Driving clinical insights into your Urology department

Available data is the only thing keeping you from answering these apparently simple questions:

  • How are new innovations ( robotic surgery) impacting the overall quality of care of my patient population? 

  • How can we get faster insights into our clinical outcomes

  • After what time does the urine stream goes back to normal and for how long is this surgery effect lasting

  • Are their types of patients that respond better to certain treatments? 

Urology dashboard
Urology dashboard
Urology dashboard
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Accurately identify patients at risk by combining +245 patient datapoints

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Combine research & knowledge with international peers and benchmark different patient populations & treatments.

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Drive superior insights into mortality rate & care pathway efficiency.

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Track efficacy of treatments & protocols holistically.

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Key challenges modern Urology departments face:

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The ever-increasing complexity of care pathways

Assessing independent risk- and outcome predictors in Urology is difficult because it necessitates adjustment for multiple co-morbidities that are not always readily available on file. As such, evaluating the added value of innovation and optimizing the quality of care pathways is difficult.

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Managing a cost-effective research unit

With the data siloed into different locations, managing a research unit with prompt access to a centralized, up-to-date real-world data platform is a costly challenge that requires lots of manual interventions.

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Managing the continuous stream of administration & data registration

With mobile health data streams, the extension of EHR data entry, and regulatory requirements - managing the administrative burden is often several FTEs worth of labour for the modern Urology department.

Lynxcare is helping leading Urologists:

"It used to take up to two years before we had the last clinical outcomes available. From now on it can be done in near real time."

Dr. Peter Schatteman
Dr. Peter Schatteman
Urologist at OLV-hospital Aalst, Belgium
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How it works.

Step 1: We use our AI-trained algorithms to automatically extract data, both retrospectively and prospectively.

Our algorithms are trained to extract the data automatically from source records, according to the latest medical standards.

We categories data in over 245 datapoints based on published Chinese research, WHO guidelines and international clinician feedback.

Our team of MD’s & PhD’s is constantly updating this list with the latest insight and we can update it in collaboration with your team.

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Step 2: We make data pseudonymous & centrally available in an OMOP database.

Our data warehouse is set up according to the latest of clinical standards (OMOP/OHDSI).

We collaborate with Microsoft Azure to set up a data infrastructure that is resilient, while maintaining high scalability as well as compliance to data privacy regulation.

Before transfer, data can be anonymized/pseudonymised. 

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Step 3: We set up off-the-shelf or custom dashboards on top of the now-available data

On top of the now-available data, we deploy our off-the shelf reports.

Your BI team can also build on top of our dashboarding engine to build custom reports.

Dashboards are always and near-real-time updated with the latest available data.

We can setup automated reporting protocols to report back to epidemiological institutes.​

Data stays under control of the respective hospital.

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