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P A de Clercq

Publications and source records attributed to P A de Clercq.

8 recordsLinked to original sources
PubMed2005

Three-Layer Model for the design of a Protocol Support System.

OBJECTIVE: The aim of the PropeR project is to investigate the impact of Active Computerized Protocol Support (ACPS) on daily care processes in different settings (home care and hospital care). ACPS consists of an active Protocol Support System (PSS) that is linked to an Electronic Patient Record system. The aim of this paper is to describe how we have taken the organizational and social aspects into account in the hospital setting and the consequences of this approach for the design of the PSS. METHODS: Socio-technical approaches have been applied. Observations and interviews with various health care providers were performed at the hematology and oncology department of the University Hospital Maastricht. Ten extensive sessions with a specialist physician and research nurse took place to further elaborate a study protocol and to discuss how it is integrated in daily practice. The knowledge editor component of Gaston was used to build a computer interpretable version of the selected protocol. RESULTS AND CONCLUSIONS: To support the representation of a study protocol integrated in routine clinical care, a Three-Layer Model was developed. This model distinguishes the protocol description, local adaptations to the protocol and communication as three separate layers. These layers have been incorporated into the knowledge acquisition tool Gaston. The Three-Layer Model makes easy updating possible, and also supports transferability of computerized (study) protocols to other organizations.

Clinical Protocols↗
PubMed2001

The application of ontologies and problem-solving methods for the development of shareable guidelines.

Recently, studies have shown the benefits of using clinical guidelines in the practice of medicine. Computer-based clinical guidelines are increasingly applied in diverse areas such as policy development, utilization management, education, conduct of clinical trials, and workflow facilitation. This paper discusses some of the representations suggested in literature, discusses their weak and strong points, and demonstrates and discusses a new approach that extends earlier developed formalisms by combining primitives, ontologies and the use of problem-solving methods (PSMs). The approach is supported by a framework that facilitates the entire guideline authoring process. The paper demonstrates this framework and presents examples of guidelines, PSMs and systems that were developed by means of this approach. The overall goal of this approach is to improve the acceptance of shareable guidelines and decision support systems in daily care by facilitating the guideline acquisition and execution phases.

Decision Support Systems, Clinical↗
PubMed2001

Design and implementation of a framework to support the development of clinical guidelines.

This paper describes and discusses a framework that facilitates the development of clinical guideline application tasks. The framework, named GASTON covers all stages in the guideline development process, ranging from the definition of models that represent guidelines to the implementation of run-time systems that provide decision support, based on the guidelines that were developed during the earlier stages. The GASTON framework consists of (1) a newly developed guideline representation formalism that uses the concepts of primitives, problem-solving methods (PSMs) and ontologies to represent the guidelines of various complexity and granularity and different application domains, (2) a guideline authoring environment that enables guideline authors to define the guidelines, based on the newly developed guideline representation formalism and (3) a guideline execution environment that translates defined guidelines into a more efficient symbol level representation, which can be read in and processed by an execution time engine. The paper describes a number of design criteria that were formulated regarding the aspects of guideline representation, guideline authoring and guideline execution and explains the framework by example in terms of the four stages that were identified in the guideline development process and the tools that were developed to support each stage. It also shows examples of systems that were developed by means of the GASTON framework.

Artificial Intelligence↗
PubMed2001

Design of a consumer health record for supporting the patient-centered management of chronic diseases.

This paper describes and discusses the design and usage of a shareable consumer health record system to investigate whether these systems can assist in the management of chronic diseases. This web-based system that can be used both by care providers and patients contains medical and patient information, provides access to websites that contain quality information, provides guideline-based advice, allows discussion between patients and allows us to interrogate both patients and care providers on a regular basis in order to get a good impression of the utility of such a consumer record for both chronic patients and the physicians and nurses. A health record system that was developed for the area of Diabetes is presented as an example.

Chronic Disease↗
PubMed2000

A test ordering system with automated reminders for primary care based on practice guidelines.

In this article we describe a real-time automated reminder system that has been developed to change Family Physicians' (FP) test ordering behavior. The system focuses on the appropriateness of test requests. We aim at using the system as a substitute for written feedback by human experts. The reminder system consists of a knowledge base, an order entry system and modules to provide passive and active support in the form of reminders to FPs. The system generates critical comments about the rationality of the test requests at the moment the FP orders a test that is not in line with national or regional guidelines. For the first validation of the knowledge base we compared the comments of a human expert to the comments of the reminder system on three random samples of test requests. The overall agreement in the subsequent validation rounds was 46, 60 and 69%. The corrections made in the knowledge base after each validation round resulted in a reminder system with 149 reminders concerning various medical problems. Due to the corrections in the knowledge base the reminder system reacts better over the subsequent validation rounds.

Diagnostic Tests, Routine↗
PubMed2000

An ontological approach for the development of shareable guidelines.

Computer-based clinical guidelines and protocols are being increasingly applied in diverse areas. Although there is still little standardization to facilitate sharing, various parties are engaged in the development of shareable guideline representation formalisms and corresponding decision support systems. This paper mentions some of these developed representations, discusses their pros en cons, and demonstrates and discusses a new approach, which combines common elements from earlier-developed formalisms with new ones to improve the reusability and shareability of developed guidelines. An ontological representation is presented that formalizes guidelines in terms of domain-specific knowledge and employed generic strategies that use this domain-specific knowledge in order to solve particular guideline tasks. Furthermore, a framework is described that supports this representation and three examples are shown of guidelines of various granularity and complexity that were developed by means of this approach.

Algorithms↗
PubMed1999

A strategy for developing practice guidelines for the ICU using automated knowledge acquisition techniques.

OBJECTIVES: To implement practice guideline entry tools in a reminder system in order to provide decision support to health care workers in clinical care and emergency care environments. To design a knowledge acquisition environment that enables physicians to formulate, update, and verify guidelines without the assistance of a knowledge engineer. METHODS: We developed a knowledge acquisition environment for the Intensive Care Unit (ICU) consisting of 1) a graphical knowledge acquisition tool, 2) tools that perform logical and semantic tests on proposed guidelines, 3) a Patient Data Management System (PDMS) containing clinical patient data, and 4) an expert system that reminds ICU health care workers of inconsistencies between a treatment plan and implemented guidelines. Physicians enter the guidelines using the knowledge acquisition tool, after which consistency and correctness tests are performed on the guidelines. The guidelines are then transferred to the knowledge base of the reminder system and validated by applying the new guidelines to a large stored data set of previous patients. If the new guidelines are approved, they are exported to the reminder system that is used in daily practice. RESULTS: ICU physicians used the knowledge acquisition tool to enter 58 guidelines into the reminder system's knowledge base. These guidelines were tested on a data set consisting of 803 previously admitted patients. As a result, 27 guidelines fired at least once, generating 406 reminders in total. Of the 406 generated reminders, 356 (88%) were issued correctly and 50 (12%) were false alarms. The reminders that were issued correctly involved 3 situations: 1) the database contained inconsistent or incomplete information, 2) the actions or decisions of the health care workers were not the most appropriate ones, and 3) there was a potential risk involved. All false alarms were caused by the fact that the corresponding guidelines were not specific enough to handle certain exceptions. As a result of this analysis, the guidelines could be improved in such a way as to eliminate all false alarms. CONCLUSIONS: These first results demonstrate that this bottom-up knowledge acquisition strategy, implemented by the automated knowledge acquisition tools, enables medical specialists to improve the quality of computer support in an ICU without assistance of a knowledge engineer.

Artificial Intelligence↗