Logibec NOAH

Accelerating clinical research by mastering data with an advanced technological solution

Replicating data and identifying cohorts from "anonymized" patient data

Logibec NOAH is an innovative data management solution that allows researchers and healthcare institutions to share and access anonymized patient data securely, to replicate data and identify cohorts using granular, customized criteria with powerful, cost-effective tools that support advances in research worldwide.

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Researcher analyzing synthetic patient data to further health science research

Logibec NOAH: Push the limits of research

Logibec NOAH was created to promote interconnectivity between specialists, expertise, and locations, so that genomic, clinical, administrative, and general research data can cross-reference and complement each other in a safe, harmonious, intuitive, and integrated manner.

Thanks to Logibec NOAH, the union of the vital forces of the health industry contributes to the optimization of patient care through data sharing, identification of cohorts, and in-depth analysis of results.

Before

Graphic without crossing genomic, clinical, administrative and general health research data

After

Graphic with integrated genomic, clinical, administrative and general health research cross-referencing data

50%

of the tasks performed by researchers are automated by Logibec NOAH.

ex

Exponential increase in capacity for modeling and identifying patient cohorts.

10x

Patients can be recruited for clinical trials 10 times faster.

Break new ground in patient care using connected data 
Logibec NOAH was created to interconnect specialists, fields of expertise and locations so that genomic, clinical, administrative and general research data can be linked, referenced and integrated in a secure, harmonious and intuitive manner. 

With Logibec NOAH, the driving forces behind the evolution of the healthcare industry can unite to optimize patient care using data sharing, cohort identification and in-depth analyses of results. 

 

 

Proper governance of automated data and accesses

Logibec NOAH automates the governance of the access to data as established by the healthcare establishment and by the patient's consent form. Aggregated data is systematically de-identified, encrypted and structured with consistency. A unique identifier is created to allow longitudinal tracking.

Access to the data is configurable for each users. The data is logged daily for audit purposes. Only the establishment and its ethics committee are able to re-identify patients for the purpose of a clinical trial, for instance.

 

Customize criteria to identify cohorts 
With i2b2, Logibec NOAH provides researchers with exponential flexibility in identifying patient cohorts. No need for IT support to navigate the data tree!

Select the research criteria in “drag and drop” mode and immediately obtain the required number of patients that meet the desired attributes. The user-friendly navigation allows researchers to be autonomous and to gain great flexibility for iterations.

 

Customized data entry and electronic forms
The Logibec NOAH solution includes a data entry tool which enables researchers to add notes and import additional data related to their patient cohorts. The complementary data are de-identified and encrypted before being aggregated with all patient files.

The right of access to this new data can be restricted if necessary.

 

 

Modeling, in-depth analysis, and translational & "omic" research
Logibec NOAH facilitates data modeling :

  • A large inventory of clinical data can be aggregated with consistency.
  • Logibec NOAH is designed to integrate with Azure ML Studio, SAS, tranSMART, Python, R, and other solutions.
  • Modeling, natural language processing, and translational research can be performed by applying the same level of governance and security everywhere.

 

Harness real processing power 

Our flexible and affordable cloud infrastructure lets you access the processing power needed, as needed. The allocation of cloud capacity is configurable for each user.

Replicate data and experiment freely

With NOAH, create as many copies of the data as necessary, manage access to each instance separately and process their contents independently. 

Secondary data that accumulates can be enhanced later.

 

More about Logibec

  • A Fully Protected and Secure Tool to Create Patient Cohorts Using a Centralized Database of De-Identified Electronic Health Records
    A Fully Protected and Secure Tool to Create Patient Cohorts Using a Centralized Database of De-Identified Electronic Health Records
    Through Logibec NOAH researchers have safe and secure access to EHRs from across their own institution and collaborating research centers. Within a few minutes, researchers can see the number of patients they could potentially study, which research centers house the data, and how to contact them.
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  • What Are the Advantages and Dangers of Using EHRs?
    What Are the Advantages and Dangers of Using EHRs?
    Patient health information, which normally resides in paper forms or Electronic Health Records (EHRs), is well protected under a number of regulations, including PIPEDA in Canada, HIPAA in the USA, and GDPR in Europe. These regulations exist to safeguard the privacy and security of sensitive information and to ensure that patients feel safe participating in research, without the fear that their personal information will be made public or fall into the wrong hands. This protects individuals’ dignity and benefits society at large.
    Read the article
  • How to Use Bioinformatics to Identify Patient Groups and Augment Research Output
    How to Use Bioinformatics to Identify Patient Groups and Augment Research Output
    Using a bioinformatics platform to narrow in on patient cohorts that match precise inclusion and exclusion criteria in 5 minutes or less allows researchers to prepare grant applications grounded in data, save time on recruitment, build collaborations across institutions, and increase their research capacity.
    Read the article
  • Benchmarking Kafka
    Benchmarking Kafka
    In the solution Logibec NOAH, we have been using Kafka as message bus to transfer data from on-premise source systems to the cloud infrastructure. While trying to assess the machine requirements, we could not find precise documentation about the minimum CPU and memory requirements for the performance of Kafka. With this in mind, we performed a benchmark of our setup on Microsoft Azure and we explored a few approaches to optimize the stack for speed. We decided to publish the results so that others can benefit from our findings.
    Read the article

Continuum Health

Optimize the patient journey at every stage of the care trajectory with in-depth analytical reports and real-time visual representations of objective achievement through performance indicators presented in a single, user-friendly interface.

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Health science researcher analyzing patient medical data

Performance & Analytics

Have access to predictive scenarios and strategic recommendations based on your healthcare facility’s Big Data to support your daily decisions and drive the achievement of your operational objectives related to patient services quality and efficiency.

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