Juan Manuel Pikatza

Evaluating Acceptance and User Experience of a Guideline-based Clinical Decision Support System Execution Platform

This study aims to determine what the initial disposition of physicians towards the use of Clinical Decision Support Systems (CDSS) based on Computerised Clinical Guidelines and Protocols (CCGP) is; and whether their prolonged utilisation has a positive effect on their intention to adopt them in the future. For a period of 3 months, 8 volunteer paediatricians monitored each up to 10 asthmatic patients using two CCGPs deployed in thee-GuidesMed CDSS. A Technology Acceptance Model (TAM) questionnaire was supplied to them before and after using the system. Results from both questionnaires are analysed searching for significant improvements in opinion between them. An additional survey was performed to analyse the usability of the system. It was found that initial disposition of physicians towards e-Guidesmed is good. Improvement between the pre and post iterationsof the TAM questionnaire has been found to be statistically significant. Nonetheless, slightly lower values in the Compatibility and Habit variables show that participants perceive possible difficulties to integrate e-GuidesMed into their daily routine. The variable Facilitators shows the highest correlation with the Intention to Use. Usabilityof the system has also been rated very high and, in this regard, no fundamental flaw has been detected. Initial views towards e-GuidesMed are positive, and become reinforced after continued utilisation of the system. In order to achieve an effective implementation, it becomes essential to facilitate conditions to integrate the system intothe physician’s daily routine.

Towards a Clinical Practice Guideline Implementation for Asthma Treatment

There is a tremendous amount of effort involved in the definition of Clinical Practice Guidelines (CPG) by physicians. Because the quality of medical assistance is highly impacted by the use of CPG, and establishing their use is difficult, we consider helpful to develop an effective solution that implements CPG through Decision Support Systems (DSS). Among the many existing representation models for CPG, we have selected and applied GLIF. In addition, we have created ontologies for the domains of asthma severity and Fuzzy Multicriteria Decision Aid approach (PROAFTN method). The results have been integrated into our DSS called Arnasa in order to provide support via Web to asthmatic patients.

Automatic retrieval of current evidence to support update of bibliography in clinical guidelines

Highlights? A system that automatically updates existing clinical guidelines. ? The method finds new relevant articles based on existing bibliography. ? It is intended as a decision support system to medical practitioners. ? Its validated using guidelines from the national guideline clearinghouse. This paper reports on a system developed to support medical experts in the process of updating clinical guidelines by automatically suggesting new articles suitable to the domain under consideration. It follows a comprehensive process based on several consecutive steps in order to (i) identify which articles from the current guideline are eligible to be updated; (ii) retrieve and filter new related articles from medline; and (iii) select the most relevant resulting articles by applying a scoring algorithm. Extensive validation is based on a set of experiments on 40 guidelines from multiple medical domains. The analysis of results shows a promising prospect as indicated by recall values greater than 90%.

Automatic construction of Fuzzy Inference Systems for computerized clinical guidelines and protocols

We create Fuzzy Inference Systems (FIS) as a means of computerizing differential diagnosis (DD) tables.Use of Model-Driven Software Engineering (MDSE) techniques to systematize FIS development process.FIS design and edition from both domain expert and knowledge engineer perspectives.Calculation results shown in an easily comprehensible and self-explanatory way.Tested in the development of two FIS for a computerized clinical guideline for hyperammonemia care. Clinical guidelines and protocols (CGPs) are standard documents with the aim of helping practitioners in their daily work. Their computerization has received much attention in recent years, but it still presents some problems, mainly due to the low sustainability and low adaptability to changes (both in knowledge and technology) of the computerized CGPs. This paper presents an approach to an easy and automatic creation of Fuzzy Inference Systems (FISs), which are suitable for the computerized interpretation of differential diagnoses. The proposed FIS development process is based on applying Model-Driven Software Engineering techniques: automatic generation of computer artefacts and separation of concerns. The process focuses on the separation of roles during the design stage: domain experts use a basic editor that allows them to define the categories and factors that will be involved in the FIS in natural language, while knowledge engineers at a later stage refine these elements using a more advanced editor. The whole system has been tested by automatically generating two FISs that have been included in a computerized CGP for the diagnosis of a rare disease called hyperammonemia. This CGP has been validated and it is currently in use.

Towards an Effective Knowledge Translation of Clinical Guidelines and Complementary Information

Clinical guidelines enable best medical evidence transfer to where best practice is needed. Although technology is considered the best way to reach this goal, the desired results have not been achieved yet.

Intrusion detection using text mining in a web-based telemedicine system

Security in telemedicine systems might be considered a particularly sensitive subject due to the type of confidential information generally handled and the responsibilities consequently derived. In this work we focus on detecting attempts of gaining unauthorised access to a telemedicine web application. We introduce a new Text Mining module that by using Text Categorisation of the web application server log entries is capable of learning the characteristics of both normal and malicious user behaviour. As a result, the detection of misuse in the web application is achieved without the need of explicit programming hence improving the system maintainability.

Improvement of newborn screening using a fuzzy inference system

Newborn screening plans are a routine for newborn care.We present a fuzzy inference system to support newborn screening.It screens for 46 inborn errors of metabolism and takes as input up to 42 analytes.It has been tested with two different types of samples from three distinct sources.High accuracy on diagnosis is obtained in 98.7% less time than laboratory personnel. This paper presents a decision support system (DSS) called DSScreening to rapidly detect inborn errors of metabolism (IEMs) in newborn screening (NS). The system has been created using the Aide-DS framework, which uses techniques imported from model-driven software engineering (MDSE) and soft computing, and it is available through eGuider, a web portal for the enactment of computerised clinical practice guidelines and protocols.MDSE provides the context and techniques to build new software artefacts based on models which conform to a specific metamodel. It also offers separation of concern, to disassociate medical from technological knowledge, thus allowing changes in one domain without affecting the other. The changes might include, for instance, the addition of new disorders to the DSS or new measures to the computation related to a disorder. Artificial intelligence and soft computing provide fuzzy logic to manage uncertainty and ambiguous situations. Fuzzy logic is embedded in an inference system to build a fuzzy inference system (FIS); specifically, a single-input rule modules connected zero-order Takagi-Sugeno FIS. The automatic creation of FISs is performed by the Aide-DS framework, which is capable of embedding the generated FISs in computerized clinical guidelines. It can also create a desktop application to execute the FIS. Technologically, it supports the addition of new target languages for the desktop applications and the inclusion of new ways of acquiring data.DSScreening has been tested by comparing its predictions with the results of 152 real analyses from two groups: (1) NS samples and (2) clinical samples belonging to individuals of all ages with symptoms that do not necessarily correspond to an IEM. The system has reduced the time needed by 98.7% when compared to the interpretation time spent by laboratory professionals. Besides, it has correctly classified 100% of the NS samples and obtained an accuracy of 70% for samples belonging to individuals with clinical symptoms.