EGADSS (Evidence-based Guideline and Decision Support System) is an open source tool that is designed to work in conjunction with primary care Electronic Medical Record (EMR) systems to provide patient specific point of care reminders in order to aid physicians provide high quality care. EGADSS is designed as a stand alone system that would respond to requests from existing Electronic Medical Records such as Wolf, Med Access, and MedOffIS to provide patient specific clinical guidance based on its internal collection of guidelines.
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Snofyre is an open source, service oriented API for creating SNOMED CT enabled applications in Java. It provides a number of SNOMED CT related services out of the box. These services can be used:
- as a starter for understanding how to add SNOMED CT functionality to an application.
- to rapidly prototype a SNOMED CT enabled application.
Snofyre API aims to
- reduce the 'ramp up' time needed to understand
- and embed SNOMED CT functionality in an application.
Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization. It is also well-suited for developing new machine learning schemes.
Laika analyzes and reports on the interoperability capabilities of EHR systems. This includes the testing for certification of EHR software products and networks.
To support EHR data interoperability testing, Laika is designed to verify the input and output of EHR data against the standards and criteria identified by the Certification Commission for Health Information Technology (CCHIT). Laika is used by the Certification Commission to perform part of the interoperability certification inspection of EHRs.
The HL7 Inspector is a useful hl7 tool for integration the HL7 in a health care environmental. It will help you to minimize the time for tuning the HL7 communication between systems such as HIS and RIS by analyzing and validating HL7 messages.
tranSMART is a knowledge management platform that enables scientists to develop and refine research hypotheses by investigating correlations between genetic and phenotypic data, and assessing their analytical results in the context of published literature and other work.
The integration, normalization, and alignment of data in tranSMART permits users to explore data very efficiently to formulate new research strategies. Some of tranSMART's specific applications include:
ACHILLES is a platform which enables the characterization, quality assessment and visualization of observational health databases. ACHILLES provides users with an interactive, exploratory framework to assess patient demographics, the prevalence of conditions, drugs and procedures, and to evaluate the distribution of values for clinical observations.
ACHILLES is intended to be implemented by organizations that have patient-level observational health databases available in their local environment.
i2b2 has turned out to be a very valuable component for secondary use of routine clinical data. Its pragmatic database schema allows merging of data from heterogeneous data sources, and the intuitive user interface enables easy querying and powerful processing. However, it's a component rather than a complete solution: The user is facing several barriers when integrating i2b2 into the operational workflow.
OBiBa software consists of a suite of stand-alone applications that support various study's data management activities. These modular applications can be integrated to create a comprehensive information management and analysis system for individual studies.
As part of the Maelstrom Research program, OBiBa suite includes advanced software components enabling data harmonization and federation for study networks that aim to harmonize and share securely data among their members.
MOLGENIS is a modular web application for scientific data. MOLGENIS was born from molecular genetics research (and was called 'molecular genetics information system') but has grown, thanks too many sponsors and contributors, to be used in many scientifc areas such as biobanking, rare disease research, patient registries and even energy research. MOLGENIS provides researchers with user friendly and scalable software infrastructures to capture, exchange, and exploit the large amounts of data that is being produced by scientific organisations all around the world.