Electronic implementation of guidelines in the EsPeR system: a knowledge specification method.

Isabelle Colombet; Angel-Ricardo Aguirre-Junco; Sylvain Zunino; Marie-Christine Jaulent; Laurence Leneveut; Gilles Chatellier

doi:10.1016/j.ijmedinf.2005.05.001

PubMed · 15921954

Electronic implementation of guidelines in the EsPeR system: a knowledge specification method.

Isabelle Colombet·Angel-Ricardo Aguirre-Junco·Sylvain Zunino·Marie-Christine Jaulent·Laurence Leneveut·Gilles Chatellier

View original source ↗Export metadata ↓

Abstract

Despite initiatives to standardize methods for the development of clinical guidelines, several barriers hinder their integration in daily clinical practice: failure to fulfil quality criteria, poor effectiveness of their dissemination. Computerization of guidelines can favor their dissemination. The initial step of computerization is the knowledge specification from the text of the guideline. We describe the method of knowledge specification, which is used in EsPeR (Personalized Estimate of Risks), a web-based decision support system in preventive medicine, which allows, for a given person, to estimate risks and access recommendations, based on clinical profile. This method is based on a structured and systematic analysis of text allowing detailed specification of a decision tree. We use decision tables to validate the decision algorithm and decision trees to specify this algorithm, along with elementary messages of recommendation. Editing tools are used to facilitate the process of validation and the workflow between expert physicians and computer scientists. Applied to eleven different guidelines, the method allows a quick and valid computerization and integration in the EsPeR system. The method used for computerization could help to define a framework usable at the initial step of guideline development in order to produce guidelines ready for electronic implementation.

Explore related subjects

Decision Support Systems, Clinical France Guideline Adherence Internet Practice Guidelines as Topic Preventive Medicine Risk Assessment Journal Article Research Support, Non-U.S. Gov't Validation Study

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Isabelle Colombet, Angel-Ricardo Aguirre-Junco, Sylvain Zunino, Marie-Christine Jaulent, Laurence Leneveut, Gilles Chatellier. 2005. Electronic implementation of guidelines in the EsPeR system: a knowledge specification method.. https://doi.org/10.1016/j.ijmedinf.2005.05.001

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2007

Rethinking electronic health records to better achieve quality and safety goals.

Health care information technology changes the ecosystem of a practice. Human roles, process work flow, and technology infrastructure are tightly interrelated. Medical errors may increase if a change in one is not accommodated by a change in the others. Introduction of information technology should be approached as an iterative process of care improvement rather than as a one-time insertion of an information system into established practice. Information technology supports a family of technological approaches, each with distinct mechanisms of action, benefits, and side effects. By matching technological approach to task and staging introduction into practice, initial benefit can be obtained more quickly, at reduced cost, while managing risk of a misfit. A staged approach to turning direct access by patients to their health information into more effective care is presented as an example of this strategy.

Decision Support Systems, Clinical↗

PubMed2006

Context-sensitive autoassociative memories as expert systems in medical diagnosis.

BACKGROUND: The complexity of our contemporary medical practice has impelled the development of different decision-support aids based on artificial intelligence and neural networks. Distributed associative memories are neural network models that fit perfectly well to the vision of cognition emerging from current neurosciences. METHODS: We present the context-dependent autoassociative memory model. The sets of diseases and symptoms are mapped onto a pair of basis of orthogonal vectors. A matrix memory stores the associations between the signs and symptoms, and their corresponding diseases. A minimal numerical example is presented to show how to instruct the memory and how the system works. In order to provide a quick appreciation of the validity of the model and its potential clinical relevance we implemented an application with real data. A memory was trained with published data of neonates with suspected late-onset sepsis in a neonatal intensive care unit (NICU). A set of personal clinical observations was used as a test set to evaluate the capacity of the model to discriminate between septic and non-septic neonates on the basis of clinical and laboratory findings. RESULTS: We show here that matrix memory models with associations modulated by context can perform automatic medical diagnosis. The sequential availability of new information over time makes the system progress in a narrowing process that reduces the range of diagnostic possibilities. At each step the system provides a probabilistic map of the different possible diagnoses to that moment. The system can incorporate the clinical experience, building in that way a representative database of historical data that captures geo-demographical differences between patient populations. The trained model succeeds in diagnosing late-onset sepsis within the test set of infants in the NICU: sensitivity 100%; specificity 80%; percentage of true positives 91%; percentage of true negatives 100%; accuracy (true positives plus true negatives over the totality of patients) 93,3%; and Cohen's kappa index 0,84. CONCLUSION: Context-dependent associative memories can operate as medical expert systems. The model is presented in a simple and tutorial way to encourage straightforward implementations by medical groups. An application with real data, presented as a primary evaluation of the validity and potentiality of the model in medical diagnosis, shows that the model is a highly promising alternative in the development of accuracy diagnostic tools.

Decision Support Systems, Clinical↗

PubMed2006

Creating a GIS application for health services at Jeddah city.

This paper explores the possibilities of using GIS for private hospitals at Jeddah city, Saudi Arabia. A GIS application is created to cover three main health planning issues which are distribution of health demand, classification of hospital patients and the definition of hospital service area. Each one of these issues is covered using several GIS functions including network analysis and overlay analysis. The former is used to produce drive-time hospital service area and the latter is applied at the selected hospital to calculate the size of its served demand. GIS has several useful functions and tools that can be used in health planning field. This paper uses some of these functions for one private hospital. These functions are used to help health planners on evaluating the spatial distribution of hospital demand and for defining hospital service area. All the produced models can be applied on any private or public hospital in Jeddah city. They can be used to build a spatial decision support system for hospitals in Jeddah city.

Decision Support Systems, Clinical↗