Exploring the use of a web-based virtual patient to support learning through reflection

This thesis explores the support of learning through reflection, in the context of medical students and practitioners, working through a series of simulated consultations involving the diagnosis and management of chronic illness. A model of the medical consultative process was defined, on which a web-based patient simulation was developed. This simulation can be accessed over the Internet using commonly available web-browsers. It enables users to interact with a virtual patient by taking a history, examining the patient, requesting and reviewing investigations, and choosing appropriate management strategies. The virtual patient can be reviewed over a number of consultations, and the patient outcome is dependant on the management strategy selected by the user. A second model was also developed, that adds a layer of reflection over the consultative process. While interacting with the virtual patient users are asked to formulate and test their hypotheses. Simple tools are included to encourage users to record their observations and thoughts for further learning, as well as providing links to web-based library resources. At the end of each consultation, users are asked to review their actions and indicate whether they think their actions were critical, relevant, or not relevant to the diagnosis and management of the patient in light of their current knowledge. Users also have the opportunity to compare their activity to their peers or an expert in the case under study. Three formal cycles of evaluation were undertaken during the design and development of the software. A number of clinicians were involved in the initial design to ensure there was an appropriate structure that matched clinical practice. Formative evaluation was conducted to review the usability of the application, and based on user feedback a number of changes were made to the user interface and structure of the application. A third, end user, evaluation was undertaken using a single case concerning the diagnosis and management of hypertriglyceridaemia in the context of Type 1B Glycogen Storage Disease. This evaluation involved ten medical students, five general practitioners and two specialists. The evaluation involved observation using a simplified think-aloud, as well as administration of a questionnaire. Users were engaged by the simulation, and were able to use the application with only a short period of training. Usability issues still exist with respect to the processing of natural language input, especially when asking questions of the virtual patient. Until such time that natural language recognition is able to provide satisfactory performance, alternative, list-based, methods of interaction will be required. Evaluation involving medical students, general practitioners, and specialist medical practitioners demonstrated that reflection can be supported and encouraged by providing appropriate tools, as well as by judiciously interrupting the consultative process and providing time for reflection to take place. Reflection could have been further enhanced if users had been educated on reflection as a learning modality prior to using SIMPRAC. Further work is also required to improve the simulation environment, improve the interfaces for supporting reflection, and further define the benefits of using this approach for medical education and professional development with respect to learning outcomes and behavioural change

Formalization and computation of quality measures based on electronic medical records

Ambiguous definitions of quality measures in natural language impede their automated computability and also the reproducibility, validity, timeliness, traceability, comparability, and interpretability of computed results. Therefore, quality measures should be formalized before their release. We have previously developed and successfully applied a method for clinical indicator formalization (CLIF). The objective of our present study is to test whether CLIF is generalizable--that is, applicable to a large set of heterogeneous measures of different types and from various domains. We formalized the entire set of 159 Dutch quality measures for general practice, which contains structure, process, and outcome measures and covers seven domains. We relied on a web-based tool to facilitate the application of our method. Subsequently, we computed the measures on the basis of a large database of real patient data. Our CLIF method enabled us to fully formalize 100% of the measures. Owing to missing functionality, the accompanying tool could support full formalization of only 86% of the quality measures into Structured Query Language (SQL) queries. The remaining 14% of the measures required manual application of our CLIF method by directly translating the respective criteria into SQL. The results obtained by computing the measures show a strong correlation with results computed independently by two other parties. The CLIF method covers all quality measures after having been extended by an additional step. Our web tool requires further refinement for CLIF to be applied completely automatically. We therefore conclude that CLIF is sufficiently generalizable to be able to formalize the entire set of Dutch quality measures for general practic

Characteristics of AIDS patients in Dutch intensive care units

Perioperative Medicine: Efficacy, Safety and Outcom

Comparison of reasoners for large ontologies in the OWL 2 EL profile

This paper provides a survey to and a comparison of state-of-the-art Semantic Web reasoners that succeed in classifying large ontologies expressed in the tractable OWL 2 EL profile. Reasoners are characterized along several dimensions: The first dimension comprises underlying reasoning characteristics, such as the employed reasoning method and its correctness as well as the expressivity and worst-case computational complexity of its supported language and whether the reasoner supports incremental classification, rules, justifications for inconsistent concepts and ABox reasoning tasks. The second dimension is practical usability: whether the reasoner implements the OWL API and can be used via OWLlink, whether it is available as Protégé plugin, on which platforms it runs, whether its source is open or closed and which license it comes with. The last dimension contains performance indicators that can be evaluated empirically, such as classification, concept satisfiability, subsumption checking and consistency checking performance as well as required heap space and practical correctness, which is determined by comparing the computed concept hierarchies with each other. For the very large ontology SNOMED CT, which is released both in stated and inferred form, we test whether the computed concept hierarchies are correct by comparing them to the inferred form of the official distribution. The reasoners are categorized along the defined characteristics and benchmarked against well-known biomedical ontologies. The main conclusion from this study is that reasoners vary significantly with regard to all included characteristics, and therefore a critical assessment and evaluation of requirements is needed before selecting a reasoner for a real-life application

Improving medication safety in the Intensive Care by identifying relevant drug-drug interactions - Results of a multicenter Delphi study

Purpose: Drug-drug interactions (DDIs) may cause adverse outcomes in patients admitted to the Intensive Care Unit (ICU). Computerized decision support systems (CDSSs) may help prevent DDIs by timely showing relevant warning alerts, but knowledge on which DDIs are clinically relevant in the ICU setting is limited. Therefore, the purpose of this study was to identify DDIs relevant for the ICU. Materials and methods: We conducted a modified Delphi procedure with a Dutch multidisciplinary expert panel consisting of intensivists and hospital pharmacists to assess the clinical relevance of DDIs for the ICU. The procedure consisted of two rounds, each included a questionnaire followed by a live consensus meeting. Results: In total the clinical relevance of 148 DDIs was assessed, of which agreement regarding the relevance was reached for 139 DDIs (94%). Of these 139 DDIs, 53 (38%) were considered not clinically relevant for the ICU setting. Conclusions: A list of clinically relevant DDIs for the ICU setting was established on a national level. The clinical value of CDSSs for medication safety could be improved by focusing on the identified clinically relevant DDIs, thereby avoiding alert fatigue