Use our standards-based healthcare data integration solution to aggregate and consolidate data from multiple different medical data sources.
The increasing ability to electronically maintain healthcare data carries with it the enormous potential to also improve overall healthcare. Collecting and analyzing data opens possibilities to further share knowledge, improve patient outcomes, optimise coordinated care processes and support evidence-based medicine. This is accomplished by comparing the effectiveness of treatments and by detecting treatment trends. A data-driven solution is a key component in providing this necessary knowledge for better care, lower cost and improved efficiency. MGRID offers such a solution for data-integration, reporting and analytics.
To drive healthcare reporting and analytics through data compiled from multiple operational data sources, it is necessary to translate source data into a common information model. Instead of developing a proprietary model, MGRID adopts the standardised and normalised models from HL7v2, HL7v3 RIM and HL7 FHIR for the Healthcare Data Model.
Benefits of using the HL7 standards for a datamodel include:
The MGRID Healthcare Data Model offers best-in-class database support and performance for all HL7 standards.
MGRID has developed applications that aid message transformation, dataset creation and dataset exploration. The modules and applications of MGRID follow the HL7 and SQL standards; they are well-suited and intended to be used as components in software stacks for the medical vertical. MGRID software makes the process of preparing data for healthcare reporting and analytics more efficient, taking the next step toward improved data analysis, and consequently, overall healthcare.
Healthcare data reporting ranges from mandatory regulated quality reports to reports that facilitate treatment planning over active patients. The main objective is to transform consolidated medical information into answers to business questions, such as:
Conceptually progressing from medical data to a report requires the following steps:
Data can be prepared in a number of ways:
BI Explorer facilitates report definition by drilling down into the prepared base reporting tables. Here queries can be constructed visually, and shared between users. Note that queries which underlie government mandated reports can be identified and shared between all Explorer users.
When all queries needed for a particular report have been constructed, they can be embedded into a report on the MGRID report server, which allows clinicians or administrators direct access to a PDF rendition for a specified period. Drill-down is supported by diving down into a specific stored query in BI Explorer. Our policy is to develop mandatory reports for a particular locality once, and then distribute them to all of our customers.
Healthcare data analytics ranges from basic statistical summaries of data and inferences to advanced predictive models. These models help to gain insight into disease progression and they support of the process of selecting the right treatment for individual patients. Statistical methods used include:
One factor that these methods have in common is that they reveal patterns in data, and from these patterns, inferences can be made to support decisions.
Data analysts spend the bulk of their time on getting, cleaning and preparing data, which is the process of preparing and selecting variables for data sets that will be used by analytical tools. Each data analysis project has its own requirements for variables (features), and during the analysis, more knowledge is gained about variables which prove to be useful, and, about the additional variables that are required. Given a repository of healthcare data, the ability to quickly prepare data sets with selected and constructed features will enable data analysts to test hypotheses quickly, thus increasing productivity.
To aid in dataset creation for healthcare research, MGRID has developed Dataset Builder. DSB removes the need to manually craft SQL or scripts to select, merge and manipulate data data, which speeds up the time of data set creation from weeks to hours. Please refer to the DSB product page for more information.
BI Explorer is a self-service BI solution that allows healthcare workers to get better insight into their patient data. The solution offers easily configurable filters, grouping and pivot functionality that answers existing questions, but also provides extra insight into the data. Timely insight into your patient's key indicators and your organization's performance can help improve the service to your patients, and reduce mistakes that could lead to life-threating complications or patient discomfort.
Any combination of filters, groupings and other functions applied on your data can be stored as presets and shared with colleagues throughout the organization. These presets can also be supplied beforehand for all users within a healthcare organizition. Besides providing insight into the organization's most important Key Performance Indicators, the presets can also help fulfill healthcare regulatory reporting requirements. With continuous insight into those answers, corrective actions can be taken leading to better scores.
BI Explorer is a multi-tenant solution that is delivered as a service, relieving healthcare organizations of the maintenance burden. To further enhance the user experience, the tool can be branded and integrated with existing (oauth-based) authentication services. Contact us for more information or a demonstration.
BI Explorer includes the following features:
BI Explorer has successfully answered questions like:
Dataset Builder speeds up creation of datasets from structured information. It can be used to extract features from a wide variety of sources into tables.
DSB includes the following features:
DSB is complementary to existing tools for data analysis and analytic workspace management, acting as a pre-processing step to produce tabular data for consumption by statistics and analytics tools. With DSB the complete transformation process from raw data to tabular data is documented and versioned, which enables re-use of constructed features, and also helps reproducible research and quality assurance of the transformation process.
Built on the MGRID Healthcare Data Model, DSB can perform in-database operations to transform physical quantity continuous variables from one unit to another, for instance from mg/dL to mmol/L. In addition, for categorical coded values, DSB has support for selections and calculations using knowledge from clinical and drug ontologies, to perform data selection and integration based on concept subtree search for hierarchical code systems such as SNOMED-CT.
Query Builder is a web-based tool for visual query building on existing databases
The Query Builder is used by researchers to select research data from existing healthcare databases, such as clinical datawarehouses, and export the data to a research location for analysis.
Actual data delivery can be controlled by an approval process that allows data stewards to either approve or deny each query before processing. If the source database contains personally identifiable data, the data can be masked or de-identified before extraction. This de-identification can be either pre-configured for the source database, or be configured per query (subject to approval by a data steward).
MGRID Message Transformation XFM provides messaging based infrastructure for the exchange of healthcare data between medical data sources and systems for secondary usage reporting and analytics. Built upon RabbitMQ from the Pivotal Big Data suite, with XFM thousands of healthcare messages per second can be received, transformed and loaded into a Healthcare Data Model (MGRID HDM or your own model).
XFM includes the following features:
A good case study of XFM was during the Portavita Benchmark, the first Big Data Healthcare Benchmark. In this benchmark many thousands of synthetic CDA and FHIR messages are generated per second, that need to be validated, transformed and loaded into a lake datawarehouse. XFM handles this pipeline, whilst allowing scaleout if more processing hardware is available.
The Messaging SDK (MSG) is delivered as a part of XFM. With this SDK, parsers can be generated for any existing or custom HL7v3 message type. The resulting parsers can convert incoming HL7 XML into SQL or JSON. Parsers for common message types such as CDA and Consolidated-CDA are included by default.
The MGRID Healthcare Data Model (HDM) provides database support for the HL7v3 and HL7 FHIR standards. After loading data, the HDM is the source of information for reporting and analytics use cases which require data from multiple sources or longer periods of time.
HDM includes the following features:
The MGRID Healthcare Data Model is the only model that provides seamless access to all HL7 standards from the same unified interface
The MGRID HDM is available on PostgreSQL and Pivotal Big Data Suite databases. PostgreSQL is a highly versatile and server suitable for OLTP deployments, and has been reported to reach over 300.000 TPS on a single node server. For Extract, Load, Transform deployments where the transform step from source data to query format data is performed on-the-fly, Pivotal Greenplum distributed parallel database server ensures query performance for large databases.
MGRID develops software for managing medical data. The advantages of MGRID are scalability for large databases, low costs in use, low investment costs for extensions and high guaranteed speed for use in interactive environments. In addition, the company provides analytical and statistical reports.
MGRID originates from Portavita and has partly the same shareholders, including Deutsche Telekom.
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