Patient-tailored Workflow Patterns from Clinical Practice Guidelines Recommendations

Abstract MobiGuide is a project devoted to the development of a patient-centric decision support system based on computerized clinical guidelines for chronic illnesses including Atrial . In this paper we describe the process o

Porcine Epidemic Diarrhea in Europe: In-Detail Analyses of Disease Dynamics and Molecular Epidemiology

Porcine epidemic diarrhea (PED) is an acute and highly contagious enteric disease of swine caused by the eponymous virus (PEDV) which belongs to the genus Alphacoronavirus within the Coronaviridae virus family. Following the disastrous outbreaks in Asia and the United States, PEDV has been detected also in Europe. In order to better understand the overall situation, the molecular epidemiology, and factors that might influence the most variable disease impact;40 samples from swine feces were collected from different PED outbreaks in Germany and other European countries and sequenced by shot-gun next-generation sequencing. A total of 38 new PEDV complete coding sequences were generated. When compared on a global scale, all investigated sequences from Central and South-Eastern Europe formed a rather homogeneous PEDV S INDEL cluster, suggesting a recent re-introduction. However, in-detail analyses revealed two new clusters and putative ancestor strains. Based on the available background data, correlations between clusters and location, farm type or clinical presentation could not be established. Additionally, the impact of secondary infections was explored using the metagenomic data sets. While several coinfections were observed, no correlation was found with disease courses. However, in addition to the PEDV genomes, ten complete viral coding sequences from nine different data sets were reconstructed each representing new virus strains. In detail, three pasivirus A strains, two astroviruses, a porcine sapelovirus, a kobuvirus, a porcine torovirus, a posavirus, and an enterobacteria phage were almost fully sequenced

MobiGuide: a personalized and patient-centric decision-support system and its evaluation in the atrial fibrillation and gestational diabetes domains

MobiGuide is a ubiquitous, distributed and personalized evidence-based decision-support system (DSS) used by patients and their care providers. Its central DSS applies computer-interpretable clinical guidelines (CIGs) to provide real-time patient-specific and personalized recommendations by matching CIG knowledge with a highly-adaptive patient model, the parameters of which are stored in a personal health record (PHR). The PHR integrates data from hospital medical records, mobile biosensors, data entered by patients, and recommendations and abstractions output by the DSS. CIGs are customized to consider the patients’ psycho-social context and their preferences; shared decision making is supported via decision trees instantiated with patient utilities. The central DSS “projects” personalized CIG-knowledge to a mobile DSS operating on the patients’ smart phones that applies that knowledge locally. In this paper we explain the knowledge elicitation and specification methodologies that we have developed for making CIGs patient-centered and enabling their personalization. We then demonstrate feasibility, in two very different clinical domains, and two different geographic sites, as part of a multi-national feasibility study, of the full architecture that we have designed and implemented. We analyze usage patterns and opinions collected via questionnaires of the 10 atrial fibrillation (AF) and 20 gestational diabetes mellitus (GDM) patients and their care providers. The analysis is guided by three hypotheses concerning the effect of the personal patient model on patients and clinicians’ behavior and on patients’ satisfaction. The results demonstrate the sustainable usage of the system by patients and their care providers and patients’ satisfaction, which stems mostly from their increased sense of safety. The system has affected the behavior of clinicians, which have inspected the patients’ models between scheduled visits, resulting in change of diagnosis for two of the ten AF patients and anticipated change in therapy for eleven of the twenty GDM patients