BackgroundThe process of creating and designing Virtual Patients for teaching students of medicine is an expensive and time-consuming task. In order to explore potential methods of mitigating these costs, our group began exploring the possibility of creating Virtual Patients based on electronic health records. This review assesses the usage of electronic health records in the creation of interactive Virtual Patients for teaching clinical decision-making.MethodsThe PubMed database was accessed programmatically to find papers relating to Virtual Patients. The returned citations were classified and the relevant full text articles were reviewed to find Virtual Patient systems that used electronic health records to create learning modalities.ResultsA total of n = 362 citations were found on PubMed and subsequently classified, of which n = 28 full-text articles were reviewed. Few articles used unformatted electronic health records other than patient CT or MRI scans. The use of patient data, extracted from electronic health records or otherwise, is widespread. The use of unformatted electronic health records in their raw form is less frequent. Patient data use is broad and spans several areas, such as teaching, training, 3D visualisation, and assessment.ConclusionsVirtual Patients that are based on real patient data are widespread, yet the use of unformatted electronic health records, abundant in hospital information systems, is reported less often. The majority of teaching systems use reformatted patient data gathered from electronic health records, and do not use these electronic health records directly. Furthermore, many systems were found that used patient data in the form of CT or MRI scans. Much potential research exists regarding the use of unformatted electronic health records for the creation of Virtual Patients.

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The MEDLINE database (Medical Literature Analysis and Retrieval System Online) contains an enormously increasing volume of biomedical articles. There is urgent need for techniques which enable the discovery, the extraction, the integration and the use of hidden knowledge in those articles. Text mining aims at developing technologies to help cope with the interpretation of these large volumes of publications. Co-occurrence analysis is a technique applied in text mining and the methodologies and statistical models are used to evaluate the significance of the relationship between entities such as disease names, drug names, and keywords in titles, abstracts or even entire publications. In this paper we present a method and an evaluation on knowledge discovery of disease-disease relationships for rheumatic diseases. This has huge medical relevance, since rheumatic diseases affect hundreds of millions of people worldwide and lead to substantial loss of functioning and mobility. In this study, we interviewed medical experts and searched the ACR (American College of Rheumatology) web site in order to select the most observed rheumatic diseases to explore disease-disease relationships. We used a web based text-mining tool to find disease names and their co-occurrence frequencies in MEDLINE articles for each disease. After finding disease names and frequencies, we normalized the names by interviewing medical experts and by utilizing biomedical resources. Frequencies are normally a good indicator of the relevance of a concept but they tend to overestimate the importance of common concepts. We also used Pointwise Mutual Information (PMI) measure to discover the strength of a relationship. PMI provides an indication of how more often the query and concept co-occur than expected by change. After finding PMI values for each disease, we ranked these values and frequencies together. The results reveal hidden knowledge in articles regarding rheumatic diseases indexed by MEDLINE, thereby exposing relationships that can provide important additional information for medical experts and researchers for medical decision-making.

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BackgroundVirtual Patients are a well-known and widely used form of interactive software used to simulate aspects of patient care that students are increasingly less likely to encounter during their studies. However, to take full advantage of the benefits of using Virtual Patients, students should have access to multitudes of cases. In order to promote the creation of collections of cases, a tablet application was developed which makes use of electronic health records as material for Virtual Patient cases. Because electronic health records are abundantly available on hospital information systems, this results in much material for the basis of case creation.ResultsAn iPad-based Virtual Patient interactive software system was developed entitled Casebook. The application has been designed to read specially formatted patient cases that have been created using electronic health records, in the form of X-ray images, electrocardiograms, lab reports, and physician notes, and present these to the medical student. These health records are organised into a timeline, and the student navigates the case while answering questions regarding the patient along the way. Each health record can also be annotated with meta-information by the case designer, such as insight into the thought processes and the decision-making rationale of the physician who originally worked with the patient. Students learn decision-making skills by observing and interacting with real patient cases in this simulated environment. This paper discusses our approach in detail.ConclusionsOur group is of the opinion that Virtual Patient cases, targeted at undergraduate students, should concern patients who exhibit prototypical symptoms of the kind students may encounter when beginning their first medical jobs. Learning theory research has shown that students learn decision-making skills best when they have access to multitudes of patient cases and it is this plurality that allows students to develop their illness scripts effectively. Casebook emphasises the use of pre-existing electronic health record data as the basis for case creation, thus, it is hoped, making it easier to produce cases in larger numbers. By creating a Virtual Patient system where cases are built from abundantly available electronic health records, collections of cases can be accumulated by institutions.