in detecting anomalous system behavior early, in recommending therapeutic actions, and in assessing the effectiveness of these actions within a certain period. We implemented our methods in VIE-VENT, an open-loop knowledge-based monitoring and therapy planning system for artificially ventilated newborn infants. The applicability and usefulness of our approach are illustrated by examples of VIE-VENT. Finally, we present our first experiences with using VIE-VENT in a real clinical setting.

The computerization of clinical practice guidelines is a significant scientific challenge for the medical informatics community. One frequently reported factor hindering this objective is the existence of deficiencies within guideline knowledge. In this paper, we focus on the detection of flaws within temporal scheduling constraints. Temporal scheduling constraints are important elements of therapy management, and are frequently incorporated in clinical practice guidelines. We present a suitable verification method that is based on calculating the minimal network of temporal constraints on the execution of guideline activities. Our method serves three purposes: (1) it checks whether temporal scheduling constraints are consistent with scheduling constraints implied by control flow operators and the hierarchical structuring of a guideline; (2) it yields suggestions for an equivalent, yet more explicit representation of non-minimal constraints; (3) it can be used by the guideline interpreter to assemble feasible time intervals for the execution of each guideline activity. We evaluate our approach by applying it to a guideline specified in the Asbru language. For this purpose, we implemented a prototype verifier. Although we concentrate on the guideline representation language Asbru as the demonstration medium of our method within this paper, our approach can be reused to verify several alternative guideline representation formats.

Calculating the daily changing composition of parenteral nutrition for small newborn infants is troublesome and time consuming routine work in neonatal intensive care. The task needs expertise and experience and is prone to inherent calculation errors. We designed VIE-PNN (Vienna Expert System for Parenteral Nutrition of Neonates), a knowledge-based system (KBS) in order to reduce daily routine work and calculation errors. VIE-PNN was redesigned several times because the clinicians accepted the system only when it saved time. The most recent version of VIE-PNN uses an Hypertext Markup Language (HTML)-based client-server architecture and is integrated into the intranet of the local patient data management system. Since more than 3 years all parenteral nutrition plans are calculated using VIE-PNN. Evaluating the system's performance and the users contentedness, we compared 50 nutrition plans calculated in parallel using VIE-PNN or a hand-held calculator, retrospectively analyzed more than 5000 nutrition plans stored in VIE-PNNs database and evaluated a user questionnaire. Nutrition plans were calculated in a mean time of 2.4 versus 7.1min using VIE-PNN or the hand-held calculator. Errors and omissions in the nutrition plans were detected in 22% versus 56% and errors in the VIE-PNN's plans occurring only with interactively changed values. Reviews of stored plans show that a mean of 4 out of 16 parameters were interactively changed. VIE-PNN was well accepted. Most important reasons for the successful operation of VIE-PNN in the daily routine work were time savings and robustness of the system.

Therapy planning plays an increasingly important role in the everyday work of physicians. Clinical protocols or guidelines are typically represented using flow-charts, decision tables, or plain text. These representations are badly suited, however, for complex medical procedures. One representation method that overcomes these problems is the language Asbru. But because Asbru has a LISP-like syntax (and also incorporates many concepts from computer science), it is not suitable for physicians. Therefore, we developed a visualization and user interface to deal with treatment plans expressed in Asbru. We use graphical metaphors to make the underlying concepts easier to grasp, employ glyphs to communicate complex temporal information and colors to make it possible to understand the connection between the two views (Topological View and Temporal View) available in the system. In this paper, we present the design ideas behind AsbruView, and discuss its usefulness based on the results of a usability study we performed with six physicians.

ions with respect to real time constraints; (ii) distributed architectures, agent models and communication protocols; (iii) knowledge acquisition and representation (for a review see [2]). The four articles we selected for this special issue are mainly concerned with the third topic knowledge acquisition and representation. Two articles propose data-driven techniques to improve the exploitation of raw data coming from medical devices present at the patient's bedside. Clearly, a set of