Health Intelligence Platform

Intelligent Healthcare Information Management

  • Semantic Discovery Exploitation Of Clinical Narratives
  • Integrated Semantic Information Model
  • Integrates HIS, EMR, HL7, Data Warehouses and other structured sources
  • Clinical Data Repository
  • Ontology Based Information Retrieval
  • Real Time Semantic Information Bus
  • Infinite Application Domains like Clinical Trials, Evidence Based Medicine, Documentation support etc.

Vision

Healthcare delivery depends heavily on appropriate information management. Tools and systems including HIS and EMR’s were not designed to support mature, clinical information retrieval.

Healthcare delivery, processes and people working together doing healthcare are not able to access all necessary information. Information sources like narratives are hidden or just inaccessible to be used in healthcare processes. Electronic records often contain wrong or incomplete information which renders them useless for healthcare delivery.

Future Healthcare depends highly on the possibility to

  • Analyze and Interpret Data in Real Time
  • Support Mature Decisions and Processes in Complex Situations
  • Align Healthcare Delivery with Clinical Science to Share Common Goals and Processes
  • Support Evidence Based Medicine by the Information Processed in Healthcare Delivery
  • Delivery of Continuous and Coordinated Care with Optimized Outcome as Main Goal and Influence
  • Real Time Semantic Information Bus
  • Patient Participation and Communication in and with the Healthcare Delivery Process

Vision

Product

Integrate

HIP integrates structured sources and information from clinical narratives into one common information model which supports effective information retrieval and mature applications to manage and perform healthcare processes.

Support

HIP supports various semantic models as domain description. Data and models converge in a model called semantic convergence to support simple application models. Web-based applications leverage user interaction with semantic information

Text Mining

Text Mining and information integration from various sources unlocks possibilities hidden so far from available systems like HIS or EPRs. NLP and text-mining together with semantic integration empowers domain users to interact with information from any document source

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Testimonials

Solutions

4 Doctors

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Physicians are struggling to focus on their main goal to improve patient outcomes while managing major influences on healthcare delivery that are often not aligned with that major goal Administration Documentation Communication Collaboration Patient Compliance and Adherence HIP supports applications for Evidence Based Medicine, analytics, Documentation and Risk Management based on all available information sources....

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4 Patients

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Empowered patients are among the most powerful influences of future healthcare delivery. Patient participation in healthcare processes highly depend on the availability of appropriate information to improve individual levels of health literacy. HIP semantic platform is able to support mature dialogs between the stakeholders of healthcare delivery including patients. HIP will be the information hub...

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4 CIOs

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Hospital infrastructures are a constant challenge for CIOs. HIP delivers a lightweight application infrastructure which scales as your hospital grows and more demand for intelligent solutions arise. The most prominent feature are Lightweight Application Development for Web, or Mobile Based Domain Specific Applications Flexible and Scalable Integration into HIS and Hospital Infrastructures Big Data Architecture...

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4 Pharma and Research

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Sponsors of clinical trials face multiple risks for clinical trial that depend on study center performance missed recruitment goals feasibility assessment with unreliable recruitment goals costly and ineffective recruitment misinterpretation of study criteria Researchers meet several challenges when planning clinical trials. Data collections must be valid and in line with the research purpose. Sampling data...

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Examples

Example Applications

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Situation

Complex identification of Patients with specific Criteria

Complex search in multiple data sources

Failure in feasibility estimations (>30%)

Time consuming

Benefits

Semantic search and matching on all relevant Data

Low Effort

High Speed

High Precision

Intuitive and user friendly interface

Clinical research

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Situation

Benefits

Difficult integration and dissemination of (non structured) data

No support of scientific process

Genotype – Phenotype correlation impossible

No integration with registries

Integration, semantic tagging and dissemination of all relevant data

Workflow integration

Integration of genetic and clinical data

Registry integration

Biobanks

dreamstimemedium_20122409_scale

Personalized medicine

Situation

Benefits

Integration and availability of all patient data not feasible

Genotype – Phenotype correlation impossible

Creation of a complete semantic patient record

Integration of genetic and clinical data

Matching with research databases

Advanced analytics

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Rare Diseases

Situation

Benefits

Common symptoms as indication often overlooked

Correlation of symptoms from patient history difficult/impossib le

No single view on patient history

Integration and correlation of all available patient data

Collection of historical patients data in semantic patient record with timestamps

Easy recognition of complex and rare findings

Timely correlation and retrieval of relevant symptoms with semantic queries and time constraints

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Diagnostic Support

Situation

Benefits

Wrong decision based on incomplete data

Recognition of unusual findings

Integration and correlation of all available patient data

Easy recognition of complex and rare findings