Computerization of workflows, guidelines and care pathways: a review of implementation challenges for process-oriented health information systems

There is a need to integrate the various theoretical frameworks and formalisms for modeling clinical guidelines, workflows, and pathways, in order to move beyond providing support for individual clinical decisions and toward the provision of process-oriented, patient-centered, health information systems (HIS). In this review, we analyze the challenges in developing process-oriented HIS that formally model guidelines, workflows, and care pathways. A qualitative meta-synthesis was performed on studies published in English between 1995 and 2010 that addressed the modeling process and reported the exposition of a new methodology, model, system implementation, or system architecture. Thematic analysis, principal component analysis (PCA) and data visualisation techniques were used to identify and cluster the underlying implementation ‘challenge’ themes. One hundred and eight relevant studies were selected for review. Twenty-five underlying ‘challenge’ themes were identified. These were clustered into 10 distinct groups, from which a conceptual model of the implementation process was developed. We found that the development of systems supporting individual clinical decisions is evolving toward the implementation of adaptable care pathways on the semantic web, incorporating formal, clinical, and organizational ontologies, and the use of workflow management systems. These architectures now need to be implemented and evaluated on a wider scale within clinical settings

Thirty years of artificial intelligence in medicine (AIME) conferences: A review of research themes

Over the past 30 years, the international conference on Artificial Intelligence in MEdicine (AIME) has been organized at different venues across Europe every 2 years, establishing a forum for scientific exchange and creating an active research community. The Artificial Intelligence in Medicine journal has published theme issues with extended versions of selected AIME papers since 1998

Data integration in eHealth: a domain/disease specific roadmap

The paper documents a series of data integration workshops held in 2006 at the UK National e-Science Centre, summarizing a range of the problem/solution scenarios in multi-site and multi-scale data integration with six HealthGrid projects using schizophrenia as a domain-specific test case. It outlines emerging strategies, recommendations and objectives for collaboration on shared ontology-building and harmonization of data for multi-site trials in this domain

Similarity measuring between patient traces for clinical pathway analysis

Clinical pathways leave traces, described as activity sequences with regard to a mixture of various latent treatment behaviors. Measuring similarities between patient traces can profitably be exploited further as a basis for providing insights into the pathways, and complementing existing techniques of clinical pathway analysis, which mainly focus on looking at aggregated data seen from an external perspective. In this paper, a probabilistic graphical model, i.e., Latent Dirichlet Allocation, is employed to discover latent treatment behaviors of patient traces for clinical pathways such that similarities of pairwise patient traces can be measured based on their underlying behavioral topical features. The presented method, as a basis for further tasks in clinical pathway analysis, are evaluated via a real-world data-set collected from a Chinese hospital

Data Integration in the Life Sciences: Scientific Workflows, Provenance, and Ranking

Biological research is a science which derives its findings from the proper analysis of experiments. Today, a large variety of experiments are carried-out in hundreds of labs around the world, and their results are reported in a myriad of different databases, web-sites, publications etc., using different formats, conventions, and schemas. Providing a uniform access to these diverse and distributed databases is the aim of data integration solutions, which have been designed and implemented within the bioinformatics community for more than 20 years. However, the perception of the problem of data integration research in the life sciences has changed: While early approaches concentrated on handling schema-dependent queries over heterogeneous and distributed databases, current research emphasizes instances rather than schemas, tries to place the human back into the loop, and intertwines data integration and data analysis. Transparency -- providing users with the illusion that they are using a centralized database and thus completely hiding the original databases -- was one of the main goals of federated databases. It is not a target anymore. Instead, users want to know exactly which data from which source was used in which way in studies (Provenance). The old model of "first integrate, then analyze" is replaced by a new, process-oriented paradigm: "integration is analysis - and analysis is integration". This paradigm change gives rise to some important research trends. First, the process of integration itself, i.e., the integration workflow, is becoming a research topic in its own. Scientific workflows actually implement the paradigm "integration is analysis". A second trend is the growing importance of sensible ranking, because data sets grow and grow and it becomes increasingly difficult for the biologist user to distinguish relevant data from large and noisy data sets. This HDR thesis outlines my contributions to the field of data integration in the life sciences. More precisely, my work takes place in the first two contexts mentioned above, namely, scientific workflows and biological data ranking. The reported results were obtained from 2005 to late 2014, first as a postdoctoral fellow at the Uniersity of Pennsylvania (Dec 2005 to Aug 2007) and then as an Associate Professor at Université Paris-Sud (LRI, UMR CNRS 8623, Bioinformactics team) and Inria (Saclay-Ile-de-France, AMIB team 2009-2014)

Composite Ontology-Based Medical Diagnosis Decision Support System Framework

Current medical decision support systems have evolved from the automation of medical decision routines to improving the quality of health care services. Knowledge-based systems, compared to conventional data-driven techniques, are promising to support medical decision making. However, knowledge acquisition is usually a bottleneck in the process of developing such systemsOne possibility for acquiring medical knowledge, particularly tacit knowledge, is to use data or cases in both syntactic and semantic ways. Case-based Reasoning (CBR) methodology provides a practical way of problem solving with recalled knowledge memory of solved cases. To reduce the difficulty of knowledge acquisition, this paper proposes a design of the system framework that utilizes the simplified medical knowledge:disease-symptom ontology for prediagnosis, given patients symptoms and signs as input. In the first stage, simple pattern matching is used to gather candidate diseases in diagnosis. Following that, case-based reasoning is used to refine diagnostic decision. The case base is structured with ontological knowledge model. The case retrieval process is based on semantic similarity. The diagnostic system uses a composite knowledge base, and will allow automated diagnosis recommendation. The system framework also aims at facilitating semantic explanations to the solution derived

The metaRbolomics Toolbox in Bioconductor and beyond

Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub